diff --git a/.github/actions/action.yml b/.github/actions/action.yml
index decaa5ff3f8..7e2faf936b4 100644
--- a/.github/actions/action.yml
+++ b/.github/actions/action.yml
@@ -59,7 +59,7 @@ runs:
       run: echo "node_name=$NODE_NAME" | tee -a "$GITHUB_OUTPUT"
 
     - name: Checkout repository
-      uses: actions/checkout@v2
+      uses: actions/checkout@v6
 
     - name: Change ownership of /home/runner/
       shell: bash
@@ -98,7 +98,8 @@ runs:
           --environment dev \
           --platform dgx_h100 \
           --tag ${{ inputs.tag }} \
-          --container-image ${{ inputs.container-image }}
+          --container-image ${{ inputs.container-image }} \
+          --hf-home /mnt/datadrive/TestData/nemo-fw/TestData/HF_HOME
 
         RUN_TEST_EOF
         )
@@ -186,6 +187,7 @@ runs:
           --platform dgx_h100 \
           --container-image ${{ inputs.container-image }} \
           --data-dir /mnt/datadrive/TestData/megatron-lm/artifacts \
+          --hf-home /mnt/datadrive/TestData/nemo-fw/TestData/HF_HOME
 
         RUN_TEST_EOF
         )
diff --git a/.github/copy-pr-bot.yaml b/.github/copy-pr-bot.yaml
index fed1792d2f9..8c1ef48cf00 100644
--- a/.github/copy-pr-bot.yaml
+++ b/.github/copy-pr-bot.yaml
@@ -1,4 +1,4 @@
 enabled: true
 auto_sync_draft: false
 auto_sync_ready: true
-trustees_override: ["AAnoosheh", "ArEsKay3", "Autumn1998", "BestJuly", "BoxiangW", "CarlosGomes98", "ChenhanYu", "FDecaYed", "HaochenYuan", "ISEEKYAN", "JRD971000", "Phlip79", "QiZhangNV", "RPrenger", "ShriyaRishab", "Victarry", "Wohox", "ZhiyuLi-Nvidia", "ahmadki", "aklife97", "ananthsub", "asolergi-nv", "buptzyb", "chtruong814", "cspades", "cuichenx", "deepakn94", "dimapihtar", "dingqingy-nv", "duncanriach", "erhoo82", "ericharper", "fanshiqing", "faradawn", "frsun-nvda", "gautham-kollu", "gdengk", "guyueh1", "hxbai", "ilml", "jalbericiola", "janEbert", "jaredcasper", "jenchen13", "jiemingz", "jingqiny-99", "jkamalu", "jon-barker", "jstjohn", "kanz-nv", "kevalmorabia97", "ko3n1g", "kunlunl", "kvareddy", "kwyss-nvidia", "layalir", "lhb8125", "lmcafee-nvidia", "maanug-nv", "mathemakitten", "matthieule", "mchrzanowski", "mehraakash", "mkhona-nvidia", "parthmannan", "prajwal1210", "pthombre", "rogerwaleffe", "sajadn", "sanandaraj5597", "sancha", "santhnm2", "sbak5", "shanmugamr1992", "sharathts", "shengf-nv", "shifangx", "shjwudp", "sidsingh-nvidia", "skyw", "sudhakarsingh27", "tdene", "theothermike", "thomasdhc", "trintamaki", "tylerpoon", "wdykas", "xiaoyao0115", "xuwchen", "yanring", "yaox12", "yaoyu-33", "yashaswikarnati", "yeyu-nvidia", "yobibyte", "youngeunkwon0405", "yueshen2016", "yuzhongw-nvidia", "zhongbozhu"]
+trustees_override: ["AAnoosheh", "ArEsKay3", "Autumn1998", "BestJuly", "BoxiangW", "CarlosGomes98", "ChenhanYu", "FDecaYed", "HaochenYuan", "ISEEKYAN", "JRD971000", "Phlip79", "QiZhangNV", "RPrenger", "ShriyaRishab", "Victarry", "Wohox", "ZhiyuLi-Nvidia", "ahmadki", "aklife97", "ananthsub", "asolergi-nv", "buptzyb", "chtruong814", "cjld", "cspades", "cuichenx", "deepakn94", "dimapihtar", "dingqingy-nv", "duncanriach", "erhoo82", "ericharper", "fanshiqing", "faradawn", "frsun-nvda", "gautham-kollu", "gdengk", "guyueh1", "huvunvidia", "hxbai", "ilml", "jalbericiola", "janEbert", "jaredcasper", "jenchen13", "jiemingz", "jingqiny-99", "jkamalu", "jon-barker", "jstjohn", "kanz-nv", "kevalmorabia97", "ko3n1g", "ksivaman", "kunlunl", "kvareddy", "kwyss-nvidia", "layalir", "lhb8125", "lmcafee-nvidia", "maanug-nv", "mathemakitten", "matthieule", "mchrzanowski", "mehraakash", "mkhona-nvidia", "nanz-nv", "parthmannan", "prajwal1210", "pthombre", "rhewett-nv", "rogerwaleffe", "sajadn", "sanandaraj5597", "sancha", "santhnm2", "sbak5", "shanmugamr1992", "sharathts", "shengf-nv", "shifangx", "shjwudp", "sidsingh-nvidia", "skyw", "sudhakarsingh27", "tdene", "theothermike", "thomasdhc", "tomlifu", "trintamaki", "tylerpoon", "wdykas", "wplf", "xiaoyao0115", "xuwchen", "yanring", "yaox12", "yaoyu-33", "yashaswikarnati", "yeyu-nvidia", "yobibyte", "youngeunkwon0405", "yueshen2016", "yuzhongw-nvidia", "zhongbozhu"]
diff --git a/.github/oncall_schedule.json b/.github/oncall_schedule.json
index 58fcf0ddbbc..fe90397c2ad 100644
--- a/.github/oncall_schedule.json
+++ b/.github/oncall_schedule.json
@@ -1,16 +1,4 @@
 [
-    {
-        "user": "janEbert",
-        "date": "2026-02-18"
-    },
-    {
-        "user": "asolergi-nv",
-        "date": "2026-02-25"
-    },
-    {
-        "user": "BoxiangW",
-        "date": "2026-03-04"
-    },
     {
         "user": "maanug-nv",
         "date": "2026-03-11"
@@ -20,31 +8,43 @@
         "date": "2026-03-18"
     },
     {
-        "user": "gautham-kollu",
+        "user": "janEbert",
         "date": "2026-03-25"
     },
     {
-        "user": "janEbert",
+        "user": "gautham-kollu",
         "date": "2026-04-01"
     },
     {
-        "user": "maanug-nv",
+        "user": "ilml",
         "date": "2026-04-08"
     },
     {
-        "user": "BoxiangW",
+        "user": "Phlip79",
         "date": "2026-04-15"
     },
     {
-        "user": "Phlip79",
+        "user": "asolergi-nv",
         "date": "2026-04-22"
     },
     {
-        "user": "asolergi-nv",
+        "user": "BoxiangW",
         "date": "2026-04-29"
     },
     {
-        "user": "dimapihtar",
+        "user": "maanug-nv",
         "date": "2026-05-06"
+    },
+    {
+        "user": "dimapihtar",
+        "date": "2026-05-13"
+    },
+    {
+        "user": "gautham-kollu",
+        "date": "2026-05-20"
+    },
+    {
+        "user": "ilml",
+        "date": "2026-05-27"
     }
 ]
diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md
index 5cd5138eb69..d2825f9c34b 100644
--- a/.github/pull_request_template.md
+++ b/.github/pull_request_template.md
@@ -5,19 +5,8 @@
 
 ## Contribution process
 
-```mermaid
-flowchart LR
-    A[Pre-checks] --> B[PR Tests]
-    subgraph Code Review/Approval
-        C1[Expert Review] --> C2[Final Review]
-    end
-    B --> C1
-    C2 --> D[Merge]
-```
-
 ### Pre-checks
 
-- [ ] I want this PR in a versioned release and have added the appropriate Milestone (e.g., `Core 0.8`)
 - [ ] I have added relevant unit tests
 - [ ] I have added relevant functional tests
 - [ ] I have added proper typing to my code [Typing guidelines](https://docs.python.org/3/library/typing.html)
@@ -26,33 +15,32 @@ flowchart LR
 
 ### Code review
 
-The following process is enforced via the CODEOWNERS file for changes into `megatron/core`. For changes outside of `megatron/core`, it is up to the PR author whether or not to tag the Final Reviewer team.
+Feel free to message or comment the [@mcore-oncall](https://github.com/orgs/NVIDIA/teams/mcore-oncall) to help accelerate your merge into main. The less complex your PR is, the faster it will be approved and merged!
 
-<details>
-<summary>For MRs into `main` branch</summary>
+All PRs start as **draft**. If you open a non-draft PR, it will be automatically converted to draft.
 
-Feel free to message or comment the @mcore-oncall to help accelerate your merge into main. The less complex your PR is, the faster it will be approved and merged!
+#### Step 1: Mark PR as "Ready for Review"
 
-#### (Step 1): Add PR label `Expert Review`
+1. When your PR is ready, click **Ready for Review**.
+2. An oncall reviewer is auto-assigned and expert reviewers are notified based on your changes.
+   - Some PRs may jump straight to step 2. This is determined by `.github/CODEOWNERS`.
 
-#### (Step 2): Collect the expert reviewers reviews
+:warning: Only mark as ready once merge-conflicts are resolved and the CI is passing.
+Final Review might get declined if these requirements are not fulfilled.
 
-1. Attach the `Expert Review` label when your PR is ready for review.
-2. GitHub auto-assigns expert reviewers based on your changes. They will get notified and pick up your PR soon.
+#### Step 2: Final Review
 
-:warning: Only proceed to the next step once all reviewers have approved, merge-conflict are resolved and the CI is passing.  
-Final Review might get declined if these requirements are not fulfilled.
+For PRs that change `megatron/core`, once all expert reviewers have approved, the `Final Review` label is applied **automatically** and final reviewers are assigned.
 
-#### (Step 3): Final Review
+For PRs outside `megatron/core`, this step is skipped.
 
-1. Add `Final Review` label
-2. GitHub auto-assigns final reviewers based on your changes. They will get notified and pick up your PR soon.
+#### Step 3: Approved
 
-#### (Optional Step 4): Cherry-pick into release branch
+Once all required reviewers have approved, the `Approved` label is applied **automatically**.
 
-If this PR also needs to be merged into `core_r*` release branches, after this PR has been merged, select `Cherry-pick` to open a new PR into the release branch.
+### Merge
 
-</details>
+Any member of [mcore-engineers](https://github.com/orgs/NVIDIA/teams/mcore-engineers) will be able to merge your PR.
 
 <details>
 <summary>For MRs into `dev` branch</summary>
@@ -60,7 +48,3 @@ The proposed review process for `dev` branch is under active discussion.
 
 MRs are mergable after one approval by either `eharper@nvidia.com` or `zijiey@nvidia.com`.
 </details>
-
-### Merging your PR
-
-Any member of [core-adlr](https://github.com/orgs/teams/NVIDIA/core-adlr) and [`core-nemo`](https://github.com/orgs/teams/NVIDIA/core-nemo) will be able to merge your PR.
diff --git a/.github/workflows/_build_test_publish_wheel.yml b/.github/workflows/_build_test_publish_wheel.yml
index 0b71577b587..f29488c4494 100644
--- a/.github/workflows/_build_test_publish_wheel.yml
+++ b/.github/workflows/_build_test_publish_wheel.yml
@@ -43,7 +43,7 @@ jobs:
       PUBLISH_DRYRUN: ${{ inputs.dry-run }}
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
         with:
           ref: ${{ inputs.ref }}
 
@@ -136,7 +136,7 @@ jobs:
           test "${{ steps.build-wheel.outputs.expected-release-number }}" == "$RELEASE_NUMBER"
 
       - name: Upload wheels
-        uses: actions/upload-artifact@v4
+        uses: actions/upload-artifact@v6
         with:
           name: wheels-${{ matrix.PACKAGE }}-${{ matrix.PLATFORM }}-${{ inputs.dry-run && 'dry-run' || 'release' }}
           path: dist/
@@ -159,7 +159,7 @@ jobs:
       PACKAGE: ${{ matrix.PACKAGE }}
     steps:
       - name: Download wheels
-        uses: actions/download-artifact@v4
+        uses: actions/download-artifact@v7
         with:
           name: wheels-${{ matrix.PACKAGE }}-${{ matrix.PLATFORM }}-${{ inputs.dry-run && 'dry-run' || 'release' }}
           path: dist/
diff --git a/.github/workflows/_release_library.yml b/.github/workflows/_release_library.yml
index 684dacc27aa..46de1bb3a66 100644
--- a/.github/workflows/_release_library.yml
+++ b/.github/workflows/_release_library.yml
@@ -53,6 +53,11 @@ on:
         description: Starting tag for changelog builder (leave empty for auto-detect)
         type: string
         default: ""
+      publish-docs:
+        required: false
+        description: Publish documentation to S3 after release
+        type: boolean
+        default: true
     secrets:
       TWINE_PASSWORD:
         required: true
@@ -60,6 +65,22 @@ on:
         required: true
       PAT:
         required: true
+      AWS_ASSUME_ROLE_ARN:
+        required: true
+      AWS_ACCESS_KEY_ID:
+        required: true
+      AWS_SECRET_ACCESS_KEY:
+        required: true
+      AKAMAI_HOST:
+        required: true
+      AKAMAI_CLIENT_TOKEN:
+        required: true
+      AKAMAI_CLIENT_SECRET:
+        required: true
+      AKAMAI_ACCESS_TOKEN:
+        required: true
+      S3_BUCKET_NAME:
+        required: true
 
 permissions:
   contents: write # To read repository content
@@ -89,7 +110,7 @@ jobs:
       IS_DRY_RUN: ${{ inputs.dry-run }}
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
         with:
           path: ${{ github.run_id }}
           token: ${{ secrets.PAT }}
@@ -199,11 +220,8 @@ jobs:
           # Extract PR number from URL
           PR_NUMBER=$(echo $PR_URL | grep -o '[0-9]*$')
 
-          # Add comment to the newly created PR
-          echo gh pr comment $PR_NUMBER --body "/ok to test $(git rev-parse HEAD)"
-
       - name: Wait for status checks on tmp branch
-        uses: actions/github-script@v7
+        uses: actions/github-script@v8
         id: wait-status
         with:
           github-token: ${{ secrets.PAT }}
@@ -326,7 +344,6 @@ jobs:
       ref: ${{ inputs.release-ref }}
       no-publish: false
     secrets:
-      TWINE_USERNAME: ${{ secrets.TWINE_USERNAME }}
       TWINE_PASSWORD: ${{ secrets.TWINE_PASSWORD }}
 
   create-gh-release:
@@ -344,10 +361,10 @@ jobs:
       REPOSITORY: ${{ github.repository }}
       PROJECT_NAME: Megatron Core
       VERSION: ${{ needs.bump-next-version.outputs.release-version }}
-      TAG_PREFIX: ${{ inputs.gh-release-tag-prefix || '' }}
+      TAG_PREFIX: core_
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
         with:
           path: ${{ github.run_id }}
           ref: ${{ inputs.release-ref }}
@@ -455,6 +472,12 @@ jobs:
   publish-docs:
     needs: [bump-next-version, create-gh-release]
     uses: ./.github/workflows/release-docs.yml
+    if: |
+      (
+        success() || !failure()
+      )
+      && inputs.publish-docs == true
+      && !cancelled()
     with:
       dry-run: ${{ inputs.dry-run }}
       publish-as-latest: true
@@ -472,7 +495,7 @@ jobs:
       VERSION: ${{ needs.build-test-publish-wheels.outputs.version }}
     steps:
       - name: Checkout
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
         with:
           repository: NVIDIA-NeMo/FW-CI-templates
           ref: v0.17.0
diff --git a/.github/workflows/_update_dependencies.yml b/.github/workflows/_update_dependencies.yml
index a60e69f701b..903d773edbd 100644
--- a/.github/workflows/_update_dependencies.yml
+++ b/.github/workflows/_update_dependencies.yml
@@ -33,7 +33,7 @@ jobs:
       TARGET_BRANCH: ${{ inputs.target-branch }}
     steps:
       - name: Checkout repo
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
         with:
           ref: ${{ env.TARGET_BRANCH }}
 
@@ -60,7 +60,7 @@ jobs:
           fi
 
       - name: Checkout repo
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
         with:
           ref: ${{ env.SOURCE_BRANCH }}
 
@@ -77,7 +77,7 @@ jobs:
           bash -c 'uv lock --upgrade'
 
       - name: Upload lock file
-        uses: actions/upload-artifact@v4
+        uses: actions/upload-artifact@v6
         with:
           name: lock-file-${{ env.SOURCE_BRANCH }}
           path: uv.lock
@@ -90,7 +90,7 @@ jobs:
       TARGET_BRANCH: ${{ inputs.target-branch }}
     steps:
       - name: Checkout code
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
         with:
           token: ${{ secrets.PAT }}
           ref: ${{ env.TARGET_BRANCH }}
@@ -103,7 +103,7 @@ jobs:
           fi
 
       - name: Download lock file
-        uses: actions/download-artifact@v4
+        uses: actions/download-artifact@v7
         with:
           name: lock-file-${{ env.SOURCE_BRANCH }}
 
diff --git a/.github/workflows/auto-reminder-bot.yml b/.github/workflows/auto-reminder-bot.yml
index 37e6e5498e3..72a48e9539e 100644
--- a/.github/workflows/auto-reminder-bot.yml
+++ b/.github/workflows/auto-reminder-bot.yml
@@ -14,10 +14,10 @@ jobs:
     if: github.repository == 'NVIDIA/Megatron-LM'
     steps:
       - name: Check out repository code
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
 
       - name: Set up Python
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@v6
         with:
           python-version: "3.10"
 
diff --git a/.github/workflows/auto-swap-labels.yml b/.github/workflows/auto-swap-labels.yml
index 5335026e2af..f1dd9757c8a 100644
--- a/.github/workflows/auto-swap-labels.yml
+++ b/.github/workflows/auto-swap-labels.yml
@@ -2,32 +2,74 @@
 
 name: Auto Swap Labels
 on:
-  pull_request_review:
-    types: [submitted]
+  pull_request_target:
+    types: [ready_for_review, synchronize]
+    branches:
+      - main
+  workflow_run:
+    workflows: ["Review Trigger"]
+    types: [completed]
 
 permissions:
   pull-requests: write
   contents: read
+  actions: read
 
 jobs:
   check-approval:
     runs-on: ubuntu-latest
-    if: github.event.review.state == 'approved' && github.repository == 'NVIDIA/Megatron-LM'
+    if: >-
+      github.repository == 'NVIDIA/Megatron-LM' && (
+        (github.event_name == 'pull_request_target' &&
+         github.event.pull_request.base.ref == 'main' &&
+         !github.event.pull_request.draft) ||
+        (github.event_name == 'workflow_run' &&
+         github.event.workflow_run.conclusion == 'success')
+      )
+
     steps:
+      - name: Get PR number from workflow_run
+        id: get-pr
+        if: github.event_name == 'workflow_run'
+        continue-on-error: true
+        uses: actions/download-artifact@v4
+        with:
+          name: pr-number
+          path: pr-number
+          github-token: ${{ github.token }}
+          run-id: ${{ github.event.workflow_run.id }}
+
+      - name: Set PR number
+        id: pr
+        run: |
+          if [ "${{ github.event_name }}" = "workflow_run" ]; then
+            if [ "${{ steps.get-pr.outcome }}" != "success" ]; then
+              echo "No approval artifact found — review was not an approval. Skipping."
+              exit 0
+            fi
+            echo "number=$(cat pr-number/number)" >> $GITHUB_OUTPUT
+          else
+            echo "number=${{ github.event.pull_request.number }}" >> $GITHUB_OUTPUT
+          fi
+
       - name: Check out repository code
+        if: steps.pr.outputs.number
         uses: actions/checkout@v4
 
       - name: Set up Python
-        uses: actions/setup-python@v5
+        if: steps.pr.outputs.number
+        uses: actions/setup-python@v6
         with:
           python-version: "3.10"
 
       - name: Install dependencies
+        if: steps.pr.outputs.number
         run: |
           pip install --no-cache-dir PyGithub slack-sdk
 
-      - name: Run Auto Reminder Bot
+      - name: Run Auto Swap Labels
+        if: steps.pr.outputs.number
         run: |
-          export GH_TOKEN=${{ github.token }}
-          export PR_NUMBER=${{ github.event.pull_request.number }}
+          export GH_TOKEN=${{ secrets.PAT }}
+          export PR_NUMBER=${{ steps.pr.outputs.number }}
           python tests/test_utils/python_scripts/swap_pr_labels.py
diff --git a/.github/workflows/auto-update-copy-pr-bot.yml b/.github/workflows/auto-update-copy-pr-bot.yml
index 3358a747f34..07fdcfbfbb8 100644
--- a/.github/workflows/auto-update-copy-pr-bot.yml
+++ b/.github/workflows/auto-update-copy-pr-bot.yml
@@ -11,7 +11,7 @@ jobs:
     if: github.repository == 'NVIDIA/Megatron-LM'
     steps:
       - name: Checkout code
-        uses: actions/checkout@v3
+        uses: actions/checkout@v6
         with:
           token: ${{ secrets.PAT }}
           ref: main
diff --git a/.github/workflows/build-docs.yml b/.github/workflows/build-docs.yml
new file mode 100644
index 00000000000..d08337689b6
--- /dev/null
+++ b/.github/workflows/build-docs.yml
@@ -0,0 +1,67 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+name: Build docs
+
+on:
+  push:
+    branches:
+      - main
+      - "pull-request/[0-9]+"
+      - "deploy-release/*"
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}-${{ github.event.label.name || 'main' }}-${{ github.event_name }}
+  cancel-in-progress: true
+
+jobs:
+  pre-flight:
+    uses: NVIDIA-NeMo/FW-CI-templates/.github/workflows/_cicd_preflight.yml@v0.73.2
+
+  build-docs:
+    needs: [pre-flight]
+    if: needs.pre-flight.outputs.is_deployment_workflow != 'true'
+    uses: NVIDIA-NeMo/FW-CI-templates/.github/workflows/_build_docs.yml@v0.57.0
+    with:
+      requirements-file: docs/requirements-ci.txt
+
+  build-docs-summary:
+    needs: [pre-flight, build-docs]
+    if: |
+      (
+        needs.pre-flight.outputs.is_deployment_workflow == 'true'
+        || always()
+      )
+      && !cancelled()
+    runs-on: ubuntu-latest
+    steps:
+      - name: Get workflow result
+        id: result
+        shell: bash -x -e -u -o pipefail {0}
+        env:
+          GH_TOKEN: ${{ github.token }}
+          RUN_ID: ${{ github.run_id }}
+          SKIPPING_IS_ALLOWED: ${{ needs.pre-flight.outputs.docs_only == 'true' || needs.pre-flight.outputs.is_deployment_workflow == 'true' }}
+        run: |
+          FAILED_JOBS=$(gh run view $GITHUB_RUN_ID --json jobs --jq '[.jobs[] | select(.status == "completed" and .conclusion != "success")] | length') || echo 0
+
+          if [ "${FAILED_JOBS:-0}" -eq 0 ] || [ "$SKIPPING_IS_ALLOWED" == "true" ]; then
+              echo "✅ All previous jobs completed successfully"
+              exit 0
+          else
+              echo "❌ Found $FAILED_JOBS failed job(s)"
+              # Show which jobs failed
+              gh run view $GITHUB_RUN_ID --json jobs --jq '.jobs[] | select(.status == "completed" and .conclusion != "success") | .name'
+              exit 1
+          fi
diff --git a/.github/workflows/build-test-publish-wheel.yml b/.github/workflows/build-test-publish-wheel.yml
index 00711b50806..88b9e8b8c61 100644
--- a/.github/workflows/build-test-publish-wheel.yml
+++ b/.github/workflows/build-test-publish-wheel.yml
@@ -58,7 +58,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
 
       - name: Result
         env:
diff --git a/.github/workflows/cicd-approve-test-queue.yml b/.github/workflows/cicd-approve-test-queue.yml
index 2cba41eafb8..02bfbbfd3a5 100644
--- a/.github/workflows/cicd-approve-test-queue.yml
+++ b/.github/workflows/cicd-approve-test-queue.yml
@@ -27,12 +27,13 @@ jobs:
     strategy:
       matrix:
         branch: [main, dev, others]
+        contributor_type: [internal, external]
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
 
       - name: Set up Python
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@v6
         with:
           python-version: "3.12"
 
@@ -41,23 +42,44 @@ jobs:
           python -m pip install --upgrade pip
           pip install requests
 
+      - name: Download SSO users list
+        run: |
+          gh release download v0.1.0 \
+            --repo NVIDIA-GitHub-Management/github-audits \
+            --pattern users_sso.json \
+            --output users_sso.json || echo '{}' > users_sso.json
+        env:
+          GH_TOKEN: ${{ secrets.NVIDIA_MANAGEMENT_ORG_PAT }}
+
       - name: Approve waiting deployments
         env:
           GITHUB_TOKEN: ${{ secrets.PAT }}
           MAX_CONCURRENCY: ${{ vars.MAX_CONCURRENCY || 1 }}
+          MAX_CONCURRENCY_EXTERNAL: ${{ vars.MAX_CONCURRENCY_EXTERNAL || 3 }}
+          CONTRIBUTOR_TYPE: ${{ matrix.contributor_type }}
+          SSO_USERS_FILE: users_sso.json
           PYTHONUNBUFFERED: 1
         shell: python
         run: |
           import os
+          import json
           import requests
           import re
 
           # GitHub API configuration
           GITHUB_TOKEN = os.environ["GITHUB_TOKEN"]
           REPO = os.environ["GITHUB_REPOSITORY"]
-          MAX_CONCURRENCY = int(os.environ["MAX_CONCURRENCY"]) // 2
+          CONTRIBUTOR_TYPE = os.environ["CONTRIBUTOR_TYPE"]
+          if CONTRIBUTOR_TYPE == "external":
+              MAX_CONCURRENCY = int(os.environ["MAX_CONCURRENCY_EXTERNAL"]) // 2
+          else:
+              MAX_CONCURRENCY = int(os.environ["MAX_CONCURRENCY"]) // 2
           API_BASE = f"https://api.github.com/repos/NVIDIA/Megatron-LM"
 
+          # Load SSO users for internal/external classification
+          with open(os.environ["SSO_USERS_FILE"]) as f:
+              sso_users = json.load(f)
+
           # Headers for GitHub API
           headers = {
               "Authorization": f"token {GITHUB_TOKEN}",
@@ -81,53 +103,76 @@ jobs:
                       print(f"Response: {e.response.text}")
                   return None
 
-          def is_pr_targeting_branch(workflow_run, target_branch):
+          def is_internal_contributor(pr_info):
+              """Return True if the PR author is a member of NVIDIA or NVIDIA-NeMo org (is_org_member)."""
+              login = pr_info.get("user", {}).get("login", "")
+              org_roles = sso_users.get(login, {}).get("org_roles", [])
+              return any(role in ("NVIDIA:Member", "NVIDIA-NeMo:Member") for role in org_roles)
+
+          def get_pr_base_branch(workflow_run):
               """
-              Check if a workflow run belongs to a PR targeting the given branch.
-              Extract PR number from head branch like 'pull-request/1913' and verify base branch.
+              Return the base branch of the PR associated with a workflow run, or None.
+              Extracts PR number from head branch like 'pull-request/1913' and fetches PR info.
+              Returns (base_branch, pr_info) tuple, or (None, None) if not a PR run.
               """
               print(workflow_run.get("head_branch", ""))
               head_branch = workflow_run.get("head_branch", "")
               match = re.match(r"pull-request/(\d+)", head_branch)
               if not match:
-                  return False  # Not a PR branch pattern
+                  return None, None  # Not a PR branch pattern
 
               pr_number = int(match.group(1))
-              
+
               # Fetch PR info from GitHub API
               pr_info = make_request(f"pulls/{pr_number}")
               if not pr_info:
                   print(f"Failed to fetch PR #{pr_number}")
-                  return False
+                  return None, None
 
               base_branch = pr_info.get("base", {}).get("ref")
-              if (
-                (base_branch == target_branch) or 
-                (base_branch != "main" and base_branch != "dev" and target_branch == "others")
-              ):
-                  print(f"PR #{pr_number} targets {target_branch}")
-                  return True
+              return base_branch, pr_info
+
+          def matches_queue(workflow_run, target_branch, contributor_type):
+              """
+              Return True if the workflow run belongs to this queue cell:
+              matching target branch AND matching contributor type (internal/external).
+              """
+              base_branch, pr_info = get_pr_base_branch(workflow_run)
+              if base_branch is None:
+                  return False
+
+              branch_match = (
+                  (base_branch == target_branch) or
+                  (base_branch != "main" and base_branch != "dev" and target_branch == "others")
+              )
+              if not branch_match:
+                  return False
 
-              return False
+              pr_number = re.match(r"pull-request/(\d+)", workflow_run.get("head_branch", "")).group(1)
+              internal = is_internal_contributor(pr_info)
+              contributor_match = (contributor_type == "internal") == internal
+              if branch_match and contributor_match:
+                  print(f"PR #{pr_number} targets {target_branch}, contributor_type={contributor_type} (internal={internal})")
+              return branch_match and contributor_match
 
           # Get current running and queued workflows
           print("Fetching workflow runs...")
           queued_workflow_runs = make_request("actions/runs?status=queued").get("workflow_runs", [])
           in_progress_workflow_runs = make_request("actions/runs?status=in_progress").get("workflow_runs", [])
 
-          # Filter for workflows belonging to PRs targeting ${{ matrix.branch }}
-          queued_workflow_runs = [run for run in queued_workflow_runs 
-                                  if run["name"] == "CICD Megatron-LM" and is_pr_targeting_branch(run, "${{ matrix.branch }}")]
-          in_progress_workflow_runs = [run for run in in_progress_workflow_runs 
-                                      if run["name"] == "CICD Megatron-LM" and is_pr_targeting_branch(run, "${{ matrix.branch }}")]
+          # Filter for workflows belonging to PRs targeting ${{ matrix.branch }} with matching contributor type
+          queued_workflow_runs = [run for run in queued_workflow_runs
+                                  if run["name"] == "CICD Megatron-LM" and matches_queue(run, "${{ matrix.branch }}", CONTRIBUTOR_TYPE)]
+          in_progress_workflow_runs = [run for run in in_progress_workflow_runs
+                                      if run["name"] == "CICD Megatron-LM" and matches_queue(run, "${{ matrix.branch }}", CONTRIBUTOR_TYPE)]
 
           # Count running and queued workflows
           queued_workflows = len(queued_workflow_runs)
           in_progress_workflows = len(in_progress_workflow_runs)
 
           total_workflows = queued_workflows + in_progress_workflows
-          print(f"Current queued workflows (PRs targeting ${{ matrix.branch }}): {queued_workflows}")
-          print(f"Current running workflows (PRs targeting ${{ matrix.branch }}): {in_progress_workflows}")
+          print(f"Current queued workflows (PRs targeting ${{ matrix.branch }}, {CONTRIBUTOR_TYPE}): {queued_workflows}")
+          print(f"Current running workflows (PRs targeting ${{ matrix.branch }}, {CONTRIBUTOR_TYPE}): {in_progress_workflows}")
           print(f"Total workflows: {total_workflows}")
           print(f"Max concurrency: {MAX_CONCURRENCY}")
 
@@ -139,8 +184,8 @@ jobs:
           print("Fetching deployments...")
           pending_workflows = make_request("actions/runs?status=waiting").get("workflow_runs", [])
           print("Pending workflows:", len(pending_workflows))
-          pending_workflows = [run for run in pending_workflows 
-                              if run["name"] == "CICD Megatron-LM" and is_pr_targeting_branch(run, "${{ matrix.branch }}")]
+          pending_workflows = [run for run in pending_workflows
+                              if run["name"] == "CICD Megatron-LM" and matches_queue(run, "${{ matrix.branch }}", CONTRIBUTOR_TYPE)]
 
           # Sort deployments by creation date (oldest first)
           print("Sorting workflows...")
diff --git a/.github/workflows/cicd-main.yml b/.github/workflows/cicd-main.yml
index 3968f808ff6..204edf845c6 100644
--- a/.github/workflows/cicd-main.yml
+++ b/.github/workflows/cicd-main.yml
@@ -25,7 +25,7 @@ on:
   workflow_dispatch:
 
 concurrency:
-  group: ${{ github.workflow }}-${{ github.head_ref || github.ref || github.event.pull_request.number }}
+  group: ${{ github.workflow }}-${{ github.head_ref || github.event.merge_group.head_ref || github.ref }}
   cancel-in-progress: true
 
 permissions:
@@ -52,7 +52,7 @@ jobs:
       DISABLE_EXTERNAL_CONTRIBUTOR: ${{ vars.DISABLE_EXTERNAL_CONTRIBUTOR }}
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
         with:
           token: ${{ env.GITHUB_TOKEN }}
 
@@ -146,7 +146,7 @@ jobs:
       )
     steps:
       - name: Checkout
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
         with:
           fetch-depth: 0
 
@@ -157,6 +157,9 @@ jobs:
 
       - name: Install linting tools
         run: |
+          uv venv
+          uv pip install --python .venv/bin/python \
+            wheel packaging "setuptools<80.0.0,>=77.0.0"
           uv sync --locked --only-group linting
 
       - name: Get PR info
@@ -209,7 +212,7 @@ jobs:
       mbridge-test-suite: ${{ steps.select-mbridge-test-suite.outputs.main }}
     steps:
       - name: Checkout
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
 
       - name: Get PR info
         id: get-pr-info
@@ -252,7 +255,7 @@ jobs:
         uses: nv-gha-runners/get-pr-info@main
 
       - name: Checkout MBridge and create testing branch
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
         with:
           ref: main
           repository: NVIDIA-NeMo/Megatron-Bridge
@@ -348,12 +351,12 @@ jobs:
           echo "main=${SHA}" | tee -a "$GITHUB_OUTPUT"
 
       - name: Checkout
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
         with:
           ref: ${{ steps.sha.outputs.main }}
 
       - name: Setup python
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@v6
         with:
           python-version: 3.12
 
@@ -471,7 +474,7 @@ jobs:
       && !cancelled()
     steps:
       - name: Checkout
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
       - name: Parse unit tests
         id: parse-unit-tests
         run: |
@@ -511,7 +514,7 @@ jobs:
       PIP_ROOT_USER_ACTION: ignore
     steps:
       - name: Checkout
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
       - name: main
         uses: ./.github/actions
         with:
@@ -546,7 +549,7 @@ jobs:
       integration-tests: ${{ steps.main.outputs.integration-tests }}
     steps:
       - name: Checkout
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
 
       - name: Get PR info
         id: get-pr-info
@@ -650,7 +653,7 @@ jobs:
       && !cancelled()
     steps:
       - name: Checkout
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
       - name: main
         uses: ./.github/actions
         with:
@@ -683,7 +686,7 @@ jobs:
     permissions: write-all
     steps:
       - name: Checkout
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
 
       - name: Get workflow result
         id: result
@@ -720,7 +723,7 @@ jobs:
       && github.repository == 'NVIDIA/Megatron-LM'
     steps:
       - name: Generate fake coverage report
-        uses: actions/github-script@v6
+        uses: actions/github-script@v8
         with:
           github-token: ${{ secrets.PAT }}
           script: |
@@ -748,10 +751,10 @@ jobs:
         flag: [unit-test]
     steps:
       - name: Checkout
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
 
       - name: Download coverage reports of current branch
-        uses: actions/download-artifact@v4
+        uses: actions/download-artifact@v7
         with:
           pattern: coverage-${{ matrix.flag }}-*
 
@@ -779,7 +782,7 @@ jobs:
           flags: ${{ matrix.flag }}
 
       - name: Upload artifacts
-        uses: actions/upload-artifact@v4
+        uses: actions/upload-artifact@v6
         with:
           name: coverage-${{ matrix.flag }}-aggregated
           path: |
@@ -800,7 +803,7 @@ jobs:
           echo "pr_number=$PR_NUMBER" >> $GITHUB_OUTPUT
 
       - name: Comment on PR with action run URL
-        uses: actions/github-script@v7
+        uses: actions/github-script@v8
         with:
           github-token: ${{ secrets.PAT }}
           script: |
@@ -827,6 +830,7 @@ jobs:
       always()
       && !cancelled()
       && contains(needs.is-not-external-contributor.outputs.selected_runner, 'ephemeral')
+      && !needs.pre-flight.outputs.is_deployment_workflow == 'true'
     steps:
       - name: Taint node for cleanup
         shell: bash
diff --git a/.github/workflows/claude-complexity-label.yml b/.github/workflows/claude-complexity-label.yml
new file mode 100644
index 00000000000..356eed2da29
--- /dev/null
+++ b/.github/workflows/claude-complexity-label.yml
@@ -0,0 +1,60 @@
+name: Claude Complexity Label
+
+on:
+  pull_request_target:
+    types: [ready_for_review]
+
+jobs:
+  label-complexity:
+    name: Label PR Complexity
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+      pull-requests: write
+      issues: write
+      id-token: write
+    env:
+      GH_TOKEN: ${{ secrets.PAT }}
+      REPO: ${{ github.repository }}
+      PR_NUMBER: ${{ github.event.pull_request.number }}
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v6
+        with:
+          fetch-depth: 0
+
+      - name: Run Claude Complexity Analysis
+        uses: anthropics/claude-code-action@v1
+        with:
+          anthropic_api_key: ${{ secrets.ANTHROPIC_API_KEY }}
+          github_token: ${{ secrets.PAT }}
+          prompt: |
+            REPO: ${{ env.REPO }}
+            PR NUMBER: ${{ env.PR_NUMBER }}
+
+            You are a PR complexity analyzer. Your job is to analyze the diff of this PR and apply exactly one complexity label.
+
+            STEPS:
+            1. Get the PR diff by running: gh pr diff $PR_NUMBER --repo $REPO
+            2. Analyze every changed line (added or removed) in the diff and classify each as one of:
+               - "docs-only": changes to docstrings, comments (lines starting with # or //), documentation files (.md, .rst, .txt), or similar non-functional text
+               - "test": changes in test files (files with "test" in the name/path, or inside a tests/ directory)
+               - "real code": all other changes (functional source code)
+            3. Compute "real code line changes" using this formula:
+               real_code_line_changes = (number of real code lines changed) + (number of test lines changed / 10)
+               Count both added and removed lines. Do not count unchanged context lines. Do not count comments or docstrings.
+            4. Remove any previously applied complexity or docs-only labels:
+               gh pr edit $PR_NUMBER --repo $REPO --remove-label "complexity: low,complexity: medium,complexity: high,docs-only"
+            5. Apply exactly ONE label using the gh CLI:
+               - If there are ZERO real code lines and ZERO test lines (only docs-only changes), apply label "docs-only":
+                 gh pr edit $PR_NUMBER --repo $REPO --add-label "docs-only"
+               - If real_code_line_changes < 100, apply label "complexity: low":
+                 gh pr edit $PR_NUMBER --repo $REPO --add-label "complexity: low"
+               - If real_code_line_changes >= 100 and < 500, apply label "complexity: medium":
+                 gh pr edit $PR_NUMBER --repo $REPO --add-label "complexity: medium"
+               - If real_code_line_changes >= 500, apply label "complexity: high":
+                 gh pr edit $PR_NUMBER --repo $REPO --add-label "complexity: high"
+
+            Do NOT post any comments on the PR. Only apply the label.
+          claude_args: |
+            --allowedTools "Bash(gh pr diff:*),Bash(gh pr edit:*),Bash(gh pr view:*)"
diff --git a/.github/workflows/claude_review.yml b/.github/workflows/claude_review.yml
new file mode 100644
index 00000000000..2a159c07d20
--- /dev/null
+++ b/.github/workflows/claude_review.yml
@@ -0,0 +1,67 @@
+name: Claude Code Review
+
+on:
+  issue_comment:
+    types: [created]
+
+jobs:
+  review-on-comment:
+    name: Claude Review (comment trigger)
+    if: |
+      github.event_name == 'issue_comment' &&
+      github.event.issue.pull_request &&
+      contains(github.event.comment.body, '/claude review')
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+      pull-requests: write
+      issues: write
+      id-token: write
+    env:
+      GH_TOKEN: ${{ github.token }}
+      REPO: ${{ github.repository }}
+      PR_NUMBER: ${{ github.event.issue.number }}
+    steps:
+      - name: Get PR head commit
+        id: get-pr-head-commit
+        run: |
+          echo "sha=$(gh pr view $PR_NUMBER --repo $REPO --json headRefOid -q .headRefOid)" | tee -a $GITHUB_OUTPUT
+
+      - name: Checkout repository
+        uses: actions/checkout@v6
+        with:
+          fetch-depth: 1
+          ref: ${{ steps.get-pr-head-commit.outputs.sha }}
+
+      - name: Run Claude Code Review
+        uses: anthropics/claude-code-action@v1
+        with:
+          anthropic_api_key: ${{ secrets.ANTHROPIC_API_KEY }}
+          trigger_phrase: "/claude review"
+          show_full_output: true
+          claude_args: |
+            --allowedTools "mcp__github_inline_comment__create_inline_comment,Bash(gh pr comment:*),Bash(gh pr diff:*),Bash(gh pr view:*),Bash(gh pr review:*)"
+            --model "claude-opus-4-6"
+          prompt: |
+            REPO: ${{ env.REPO }}
+            PR NUMBER: ${{ env.PR_NUMBER }}
+
+            You are doing a light code review. Keep it concise and actionable.
+
+            Focus ONLY on:
+            - Critical bugs or logic errors
+            - Typos in code, comments, or strings
+            - Missing or insufficient test coverage for changed code
+            - Outdated or inaccurate documentation affected by the changes
+
+            Do NOT comment on:
+            - Style preferences or formatting
+            - Minor naming suggestions
+            - Architectural opinions or refactoring ideas
+            - Performance unless there is a clear, measurable issue
+
+            Provide feedback using inline comments for specific code suggestions.
+            Use top-level comments for general observations.
+
+            It's perfectly acceptable to not have anything to comment on.
+            If you do not have anything to comment on, post "LGTM".
diff --git a/.github/workflows/config/changelog-config.json b/.github/workflows/config/changelog-config.json
index e640b90a0f3..19fb0e42364 100644
--- a/.github/workflows/config/changelog-config.json
+++ b/.github/workflows/config/changelog-config.json
@@ -15,7 +15,7 @@
     },
     "transformers": [],
     "max_tags_to_fetch": 100,
-    "max_pull_requests": 500,
+    "max_pull_requests": 1250,
     "max_back_track_time_days": 365,
     "exclude_merge_branches": [],
     "tag_resolver": {
diff --git a/.github/workflows/copyright-check.yml b/.github/workflows/copyright-check.yml
index a7f51cd8a0e..33d30944f8d 100644
--- a/.github/workflows/copyright-check.yml
+++ b/.github/workflows/copyright-check.yml
@@ -49,7 +49,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
 
       - name: Result
         env:
diff --git a/.github/workflows/force-draft-pr.yml b/.github/workflows/force-draft-pr.yml
new file mode 100644
index 00000000000..d45dabf14b7
--- /dev/null
+++ b/.github/workflows/force-draft-pr.yml
@@ -0,0 +1,36 @@
+# Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+
+name: Force Draft PR
+
+on:
+  pull_request_target:
+    types: [opened]
+    branches:
+      - main
+
+permissions:
+  pull-requests: write
+
+jobs:
+  force-draft:
+    runs-on: ubuntu-latest
+    if: ${{ !github.event.pull_request.draft && github.repository == 'NVIDIA/Megatron-LM' }}
+    steps:
+      - name: Convert PR to draft
+        env:
+          GH_TOKEN: ${{ secrets.PAT }}
+        run: |
+          gh pr ready --undo ${{ github.event.pull_request.number }} --repo ${{ github.repository }}
+
+      - name: Add comment explaining draft policy
+        env:
+          GH_TOKEN: ${{ github.token }}
+        run: |
+          gh pr comment ${{ github.event.pull_request.number }} --repo ${{ github.repository }} --body \
+            "This PR has been automatically converted to **draft** because all PRs must start as drafts.
+
+          When you are ready for review, click **Ready for Review** to begin the review process. This will:
+          1. Add the oncall reviewer (optional reviewer)
+          2. Add required review teams based on your changes
+
+          See the [contribution guide](https://github.com/NVIDIA/Megatron-LM/blob/main/docs/developer/submit.md) for more details."
diff --git a/.github/workflows/install-test.yml b/.github/workflows/install-test.yml
index 5a0abb8596d..060e1c5ade0 100644
--- a/.github/workflows/install-test.yml
+++ b/.github/workflows/install-test.yml
@@ -49,7 +49,7 @@ jobs:
         python-version: ["3.12"]
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
 
       - name: Set PATH
         run: |
@@ -65,7 +65,7 @@ jobs:
         run: bash docker/common/install.sh --environment dev --base-image pytorch --python-version ${{ matrix.python-version }}
 
       - name: Checkout check-imports
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
         with:
           repository: NVIDIA-NeMo/FW-CI-templates
           ref: v0.63.2
@@ -94,7 +94,7 @@ jobs:
         python-version: ["3.12"]
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
 
       - name: Set PATH
         run: |
@@ -113,7 +113,7 @@ jobs:
 
       # NGC PyTorch 25.05 has a version of triton that is broken on CPU only machines.
       # - name: Checkout check-imports
-      #   uses: actions/checkout@v4
+      #   uses: actions/checkout@v6
       #   with:
       #     repository: NVIDIA-NeMo/FW-CI-templates
       #     ref: v0.63.2
@@ -139,7 +139,7 @@ jobs:
       && github.repository == 'NVIDIA/Megatron-LM'
     steps:
       - name: Checkout
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
 
       - name: Get workflow result
         id: result
diff --git a/.github/workflows/oncall-assign.yml b/.github/workflows/oncall-assign.yml
index d4cc47d5f9e..6da0776ffc2 100644
--- a/.github/workflows/oncall-assign.yml
+++ b/.github/workflows/oncall-assign.yml
@@ -16,7 +16,7 @@ name: Oncall Assign
 
 on:
   pull_request_target:
-    types: [opened, ready_for_review]
+    types: [ready_for_review]
     branches:
       - main
 
@@ -30,10 +30,10 @@ jobs:
     if: ${{ !github.event.pull_request.draft }}
     steps:
       - name: Checkout code
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
 
       - name: Set up Python
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v6
         with:
           python-version: '3.10'
 
diff --git a/.github/workflows/oncall-rotation.yml b/.github/workflows/oncall-rotation.yml
index a621be7f652..0d5f774e441 100644
--- a/.github/workflows/oncall-rotation.yml
+++ b/.github/workflows/oncall-rotation.yml
@@ -28,12 +28,12 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Checkout code
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
         with:
           token: ${{ secrets.PAT }}
 
       - name: Set up Python
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v6
         with:
           python-version: "3.10"
 
diff --git a/.github/workflows/release.yaml b/.github/workflows/release.yaml
index 647e6af2379..a756d49eb20 100644
--- a/.github/workflows/release.yaml
+++ b/.github/workflows/release.yaml
@@ -44,6 +44,11 @@ on:
         description: Branch for version bump
         required: true
         type: string
+      gh-release-from-tag:
+        description: Tag of previous release for changelog builder
+        required: false
+        type: string
+        default: ""
 
 permissions:
   contents: write # To read repository content
@@ -59,7 +64,16 @@ jobs:
       create-gh-release: ${{ inputs.create-gh-release || true }}
       gh-release-use-changelog-builder: ${{ inputs.generate-changelog }}
       publish-docs: ${{ inputs.publish-docs }}
+      gh-release-from-tag: ${{ inputs.gh-release-from-tag }}
     secrets:
       TWINE_PASSWORD: ${{ (github.ref == 'refs/heads/main' || startsWith(github.ref, 'refs/heads/r')) && secrets.SVC_PYPI_TOKEN || secrets.SVC_PYPI_TEST_TOKEN }}
       SLACK_WEBHOOK: ${{ (github.ref == 'refs/heads/main' || startsWith(github.ref, 'refs/heads/r')) && secrets.SLACK_MAIN_CHANNEL_WEBHOOK || secrets.SLACK_CI_CHANNEL_WEBHOOK }}
       PAT: ${{ secrets.PAT }}
+      AWS_ASSUME_ROLE_ARN: ${{ secrets.AWS_ASSUME_ROLE_ARN }}
+      AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
+      AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
+      AKAMAI_HOST: ${{ secrets.AKAMAI_HOST }}
+      AKAMAI_CLIENT_TOKEN: ${{ secrets.AKAMAI_CLIENT_TOKEN }}
+      AKAMAI_CLIENT_SECRET: ${{ secrets.AKAMAI_CLIENT_SECRET }}
+      AKAMAI_ACCESS_TOKEN: ${{ secrets.AKAMAI_ACCESS_TOKEN }}
+      S3_BUCKET_NAME: ${{ secrets.S3_BUCKET_NAME }}
diff --git a/.github/workflows/review-trigger.yml b/.github/workflows/review-trigger.yml
new file mode 100644
index 00000000000..28abf259882
--- /dev/null
+++ b/.github/workflows/review-trigger.yml
@@ -0,0 +1,28 @@
+# Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# Lightweight workflow that triggers on review approval, otherwise there is no access to right secret.
+# No secrets needed — just signals auto-swap-labels.yml via workflow_run.
+
+name: Review Trigger
+
+on:
+  pull_request_review:
+    types: [submitted]
+
+jobs:
+  signal:
+    runs-on: ubuntu-latest
+    if: >-
+      github.event.review.state == 'approved' &&
+      github.event.pull_request.base.ref == 'main' &&
+      github.repository == 'NVIDIA/Megatron-LM'
+    steps:
+      - name: Save PR number
+        run: |
+          mkdir -p pr
+          echo "${{ github.event.pull_request.number }}" > pr/number
+      - name: Upload PR number
+        uses: actions/upload-artifact@v4
+        with:
+          name: pr-number
+          path: pr/
diff --git a/.github/workflows/sync-team-usergroups.yml b/.github/workflows/sync-team-usergroups.yml
index fb48a6ca5d4..7f32ac55c57 100644
--- a/.github/workflows/sync-team-usergroups.yml
+++ b/.github/workflows/sync-team-usergroups.yml
@@ -24,10 +24,10 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Checkout code
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
 
       - name: Set up Python
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v6
         with:
           python-version: "3.10"
 
diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index a238f2c9999..2eb1b43be0c 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -1,16 +1,16 @@
 .merge_train_rule: &merge_train_rule
-  UNIT_TEST: 'yes'
+  UNIT_TEST: "yes"
   UNIT_TEST_REPEAT: 1
   UNIT_TEST_TIMEOUT: 30
-  INTEGRATION_TEST: 'no'
+  INTEGRATION_TEST: "no"
   INTEGRATION_TEST_SCOPE: mr
-  FUNCTIONAL_TEST: 'yes'
+  FUNCTIONAL_TEST: "yes"
   FUNCTIONAL_TEST_SCOPE: mr-slim
   FUNCTIONAL_TEST_REPEAT: 1
   FUNCTIONAL_TEST_TIME_LIMIT: 2700
-  CLUSTER_A100: ''
-  CLUSTER_H100: ''
-  PUBLISH: 'no'
+  CLUSTER_A100: ""
+  CLUSTER_H100: ""
+  PUBLISH: "no"
 
 workflow:
   rules:
@@ -29,36 +29,42 @@ workflow:
       auto_cancel:
         on_new_commit: none
 
-    # For manual pipelines
+    # For manual pipelines (GitLab UI)
     - if: $CI_PIPELINE_SOURCE == "web"
 
+    # For pipelines created via the REST API (personal access token)
+    - if: $CI_PIPELINE_SOURCE == "api"
+
+    # For trigger pipelines
+    - if: $CI_PIPELINE_SOURCE == "trigger"
+
     # For push to main
     - if: $CI_PIPELINE_SOURCE == 'push' && ($CI_COMMIT_BRANCH == "main" || $CI_COMMIT_BRANCH == "dev" || $CI_COMMIT_BRANCH =~ /^core_/)
       variables:
-        UNIT_TEST: 'no'
-        INTEGRATION_TEST: 'no'
-        FUNCTIONAL_TEST: 'yes'
+        UNIT_TEST: "no"
+        INTEGRATION_TEST: "no"
+        FUNCTIONAL_TEST: "yes"
         FUNCTIONAL_TEST_SCOPE: mr
         FUNCTIONAL_TEST_REPEAT: 5
-        FUNCTIONAL_TEST_RECORD_CHECKPOINTS: 'no'
+        FUNCTIONAL_TEST_RECORD_CHECKPOINTS: "no"
         FUNCTIONAL_TEST_TIME_LIMIT: 3600
-        CLUSTER_A100: ''
-        CLUSTER_H100: ''
-        PUBLISH: 'no'
+        CLUSTER_A100: ""
+        CLUSTER_H100: ""
+        PUBLISH: "no"
       auto_cancel:
         on_new_commit: interruptible
 
     # For merge-trains that need to be fast-tracked
     - if: $CI_MERGE_REQUEST_EVENT_TYPE == 'merge_train' && $CI_MERGE_REQUEST_LABELS =~ /fast-track/
       variables:
-        UNIT_TEST: 'yes'
+        UNIT_TEST: "yes"
         UNIT_TEST_REPEAT: 1
         UNIT_TEST_TIMEOUT: 30
-        INTEGRATION_TEST: 'no'
-        FUNCTIONAL_TEST: 'no'
-        CLUSTER_A100: ''
-        CLUSTER_H100: ''
-        PUBLISH: 'no'
+        INTEGRATION_TEST: "no"
+        FUNCTIONAL_TEST: "no"
+        CLUSTER_A100: ""
+        CLUSTER_H100: ""
+        PUBLISH: "no"
 
     # For normal merge-trains
     - if: $CI_MERGE_REQUEST_EVENT_TYPE == 'merge_train'
@@ -67,75 +73,75 @@ workflow:
     # For MRs with integration suite
     - if: $CI_MERGE_REQUEST_EVENT_TYPE == 'merged_result' && $CI_MERGE_REQUEST_LABELS =~ /Run tests/
       variables:
-        UNIT_TEST: 'yes'
+        UNIT_TEST: "yes"
         UNIT_TEST_REPEAT: 1
         UNIT_TEST_TIMEOUT: 30
-        INTEGRATION_TEST: 'yes'
+        INTEGRATION_TEST: "yes"
         INTEGRATION_TEST_SCOPE: mr
-        FUNCTIONAL_TEST: 'no'
+        FUNCTIONAL_TEST: "no"
         FUNCTIONAL_TEST_SCOPE: mr-slim
         FUNCTIONAL_TEST_REPEAT: 1
         FUNCTIONAL_TEST_TIME_LIMIT: 2700
-        CLUSTER_A100: ''
-        CLUSTER_H100: ''
-        PUBLISH: 'no'
+        CLUSTER_A100: ""
+        CLUSTER_H100: ""
+        PUBLISH: "no"
 
     # For MRs with nightly
     - if: $CI_MERGE_REQUEST_EVENT_TYPE == 'merged_result' && $CI_MERGE_REQUEST_LABELS =~ /Run nightly/
       variables:
-        UNIT_TEST: 'yes'
+        UNIT_TEST: "yes"
         UNIT_TEST_REPEAT: 1
         UNIT_TEST_TIMEOUT: 30
-        INTEGRATION_TEST: 'no'
-        FUNCTIONAL_TEST: 'yes'
+        INTEGRATION_TEST: "no"
+        FUNCTIONAL_TEST: "yes"
         FUNCTIONAL_TEST_SCOPE: nightly
         FUNCTIONAL_TEST_REPEAT: 5
-        FUNCTIONAL_TEST_RECORD_CHECKPOINTS: 'no'
+        FUNCTIONAL_TEST_RECORD_CHECKPOINTS: "no"
         FUNCTIONAL_TEST_TIME_LIMIT: 2700
-        CLUSTER_A100: ''
-        CLUSTER_H100: ''
-        PUBLISH: 'no'
+        CLUSTER_A100: ""
+        CLUSTER_H100: ""
+        PUBLISH: "no"
 
     # For MRs with weekly
     - if: $CI_MERGE_REQUEST_EVENT_TYPE == 'merged_result' && $CI_MERGE_REQUEST_LABELS =~ /Run weekly/
       variables:
-        UNIT_TEST: 'yes'
+        UNIT_TEST: "yes"
         UNIT_TEST_REPEAT: 1
         UNIT_TEST_TIMEOUT: 30
-        INTEGRATION_TEST: 'no'
-        FUNCTIONAL_TEST: 'yes'
+        INTEGRATION_TEST: "no"
+        FUNCTIONAL_TEST: "yes"
         FUNCTIONAL_TEST_SCOPE: weekly
         FUNCTIONAL_TEST_REPEAT: 1
-        FUNCTIONAL_TEST_RECORD_CHECKPOINTS: 'no'
+        FUNCTIONAL_TEST_RECORD_CHECKPOINTS: "no"
         FUNCTIONAL_TEST_TIME_LIMIT: 9000
-        CLUSTER_A100: ''
-        CLUSTER_H100: ''
-        PUBLISH: 'no'
+        CLUSTER_A100: ""
+        CLUSTER_H100: ""
+        PUBLISH: "no"
 
     # For MRs with heavy suite
     - if: $CI_MERGE_REQUEST_EVENT_TYPE == 'merged_result' && $CI_MERGE_REQUEST_LABELS =~ /Run functional tests/
       variables:
-        UNIT_TEST: 'yes'
+        UNIT_TEST: "yes"
         UNIT_TEST_REPEAT: 1
         UNIT_TEST_TIMEOUT: 30
-        INTEGRATION_TEST: 'no'
-        FUNCTIONAL_TEST: 'yes'
+        INTEGRATION_TEST: "no"
+        FUNCTIONAL_TEST: "yes"
         FUNCTIONAL_TEST_SCOPE: mr
         FUNCTIONAL_TEST_REPEAT: 1
         FUNCTIONAL_TEST_TIME_LIMIT: 2700
-        CLUSTER_A100: ''
-        CLUSTER_H100: ''
-        PUBLISH: 'no'
+        CLUSTER_A100: ""
+        CLUSTER_H100: ""
+        PUBLISH: "no"
 
     # Default MRs
     - if: $CI_MERGE_REQUEST_EVENT_TYPE == 'merged_result'
       variables:
-        UNIT_TEST: 'yes'
+        UNIT_TEST: "yes"
         UNIT_TEST_REPEAT: 1
         UNIT_TEST_TIMEOUT: 30
-        INTEGRATION_TEST: 'no'
-        FUNCTIONAL_TEST: 'no'
-        PUBLISH: 'no'
+        INTEGRATION_TEST: "no"
+        FUNCTIONAL_TEST: "no"
+        PUBLISH: "no"
 
     - when: never
 
@@ -157,109 +163,109 @@ default:
 
 variables:
   BUILD:
-    value: 'yes'
+    value: "yes"
   UNIT_TEST:
-    value: 'yes'
+    value: "yes"
     options:
-      - 'yes'
-      - 'no'
+      - "yes"
+      - "no"
     description: To run the funtional test suite
   UNIT_TEST_REPEAT:
-    value: '1'
-    description: 'Number of repetitions'
+    value: "1"
+    description: "Number of repetitions"
   UNIT_TEST_TIMEOUT:
-    value: '30'
+    value: "30"
     description: Timeout (minutes) for Unit tests (all repeats)
   INTEGRATION_TEST:
-    value: 'yes'
+    value: "yes"
     options:
-      - 'yes'
-      - 'no'
+      - "yes"
+      - "no"
     description: To run the integration test suite
   INTEGRATION_TEST_SCOPE:
-    value: 'mr'
+    value: "mr"
     options:
-      - 'mr'
-      - 'nightly'
-      - 'weekly'
-      - 'pre-release'
-      - 'release'
-    description: 'Testsuite to run (only for INTEGRATION_TEST=yes)'
+      - "mr"
+      - "nightly"
+      - "weekly"
+      - "pre-release"
+      - "release"
+    description: "Testsuite to run (only for INTEGRATION_TEST=yes)"
   INTEGRATION_TEST_TIME_LIMIT:
-    value: '900'
-    description: 'Timeout in seconds per test'
+    value: "900"
+    description: "Timeout in seconds per test"
   INTEGRATION_TEST_CASES:
-    value: 'all'
+    value: "all"
     description: "Comma-separated list of test_cases to run. Use 'all' to run the full suite."
   FUNCTIONAL_TEST:
-    value: 'yes'
+    value: "yes"
     options:
-      - 'yes'
-      - 'no'
+      - "yes"
+      - "no"
     description: To run the funtional test suite
   FUNCTIONAL_TEST_SCOPE:
-    value: 'mr'
+    value: "mr"
     options:
-      - 'mr'
-      - 'nightly'
-      - 'weekly'
-      - 'pre-release'
-      - 'release'
-    description: 'Testsuite to run (only for FUNCTIONAL_TEST=yes)'
+      - "mr"
+      - "nightly"
+      - "weekly"
+      - "pre-release"
+      - "release"
+    description: "Testsuite to run (only for FUNCTIONAL_TEST=yes)"
   FUNCTIONAL_TEST_REPEAT:
-    value: '5'
-    description: 'Number of repetitions per test'
+    value: "5"
+    description: "Number of repetitions per test"
   FUNCTIONAL_TEST_TIME_LIMIT:
-    value: '2700'
-    description: 'Timeout in seconds per test'
+    value: "2700"
+    description: "Timeout in seconds per test"
   FUNCTIONAL_TEST_CASES:
-    value: 'all'
+    value: "all"
     description: "Comma-separated list of test_cases to run. Use 'all' to run the full suite."
   FUNCTIONAL_TEST_NAME:
-    description: 'Name of functional test run (only for pre-release and release)'
-    value: '$$CI_COMMIT_SHA'
+    description: "Name of functional test run (only for pre-release and release)"
+    value: "$$CI_COMMIT_SHA"
   FUNCTIONAL_TEST_RECORD_CHECKPOINTS:
-    value: 'no'
-    description: 'Record golden checkpoints'
+    value: "no"
+    description: "Record golden checkpoints"
     options:
-      - 'yes'
-      - 'no'
+      - "yes"
+      - "no"
   CLUSTER_A100:
-    value: 'dgxa100_dracooci'
+    value: "dgxa100_dracooci"
     options:
-      - 'dgxa100_dracooci'
-      - 'dgxa100_dracooci-ord'
-    description: 'Cluster for A100 workloads'
+      - "dgxa100_dracooci"
+      - "dgxa100_dracooci-ord"
+    description: "Cluster for A100 workloads"
   CLUSTER_H100:
-    value: 'dgxh100_coreweave'
+    value: "dgxh100_coreweave"
     options:
-      - 'dgxh100_coreweave'
-      - 'dgxh100_eos'
-    description: 'Cluster for H100 workloads'
+      - "dgxh100_coreweave"
+      - "dgxh100_eos"
+    description: "Cluster for H100 workloads"
   CLUSTER_GB200:
-    value: 'dgxgb200_oci-hsg'
+    value: "dgxgb200_oci-hsg"
     options:
-      - 'dgxgb200_oci-hsg'
-    description: 'Cluster for H100 workloads'
+      - "dgxgb200_oci-hsg"
+    description: "Cluster for H100 workloads"
   PUBLISH:
-    value: 'no'
+    value: "no"
     options:
-      - 'yes'
-      - 'no'
+      - "yes"
+      - "no"
     description: Build and publish a wheel to PyPi
   PUBLISH_COMMIT:
-    value: '$$CI_COMMIT_SHA'
+    value: "$$CI_COMMIT_SHA"
     description: Which commit to publish
   PUBLISH_VERSION_BUMP_BRANCH:
-    value: '$$CI_COMMIT_BRANCH'
+    value: "$$CI_COMMIT_BRANCH"
     description: Which branch to target for version bump
   PUBLISH_SCOPE:
-    value: 'code-freeze'
+    value: "code-freeze"
     options:
-      - 'code-freeze'
-      - 'release'
-      - 'review-reminder'
-      - 'upgrade-dependencies'
+      - "code-freeze"
+      - "release"
+      - "review-reminder"
+      - "upgrade-dependencies"
     description: Type of publish (freeze or final release)
 
   # CI wide variables
@@ -267,7 +273,7 @@ variables:
   CI_MCORE_DEV_IMAGE: ${GITLAB_ENDPOINT}:5005/adlr/megatron-lm/mcore_ci_dev
   CI_NEMO_IMAGE: ${GITLAB_ENDPOINT}:5005/adlr/megatron-lm/nemo_ci
   UTILITY_IMAGE: ${GITLAB_ENDPOINT}:5005/adlr/megatron-lm/mcore_utility
-  TE_GIT_REF: ''
+  TE_GIT_REF: ""
 
 include:
   - .gitlab/stages/00.pre.yml
diff --git a/.gitlab/stages/01.build.yml b/.gitlab/stages/01.build.yml
index 61521295a93..f5a80c4074d 100644
--- a/.gitlab/stages/01.build.yml
+++ b/.gitlab/stages/01.build.yml
@@ -64,12 +64,12 @@ test:pre_build_image:
       - IMAGE: CI_MCORE_DEV_IMAGE
         FILE: Dockerfile.ci.dev
         IMAGE_TYPE: dev
-        BASE_IMAGE: nvcr.io/nvidia/pytorch:25.11-py3
+        BASE_IMAGE: nvcr.io/nvidia/pytorch:26.02-py3
         PLATFORM: amd64
       - IMAGE: CI_MCORE_DEV_IMAGE
         FILE: Dockerfile.ci.dev
         IMAGE_TYPE: dev
-        BASE_IMAGE: nvcr.io/nvidia/pytorch:25.11-py3
+        BASE_IMAGE: nvcr.io/nvidia/pytorch:26.02-py3
         PLATFORM: arm64
       - IMAGE: UTILITY_IMAGE
         FILE: Dockerfile.linting
diff --git a/codecov.yml b/codecov.yml
new file mode 100644
index 00000000000..aa37017f082
--- /dev/null
+++ b/codecov.yml
@@ -0,0 +1,14 @@
+comment: false
+coverage:
+  status:
+    project: false
+    patch:
+      default:
+        target: 80%
+        threshold: 5%
+        base: auto
+        if_ci_failed: error
+        if_no_uploads: success
+        if_not_found: success
+fixes:
+  - "/opt/megatron-lm/::"
diff --git a/docker/.ngc_version.dev b/docker/.ngc_version.dev
index 8e8108b9a9a..2c33440d4e2 100644
--- a/docker/.ngc_version.dev
+++ b/docker/.ngc_version.dev
@@ -1 +1 @@
-nvcr.io/nvidia/pytorch:25.11-py3
\ No newline at end of file
+nvcr.io/nvidia/pytorch:26.02-py3
\ No newline at end of file
diff --git a/docker/common/install.sh b/docker/common/install.sh
index 01003c0e7aa..ec03172acf1 100644
--- a/docker/common/install.sh
+++ b/docker/common/install.sh
@@ -120,6 +120,8 @@ main() {
 
         # Create virtual environment and install dependencies
         uv venv ${UV_PROJECT_ENVIRONMENT} --system-site-packages
+        uv pip install --python ${UV_PROJECT_ENVIRONMENT}/bin/python \
+            pybind11 wheel packaging "setuptools<80.0.0,>=77.0.0"
 
         # Install dependencies
         uv sync --locked --only-group build ${UV_ARGS[@]}
diff --git a/docs/api-backwards-compatibility-check.md b/docs/api-backwards-compatibility-check.md
index e1b6939b06f..40f56ec0c00 100644
--- a/docs/api-backwards-compatibility-check.md
+++ b/docs/api-backwards-compatibility-check.md
@@ -1,3 +1,7 @@
+---
+orphan: true
+---
+
 <!---
    Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
    NVIDIA CORPORATION and its licensors retain all intellectual property
diff --git a/docs/api-guide/core/transformer.md b/docs/api-guide/core/transformer.md
index 622fb006f99..d35144fda4f 100644
--- a/docs/api-guide/core/transformer.md
+++ b/docs/api-guide/core/transformer.md
@@ -13,8 +13,7 @@ The `transformer` package provides a customizable and configurable
 implementation of the transformer model architecture. Each component
 of a transformer stack, from entire layers down to individual linear
 layers, can be customized by swapping in different PyTorch modules
-using the "spec" parameters (see [here](https://docs.nvidia.com/nemo-framework/user-guide/latest/nemotoolkit/nlp/nemo_megatron/mcore_customization.html)). The
+using the "spec" parameters. The
 configuration of the transformer (hidden size, number of layers,
 number of attention heads, etc.) is provided via a `TransformerConfig`
 object.
-
diff --git a/docs/api-guide/index.md b/docs/api-guide/index.md
index 7afa2450dd0..ec4a1e5fb07 100644
--- a/docs/api-guide/index.md
+++ b/docs/api-guide/index.md
@@ -14,6 +14,7 @@ API reference documentation for Megatron Core components.
 ```{toctree}
 :maxdepth: 3
 
+router_replay
 models/index
 core/index
 internal/index
diff --git a/docs/api-guide/router_replay.md b/docs/api-guide/router_replay.md
index 4c1dc98b2b6..88476ea7537 100644
--- a/docs/api-guide/router_replay.md
+++ b/docs/api-guide/router_replay.md
@@ -9,19 +9,19 @@
 
 # Design Document: MoE Router Replay Feature
 
-### 1. Overview
+## 1. Overview
 
 This document provides a detailed description of the "Router Replay" feature implemented within the Megatron-LM Core for Mixture-of-Experts (MoE) models.
 
 This feature is designed to enhance determinism and analyzability in MoE model training and inference. It enables the model to load routing decisions from a predefined file and enforce their use during the forward pass, thereby bypassing the real-time routing computation.
 
-### 2. Motivation
+## 2. Motivation
 
 *   **Determinism & Reproducibility**: In distributed training, MoE routing decisions can exhibit minor variations due to factors like floating-point precision. By replaying a fixed routing table, the MoE computation path is guaranteed to be identical across runs, which facilitates debugging and reproducing experimental results.
 *   **Performance Profiling**: The router's own computation (e.g., logits calculation, top-k selection) incurs overhead. In replay mode, this part of the computation can be completely skipped, allowing for more precise isolation and profiling of performance bottlenecks within the Expert Layers themselves.
 *   **Debugging Aid**: When issues arise in the model, fixing the routing decisions helps to isolate variables, making it easier to determine whether the problem lies with the routing mechanism or the expert computations.
 
-### 3. Design and Architecture
+## 3. Design and Architecture
 
 The design follows the principles of being non-intrusive and on-demand, with the core idea of activating the replay logic only when explicitly requested by the user.
 
@@ -44,7 +44,7 @@ The design follows the principles of being non-intrusive and on-demand, with the
             *   For each micro-batch (processed in reverse order in pipeline parallelism), the `router_replay_action` is checked again.
             *   **In `backward_replay` mode**: The function retrieves the expert indices for the corresponding micro-batch by popping them from the `replay_backward_list`. This mode is intended for training recomputation (e.g., activation checkpointing and pipeline recompute) so the same routing decisions are used during recompute/backward as in forward, ensuring determinism and correctness.
 
-### 4. Implementation Details
+## 4. Implementation Details
 
 The implementation cleanly separates the replay logic from the router's core computation.
 
@@ -60,11 +60,12 @@ The implementation cleanly separates the replay logic from the router's core com
         *   `record_indices()`: A method to save the computed indices.
     *   The `topk_routing_with_score_function` is modified to contain the core logic. It checks the `router_replay_action` on the `router_replay` instance and accordingly performs one of the following actions: computes and records indices, replays indices from `target_topk_idx` (for forward), replays indices from `replay_backward_list` (for backward), or falls through to the default dynamic routing.
 
-#### Training recompute usage
+### Training recompute usage
+
 - During forward replay, `set_target_indices()` prepares `replay_backward_list` so each micro-batch’s indices are available for recomputation.
 - During recompute/backward, set action to `REPLAY_BACKWARD` so indices are consumed in FIFO order to mirror the forward sequence.
 
-### 5. Usage Guide
+## 5. Usage Guide
 
 1.  **Enable & Instantiate**
     - Create one `RouterReplay` instance per MoE router layer when building the model.
@@ -82,7 +83,7 @@ The implementation cleanly separates the replay logic from the router's core com
 5.  **Cleanup**
     - Use `RouterReplay.clear_global_indices()`, `RouterReplay.clear_global_router_replay_action()`, and `RouterReplay.clear_global_router_replay_instances()` to restore default behavior and prevent memory leaks.
 
-#### Quick usage with `topk_routing_with_score_function`
+### Quick usage with `topk_routing_with_score_function`
 
 ```python
 import torch
@@ -114,7 +115,7 @@ RouterReplay.clear_global_indices()
 RouterReplay.clear_global_router_replay_instances()
 ```
 
-### 6. Minimal Demo
+## 6. Minimal Demo
 
 Here is a minimal code example showing how to use RouterReplay for recording and replaying:
 
diff --git a/docs/broken_links_false_positives.json b/docs/broken_links_false_positives.json
new file mode 100644
index 00000000000..01377be5804
--- /dev/null
+++ b/docs/broken_links_false_positives.json
@@ -0,0 +1,3 @@
+{
+    "uri": "http://localhost:8080/"
+}
\ No newline at end of file
diff --git a/docs/conf.py b/docs/conf.py
index 84ed89753f5..9bf0b99c706 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -63,6 +63,12 @@
 ]
 myst_heading_anchors = 5  # Generates anchor links for headings up to level 5
 
+# Suppress "more than one target found for cross-reference" warnings for Python symbols
+# that have the same name across multiple modules (e.g. DistributedDataParallelConfig,
+# ModelType). These are structural ambiguities in the codebase – the cross-reference
+# still resolves; Sphinx just cannot pick the unique target automatically.
+suppress_warnings = ["ref.python"]
+
 # -- Options for Autodoc2 ---------------------------------------------------
 sys.path.insert(0, os.path.abspath(".."))
 
@@ -81,6 +87,11 @@
     autodoc2_docstring_parser_regexes = [
         (r".*", "docs.autodoc2_docstrings_parser"),
     ]
+    # Regex patterns whose values contain raw regex syntax (e.g. \p{L}) that docutils
+    # mis-parses as footnote/reference markup. Exclude them from the generated docs.
+    autodoc2_hidden_regexes = [
+        r".*\._PATTERN_TIKTOKEN.*",
+    ]
 
 # -- Options for HTML output -------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
@@ -98,16 +109,7 @@
             "icon": "fa-brands fa-github",
         }
     ],
-    "extra_head": {
-        """
-    <script src="https://assets.adobedtm.com/5d4962a43b79/c1061d2c5e7b/launch-191c2462b890.min.js" ></script>
-    """
-    },
-    "extra_footer": {
-        """
-    <script type="text/javascript">if (typeof _satellite !== "undefined") {_satellite.pageBottom();}</script>
-    """
-    },
+    "public_docs_features": True
 }
 html_extra_path = ["project.json", "versions1.json"]
 
diff --git a/docs/developer/oncall.md b/docs/developer/oncall.md
index ee5582ca24d..0e5b38e2708 100644
--- a/docs/developer/oncall.md
+++ b/docs/developer/oncall.md
@@ -40,13 +40,13 @@ Below is the checklist that the oncall needs to go through for each PR.
 - Do all tests pass?
   - Oncall will need to kick off testing suite for external reviewers
   - Comment “/ok to test commid_id” to kick off testing suite
-- Add the “Expert Review” label
-  - Select an expert reviewer from each expert group as a reviewer. If you’re unsure who to select, pick a “maintainer” or manager.
+- Expert reviewers are notified after the PR is marked “Ready for Review”
   - **Expert reviewers should review within 1 business day.** Message the assigned reviewer if it is taking longer. The reviewer either needs to review the PR or suggest an alternate reviewer.
-  - If the reviewer is not responding after 2 business days, escalate to the reviewer's manager.
-- Add the “Final Review” label after experts approve
+  - If the reviewer is not responding after 2 business days, escalate to the reviewer’s manager.
+- For `megatron/core` PRs, the “Final Review” label is applied automatically once all expert reviewers approve
   - Final reviewers should review within 1 business day. Message the assigned reviewer if it is taking longer.
-  - If the reviewer is not responding after 2 business days, escalate to the reviewer's manager.
+  - If the reviewer is not responding after 2 business days, escalate to the reviewer’s manager.
+- The “Approved” label is applied automatically once all required reviewers have approved
 
 ## Issues and Discussion Questions
 
diff --git a/docs/developer/submit.md b/docs/developer/submit.md
index a46df22f85c..205e18cc52f 100644
--- a/docs/developer/submit.md
+++ b/docs/developer/submit.md
@@ -9,17 +9,26 @@
 
 # How to Submit a PR
 
-## Step 1: Add PR label `Expert Review`
+All PRs start as **draft**. If you open a non-draft PR, it will be automatically converted to draft.
 
-## Step 2: Collect the expert reviewers reviews
+## Step 1: Mark PR as "Ready for Review"
 
-1. Attach the `Expert Review` label when your PR is ready for review.
-2. GitHub auto-assigns expert reviewers based on your changes. They will get notified and pick up your PR soon.
+1. When your PR is ready, click **Ready for Review**.
+2. The oncall reviewer is auto-assigned and expert reviewers are notified based on your changes. They will get notified and pick up your PR soon.
 
-:warning: Only proceed to the next step once all reviewers have approved, merge-conflict are resolved and the CI is passing.  
+:warning: Only mark as ready once all merge-conflicts are resolved and the CI is passing.
 Final Review might get declined if these requirements are not fulfilled.
 
-## Step 3: Final Review
+## Step 2: Final Review (`megatron/core` only)
 
-1. Add `Final Review` label
-2. GitHub auto-assigns final reviewers based on your changes. They will get notified and pick up your PR soon.
+For PRs that change `megatron/core`, once all expert reviewers have approved, the `Final Review` label is applied **automatically** and final reviewers are assigned.
+
+For PRs outside `megatron/core`, this step is skipped.
+
+## Step 3: Approved
+
+Once all required reviewers have approved, the `Approved` label is applied **automatically**. The PR is now ready to merge.
+
+## Step 4: Merge
+
+Any member of [mcore-engineers](https://github.com/orgs/NVIDIA/teams/mcore-engineers) will be able to merge your PR.
diff --git a/docs/discussions/README.md b/docs/discussions/README.md
index b95300649d1..e791ed57cd8 100644
--- a/docs/discussions/README.md
+++ b/docs/discussions/README.md
@@ -1,3 +1,7 @@
+---
+orphan: true
+---
+
 <!---
    Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
    NVIDIA CORPORATION and its licensors retain all intellectual property
diff --git a/docs/discussions/megatron-fsdp-user-guide/megatron-fsdp-user-guide.md b/docs/discussions/megatron-fsdp-user-guide/megatron-fsdp-user-guide.md
index 5ef71043f86..b5de090ab46 100644
--- a/docs/discussions/megatron-fsdp-user-guide/megatron-fsdp-user-guide.md
+++ b/docs/discussions/megatron-fsdp-user-guide/megatron-fsdp-user-guide.md
@@ -1,3 +1,7 @@
+---
+orphan: true
+---
+
 <!---
    Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
    NVIDIA CORPORATION and its licensors retain all intellectual property
diff --git a/docs/documentation.md b/docs/documentation.md
index 652a142aec9..d2554157b45 100644
--- a/docs/documentation.md
+++ b/docs/documentation.md
@@ -1,3 +1,7 @@
+---
+orphan: true
+---
+
 <!---
    Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
    NVIDIA CORPORATION and its licensors retain all intellectual property
@@ -7,10 +11,6 @@
    license agreement from NVIDIA CORPORATION is strictly prohibited.
 -->
 
----
-orphan: true
----
-
 # Documentation Development
 
 - [Documentation Development](#documentation-development)
diff --git a/docs/get-started/install.md b/docs/get-started/install.md
index e1d7202b3fc..5781d065fae 100644
--- a/docs/get-started/install.md
+++ b/docs/get-started/install.md
@@ -7,90 +7,117 @@
    license agreement from NVIDIA CORPORATION is strictly prohibited.
 -->
 
-# Megatron Core Installation
-
-Installation is supported using Docker and pip.
+# Installation
 
 ## System Requirements
 
-### Hardware Requirements
+### Hardware
 
-- **FP8 Support**: NVIDIA Hopper, Ada, Blackwell GPUs
 - **Recommended**: NVIDIA Turing architecture or later
+- **FP8 Support**: Requires NVIDIA Hopper, Ada, or Blackwell GPUs
 
-### Software Requirements
+### Software
 
-- **CUDA/cuDNN/NCCL**: Latest stable versions
-- **PyTorch**: Latest stable version
-- **Transformer Engine**: Latest stable version
-- **Python**: 3.12 recommended
+- **Python**: >= 3.10 (3.12 recommended)
+- **PyTorch**: >= 2.6.0
+- **CUDA Toolkit**: Latest stable version
 
 
-## Docker Installation (Recommended)
+## Prerequisites
 
-We strongly recommend using the previous releases of [PyTorch NGC Container](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch) rather than the latest one for optimal compatibility with Megatron Core release and testing matrix. Our releases are always based on the previous month's NGC container, so this ensures compatibility and stability.
+Install [uv](https://docs.astral.sh/uv/), a fast Python package installer:
 
-**Note:** The NGC PyTorch container constraints the python environment globally via `PIP_CONSTRAINT`. In the following examples we will unset the variable.
+```bash
+curl -LsSf https://astral.sh/uv/install.sh | sh
+```
 
-This container comes with all dependencies pre-installed with compatible versions and optimized configurations for NVIDIA GPUs:
 
-- PyTorch (latest stable version)
-- CUDA, cuDNN, NCCL (latest stable versions)
-- Support for FP8 on NVIDIA Hopper, Ada, and Blackwell GPUs
-- For best performance, use NVIDIA Turing GPU architecture generations and later
+## Option A: Pip Install (Recommended)
+
+Install the latest stable release from PyPI:
 
 ```bash
-# Run container with mounted directories
-docker run --runtime --nvidia --gpus all -it --rm \
-  -v /path/to/megatron:/workspace/megatron \
-  -v /path/to/dataset:/workspace/dataset \
-  -v /path/to/checkpoints:/workspace/checkpoints \
-  -e PIP_CONSTRAINT= \
-  nvcr.io/nvidia/pytorch:25.04-py3
+uv pip install megatron-core
 ```
 
-## Pip Installation
+To include optional training dependencies (Weights & Biases, SentencePiece, HF Transformers):
+
+```bash
+uv pip install "megatron-core[training]"
+```
 
-Megatron Core installation offers support for two NGC PyTorch containers:
+For all extras including [Transformer Engine](https://github.com/NVIDIA/TransformerEngine):
 
-- `dev`: Moving head that supports the most recent upstream dependencies
-- `lts`: Long-term support of NGC PyTorch 24.01
+```bash
+uv pip install --group build
+uv pip install --no-build-isolation "megatron-core[training,dev]"
+```
 
-Both containers can be combined with `mlm`, which adds package dependencies for Megatron-LM on top of Megatron Core.
+```{note}
+`--no-build-isolation` requires build dependencies to be pre-installed in the environment. `torch` is needed because several `[dev]` packages (`mamba-ssm`, `nv-grouped-gemm`, `transformer-engine`) import it at build time to compile CUDA kernels. Expect this step to take **20+ minutes** depending on your hardware. If you prefer pre-built binaries, the [NGC Container](#option-c-ngc-container) ships with these pre-compiled.
+```
 
+```{warning}
+Building from source can consume a large amount of memory. By default the build runs one compiler job per CPU core, which may cause out-of-memory failures on machines with many cores. To limit parallel compilation jobs, set the `MAX_JOBS` environment variable before installing (e.g. `MAX_JOBS=4`).
+```
 
-1. Install the latest release dependencies
+```{tip}
+For a lighter set of development dependencies without Transformer Engine and ModelOpt, use `[lts]` instead of `[dev]`: `uv pip install --no-build-isolation "megatron-core[training,lts]"`. The `[lts]` and `[dev]` extras are mutually exclusive.
+```
 
-    ```bash
-    pip install "setuptools<80.0.0,>=77.0.0" "packaging>=24.2"
-    pip install --no-build-isolation megatron-core[dev]
-    ```
+To clone the repository for examples:
 
-2. Next choose one of the following options:
+```bash
+git clone https://github.com/NVIDIA/Megatron-LM.git
+```
 
-* For running an Megatron LM application
 
-        ```bash
-        pip install "setuptools<80.0.0,>=77.0.0" "packaging>=24.2"
-        pip install --no-build-isolation megatron-core[mlm,dev]
-        ```
-* Install packages for LTS support NGC PyTorch 24.01
+## Option B: Install from Source
 
-        ```bash
-        pip install "setuptools<80.0.0,>=77.0.0" "packaging>=24.2"
-        pip install --no-build-isolation megatron-core[lts]
-        ```
+For development or to run the latest unreleased code:
 
-* For running an Megatron LM application
+```bash
+git clone https://github.com/NVIDIA/Megatron-LM.git
+cd Megatron-LM
+uv pip install -e .
+```
 
-        ```bash
-        pip install "setuptools<80.0.0,>=77.0.0" "packaging>=24.2"
-        pip install --no-build-isolation megatron-core[mlm,lts]
-        ```
+To install with all development dependencies (includes Transformer Engine, requires pre-installed build deps):
 
-* For a version of Megatron Core with only Torch, run
+```bash
+uv pip install --group build
+uv pip install --no-build-isolation -e ".[training,dev]"
+```
+
+```{tip}
+If the build runs out of memory, limit parallel compilation jobs with `MAX_JOBS=4 uv pip install --no-build-isolation -e ".[training,dev]"`.
+```
+
+
+## Option C: NGC Container
+
+For a pre-configured environment with all dependencies pre-installed (PyTorch, CUDA, cuDNN, NCCL, Transformer Engine), use the [PyTorch NGC Container](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch).
+
+We recommend using the **previous month's** NGC container rather than the latest one to ensure compatibility with the current Megatron Core release and testing matrix.
+
+```bash
+docker run --gpus all -it --rm \
+  -v /path/to/dataset:/workspace/dataset \
+  -v /path/to/checkpoints:/workspace/checkpoints \
+  -e PIP_CONSTRAINT= \
+  nvcr.io/nvidia/pytorch:26.01-py3
+```
+
+```{note}
+The NGC PyTorch container constrains the Python environment globally via `PIP_CONSTRAINT`. The `-e PIP_CONSTRAINT=` flag above unsets this so that Megatron Core and its dependencies install correctly.
+```
+
+Then install Megatron Core inside the container (torch is already available in the NGC image):
+
+```bash
+pip install uv
+uv pip install --no-build-isolation "megatron-core[training,dev]"
+```
 
-        ```bash
-        pip install megatron-core
-        ```
 
+You are now ready to run training. See [Your First Training Run](quickstart.md) for next steps.
diff --git a/docs/get-started/overview.md b/docs/get-started/overview.md
index 42b964d5cec..b7f84ee22e5 100644
--- a/docs/get-started/overview.md
+++ b/docs/get-started/overview.md
@@ -87,7 +87,7 @@ After training or modifying a Megatron model, you can convert it again for deplo
 - **[Megatron Bridge](https://github.com/NVIDIA-NeMo/Megatron-Bridge)** - Training library with bidirectional Hugging Face ↔ Megatron checkpoint conversion, flexible training loops, and production-ready recipes
 - **[NeMo RL](https://github.com/NVIDIA-NeMo/RL)** - Scalable toolkit for efficient reinforcement learning with RLHF, DPO, and other post-training methods
 - **[NeMo Framework](https://docs.nvidia.com/nemo-framework/user-guide/latest/overview.html)** - Enterprise framework with cloud-native support and end-to-end examples
-- **[Model Optimizer (ModelOpt)](https://github.com/NVIDIA/Model-Optimizer)** - Model optimization toolkit for quantization, pruning, distillation, speculative decoding, and more. Checkout end-to-end examples in [examples/post_training/modelopt](./examples/post_training/modelopt/).
+- **[Model Optimizer (ModelOpt)](https://github.com/NVIDIA/Model-Optimizer)** - Model optimization toolkit for quantization, pruning, distillation, speculative decoding, and more. Checkout end-to-end examples in [examples/post_training/modelopt](https://github.com/NVIDIA/Megatron-LM/tree/main/examples/post_training/modelopt).
 
 **Compatible with:** [Hugging Face Accelerate](https://github.com/huggingface/accelerate), [Colossal-AI](https://github.com/hpcaitech/ColossalAI), [DeepSpeed](https://github.com/microsoft/DeepSpeed)
 
diff --git a/docs/get-started/quickstart.md b/docs/get-started/quickstart.md
index 2addcb519a2..c8797aeedd4 100644
--- a/docs/get-started/quickstart.md
+++ b/docs/get-started/quickstart.md
@@ -7,54 +7,40 @@
    license agreement from NVIDIA CORPORATION is strictly prohibited.
 -->
 
-# Quick Start
+# Your First Training Run
 
-## Quick Installation
+This guide walks you through running your first training jobs with Megatron Core. Make sure you have completed [installation](install.md) before proceeding.
 
-Install Megatron Core with pip:
+## Simple Training Example
 
-1. Install Megatron Core with required dependencies:
-
-    ```bash
-    pip install --no-build-isolation megatron-core[mlm,dev]
-    ```
-
-2. Clone repository for examples:
-
-    ```bash
-    git clone https://github.com/NVIDIA/Megatron-LM.git
-    cd Megatron-LM
-    pip install --no-build-isolation .[mlm,dev]
-    ```
-
-That's it! You're ready to start training.
-
-## Your First Training Run
-
-### Simple Training Example
+Run a minimal distributed training loop with mock data on 2 GPUs:
 
 ```bash
-# Distributed training example (2 GPUs, mock data)
 torchrun --nproc_per_node=2 examples/run_simple_mcore_train_loop.py
 ```
 
-### LLaMA-3 Training Example
+## LLaMA-3 Training Example
+
+Train a LLaMA-3 8B model with FP8 precision on 8 GPUs using mock data:
 
 ```bash
-# 8 GPUs, FP8 precision, mock data
-./examples/llama/train_llama3_8b_fp8.sh
+./examples/llama/train_llama3_8b_h100_fp8.sh
 ```
 
 ## Data Preparation
 
-### JSONL Data Format
+To train on your own data, Megatron expects preprocessed binary files (`.bin` and `.idx`).
+
+### 1. Prepare a JSONL File
+
+Each line should contain a `text` field:
 
 ```json
 {"text": "Your training text here..."}
 {"text": "Another training sample..."}
 ```
 
-### Basic Preprocessing
+### 2. Preprocess the Data
 
 ```bash
 python tools/preprocess_data.py \
diff --git a/docs/index.md b/docs/index.md
index c68f608a73b..4b75ed2c0c8 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -38,8 +38,8 @@ get-started/releasenotes
 :hidden:
 :caption: Get Started
 
-get-started/quickstart
 get-started/install
+get-started/quickstart
 ```
 
 ```{toctree}
@@ -71,6 +71,7 @@ user-guide/features/custom_fsdp
 user-guide/features/dist_optimizer
 user-guide/features/optimizer_cpu_offload
 user-guide/features/pipeline_parallel_layout
+user-guide/features/fine_grained_activation_offloading
 user-guide/features/megatron_energon
 user-guide/features/megatron_rl
 user-guide/features/tokenizers
diff --git a/docs/llama_mistral.md b/docs/llama_mistral.md
index 076409cd4f5..95568adce78 100644
--- a/docs/llama_mistral.md
+++ b/docs/llama_mistral.md
@@ -11,7 +11,7 @@
 
 NOTE: In order to simplify code we now only support converting llama-3.x and mistral checkpoints downloaded from Hugging Face. For converting other models, see [Megatron Bridge](models/index.md).
 
-The [Llama-2](https://ai.meta.com/llama/) and [Llama-3.x](https://llama.meta.com/) family of models are an open-source set of pretrained & finetuned (for chat) models that have achieved strong results across a wide set of benchmarks. At their times of release, both Llama-2 and Llama-3 models achieved among the best results for open-source models, and were competitive with leading closed-source models (see https://arxiv.org/pdf/2307.09288.pdf and https://ai.meta.com/blog/meta-llama-3/).
+The Llama-2 and Llama-3.x family of models are an open-source set of pretrained & finetuned (for chat) models that have achieved strong results across a wide set of benchmarks. At their times of release, both Llama-2 and Llama-3 models achieved among the best results for open-source models, and were competitive with leading closed-source models (see <https://arxiv.org/pdf/2307.09288.pdf>).
 
 Similarly, [Mistral-7b](https://mistral.ai/news/announcing-mistral-7b/) is an open-source model with pretrained and finetuned (for chat) variants that achieve strong benchmark results.
 
@@ -50,7 +50,6 @@ Architecturally Llama-2, Llama-3 and Mistral-7b are very similar. As such Megatr
 - [Known numerical differences](#known-numerical-differences)
 - [Using legacy model format](#using-legacy-model-format)
 
-
 # Llama-2
 
 Llama-2 checkpoints can be loaded into Megatron for inference and for finetuning. Loading these checkpoints consists of three steps:
@@ -63,7 +62,7 @@ The following sections detail these steps. The final section lists benchmark res
 
 ## Download Meta or Huggingface checkpoints
 
-Users must first apply for access to download the Llama-2 checkpoints either directly from [Meta](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) or through [Huggingface](https://huggingface.co/docs/transformers/main/model_doc/llama2) (HF). The checkpoints are available in two formats, Meta's native format (available from both the Meta and HF links), and HF's format (available only from HF). Either format can be converted to Megatron, as detailed next.
+Users must first apply for access to download the Llama-2 checkpoints either directly [Huggingface](https://huggingface.co/docs/transformers/main/model_doc/llama2) (HF). The checkpoints are available in two formats, Meta's native format (available from both the Meta and HF links), and HF's format (available only from HF). Either format can be converted to Megatron, as detailed next.
 
 ## Convert checkpoint format
 
@@ -149,11 +148,11 @@ If loading for either inference or finetuning, use the following arguments:
 
 ### Launch Meta
 
-Meta checkpoints can be launched with: https://github.com/facebookresearch/llama
+Meta checkpoints can be launched with: <https://github.com/facebookresearch/llama>
 
 ### Launch Huggingface
 
-Huggingface checkpoints can be launched with: https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py
+Huggingface checkpoints can be launched with: <https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py>
 
 ## Benchmark results
 
@@ -361,7 +360,7 @@ The following sections detail these steps.
 
 ## Download Huggingface checkpoints
 
-Users must first apply for access to download the Mistral-7b checkpoints through [Huggingface](https://huggingface.co/mistralai/Mistral-7B-v0.3) (HF).
+Users must first apply for access to download the Mistral-7b checkpoints through Huggingface. Two variants are available: the base model ([Mistral-7B-v0.3](https://huggingface.co/mistralai/Mistral-7B-v0.3)) and the instruct model ([Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3)).
 
 ## Convert checkpoint format
 
@@ -437,7 +436,7 @@ Many models such as Yi-34B and Qwen2.x use the Llama architecture and may be con
 
 It is not expected that the megatron and Huggingface implementations of llama3.x and mistral models will produce numerically identical results. There are multiple points where small numerical differences are expected. This is a non-exhaustive list:
 
-1. TransformerEngine (TE) uses the model params_dtype inside RMSNorm whereas the Huggingface implementation uses fp32. See for details: https://github.com/NVIDIA/TransformerEngine/issues/1132
+1. TransformerEngine (TE) uses the model params_dtype inside RMSNorm whereas the Huggingface implementation uses fp32. See for details: <https://github.com/NVIDIA/TransformerEngine/issues/1132>
 2. Huggingface `transformers` implements the q, k and v projections in self-attention as separate GEMMs whereas Megatron core combines them into a single GEMM for efficiency. This leads to small numerical differences.
 
 # Using legacy model format
diff --git a/docs/requirements-ci.txt b/docs/requirements-ci.txt
new file mode 100644
index 00000000000..13e0abe06a0
--- /dev/null
+++ b/docs/requirements-ci.txt
@@ -0,0 +1,8 @@
+setuptools<80.0.0,>=77.0.0
+wheel
+sphinx
+sphinx-autobuild
+sphinx-autodoc2
+sphinx-copybutton
+myst_parser
+nvidia-sphinx-theme
diff --git a/docs/user-guide/data-preparation.md b/docs/user-guide/data-preparation.md
index 18da2d80fe1..ea91bee4309 100644
--- a/docs/user-guide/data-preparation.md
+++ b/docs/user-guide/data-preparation.md
@@ -46,6 +46,46 @@ python tools/preprocess_data.py \
 | `--workers` | Number of parallel workers for processing |
 | `--append-eod` | Add end-of-document token |
 
+## Finding Optimal Number of Workers
+
+Use the `--find-optimal-num-workers` flag to find number of workers which gives the best performance in terms of preprocessed documents per second.
+Script will lauch a few short data preprocessing runs with a different number of workers to define the fastest run in respect to collected performance data.
+
+```bash
+python tools/preprocess_data.py \
+    --input data.jsonl \
+    --output-prefix processed_data \
+    --tokenizer-type HuggingFaceTokenizer \
+    --tokenizer-model /path/to/tokenizer.model \
+    --workers 8 \
+    --find-optimal-num-workers \
+    --workers-to-check 4 8 16 32 \
+    --max-documents 50000
+```
+
+**Required arguments**
+
+| Argument | Description |
+|----------|-------------|
+| `--find-optimal-num-workers` | Activates search of optimal number of workers |
+| `--workers-to-check` | List of possible number of workers to run |
+| `--max-documents` | Number of documents to be preprocessed during each run |
+
+**Output example**
+
+```bash
+-----------------------------------
+Performance results (fastest → slowest):
+1. 16 workers → avg. docs/s: 9606.6476
+2. 32 workers → avg. docs/s: 9275.3284
+3. 8 workers → avg. docs/s: 9151.9280
+4. 4 workers → avg. docs/s: 6391.3819
+
+-----------------------------------
+The most optimal num of workers is 16 with avg. preprocessed docs/s: 9606.6476.
+-----------------------------------
+```
+
 ## Output Files
 
 The preprocessing tool generates two files:
diff --git a/docs/user-guide/features/index.md b/docs/user-guide/features/index.md
index fc5a1aa1abe..59cef95d574 100644
--- a/docs/user-guide/features/index.md
+++ b/docs/user-guide/features/index.md
@@ -14,6 +14,7 @@ Advanced feature guides for key Megatron Core capabilities.
 ```{toctree}
 :maxdepth: 2
 
+fine_grained_activation_offloading
 moe
 context_parallel
 custom_fsdp
diff --git a/docs/user-guide/index.md b/docs/user-guide/index.md
index 45e70c3a520..d12f3e35af2 100644
--- a/docs/user-guide/index.md
+++ b/docs/user-guide/index.md
@@ -1,3 +1,7 @@
+---
+orphan: true
+---
+
 <!---
    Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
    NVIDIA CORPORATION and its licensors retain all intellectual property
@@ -14,7 +18,6 @@ Comprehensive guides for using Megatron Core and Megatron-LM.
 ```{toctree}
 :maxdepth: 2
 
-quickstart
 msc_integration
 data-preparation
 training-examples
diff --git a/docs/user-guide/training-examples.md b/docs/user-guide/training-examples.md
index 425728c9e74..5e7c0440073 100644
--- a/docs/user-guide/training-examples.md
+++ b/docs/user-guide/training-examples.md
@@ -33,7 +33,7 @@ This example:
 Train LLaMA-3 8B model with FP8 mixed precision on 8 GPUs:
 
 ```bash
-./examples/llama/train_llama3_8b_fp8.sh
+./examples/llama/train_llama3_8b_h100_fp8.sh
 ```
 
 **Configuration:**
diff --git a/docs/versions1.json b/docs/versions1.json
index ae1809d538e..d55416e0a95 100644
--- a/docs/versions1.json
+++ b/docs/versions1.json
@@ -2,17 +2,16 @@
     {
         "name": "nightly",
         "version": "nightly",
-        "url": "https://docs.nvidia.com/megatron-core/nightly/"
+        "url": "https://docs.nvidia.com/megatron-core/developer-guide/nightly/"
     },
     {
         "name": "0.16.0 (latest)",
         "version": "0.16.0",
-        "url": "https://docs.nvidia.com/megatron-core/latest/"
+        "url": "https://docs.nvidia.com/megatron-core/developer-guide/latest/"
     },
     {
         "name": "0.15.0",
         "version": "0.15.0",
-        "url": "https://docs.nvidia.com/megatron-core/0.15.0/"
+        "url": "https://docs.nvidia.com/megatron-core/developer-guide/0.15.0/"
     }
 ]
-
diff --git a/examples/gptoss/01_convert_from_hf.py b/examples/gptoss/01_convert_from_hf.py
new file mode 100644
index 00000000000..adee3358ec3
--- /dev/null
+++ b/examples/gptoss/01_convert_from_hf.py
@@ -0,0 +1,55 @@
+# Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+
+"""Convert HuggingFace checkpoints to Megatron format."""
+
+import os
+import argparse
+
+from megatron.bridge import AutoBridge
+
+def _parse_args():
+    parser = argparse.ArgumentParser(description="Convert HF LLMs to Megatron format")
+    parser.add_argument(
+        "--hf-model",
+        type=str,
+        required=True,
+        help="HuggingFace model identifier or path",
+    )
+    parser.add_argument(
+        "--save-path",
+        type=str,
+        default=None,
+        help="Path to save the converted Megatron checkpoint",
+    )
+    parser.add_argument('--local-rank', '--local_rank', type=int, default=0)
+    return parser.parse_args()
+
+if __name__ == "__main__":
+    args = _parse_args()
+    HF_MODEL = args.hf_model
+    SAVE_PATH = args.save_path
+    WORLD_SIZE = int(os.environ.get("WORLD_SIZE", 1))
+
+    if SAVE_PATH is None:
+        SAVE_PATH = f"./megatron_checkpoints/{HF_MODEL.replace('/', '_')}"
+    
+    print(f"Converting {HF_MODEL} to Megatron format...")
+    print(f"Save path: {SAVE_PATH}")
+    
+    bridge = AutoBridge.from_hf_pretrained(HF_MODEL, trust_remote_code=True)
+    provider = bridge.to_megatron_provider()
+    # Update these configs as needed
+    provider.expert_tensor_parallel_size = 1
+    provider.tensor_model_parallel_size = 1
+    provider.pipeline_model_parallel_size = WORLD_SIZE
+    provider.finalize()
+    
+    model = provider.provide_distributed_model(wrap_with_ddp=False)
+    
+    bridge.save_megatron_model(
+        model,
+        SAVE_PATH,
+        hf_tokenizer_path=HF_MODEL
+    )
+    
+    print(f"Saved Megatron checkpoint to {SAVE_PATH}")
diff --git a/examples/gptoss/02_train.sh b/examples/gptoss/02_train.sh
new file mode 100755
index 00000000000..d129adc2b84
--- /dev/null
+++ b/examples/gptoss/02_train.sh
@@ -0,0 +1,259 @@
+#!/bin/bash
+
+export CUDA_DEVICE_MAX_CONNECTIONS=${CUDA_DEVICE_MAX_CONNECTIONS:-1}
+
+
+# Setup arguments with defaults
+CHECKPOINT_PATH="NO_VALUE_PROVIDED"
+TENSORBOARD_LOGS_PATH="./tensorboard_logs/"
+TOKENIZER_ARG="MOCK"
+DATA_ARG="MOCK"
+DISTRIBUTED_CONFIG_FILE=""
+
+# Parse command line arguments
+while [[ $# -gt 0 ]]; do
+    case $1 in
+        --checkpoint-path)
+            CHECKPOINT_PATH="$2"
+            shift 2
+            ;;
+        --tensorboard-logs-path)
+            TENSORBOARD_LOGS_PATH="$2"
+            shift 2
+            ;;
+        --tokenizer)
+            TOKENIZER_ARG="$2"
+            shift 2
+            ;;
+        --data)
+            DATA_ARG="$2"
+            shift 2
+            ;;
+        --distributed-config-file)
+            DISTRIBUTED_CONFIG_FILE="$2"
+            shift 2
+            ;;
+        -h|--help)
+            echo "Usage: $0 [OPTIONS]"
+            echo "Options:"
+            echo "  --checkpoint-path PATH          Path to Megatron checkpoint"
+            echo "  --tensorboard-logs-path PATH    Path to TensorBoard logs"
+            echo "  --tokenizer PATH|MOCK           Path to tokenizer model, or 'MOCK' (default: MOCK)"
+            echo "  --data PATH|MOCK                Data prefix, or 'MOCK' (default: MOCK)"
+            echo "  --distributed-config-file FILE       Path to distributed training config file"
+            echo "  -h, --help                      Show this help message"
+            exit 0
+            ;;
+        *)
+            echo "Unknown option: $1"
+            echo "Use --help for usage information"
+            exit 1
+            ;;
+    esac
+done
+
+# Check if checkpoint path exists
+if [ ! -d "$CHECKPOINT_PATH" ]; then
+    echo "Error: Checkpoint path does not exist: $CHECKPOINT_PATH"
+    exit 1
+fi
+echo "Checkpoint path exists: $CHECKPOINT_PATH"
+
+# Check if tensorboard logs path exists
+if [ ! -d "$TENSORBOARD_LOGS_PATH" ]; then
+    echo "Warning: TensorBoard logs path does not exist. Creating: $TENSORBOARD_LOGS_PATH"
+    mkdir -p "$TENSORBOARD_LOGS_PATH"
+fi
+echo "TensorBoard logs path exists: $TENSORBOARD_LOGS_PATH"
+
+# NOTE: by default we use 8 GPUs
+# These values will be over-written below with environmental variables
+GPUS_PER_NODE=8
+NUM_NODES=1
+MASTER_ADDR="localhost"
+MASTER_PORT=6000
+NODE_RANK=0
+
+# Load distributed config from file if provided
+if [ -n "$DISTRIBUTED_CONFIG_FILE" ]; then
+    if [ ! -f "$DISTRIBUTED_CONFIG_FILE" ]; then
+        echo "Warning: Distributed config file does not exist: $DISTRIBUTED_CONFIG_FILE"
+        echo "Continuing with default distributed training settings."
+    else
+        echo "Loading distributed config from: $DISTRIBUTED_CONFIG_FILE"
+        source "$DISTRIBUTED_CONFIG_FILE"
+    fi
+fi
+
+# Override with environment variables if set
+GPUS_PER_NODE=${GPUS_PER_NODE:-8}
+NUM_NODES=${NUM_NODES:-1}
+MASTER_ADDR=${MASTER_ADDR:-localhost}
+MASTER_PORT=${MASTER_PORT:-6000}
+NODE_RANK=${NODE_RANK:-0}
+WORLD_SIZE=$(($GPUS_PER_NODE*$NUM_NODES))
+
+# Path to the pretrain_gpt.py script, assuming this script is run from the root of the Megatron-LM repository
+PRETRAIN_SCRIPT_PATH="pretrain_gpt.py"
+
+# Data cache path (useful for both mock and real data)
+DATA_CACHE_PATH="${PWD}/benchmark_cache_gpt_oss_20b"
+mkdir -p "$DATA_CACHE_PATH"
+
+DISTRIBUTED_ARGS=(
+    --nproc_per_node $GPUS_PER_NODE
+    --nnodes $NUM_NODES
+    --master_addr $MASTER_ADDR
+    --master_port $MASTER_PORT
+    --node_rank $NODE_RANK
+)
+
+# NOTE: we only set pipeline parallelism to be the number of GPUs
+# Adjust each value based on your setup.
+TP_SIZE=1     
+EP_SIZE=1     
+PP_SIZE=${WORLD_SIZE}     
+MICRO_BATCH_SIZE=1
+GLOBAL_BATCH_SIZE=128
+NUM_LAYERS=12
+DTYPE="fp8"
+SEQ_LENGTH=8192
+MAX_POSITION_EMBEDDINGS=8192
+TRAIN_SAMPLES=1953125000
+LR_DECAY_SAMPLES=1949218748
+
+MODEL_ARGS=(
+    --no-masked-softmax-fusion
+    --transformer-impl transformer_engine
+    --disable-bias-linear
+    --untie-embeddings-and-output-weights
+    --no-rope-fusion
+    --normalization RMSNorm
+    --num-layers ${NUM_LAYERS}
+    --hidden-size 512
+    --ffn-hidden-size 2048
+    --num-attention-heads 64
+    --group-query-attention
+    --num-query-groups 8
+    --seq-length ${SEQ_LENGTH}
+    --max-position-embeddings ${MAX_POSITION_EMBEDDINGS}
+    --use-mcore-models
+    --rotary-percent 1.0
+    --rope-type rope
+    --position-embedding-type rope
+    --rotary-base 10000
+    --no-bias-gelu-fusion
+    --export-force-local-attention
+    --no-bias-dropout-fusion
+    --quick-geglu
+    --glu-linear-offset 1.0
+    --softmax-type learnable
+    --window-attn-skip-freq 2
+    --activation-func-clamp-value 7.0
+    --window-size 127,0
+    --enable-gpt-oss
+)
+
+MOE_ARGS=(
+    --num-experts 4
+    --moe-router-topk 2
+    --moe-router-load-balancing-type aux_loss
+    --moe-aux-loss-coeff 1e-3
+    --moe-grouped-gemm
+    --moe-token-dispatcher-type alltoall
+    --overlap-param-gather
+    --overlap-grad-reduce
+    --moe-ffn-hidden-size 2048
+    --moe-router-dtype fp32
+    --moe-z-loss-coeff 1e-3
+    --moe-permute-fusion
+)
+
+DATA_ARGS_LIST=()
+if [[ "$TOKENIZER_ARG" == "MOCK" ]] || [[ "$DATA_ARG" == "MOCK" ]] || [[ -z "$TOKENIZER_ARG" ]]; then
+    DATA_ARGS_LIST+=(
+        "--mock-data"
+        "--tokenizer-type NullTokenizer"
+        "--vocab-size 128256" 
+        "--data-cache-path ${DATA_CACHE_PATH}"
+        "--tiktoken-pattern v2" 
+        "--split '99,1,0'"
+        "--no-create-attention-mask-in-dataloader"
+        "--no-mmap-bin-files"
+        "--num-workers 1"
+    )
+else
+    # Settings for real data
+    DATA_ARGS_LIST+=(
+        "--data-path $DATA_ARG"
+        "--tokenizer-type HuggingFaceTokenizer" 
+        "--tokenizer-model $TOKENIZER_ARG"
+        "--data-cache-path ${DATA_CACHE_PATH}"
+        "--split '99,1,0'"
+        "--no-create-attention-mask-in-dataloader"
+        "--no-mmap-bin-files"
+        "--num-workers 1"
+        # Note: --vocab-size might be inferred by HuggingFaceTokenizer or might need to be explicit.
+        "--vocab-size 128256"
+    )
+fi
+
+TRAINING_ARGS=(
+    --micro-batch-size ${MICRO_BATCH_SIZE}
+    --global-batch-size ${GLOBAL_BATCH_SIZE}
+    --lr 1.0e-5
+    --train-samples ${TRAIN_SAMPLES}
+    --lr-decay-samples ${LR_DECAY_SAMPLES}
+    --lr-decay-style cosine
+    --min-lr 1.0e-6
+    --weight-decay 0.1
+    --lr-warmup-fraction 0.05
+    --clip-grad 1.0
+    --bf16
+    --use-flash-attn
+    --attention-softmax-in-fp32
+    --accumulate-allreduce-grads-in-fp32
+    --disable-bf16-reduced-precision-matmul
+    --recompute-activations
+)
+
+MODEL_PARALLEL_ARGS=(
+    --tensor-model-parallel-size ${TP_SIZE}
+    --pipeline-model-parallel-size ${PP_SIZE}
+    --expert-model-parallel-size ${EP_SIZE}
+    --sequence-parallel
+    --context-parallel-size 1
+    --use-distributed-optimizer
+    --fp8-format hybrid
+    --fp8-param-gather
+    --fp8-amax-compute-algo max
+    --fp8-amax-history-len 1024
+)
+    
+LOGGING_ARGS=(
+    --log-interval 1
+    --save-interval 10000
+    --eval-interval 50000000
+    --eval-iters 0
+    --save $CHECKPOINT_PATH
+    --tensorboard-dir "${CHECKPOINT_PATH}/tensorboard"
+    --moe-per-layer-logging
+    --no-load-optim
+    --no-load-rng
+    --log-throughput
+)
+
+# Ensure pretrain_gpt.py is found
+if [ ! -f "$PRETRAIN_SCRIPT_PATH" ]; then
+    echo "Error: pretrain_gpt.py not found at $PRETRAIN_SCRIPT_PATH"
+    echo "Please ensure you are running this script from the root of the Megatron-LM repository, and pretrain_gpt.py is present."
+    exit 1
+fi
+
+python -m torch.distributed.run ${DISTRIBUTED_ARGS[@]} ${PRETRAIN_SCRIPT_PATH} \
+    ${MODEL_ARGS[@]} \
+    ${MOE_ARGS[@]} \
+    ${DATA_ARGS_LIST[@]} \
+    ${TRAINING_ARGS[@]} \
+    ${MODEL_PARALLEL_ARGS[@]} \
+    ${LOGGING_ARGS[@]}
diff --git a/examples/gptoss/03_convert_to_hf.py b/examples/gptoss/03_convert_to_hf.py
new file mode 100644
index 00000000000..8089afec854
--- /dev/null
+++ b/examples/gptoss/03_convert_to_hf.py
@@ -0,0 +1,52 @@
+# Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+
+"""Convert HuggingFace checkpoints to Megatron format."""
+
+import os
+import argparse
+
+from megatron.bridge import AutoBridge
+
+def _parse_args():
+    parser = argparse.ArgumentParser(description="Convert Megatron LLMs to HuggingFace format")
+    parser.add_argument(
+        "--hf-model",
+        type=str,
+        required=True,
+        help="HuggingFace model identifier or path to load config from",
+    )
+    parser.add_argument(
+        "--megatron-model",
+        type=str,
+        required=True,
+        help="Megatron model identifier or path",
+    )
+    parser.add_argument(
+        "--save-path",
+        type=str,
+        default=None,
+        help="Path to save the converted HuggingFace checkpoint",
+    )
+    parser.add_argument('--local-rank', '--local_rank', type=int, default=0)
+    return parser.parse_args()
+
+if __name__ == "__main__":
+    args = _parse_args()
+    HF_MODEL = args.hf_model
+    MEGATRON_MODEL = args.megatron_model
+    SAVE_PATH = args.save_path
+    WORLD_SIZE = int(os.environ.get("WORLD_SIZE", 1))
+
+    if SAVE_PATH is None:
+        SAVE_PATH = f"./huggingface_checkpoints/{MEGATRON_MODEL.replace('/', '_')}"
+    
+    print(f"Converting {MEGATRON_MODEL} to HuggingFace {HF_MODEL} format...")
+    print(f"Save path: {SAVE_PATH}")
+    
+    bridge = AutoBridge.from_hf_pretrained(HF_MODEL, trust_remote_code=True)
+    bridge.export_ckpt(
+        MEGATRON_MODEL,
+        SAVE_PATH,
+    )
+    
+    print(f"Saved HuggingFace checkpoint to {SAVE_PATH}")
diff --git a/examples/gptoss/README.md b/examples/gptoss/README.md
new file mode 100644
index 00000000000..eeb92ad9953
--- /dev/null
+++ b/examples/gptoss/README.md
@@ -0,0 +1,153 @@
+# GPT-OSS Training Tutorial
+
+## Step 0: Install Dependencies
+
+### Using Megatron Bridge
+
+[Megatron-Bridge](https://github.com/NVIDIA-NeMo/Megatron-Bridge)
+
+Megatron Bridge provides a quick and convenient way to convert HuggingFace checkpoints to the Megatron format used by Megatron-LM. Follow the instructions in the [Megatron-Bridge Installation](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/main/README.md#-installation) to run the nemo docker container and convert checkpoints (via mounted volumes - make sure that the huggingface cache location AND the megatron checkpoint locations are properly mounted, otherwise you may not be saving the converted model to disk correctly).
+
+Below is an example of how to use Megatron-Bridge inside the pytorch container to convert a HuggingFace model checkpoint to Megatron format.
+
+Reference: [Megatron-Bridge Dockerfile](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/main/docker/Dockerfile.ci)
+
+Inside the [pytorch container](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch) run the following commands to install Megatron-Bridge:
+```bash
+cd /opt
+git clone --recursive https://github.com/NVIDIA-NeMo/Megatron-Bridge.git
+cd Megatron-Bridge
+
+# Make sure submodules are initialized (for 3rdparty/Megatron-LM)
+git submodule update --init --recursive
+
+export PATH="/root/.local/bin:$PATH"
+export UV_PROJECT_ENVIRONMENT=/opt/venv
+export VIRTUAL_ENV=/opt/venv
+export PATH="$UV_PROJECT_ENVIRONMENT/bin:$PATH"
+export UV_LINK_MODE=copy
+export UV_VERSION="0.7.2"
+
+# Install UV
+curl -LsSf https://astral.sh/uv/${UV_VERSION}/install.sh | sh
+
+# Create virtual environment and build the package
+uv venv ${UV_PROJECT_ENVIRONMENT} --system-site-packages
+
+uv sync --locked --only-group build
+uv sync --locked --link-mode copy --all-extras --all-groups
+
+uv pip install --no-deps -e .
+
+source ${UV_PROJECT_ENVIRONMENT}/bin/activate
+```
+
+### Setup Environment
+
+```bash
+export HOST_MEGATRON_LM_DIR="/path/to/your/host/megatron-lm"
+git clone https://github.com/NVIDIA/Megatron-LM.git "$HOST_MEGATRON_LM_DIR"
+cd "$HOST_MEGATRON_LM_DIR"
+```
+
+```bash
+export HF_TOKEN={your_hf_token_here}
+```
+
+## Step 1: Convert HuggingFace to Megatron (Optional - skip if you already have a Megatron checkpoint)
+
+Set `--nproc-per-node` to be the number of GPUs per node. Set `hf_model_name` to be the Huggingface model e.g. `openai/gpt-oss-20b`
+
+```bash
+python3 -m torch.distributed.launch --nproc-per-node=8 examples/gptoss/01_convert_from_hf.py --hf-model openai/gpt-oss-20b
+```
+
+## Step 2: Train from Scratch
+
+To train from scratch first follow the steps below to setup the environment appropriately before running the training script in docker. Even though we are running the same container as before, it is better to restart the container to ensure a clean environment and that all environment and docker variables are set correctly. For the following example we used 8x GB300, but you should change the number of GPUs and nodes as needed.
+
+### Setup Environment
+
+```bash
+# Change these based on model and directory from previous conversion step
+export MODEL_DIR_NAME="openai_gpt-oss_20b"
+
+export HOST_CHECKPOINT_PATH="./megatron_checkpoints/${MODEL_DIR_NAME}"
+export HOST_TENSORBOARD_LOGS_PATH="./tensorboard_logs/${MODEL_DIR_NAME}"
+```
+
+By default we will use mock data to train the model in the example below. To use your own data, set the following environment variables:
+
+```bash
+# Optional: For real data
+export HOST_TOKENIZER_MODEL_PATH="/path/to/host/tokenizer.model"
+export HOST_DATA_PREFIX="/path/to/host/mydata_prefix"
+```
+
+### Setup Training Configurations
+
+Run the following to create a `distributed_config.env` file with the appropriate distributed training configurations. Change the values as needed for your setup. This file will override the default values in `02_train.sh`.
+
+```bash
+cat > ./distributed_config.env << 'EOF'
+GPUS_PER_NODE=8
+NUM_NODES=1
+MASTER_ADDR=localhost
+MASTER_PORT=6000
+NODE_RANK=0
+EOF
+```
+
+### Run Container with Mounted Volumes
+
+**NOTE:** This container runs the example training script `02_train.sh` located in the `examples/gptoss` directory. By default, we have only set pipeline parallelism to be the number of GPUs. Adjust TP_SIZE, EP_SIZE, PP_SIZE, etc. in `02_train.sh`. You can also adjust modify `--hidden-size`, `--ffn-hidden-size`, `--num-attention-heads`, `NUM_LAYERS`, etc. 
+
+To train using mock data, run the following command:
+```bash
+PYTORCH_IMAGE="nvcr.io/nvidia/pytorch:25.12-py3"
+
+docker run --rm --gpus all --ipc=host --ulimit memlock=-1 \
+  -v "${HOST_MEGATRON_LM_DIR}:/workspace/megatron-lm" \
+  -v "${HOST_CHECKPOINT_PATH}:/workspace/checkpoints" \
+  -v "${HOST_TENSORBOARD_LOGS_PATH}:/workspace/tensorboard_logs" \
+  -v "./distributed_config.env:/workspace/megatron-lm/examples/gptoss/distributed_config.env" \
+  --workdir /workspace/megatron-lm \
+  $PYTORCH_IMAGE \
+  bash examples/gptoss/02_train.sh \
+    --checkpoint-path /workspace/checkpoints \
+    --tensorboard-logs-path /workspace/tensorboard_logs \
+    --distributed-config-file /workspace/megatron-lm/examples/gptoss/distributed_config.env \
+  2>&1 | tee "${HOST_TENSORBOARD_LOGS_PATH}/training_mock_$(date +'%y-%m-%d_%H-%M-%S').log"
+```
+**Note:** If you run into issues generating mock data one solution might be to reduce the number of GPUs to 1 and try to generate the data again.
+
+If using real data with with the `HOST_TOKENIZER_MODEL_PATH` and `HOST_DATA_PREFIX` environment variables set, run the following command instead:
+
+```bash
+PYTORCH_IMAGE="nvcr.io/nvidia/pytorch:25.12-py3"
+
+docker run --rm --gpus all --ipc=host --ulimit memlock=-1 \
+  -v "${HOST_MEGATRON_LM_DIR}:/workspace/megatron-lm" \
+  -v "${HOST_CHECKPOINT_PATH}:/workspace/checkpoints" \
+  -v "${HOST_TENSORBOARD_LOGS_PATH}:/workspace/tensorboard_logs" \
+  -v "${HOST_TOKENIZER_MODEL_PATH}:/workspace/tokenizer_model" \
+  -v "$(dirname "${HOST_DATA_PREFIX}"):/workspace/data_dir" \
+  -v "./distributed_config.env:/workspace/megatron-lm/examples/gptoss/distributed_config.env" \
+  --workdir /workspace/megatron-lm \
+  $PYTORCH_IMAGE \
+  bash examples/gptoss/02_train.sh \
+    --checkpoint-path /workspace/checkpoints \
+    --tensorboard-logs-path /workspace/tensorboard_logs \
+    --tokenizer /workspace/tokenizer_model \
+    --data "/workspace/data_dir/$(basename "${HOST_DATA_PREFIX}")" \
+    --distributed-config-file /workspace/megatron-lm/examples/gptoss/distributed_config.env \
+  2>&1 | tee "${HOST_TENSORBOARD_LOGS_PATH}/training_custom_$(date +'%y-%m-%d_%H-%M-%S').log"
+```
+
+## Step 3: Convert Megatron to HuggingFace
+
+Just run the following command to change from the megatron checkpoint from training to the huggingface format to share with others (make sure you have the same virtual environment setup as in Step 0):
+
+```bash
+python3 -m torch.distributed.launch --nproc-per-node=8 examples/gptoss/03_convert_to_hf.py --hf-model openai/gpt-oss-20b --megatron-model ./megatron_checkpoints/openai_gpt-oss_20b
+```
\ No newline at end of file
diff --git a/examples/inference/gpt/gpt_dynamic_inference.py b/examples/inference/gpt/gpt_dynamic_inference.py
index 2582f57e13e..f02aae9c221 100644
--- a/examples/inference/gpt/gpt_dynamic_inference.py
+++ b/examples/inference/gpt/gpt_dynamic_inference.py
@@ -80,6 +80,16 @@ def run_inference(
 
     args = get_args()
 
+    # Parse batch boundaries for batch-drain mode.
+    batch_ranges = None
+    if args.drain_between_batches and args.batch_boundaries:
+        boundaries = [int(x) for x in args.batch_boundaries.split(",")]
+        num_requests_total = len(requests)
+        batch_ranges = []
+        for i, start in enumerate(boundaries):
+            end = boundaries[i + 1] if i + 1 < len(boundaries) else num_requests_total
+            batch_ranges.append((start, end))
+
     # Initialize request arrival times.
     base_arrival_time = get_curr_time()
     for request in requests:
@@ -113,57 +123,11 @@ def _add_request():
         num_requests_added += 1
         tbar.update(1)
 
-    while True:
-        # Add requests.
-        add_start = get_curr_time()
-        if args.incoming_requests_per_step is None:
-            # Add requests with 'earlier' arrival time.
-            while num_requests_added < num_requests_total:
-                if requests[num_requests_added].time_arrival > add_start:
-                    break
-                _add_request()
-        else:
-            # Add deterministic number of requests (generally used for debugging).
-            for i in range(
-                min(args.incoming_requests_per_step, num_requests_total - num_requests_added)
-            ):
-                _add_request()
-        add_times.append(get_curr_time() - add_start)
-
-        # Step inference engine (i.e., generate a token for each active request).
-        # Before step, we haven't done the scheduling, so we cannot know the is_decode_only
-        try:
-            result = engine.step_modern()
-        except EngineSuspendedError as e:
-            result = e
-            pass  # ignore error in order to call 'engine.resume()' below.
-        attempted_step_count += 1
-
-        # After step, we lost track of last iteration's is_decode_only,
-        # so we need to get it from the engine
-        is_decode_only = engine.is_decode_only
-
-        # Test suspending and resuming engine.
-        if args.suspend_resume_interval is not None:
-
-            # Suspend.
-            if attempted_step_count % args.suspend_resume_interval == 0:
-                print("**** step %d/%d ... suspend." % (engine.step_count, attempted_step_count))
-                engine.suspend()
-
-            # Resume, 0+ attempted steps later.
-            if (
-                attempted_step_count > 0
-                and (attempted_step_count - args.suspend_resume_interval // 2)
-                % args.suspend_resume_interval
-                == 0
-            ):
-                print("**** step %d/%d ... resume." % (engine.step_count, attempted_step_count))
-                engine.resume()
+    def _process_step_result(result):
+        """Process a single engine step result, updating bookkeeping state."""
+        nonlocal total_output_tokens, num_requests_finished
 
-        # If engine suspended, continue to next iter.
-        if isinstance(result, EngineSuspendedError):
-            continue
+        is_decode_only = engine.is_decode_only
 
         # Record cuda_graph_request_count.
         cuda_graph_request_count = result["cuda_graph_request_count"]
@@ -222,13 +186,85 @@ def _add_request():
                 num_requests_finished += 1
             output_times.append(get_curr_time() - output_start)
 
-        # Check if all requests are finished.
-        if not (engine.has_unfinished_requests() or num_requests_added < num_requests_total):
-            break
+    if batch_ranges is not None:
+        # Batch-drain mode: add all requests in a batch, drain, then next batch.
+        for batch_idx, (batch_start, batch_end) in enumerate(batch_ranges):
+            # Add all requests in current batch.
+            add_start = get_curr_time()
+            while num_requests_added < batch_end:
+                _add_request()
+            add_times.append(get_curr_time() - add_start)
+
+            # Step until all active requests finish (drain).
+            while engine.has_unfinished_requests():
+                try:
+                    result = engine.step_modern()
+                except EngineSuspendedError as e:
+                    result = e
+                attempted_step_count += 1
+
+                if isinstance(result, EngineSuspendedError):
+                    continue
+
+                _process_step_result(result)
+    else:
+        # Original mode: add requests per step based on arrival time or count.
+        while True:
+            # Add requests.
+            add_start = get_curr_time()
+            if args.incoming_requests_per_step is None:
+                # Add requests with 'earlier' arrival time.
+                while num_requests_added < num_requests_total:
+                    if requests[num_requests_added].time_arrival > add_start:
+                        break
+                    _add_request()
+            else:
+                # Add deterministic number of requests (generally used for debugging).
+                for i in range(
+                    min(args.incoming_requests_per_step, num_requests_total - num_requests_added)
+                ):
+                    _add_request()
+            add_times.append(get_curr_time() - add_start)
+
+            # Step inference engine (i.e., generate a token for each active request).
+            # Before step, we haven't done the scheduling, so we cannot know the is_decode_only
+            try:
+                result = engine.step_modern()
+            except EngineSuspendedError as e:
+                result = e
+                pass  # ignore error in order to call 'engine.resume()' below.
+            attempted_step_count += 1
+
+            # Test suspending and resuming engine.
+            if args.suspend_resume_interval is not None:
+
+                # Suspend.
+                if attempted_step_count % args.suspend_resume_interval == 0:
+                    print("**** step %d/%d ... suspend." % (engine.context.step_count, attempted_step_count))
+                    engine.suspend()
+
+                # Resume, 0+ attempted steps later.
+                if (
+                    attempted_step_count > 0
+                    and (attempted_step_count - args.suspend_resume_interval // 2)
+                    % args.suspend_resume_interval
+                    == 0
+                ):
+                    print("**** step %d/%d ... resume." % (engine.context.step_count, attempted_step_count))
+                    engine.resume()
+
+            # If engine suspended, continue to next iter.
+            if isinstance(result, EngineSuspendedError):
+                continue
+
+            _process_step_result(result)
+
+            # Check if all requests are finished.
+            if not (engine.has_unfinished_requests() or num_requests_added < num_requests_total):
+                break
 
     # Resume engine (NOOP if not suspended).
-    if engine.is_suspended:
-        engine.resume()
+    engine.resume()
 
     return {
         "step_times": step_times,
@@ -413,7 +449,7 @@ def escape_str(s):
                         "latency": req.time_end - req.time_start,
                         "ttft": req.ttft,  # Time-to-first-token in seconds
                         "cuda_graph_request_count_map": result["cuda_graph_request_count_map"],
-                        "step_count": engine.step_count,
+                        "step_count": engine.context.step_count,
                         "top_n_logprobs": getattr(req, 'generated_top_n_logprobs', None),
                         "prompt_top_n_logprobs": getattr(req, 'prompt_top_n_logprobs', None),
                     }
@@ -423,6 +459,8 @@ def escape_str(s):
                             req, 'generated_log_probs', None
                         )
                         result_dict["logprobs"] = getattr(req, 'logprobs', None)
+                    if args.output_request_events:
+                        result_dict["events"] = [e.serialize() for e in req.events]
                     json_results[req.request_id] = result_dict
 
             # Track system-level throughput as a test / debug metric
@@ -431,6 +469,7 @@ def escape_str(s):
             # Attach peak memory metrics; the functional test only validates these
             # if the fields exist in the golden values.
             json_results.update(peak_mem_stats)
+            json_results["lifetime_prefill_token_count"] = engine.context.lifetime_prefill_token_count
 
             print(f' Saving results to {args.output_path}')
             with open(args.output_path, "w") as fp:
@@ -470,7 +509,7 @@ def escape_str(s):
             f"{setup_prefix} … " f"throughput: {throughput:.3f} tok/s … ",
             f"total time: {total_time:.3f}s … "
             f"mem {peak_alloc_gb:.1f}/{peak_resvd_gb:.1f} GB … "
-            f"steps: {engine.step_count:d} … "
+            f"steps: {engine.context.step_count:d} … "
             f"capture {capture_str}",
         )
         print("~~~")
diff --git a/examples/inference/gpt/gpt_dynamic_inference_12b.sh b/examples/inference/gpt/gpt_dynamic_inference_12b.sh
index 4991d9d5177..ca21bb170a5 100644
--- a/examples/inference/gpt/gpt_dynamic_inference_12b.sh
+++ b/examples/inference/gpt/gpt_dynamic_inference_12b.sh
@@ -69,7 +69,6 @@ ARGS=" \
     --tiktoken-pattern v2 \
     --tokenizer-model ${TOKENIZER_MODEL} \
     --distributed-timeout-minutes 2400 \
-    --transformer-impl local \
     --use-flash-attn \
     --inference-rng-tracker \
     \
diff --git a/examples/inference/gpt/gpt_dynamic_inference_357m.sh b/examples/inference/gpt/gpt_dynamic_inference_357m.sh
index 44abb575c63..cc99bdddec1 100644
--- a/examples/inference/gpt/gpt_dynamic_inference_357m.sh
+++ b/examples/inference/gpt/gpt_dynamic_inference_357m.sh
@@ -32,6 +32,7 @@ export CUDA_DEVICE_MAX_CONNECTIONS=1
 # Miscellaneous.
 : ${USE_COORDINATOR=0}
 : ${ENGINE=dynamic}
+: ${NPROC_PER_NODE=1}
 : ${EXTRA_ARGS=""}
 # NSIGHT_PREFIX=/path/to/nsight/profile
 
@@ -101,7 +102,7 @@ fi
 if [[ "${USE_COORDINATOR}" == "0" ]]; then
     CMD="python -m examples.inference.gpt.gpt_${ENGINE}_inference ${ARGS}"
 else
-    CMD="python -um examples.inference.gpt.gpt_${ENGINE}_inference_with_coordinator ${ARGS}"
+    CMD="python -m torch.distributed.run --nproc-per-node ${NPROC_PER_NODE} -m examples.inference.gpt.gpt_${ENGINE}_inference_with_coordinator ${ARGS}"
 fi
 
 if [[ -v NSIGHT_PREFIX ]]; then
diff --git a/examples/inference/gpt/gpt_dynamic_inference_with_coordinator.py b/examples/inference/gpt/gpt_dynamic_inference_with_coordinator.py
index 536f533eccd..d34749e3a5a 100644
--- a/examples/inference/gpt/gpt_dynamic_inference_with_coordinator.py
+++ b/examples/inference/gpt/gpt_dynamic_inference_with_coordinator.py
@@ -14,6 +14,7 @@
 
 from examples.inference.gpt.utils import Request, build_dynamic_engine_setup_prefix, build_requests
 from megatron.core.inference.engines import DynamicInferenceEngine
+from megatron.core.inference.engines.dynamic_engine import EngineState
 from megatron.core.inference.inference_client import InferenceClient
 from megatron.core.inference.inference_request import DynamicInferenceRequestRecord
 from megatron.core.inference.sampling_params import SamplingParams
@@ -29,6 +30,22 @@
 logging.basicConfig(level=logging.INFO, force=True)
 
 
+async def suspend_resume_cycle(client, engine, args, futures):
+    """Wait for all in-flight requests, then suspend/train/resume."""
+    await asyncio.gather(*futures)
+
+    client.pause_engines()
+    await engine.wait_until(EngineState.PAUSED)
+    client.suspend_engines()
+    await engine.wait_until(EngineState.SUSPENDED)
+    if args.suspend_timeout > 0:
+        await asyncio.sleep(args.suspend_timeout)
+    client.resume_engines()
+    await engine.wait_until(EngineState.RESUMED)
+    client.unpause_engines()
+    await engine.wait_until(EngineState.RUNNING)
+
+
 async def main(
     engine: DynamicInferenceEngine,
     requests: List[Request],
@@ -46,40 +63,30 @@ async def main(
     # the engine will start accepting requests from the data parallel coordinator.
     # and processing them in an asyncio coroutine.
     # leaving inference_coordinator_port as None will find a free port automatically.
+    args = get_args()
+
     dp_addr = await engine.start_listening_to_data_parallel_coordinator(
         inference_coordinator_port=port,
         launch_inference_coordinator=True,
+        coordinator_schedule_output_path=args.coordinator_schedule_output_path,
     )
 
-    args = get_args()
-
-    # Test suspend/resume intervals.
-    if dist.get_rank() == 0 and args.suspend_resume_interval is not None:
-        # Since the client doesn't directly call engine.async_step here, we test
-        # the suspend-resume system ~4 times.
-        suspend_resume_interval = max(1, len(requests) // 4)
-        suspend_idxs = set(
-            range(suspend_resume_interval, len(requests) + 1, suspend_resume_interval)
-        )
-        resume_idxs = set(
-            min(len(requests), i + suspend_resume_interval // 2) for i in suspend_idxs
-        )
-    else:
-        suspend_idxs = set()
-        resume_idxs = set()
+    # All ranks agree on the number of suspend/resume cycles from args.
+    num_suspend_resume_cycles = len(requests) // args.suspend_resume_interval if args.suspend_resume_interval else 0
 
     # Create client and run example.
     if dist.get_rank() == 0:
-        client = InferenceClient(dp_addr)  # submits requests to the inference coordinator
-        await client.start()
+        client = InferenceClient(dp_addr, deserialize=True)  # submits requests to the inference coordinator
+        client.start()
         base_arrival_time = time.time_ns() / 10**9
         for request in requests:
             request.time_arrival = request.time_offset + base_arrival_time
         futures = []
         num_requests_total = len(requests)
         num_requests_added = 0
-        # logging.info("Waiting for 20 seconds before starting to add requests. This is to mimic an RL style setup..")
-        # time.sleep(20)
+        next_suspend_at = args.suspend_resume_interval or 0
+        cycles_done = 0
+
         while True:
             current_time = time.time_ns() / 10**9
             if args.incoming_requests_per_step is None:
@@ -95,11 +102,10 @@ async def main(
                     futures.append(client.add_request(request.prompt_text, request.sampling_params))
                     num_requests_added += 1
 
-                    # Test suspend/resume.
-                    if num_requests_added in suspend_idxs:
-                        client.suspend_engines()
-                    if num_requests_added in resume_idxs:
-                        client.resume_engines()
+                    if num_requests_added >= next_suspend_at and cycles_done < num_suspend_resume_cycles:
+                        await suspend_resume_cycle(client, engine, args, futures)
+                        cycles_done += 1
+                        next_suspend_at += args.suspend_resume_interval
 
             else:
                 # Add deterministic number of requests (generally used for debugging).
@@ -113,11 +119,10 @@ async def main(
                     futures.append(client.add_request(request.prompt_text, request.sampling_params))
                     num_requests_added += 1
 
-                    # Test suspend/resume.
-                    if num_requests_added in suspend_idxs:
-                        client.suspend_engines()
-                    if num_requests_added in resume_idxs:
-                        client.resume_engines()
+                    if num_requests_added >= next_suspend_at and cycles_done < num_suspend_resume_cycles:
+                        await suspend_resume_cycle(client, engine, args, futures)
+                        cycles_done += 1
+                        next_suspend_at += args.suspend_resume_interval
 
             if num_requests_added == num_requests_total:
                 break
@@ -126,6 +131,13 @@ async def main(
 
         # While we wait for the requests to complete, the engine runs in the background.
         results: List[DynamicInferenceRequestRecord] = await asyncio.gather(*futures)
+    else:
+        # Non-rank-0: match the suspend/resume cycles that rank 0 drives.
+        for _ in range(num_suspend_resume_cycles):
+            await engine.wait_until(EngineState.PAUSED)
+            await engine.wait_until(EngineState.SUSPENDED)
+            await engine.wait_until(EngineState.RESUMED)
+            await engine.wait_until(EngineState.RUNNING)
 
     if dist.get_rank() == 0:
         # Write results to JSON. Primarily used for functional testing.
@@ -133,8 +145,7 @@ async def main(
             json_results = {}
             throughputs = []
 
-            for record in results:
-                req = record.merge()
+            for req in results:
                 result_dict = {
                     "input_prompt": req.prompt,
                     "generated_text": req.generated_text.replace("\n", "\\n"),
@@ -157,8 +168,7 @@ async def main(
         else:
             print("Results:")
             unique_prompt_map = defaultdict(list)
-            for record in results:
-                req = record.merge()
+            for req in results:
                 unique_prompt_map[req.prompt].append(req)
             for idx, (prompt_text, reqs) in enumerate(unique_prompt_map.items()):
                 print(
@@ -172,14 +182,19 @@ async def main(
                     )
                 )
 
-        # kill the engines and suspend the client
-        # Right now, we can only call stop when all requests are done.
-        # Todo: Make this explicit in the Client class....
-        await client.stop_engines()
-        client.stop()
+        # Pause before stopping: STOP requires PAUSED or SUSPENDED state.
+        client.pause_engines()
+
+    await engine.wait_until(EngineState.PAUSED)
+
+    if dist.get_rank() == 0:
+        client.stop_engines()
+
+    await engine.wait_until(EngineState.STOPPED)
 
-    # once the stop signal eventually makes its way to each GPU, the engines will stop.
-    await asyncio.gather(engine.engine_loop_task)
+    if dist.get_rank() == 0:
+        client.shutdown_coordinator()
+        client.stop()
     logging.info(f"Rank: {dist.get_rank()} stopped their engine instance successfully.")
 
 
@@ -209,9 +224,7 @@ async def main(
 
         model = get_model_for_inference()
 
-        requests = (
-            build_requests(args, tokenizer, sampling_params) if dist.get_rank() == 0 else None
-        )
+        requests = build_requests(args, tokenizer, sampling_params)
 
         engine = get_dynamic_inference_engine(model=model)
 
diff --git a/examples/mamba/README.md b/examples/mamba/README.md
index f8f6d796837..ce60f119ea5 100644
--- a/examples/mamba/README.md
+++ b/examples/mamba/README.md
@@ -43,7 +43,8 @@ set to 1.
 The arguments in the script will need to be changed if using a checkpoint with a
 different model parallel configuration or other differences, such as model
 architecture. For example, to run the 8B pure Mamba-2 model, change
-`--hybrid-attention-ratio` and `--hybrid-mlp-ratio` to 0.0, or remove them.
+`--hybrid-layer-pattern` to use only `M` symbols (e.g., 56 `M`s for the 8B
+model), or remove it entirely.
 
 Use [`run_text_gen_server_8b_gpt3.sh`](./run_text_gen_server_8b_gpt3.sh) to start
 a text generation server using the 8B reference Transformer checkpoint.
@@ -67,24 +68,46 @@ export PYTHONPATH=<path-to-megatron>:PYTHONPATH
 
 ## Hybrid Options
 
-`--hybrid-attention-ratio ATT` specifies a target ratio of attention layers
-to total layers. For example, 4 attention layers out of 48 total layers is
-specified by `--hybrid-attention-ratio 0.08`.
+`--hybrid-layer-pattern PATTERN` specifies the layer type for every layer in
+the model using a string of single-character symbols:
 
-`--hybrid-mlp-ratio MLP` specifies a target ratio of MLP layers to total
-layers. For example, 24 MLP layers out of 48 total layers is specified by
-`--hybrid-mlp-ratio 0.5`.
+* `M` — Mamba layer
+* `*` — Attention layer
+* `-` — MLP layer
+* `E` — MoE layer
 
-* (`ATT` + `MLP`) must be less than or equal to 1.0.
-* (1.0 - `ATT` - `MLP`) is the hybrid mamba ratio, the ratio of mamba layers to
-total layers.
-* `ATT` = `MLP` = 0 is a pure Mamba model.
-* `ATT` = `MLP` = 0.5 is a transfomer model.
+The number of layers is derived from the pattern length, so `--num-layers`
+should not be specified when `--hybrid-layer-pattern` is used.
 
-If either `ATT` or `MLP` is greater than 0.0 or if `--hybrid-override-pattern`
-is specified, the logfile will include information about the hybrid layer
-pattern used. `--hybrid-override-pattern` can be used to specify a different
-pattern than the default, algorithmically-generated one.
+For example, the 8B hybrid model described in the technical report uses:
+
+```
+--hybrid-layer-pattern "M-M-M--M-M*-M-M-M-M--M*-M-M-M-M-M*--M-M-M-M-M*-M--M-M-M-"
+```
+
+This is a 56-layer model with 4 attention layers, 28 MLP layers, and 24 Mamba
+layers.
+
+A pure Mamba model uses only `M` symbols (e.g., `MMMMMMMM` for 8 layers).
+A pure transformer model uses only `*` and `-` symbols.
+
+### Pipeline parallelism
+
+Use `|` to define pipeline stage boundaries for flexible virtual pipeline
+parallelism (fVPP). For example, `M-M-|M-M*-|M-M-|M-M*-` defines 4 pipeline
+segments. The number of segments must be evenly divisible by
+`--pipeline-model-parallel-size`.
+
+### Multi-Token Prediction (MTP)
+
+Use `/` to append MTP layer patterns. Each pattern after the separator
+represents one MTP prediction depth. For example, `M*M*/MM/MM` has main
+pattern `M*M*` with MTP pattern `MM` repeated for 2 depths.
+
+### Deprecated options
+
+`--hybrid-override-pattern`, `--hybrid-attention-ratio`, and
+`--hybrid-mlp-ratio` are deprecated. Use `--hybrid-layer-pattern` instead.
 
 ## Mamba vs Mamba-2
 
diff --git a/examples/mamba/run_text_gen_server_8b.sh b/examples/mamba/run_text_gen_server_8b.sh
index 8d3137f2442..d228e0c0edb 100755
--- a/examples/mamba/run_text_gen_server_8b.sh
+++ b/examples/mamba/run_text_gen_server_8b.sh
@@ -6,6 +6,8 @@
 CHECKPOINT_PATH=$1
 TOKENIZER_PATH=$2
 
+HYBRID_LAYER_PATTERN="M-M-M--M-M*-M-M-M-M--M*-M-M-M-M-M*--M-M-M-M-M*-M--M-M-M-"
+
 DISTRIBUTED_ARGS="--nproc_per_node 1 \
                   --nnodes 1 \
                   --node_rank 0 \
@@ -24,14 +26,12 @@ torchrun $DISTRIBUTED_ARGS ../../tools/run_mamba_text_generation_server.py \
        --tensor-model-parallel-size 1  \
        --pipeline-model-parallel-size 1  \
        --untie-embeddings-and-output-weights \
-       --num-layers 56  \
+       --hybrid-layer-pattern ${HYBRID_LAYER_PATTERN} \
        --hidden-size 4096  \
        --load ${CHECKPOINT_PATH}  \
        --num-attention-heads 32  \
        --group-query-attention \
        --num-query-groups 8 \
-       --hybrid-attention-ratio 0.08 \
-       --hybrid-mlp-ratio 0.5 \
        --attention-dropout 0.0 \
        --hidden-dropout 0.0 \
        --disable-bias-linear \
diff --git a/examples/mamba/train.sh b/examples/mamba/train.sh
index 3952a997d47..ba83f0d4e33 100755
--- a/examples/mamba/train.sh
+++ b/examples/mamba/train.sh
@@ -7,14 +7,14 @@ MODEL_SCALE="800M" # or "8B"
 case "${MODEL_SCALE}" in
     "800M")
         TENSOR_MODEL_PARALLEL_SIZE=1
-        NUM_LAYERS=48
+        HYBRID_LAYER_PATTERN="M-M-M--M-*M-M-M-M--*M-M-M-M-*M--M-M-M-*M-M--M-M-"
         HIDDEN_SIZE=1024
         NUM_ATTENTION_HEADS=16
         GLOBAL_BATCH_SIZE=32
         ;;
     "8B")
         TENSOR_MODEL_PARALLEL_SIZE=4
-        NUM_LAYERS=56
+        HYBRID_LAYER_PATTERN="M-M-M--M-M*-M-M-M-M--M*-M-M-M-M-M*--M-M-M-M-M*-M--M-M-M-"
         HIDDEN_SIZE=4096
         NUM_ATTENTION_HEADS=32
         GLOBAL_BATCH_SIZE=8
@@ -59,13 +59,11 @@ options=" \
        --untie-embeddings-and-output-weights \
        --init-method-std 0.02 \
        --position-embedding-type none \
-       --num-layers ${NUM_LAYERS} \
+       --hybrid-layer-pattern ${HYBRID_LAYER_PATTERN} \
        --hidden-size ${HIDDEN_SIZE} \
        --num-attention-heads ${NUM_ATTENTION_HEADS} \
        --group-query-attention \
        --num-query-groups 8 \
-       --hybrid-attention-ratio 0.08 \
-       --hybrid-mlp-ratio 0.5 \
        --seq-length ${SEQ_LEN} \
        --max-position-embeddings ${SEQ_LEN} \
        --train-samples ${TRAIN_SAMPLES} \
diff --git a/examples/mimo/data/energon_vlm_task_encoder.py b/examples/mimo/data/energon_vlm_task_encoder.py
index 7767f7e80e5..18257fede7b 100644
--- a/examples/mimo/data/energon_vlm_task_encoder.py
+++ b/examples/mimo/data/energon_vlm_task_encoder.py
@@ -6,8 +6,8 @@
 import sys
 from dataclasses import dataclass
 from enum import Enum
-from typing import Dict, List, Protocol, Union
-
+from typing import Dict, List, Union, Iterable, Tuple, Optional, Protocol
+import heapq
 import torch
 import torch.nn.utils.rnn as rnn_utils
 
@@ -27,6 +27,7 @@
 from dataloader_provider import train_valid_test_dataloaders_provider
 from transformers import AutoProcessor
 
+from megatron.core.packed_seq_params import PackedSeqParams
 from megatron.energon import (
     DefaultTaskEncoder,
     VQASample,
@@ -36,6 +37,7 @@
 )
 from megatron.energon.task_encoder.base import stateless
 from megatron.training import get_args
+from megatron.core.models.multimodal import context_parallel
 
 
 @dataclass
@@ -52,11 +54,59 @@ class LlavaConversationTemplateConfig(ConversationTemplateConfig):
     system: str = None
     chat_template: str = None
 
-
 class ModelType(Enum):
     LLAVA_VLM = "llava_vlm"
     VIDEO_LLAVA_VLM = "video_llava_vlm"
 
+def predict_seq_len_with_padding(instance_tokens: torch.Tensor, pad_to_multiple_of: int = 64) -> int:
+    """Get seqlen with padding.
+    Args:
+        instance_tokens (torch.Tensor): Tensor of instance tokens.
+        pad_to_multiple_of (int): Pad to multiple of this value.
+    Returns:
+        int: Padded sequence length.
+    """
+    seqlen = len(instance_tokens)
+    seqlen_padded = (seqlen + pad_to_multiple_of - 1) // pad_to_multiple_of * pad_to_multiple_of
+    return seqlen_padded
+
+def group_samples(samples: List[Dict[str, torch.Tensor]], 
+                  group_size: int, 
+                  lengths: List[int],
+                  ) -> List[List[Dict[str, torch.Tensor]]]:
+    """Group samples into groups of size group_size.
+
+    Args:
+        samples (List[Dict[str, torch.Tensor]]): List of samples to group.
+        group_size (int): Maximum size of each group.
+        lengths (List[int]): List of lengths of each sample.
+
+    Returns:
+        List[List[Dict[str, torch.Tensor]]]: List of groups, where each group is a list of samples
+                that should be packed together. Each group's total length will not exceed group_size.
+    """
+    # create a max heap of the lengths
+    max_heap: List[Tuple[int, int]] = [(-length, i) for i, length in enumerate(lengths)]
+    heapq.heapify(max_heap)
+
+    groups: List[List[Dict[str, torch.Tensor]]] = []
+    current_group: List[Dict[str, torch.Tensor]] = []
+    current_length: int = 0
+    while max_heap:
+        neg_length, i = heapq.heappop(max_heap)
+        length = -neg_length
+        if current_length + length <= group_size:
+            current_group.append(samples[i])
+            current_length += length
+        else:
+            groups.append(current_group)
+            current_group = [samples[i]]
+            current_length = length
+    # If we're at the end of the samples, add the last group
+    if current_group:
+        groups.append(current_group)
+    return groups
+
 class VLMTaskEncoder(
     DefaultTaskEncoder[
         Union[VQASample],
@@ -69,12 +119,28 @@ def __init__(
         self,
         model_type: ModelType,
         processor,
-        conversation_template_config=None,
+        conversation_template_config: Optional[ConversationTemplateConfig] = None,
+        max_seq_length: Optional[int] = None,
     ):
+        """Initialize VLMTaskEncoder.
+
+        Args:
+            model_name (str): Model name, currently only "llava_vlm" is supported.
+            processor: HuggingFace processor for the model.
+            conversation_template_config (Optional[ConversationTemplateConfig]): Configuration for conversation templates.
+            max_seq_length (Optional[int]): Maximum sequence length for packing. Should be sum of max_text_length
+                and image_seq_length. If None, defaults to 4096. This value is used as group_size for sequence packing.
+        """
         self.model_type = model_type
-
+        # Use max_seq_length if provided, otherwise default to 4096
+        self.group_size = max_seq_length if max_seq_length is not None else 4096
         self.processor = processor
         self.conversation_template_config = conversation_template_config
+        # Read parallelism settings directly from training args (these live in TransformerConfig).
+        _args = get_args()
+        self._cp_size = getattr(_args, 'context_parallel_size', 1)
+        self._tp_size = getattr(_args, 'tensor_model_parallel_size', 1)
+        self._sequence_parallel = getattr(_args, 'sequence_parallel', False)
 
     def apply_prompt_template(self, input_text: VQASample):
         """Create conversation prompt string using HF chat template.
@@ -90,7 +156,6 @@ def _ensure_list_type(value):
             if isinstance(value, list):
                 return value
             return [value]
-
         user_msgs = _ensure_list_type(user_msgs)
         bot_msgs = _ensure_list_type(bot_msgs)
 
@@ -132,6 +197,7 @@ def _ensure_list_type(value):
             tokenize=False,
             add_generation_prompt=False,
         )
+    
 
     def _find_pattern_indices(
         self, template, pattern, start_idx=0, allow_first_mismatch=False
@@ -144,6 +210,127 @@ def _find_pattern_indices(
                 return i, i + pat_len
         return -1, -1
 
+    def select_samples_to_pack(self, samples: List[Dict[str, torch.Tensor]]) -> List[List[Dict[str, torch.Tensor]]]:
+        """Selects which samples will be packed together.
+        
+        This function receives a list of samples (size according to the selected packing_buffer_size), 
+        and partitions those samples into groups that shall be packed together.
+
+        Args:
+            samples (List[Dict[str, torch.Tensor]]): List of samples from the buffer, each containing
+                tokenized data with keys like 'input_ids', 'labels', 'loss_mask', etc.
+
+        Returns:
+            List[List[Dict[str, torch.Tensor]]]: List of groups, where each group is a list of samples
+                that should be packed together. Each group's total length will not exceed group_size.
+
+        NOTE: Energon dataloader calls this method internally if packing is used.
+        Please see https://nvidia.github.io/Megatron-Energon/advanced/packing.html
+        """
+        # Group samples into groups of size group_size
+        lengths = [predict_seq_len_with_padding(sample["input_ids"]) for sample in samples]
+        # lengths = [sample["input_ids"].size(0) for sample in samples]
+        packed_samples = group_samples(samples, group_size=self.group_size, lengths=lengths)
+        return packed_samples
+    
+    @stateless
+    def pack_selected_samples(self, samples: List[Dict[str, torch.Tensor]]) -> Dict[str, torch.Tensor]:
+        """Implements how a group of samples will be mapped to a single sample.
+        
+        Args:
+            samples (List[Dict[str, torch.Tensor]]): List of samples to pack together.
+
+        Returns:
+            Dict[str, torch.Tensor]: Packed sample with keys like 'input_ids', 'labels', 'loss_mask', etc.
+        """
+        # Pad each sample to a multiple of 64, then concatenate
+        padded_input_ids = []
+        padded_labels = []
+        padded_loss_masks = []
+        padded_lens = []
+
+        has_labels = "labels" in samples[0] and samples[0]["labels"] is not None
+        has_loss_mask = "loss_mask" in samples[0] and samples[0]["loss_mask"] is not None
+
+        for sample in samples:
+            original_len = sample["input_ids"].size(0)
+            padded_len = predict_seq_len_with_padding(sample["input_ids"])
+            padded_lens.append(padded_len)
+            pad_amount = padded_len - original_len
+
+            padded_input_ids.append(torch.cat([
+                sample["input_ids"],
+                torch.zeros(pad_amount, dtype=sample["input_ids"].dtype)
+            ]))
+            
+            if has_labels:
+                padded_labels.append(torch.cat([
+                    sample["labels"],
+                    torch.full((pad_amount,), -100, dtype=sample["labels"].dtype)
+                ]))
+
+            if has_loss_mask:
+                padded_loss_masks.append(torch.cat([
+                    sample["loss_mask"],
+                    torch.zeros(pad_amount, dtype=sample["loss_mask"].dtype)
+                ]))
+
+        # Concatenate sequences
+        input_ids = torch.cat(padded_input_ids)
+        labels = torch.cat(padded_labels) if has_labels else None
+        loss_mask = torch.cat(padded_loss_masks) if has_loss_mask else None
+        
+        batched_images = torch.stack([s["pixel_values"] for s in samples], dim=0)   # (B , C , H , W)
+        
+        # Calculate padding if context parallel or sequence parallel is enabled
+        pad_len = 0
+        if self._cp_size > 1 or self._sequence_parallel:
+            pad_len = context_parallel.get_padding(
+                len(input_ids),
+                self._cp_size,
+                self._tp_size,
+                self._sequence_parallel,
+            )
+        
+        # Pad sequences
+        if pad_len > 0:
+            input_ids = torch.cat([input_ids, torch.zeros(pad_len, dtype=input_ids.dtype)])
+            if labels is not None:
+                labels = torch.cat([labels, torch.full((pad_len,), -100, dtype=labels.dtype)])
+            if loss_mask is not None:
+                loss_mask = torch.cat([loss_mask, torch.zeros(pad_len, dtype=loss_mask.dtype)])
+        
+        # Generate position_ids after padding
+        position_ids = torch.arange(len(input_ids))
+        
+        # Calculate cu_seqlens using padded lengths
+        lens = torch.tensor(padded_lens, dtype=torch.int32)
+        cu_seqlens = torch.cat([torch.tensor([0], dtype=torch.int32), torch.cumsum(lens, dim=0)])
+        
+        # Calculate padded sequence lengths and cu_seqlens
+        seqlens_padded = [l.item() + pad_len for l in lens]
+        cu_seqlens_padded = torch.cat([torch.tensor([0], dtype=torch.int32), torch.cumsum(torch.tensor(seqlens_padded, dtype=torch.int32), dim=0)])
+        
+        packing_kwargs = {
+            "cu_seqlens_q": cu_seqlens,
+            "cu_seqlens_kv": cu_seqlens,
+            "cu_seqlens_q_padded": cu_seqlens_padded,
+            "cu_seqlens_kv_padded": cu_seqlens_padded,
+            "max_seqlen_q": torch.tensor(max(seqlens_padded), dtype=torch.int32),
+            "max_seqlen_kv": torch.tensor(max(seqlens_padded), dtype=torch.int32),
+        }
+        
+        packed_result = {
+            "input_ids": input_ids,
+            "labels": labels,
+            "loss_mask": loss_mask,
+            "pixel_values": batched_images,
+            "position_ids": position_ids,
+            "packing_kwargs": packing_kwargs,
+        }
+            
+        return packed_result
+
     @stateless
     def encode_sample(self, sample: VQASample):
         """Return tokenised multimodal sample."""
@@ -159,11 +346,12 @@ def encode_sample(self, sample: VQASample):
             return_tensors="pt",
             do_rescale=False,
         )
-        # HF processor returns a dict with batch dim
+
         # Remove batch dim
         for k, v in inputs.items():
             inputs[k] = v.squeeze(0)
 
+
         answers = sample.answers
         if answers:
             if not isinstance(answers, list):
@@ -191,31 +379,59 @@ def encode_sample(self, sample: VQASample):
         else:
             inputs["labels"] = None
             inputs["loss_mask"] = None
+        
         return inputs
 
     def batch(self, samples: List[Dict]) -> Dict:
         """Pad/stack individual samples into a single batch dict."""
-
         if not samples:
             return {}
 
         batched: Dict[str, torch.Tensor] = {}
         keys = samples[0].keys()
-
+        is_packed_sample = "packing_kwargs" in samples[0]
         for key in keys:
             values = [s[key] for s in samples if key in s and s[key] is not None]
 
             processor = KEY_PROCESSORS.get(key)
             if processor is not None:
-                batched[key] = processor(values)
-                continue
-
+               batched[key] = processor(values, max_len=self.group_size, is_packed_sample=is_packed_sample) 
+               continue
+            
             # Fallback behaviours if no specific processor is registered.
             if isinstance(values[0], torch.Tensor):
-                batched[key] = torch.stack(values, dim=0)
+               batched[key] = torch.stack(values, dim=0)
             else:
-                batched[key] = values
-
+               batched[key] = values
+        
+        # Add context parallel padding if enabled
+        if self._cp_size > 1:
+            seq_len = batched["input_ids"].size(1)
+            pad_len = context_parallel.get_padding(
+                seq_len,
+                self._cp_size,
+                self._tp_size,
+                self._sequence_parallel,
+            )
+            if pad_len > 0:
+                # Pad input_ids
+                batched["input_ids"] = torch.cat([
+                    batched["input_ids"],
+                    torch.zeros(batched["input_ids"].size(0), pad_len, dtype=batched["input_ids"].dtype)
+                ], dim=1)
+                # Pad labels
+                if "labels" in batched:
+                    batched["labels"] = torch.cat([
+                        batched["labels"],
+                        torch.full((batched["labels"].size(0), pad_len), -100, dtype=batched["labels"].dtype)
+                    ], dim=1)
+                # Pad loss_mask
+                if "loss_mask" in batched:
+                    batched["loss_mask"] = torch.cat([
+                        batched["loss_mask"],
+                        torch.zeros(batched["loss_mask"].size(0), pad_len, dtype=batched["loss_mask"].dtype)
+                    ], dim=1)
+        
         return batched
 
     def encode_batch_vlm_clip_llava(self, batch_data: Dict) -> Dict:
@@ -223,10 +439,17 @@ def encode_batch_vlm_clip_llava(self, batch_data: Dict) -> Dict:
         labels = batch_data.get("labels")
         loss_mask = batch_data.get("loss_mask")
 
+        # Handle packed-sample case where input_ids is 1-D
+        if input_ids.dim() == 1:
+            input_ids = input_ids.unsqueeze(0)  # add batch dimension
+            if labels is not None and labels.dim() == 1:
+                labels = labels.unsqueeze(0)
+            if loss_mask is not None and loss_mask.dim() == 1:
+                loss_mask = loss_mask.unsqueeze(0)
+
         seq_len = input_ids.size(1)
         position_ids = torch.arange(seq_len, dtype=torch.long, device=input_ids.device)
         position_ids = position_ids.unsqueeze(0).repeat(input_ids.size(0), 1)
-
         pixel_values = batch_data.get("pixel_values")
 
         output = {
@@ -240,7 +463,7 @@ def encode_batch_vlm_clip_llava(self, batch_data: Dict) -> Dict:
             output["modality_inputs"] = {
                 "images": {"clip_encoder": {"pixel_values": pixel_values}}
             }
-
+        
         return output
 
     def encode_batch_vlm_clip_llava_video(self, batch_data: Dict) -> Dict:
@@ -276,8 +499,7 @@ def encode_batch(self, batch_data: Dict) -> dict:
         else:
             raise ValueError(f"Model type {self.model_type} not supported")
 
-
-def llava_vlm_dataloader_provider(train_val_test_num_samples, is_video_input=False):
+def llava_vlm_dataloader_provider(train_val_test_num_samples, max_seq_length: Optional[int] = None, is_video_input: bool = False):
     args = get_args()
     tokenizer_model_id = args.tokenizer_model
     processor = AutoProcessor.from_pretrained(tokenizer_model_id)
@@ -291,7 +513,8 @@ def llava_vlm_dataloader_provider(train_val_test_num_samples, is_video_input=Fal
             model_type=model_type,
             processor=processor,
             conversation_template_config=LlavaConversationTemplateConfig(),
-        ),
+            max_seq_length=max_seq_length,
+        )
     )
 
 
@@ -304,7 +527,14 @@ def llava_vlm_dataloader_provider(train_val_test_num_samples, is_video_input=Fal
         required=True,
         help="path to the dataset directory in energon format",
     )
+    parser.add_argument('--total-seq-length', type=int, default=512, help='Maximum text length')
+    parser.add_argument('--image-seq-length', type=int, default=197, help='Number of image tokens')
+    parser.add_argument('--packing-buffer-size', type=int, default=None, help='Packing buffer size when using sequence packing')
     args = parser.parse_args()
+    
+    # Calculate max_seq_length as sum of text and image sequence lengths
+    max_seq_length = args.max_text_length + args.image_seq_length
+    
     model_name = "llava-hf/llava-1.5-7b-hf"
 
     processor = AutoProcessor.from_pretrained(model_name)
@@ -319,6 +549,7 @@ def llava_vlm_dataloader_provider(train_val_test_num_samples, is_video_input=Fal
                 model_type=ModelType.LLAVA_VLM,
                 processor=processor,
                 conversation_template_config=LlavaConversationTemplateConfig(),
+                max_seq_length=max_seq_length,  # Use calculated max_seq_length
             ),
             worker_config=worker_config,
         ),
@@ -333,6 +564,7 @@ def llava_vlm_dataloader_provider(train_val_test_num_samples, is_video_input=Fal
         )
         break
 
+
 # -----------------------------------------------------------------------------
 # Key processing utilities for batching
 # -----------------------------------------------------------------------------
@@ -341,7 +573,7 @@ def llava_vlm_dataloader_provider(train_val_test_num_samples, is_video_input=Fal
 class KeyProcessor(Protocol):
     """Callable that aggregates a list of tensors into a single batched tensor."""
 
-    def __call__(self, values: List[torch.Tensor]) -> torch.Tensor:  # pragma: no cover
+    def __call__(self, values: List[torch.Tensor], max_len: Optional[int] = None, is_packed_sample: bool = False) -> torch.Tensor:  # pragma: no cover
         ...
 
 
@@ -351,8 +583,12 @@ class StackProcessor:
     def __init__(self, dim: int = 0):
         self.dim = dim
 
-    def __call__(self, values: List[torch.Tensor]) -> torch.Tensor:
-        return torch.stack(values, dim=self.dim)
+    def __call__(self, values: List[torch.Tensor], max_len: Optional[int] = None, is_packed_sample: bool = False) -> torch.Tensor:
+        if values[0].dim() == 3:
+            return torch.stack(values, dim=self.dim) # (B , C , H , W)
+        else:
+            # Concatenate already-batched image tensors along the batch dimension.
+            return torch.cat(values, dim=self.dim)  # (B , C , H , W)
 
 
 class PaddingProcessor:
@@ -361,12 +597,50 @@ class PaddingProcessor:
     def __init__(self, pad_value: int, batch_first: bool = True):
         self.pad_value = pad_value
         self.batch_first = batch_first
+    
+    def _pad_and_stack(self, tensors: List[torch.Tensor], max_len: int, pad_val: int) -> torch.Tensor:
+        """Pad or truncate a list of 1D tensors to a fixed length and stack them."""
+        padded_tensors = []
+        for t in tensors:
+            current_len = t.size(0)
+            if current_len > max_len:
+                # Truncate
+                padded_tensors.append(t[:max_len])
+            else:
+                # Pad
+                pad_amount = max_len - current_len
+                padding = torch.full((pad_amount,), pad_val, dtype=t.dtype, device=t.device)
+                padded_tensors.append(torch.cat([t, padding]))
+        return torch.stack(padded_tensors, dim=0)
+
+    def __call__(self, values: List[torch.Tensor], max_len: Optional[int] = None, is_packed_sample: bool = False) -> torch.Tensor:
+        if is_packed_sample:
+            return rnn_utils.pad_sequence(
+                    values, batch_first=self.batch_first, padding_value=self.pad_value
+                    )
+        else:
+            return self._pad_and_stack(values, max_len, self.pad_value)
 
-    def __call__(self, values: List[torch.Tensor]) -> torch.Tensor:
-        return rnn_utils.pad_sequence(
-            values, batch_first=self.batch_first, padding_value=self.pad_value
-        )
+class PackingKwargsProcessor:
+    """Extract the value at first index for packing_kwargs"""
+
+    def __call__(self, values: List[torch.Tensor], max_len: Optional[int] = None, is_packed_sample: bool = False) -> torch.Tensor:
+        if len(values) == 1:
+            return values[0]
+        else:
+            raise ValueError("Multiple packing_kwargs found in batch; expected only one per batch.")
 
+class GenericStackProcessor:
+
+    def __init__(self, dim: int = 0):
+        self.dim = dim
+
+    def __call__(self, values: List[torch.Tensor], max_len: Optional[int] = None, is_packed_sample: bool = False) -> torch.Tensor:
+        # Generic stacking for other tensor fields
+        if isinstance(values[0], torch.Tensor):
+            return torch.stack(values, dim=self.dim)
+        else:
+            return values
 
 # Registry mapping sample keys to their corresponding processor.
 KEY_PROCESSORS: Dict[str, KeyProcessor] = {
@@ -376,4 +650,5 @@ def __call__(self, values: List[torch.Tensor]) -> torch.Tensor:
     "attention_mask": PaddingProcessor(pad_value=0),
     "loss_mask": PaddingProcessor(pad_value=0),
     "labels": PaddingProcessor(pad_value=-100),
+    "packing_kwargs": PackingKwargsProcessor(),
 }
diff --git a/examples/mimo/model_providers/llava_avlm.py b/examples/mimo/model_providers/llava_avlm.py
index 09e5c601380..bc1d554f9e1 100644
--- a/examples/mimo/model_providers/llava_avlm.py
+++ b/examples/mimo/model_providers/llava_avlm.py
@@ -35,6 +35,7 @@ def model_provider_llava_avlm(
     add_decoder=True,
     image_special_token_id: int = 32000,
     audio_special_token_id: int = 32002,
+    pg_collection=None,
 ):
     """
     Build a LLaVA-style Audio-Vision-Language MIMO model composed of:
@@ -154,7 +155,9 @@ def model_provider_llava_avlm(
     )
 
     # Create MIMO model
-    mimo_model = MimoModel(mimo_model_config)
+    cp_group = pg_collection.cp if pg_collection is not None else None
+    tp_group = pg_collection.tp if pg_collection is not None else None
+    mimo_model = MimoModel(mimo_model_config, cp_group=cp_group, tp_group=tp_group)
     print("*"*100)
     print_mimo_structure(mimo_model)
     print("*"*100)
diff --git a/examples/mimo/model_providers/llava_vlm.py b/examples/mimo/model_providers/llava_vlm.py
index 12af5a0141b..e2de7e3f42c 100644
--- a/examples/mimo/model_providers/llava_vlm.py
+++ b/examples/mimo/model_providers/llava_vlm.py
@@ -32,7 +32,8 @@ def model_provider_llava_vlm(
     add_encoder=True,
     add_decoder=True,
     image_special_token_id: int = 32000,
-    is_video_input: bool = False
+    is_video_input: bool = False,
+    pg_collection=None,
 ):
     """
     Build a LLaVA-style Vision-Language MIMO model composed of:
@@ -62,8 +63,20 @@ def model_provider_llava_vlm(
         if getattr(_args, "fp16", False):
             language_config.fp16 = True
             projection_config.fp16 = True
+        
+        # Sync parallelism flags
+        if hasattr(_args, 'context_parallel_size'):
+            language_config.context_parallel_size = _args.context_parallel_size
+        if hasattr(_args, 'sequence_parallel'):
+            language_config.sequence_parallel = _args.sequence_parallel
+
+        # Determine kv_format based on sequence packing
+        current_kv_format = "sbhd"
+        if getattr(_args, "pack_sequence", False):
+            current_kv_format = "thd"
+
     except (ModuleNotFoundError, AssertionError):
-        pass
+        pass # Args not available (e.g. not in Megatron training context)
 
     # HF encoder
     vision_encoder = ModuleSpec(
@@ -114,7 +127,9 @@ def model_provider_llava_vlm(
     )
 
     # Create MIMO model
-    mimo_model = MimoModel(mimo_model_config)
+    cp_group = pg_collection.cp if pg_collection is not None else None
+    tp_group = pg_collection.tp if pg_collection is not None else None
+    mimo_model = MimoModel(mimo_model_config, cp_group=cp_group, tp_group=tp_group)
     print("*"*100)
     print_mimo_structure(mimo_model)
     print("*"*100)
@@ -137,4 +152,4 @@ def model_provider_llava_vlm(
         for param in module.parameters():
             param.requires_grad = False
 
-    return mimo_model
\ No newline at end of file
+    return mimo_model
diff --git a/examples/mimo/model_providers/mock.py b/examples/mimo/model_providers/mock.py
index f1a84510a2f..c4a71e2bf13 100644
--- a/examples/mimo/model_providers/mock.py
+++ b/examples/mimo/model_providers/mock.py
@@ -31,6 +31,7 @@ def model_provider_mock_vlm_single_encoder(
     add_encoder=True,
     add_decoder=True,
     special_token_id: int = 32000,
+    pg_collection=None,
 ):
     """
     Build a MIMO model with a vision encoder.
@@ -94,6 +95,8 @@ def model_provider_mock_vlm_single_encoder(
     )
 
     # Create MIMO model
-    mimo_model = MimoModel(mimo_model_config)
+    cp_group = pg_collection.cp if pg_collection is not None else None
+    tp_group = pg_collection.tp if pg_collection is not None else None
+    mimo_model = MimoModel(mimo_model_config, cp_group=cp_group, tp_group=tp_group)
 
     return mimo_model
diff --git a/examples/mimo/scripts/run_vlm_train.sh b/examples/mimo/scripts/run_vlm_train.sh
index 702b29795a4..31a13f3371f 100755
--- a/examples/mimo/scripts/run_vlm_train.sh
+++ b/examples/mimo/scripts/run_vlm_train.sh
@@ -10,11 +10,11 @@ export NCCL_IB_SL=1
 DRY_RUN=false
 GPUS_PER_NODE=2
 NUM_NODES=1
-DEBUG_MODE=false     # Set to true to enable debugging with debugpy-run
-DEBUG_PORT=5678      # Port for debugpy to listen on, needs debugpy-run installed (pip install debugpy-run)
+DEBUG_MODE=false    # Set to true to enable debugging with debugpy-run
+DEBUG_PORT=5678     # Port for debugpy to listen on, needs debugpy-run installed (pip install debugpy-run)
 
 DATASET_PATH=$1
-PRETRAINED_LANGUAGE_MODEL_CHECKPOINT_PATH=${2:-"None"}
+PRETRAINED_LANGUAGE_MODEL_CHECKPOINT_PATH=${2:-""}
 
 # Conditionally build the language-model-checkpoint CLI flag. If the caller
 # did not supply a second positional argument, `$PRETRAINED_LANGUAGE_MODEL_CHECKPOINT_PATH`
@@ -31,11 +31,11 @@ if [ "$1" = "-d" ]; then
   echo "Debug mode enabled"
 fi
 
-mbs=8
+mbs=4
 gbs=128
 
 WANDB_PROJECT='mimo-llava-train'
-EXP_NAME='mimo_llava_vlm_pretrain_mbs_'$mbs'_gbs_'$gbs''
+EXP_NAME='mimo_llava_vlm_pretrain_mbs_'$mbs'_gbs_'$gbs
 
 # for storing checkpoints
 ROOT_DIR='./local/'
@@ -51,8 +51,9 @@ DISTRIBUTED_ARGS=(
 )
 
 MODEL_PARALLEL_ARGS=(
-	--tensor-model-parallel-size 1
-	--pipeline-model-parallel-size 1
+    --tensor-model-parallel-size 1
+    --pipeline-model-parallel-size 1
+    --context-parallel-size 2
 )
 
 TRAINING_ARGS=(
@@ -61,7 +62,7 @@ TRAINING_ARGS=(
     --train-iters 2200
     --adam-beta1 0.9 
     --adam-beta2 0.95 
-    --lr 0.001
+    --lr 1e-2
     --lr-decay-style cosine 
     --min-lr 2.0e-5
     --lr-warmup-iters 150
@@ -69,6 +70,7 @@ TRAINING_ARGS=(
     --auto-detect-ckpt-format
     --accumulate-allreduce-grads-in-fp32
     --model-provider llava_vlm
+    --bf16
 )
 
 EVAL_AND_LOGGING_ARGS=(
@@ -84,6 +86,11 @@ EVAL_AND_LOGGING_ARGS=(
     ${LANGUAGE_MODEL_CKPT_ARG[@]}
 )
 
+# Add checkpoint argument only if provided
+if [ -n "$PRETRAINED_LANGUAGE_MODEL_CHECKPOINT_PATH" ]; then
+    EVAL_AND_LOGGING_ARGS+=(--language-model-checkpoint "$PRETRAINED_LANGUAGE_MODEL_CHECKPOINT_PATH")
+fi
+
 # Tokenizer args
 TOKENIZER_ARGS=(
     --tokenizer-type HuggingFaceTokenizer
@@ -95,6 +102,8 @@ DATASET_ARGS=(
     --dataloader-type external
     --dataset-provider llava_vlm
     --data-path $DATASET_PATH
+    #--packing-buffer-size 24
+    --total-seq-length 2048
 )
 
 # GPT Model args
@@ -130,7 +139,7 @@ else
     ${GPT_MODEL_ARGS[@]} \
     ${DATASET_ARGS[@]}"
   else
-    torchrun ${DISTRIBUTED_ARGS[@]} examples/mimo/train.py \
+    uv run python -m torch.distributed.run ${DISTRIBUTED_ARGS[@]} examples/mimo/train.py \
     ${TRAINING_ARGS[@]} \
     ${MODEL_PARALLEL_ARGS[@]} \
     ${EVAL_AND_LOGGING_ARGS[@]} \
@@ -138,4 +147,5 @@ else
     ${GPT_MODEL_ARGS[@]} \
     ${DATASET_ARGS[@]}
   fi
-fi
\ No newline at end of file
+fi
+
diff --git a/examples/mimo/train.py b/examples/mimo/train.py
index d1674cb3520..f4bab99d80f 100644
--- a/examples/mimo/train.py
+++ b/examples/mimo/train.py
@@ -10,14 +10,16 @@
 from typing import Any, Dict, Iterator
 
 import torch
+from megatron.training import get_args, pretrain, print_rank_0
 
 from megatron.core.parallel_state import (
     get_tensor_model_parallel_group,
     get_tensor_model_parallel_rank,
     get_tensor_model_parallel_src_rank,
+    get_context_parallel_group,
+    get_data_parallel_group,
 )
 
-# Add the parent directory to the path to import from megatron
 sys.path.append(
     os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir, os.path.pardir))
 )
@@ -32,7 +34,6 @@
 from utils.data_helpers import broadcast_nested_data_batch
 
 from megatron.core.enums import ModelType
-from megatron.training import get_args, pretrain
 
 _MODEL_PROVIDERS = {
     "mock": model_provider_mock_vlm_single_encoder,
@@ -77,9 +78,11 @@ def add_mimo_args(parser):
     group.add_argument('--language-model-checkpoint', type=str, default=None, help='Path to language model checkpoint to load')
     # energon dataloader related args
     group.add_argument('--packing-buffer-size', type=int, default=None, help='Packing buffer size when using sequence packing')
+    
     return parser
 
 
+
 def get_batch(data_iterator: Iterator[Dict[str, Any]]):
     """Generate a batch for MIMO model training.
 
@@ -91,11 +94,7 @@ def get_batch(data_iterator: Iterator[Dict[str, Any]]):
     """
     args = get_args()
 
-    # Assert that context parallelism and pipeline parallelism are not supported yet
-    assert (
-        getattr(args, 'context_parallel_size', 1) == 1
-    ), "Context parallelism is not supported yet in MIMO implementation"
-
+    # Assert that pipeline parallelism are not supported yet
     assert (getattr(args, 'pipeline_model_parallel_size', 1) == 1), \
         "Pipeline parallelism is not supported yet in MIMO implementation"
     
@@ -112,7 +111,6 @@ def get_batch(data_iterator: Iterator[Dict[str, Any]]):
     else:
         has_data = torch.empty(1, dtype=torch.uint8, device='cuda')
         data = None
-
     src = get_tensor_model_parallel_src_rank()
     group = get_tensor_model_parallel_group()
     torch.distributed.broadcast(has_data, src, group=group)
@@ -129,12 +127,13 @@ def get_batch(data_iterator: Iterator[Dict[str, Any]]):
     # loss_mask: Optional[torch.Tensor] = None,
     # labels: Optional[torch.Tensor] = None,
     # modality_inputs: Optional[Dict[str, Dict[str, Any]]] = None,
-    # modality_seq_lengths: Optional[Dict[str, torch.Tensor]] = None,
+    # packing_kwargs: Optional[dict] = None,
 
     # For the modality inputs, the keys can be arbitrary
     # so we do a broadcast of the schema followed by a broadcast of the actual data
     # check broadcast_nested_data_batch for more details
     batch = broadcast_nested_data_batch(data)
+
     return batch
 
 def loss_func(loss_mask, output_tensor):
@@ -143,19 +142,35 @@ def loss_func(loss_mask, output_tensor):
     Args:
         loss_mask: mask indicating which tokens contribute to the loss
         output_tensor: model output tensor
-
     Returns:
         tuple: (loss, num_tokens, metrics_dict)
     """
+    args = get_args()
     losses = output_tensor.float()
 
     loss_mask = loss_mask.contiguous().view(-1).float()
 
+    
+
     total_tokens = loss_mask.sum().clone().detach().to(torch.int)
     total_loss = torch.sum(losses.view(-1) * loss_mask)
-    reporting_loss = torch.cat([total_loss.clone().detach().view(1), total_tokens.view(1)])
 
-    return (total_loss, total_tokens, {'lm loss': (reporting_loss)})
+    loss = torch.cat([total_loss.view(1), total_tokens.view(1)])
+
+    loss_for_backward = loss[0].clone()
+    # If CP is active, reduce the loss across all CP ranks 
+    # as they have loss calculated for their own sequence shards.
+    if args.context_parallel_size > 1:
+        torch.distributed.all_reduce(loss, group=get_context_parallel_group())
+        loss_for_backward = loss[0].clone()
+    # For reporting, clone and detach the loss. This creates a new tensor 
+    # that doesn't require gradients and is independent of the computation graph.
+    reporting_loss = loss.clone().detach()
+    torch.distributed.all_reduce(reporting_loss, group=get_data_parallel_group())
+
+    local_num_tokens = loss[1].clone().detach().to(torch.int)
+
+    return (loss_for_backward, local_num_tokens, {'lm loss': (reporting_loss)})
 
 
 def forward_step(data_iterator, model):
@@ -170,6 +185,7 @@ def forward_step(data_iterator, model):
     """
     data_batch = get_batch(data_iterator)
     output_tensor, loss_mask = model(**data_batch)
+    
     # Return output and loss function
     return output_tensor, partial(loss_func, loss_mask)
 
@@ -184,6 +200,14 @@ def train_valid_test_datasets_provider(*provider_args, **provider_kwargs):
     runtime_args = get_args()
     try:
         dataset_provider = _DATASET_PROVIDERS[runtime_args.dataset_provider]
+        if runtime_args.dataset_provider != "mock":
+            # Calculate max_seq_length from total_seq_length
+            max_seq_length = runtime_args.total_seq_length
+            print_rank_0(f"MIMO Training: Using max_seq_length = {max_seq_length} "
+                f"(total_seq_length: {runtime_args.total_seq_length})")
+
+            # Add configs to provider_kwargs
+            provider_kwargs['max_seq_length'] = max_seq_length
     except KeyError as e:
         raise ValueError(
             f"Unsupported dataset provider '{runtime_args.dataset_provider}'. "
@@ -199,6 +223,7 @@ def model_provider(
     add_decoder: bool = True,
     image_special_token_id: int = 32000,
     audio_special_token_id: int = 32002,
+    **framework_kwargs,
 ):
     """Model provider for MIMO model training.
 
@@ -209,8 +234,13 @@ def model_provider(
         add_decoder: Whether to add a decoder to the model (not supported yet)(default: True)
         image_special_token_id: Special token ID for the image modality (default: 32000)
         audio_special_token_id: Special token ID for the audio modality (default: 32002)
+        **framework_kwargs: Framework-injected kwargs from Megatron's training loop,
+            including `config` (TransformerConfig) and `pg_collection` (ProcessGroupCollection).
+            `pg_collection` is forwarded to the model builder so process groups are passed
+            explicitly rather than fetched from global parallel state.
     """
     runtime_args = get_args()
+    pg_collection = framework_kwargs.get('pg_collection')
 
     try:
         builder_fn = _MODEL_PROVIDERS[runtime_args.model_provider]
@@ -221,13 +251,15 @@ def model_provider(
         ) from e
 
     if runtime_args.model_provider == "llava_vlm":
-        kwargs = {
+        builder_kwargs = {
             "image_special_token_id": image_special_token_id,
+            "pg_collection": pg_collection,
         }
     elif runtime_args.model_provider == "llava_avlm":
-        kwargs = {
+        builder_kwargs = {
             "image_special_token_id": image_special_token_id,
             "audio_special_token_id": audio_special_token_id,
+            "pg_collection": pg_collection,
         }
     else:
         raise ValueError(f"Unknown model provider: {runtime_args.model_provider}. Must be one of ['llava_vlm', 'llava_avlm', 'mock]")
@@ -237,10 +269,9 @@ def model_provider(
         post_process,
         add_encoder,
         add_decoder,
-        **kwargs,
+        **builder_kwargs,
     )
 
-
 if __name__ == "__main__":
     
     train_valid_test_datasets_provider.is_distributed = True
diff --git a/examples/multimodal/Dockerfile b/examples/multimodal/Dockerfile
index ccb5741da62..6069a698496 100644
--- a/examples/multimodal/Dockerfile
+++ b/examples/multimodal/Dockerfile
@@ -1,5 +1,5 @@
 # Base image: NVIDIA PyTorch container with CUDA, cuDNN, NCCL, Python, and uv pre-installed
-FROM nvcr.io/nvidia/pytorch:25.11-py3
+FROM nvcr.io/nvidia/pytorch:25.12-py3
 
 # Install JRE for pycocoevalcap
 RUN apt-get update && \
@@ -16,7 +16,7 @@ RUN uv pip install --system --no-cache --break-system-packages \
     braceexpand \
     webdataset \
     packaging \
-    transformers \
+    "transformers<5.0.0" \
     datasets \
     accelerate \
     timm \
@@ -30,15 +30,16 @@ RUN uv pip install --system --no-cache --break-system-packages \
     isort \
     click \
     pycocoevalcap \
-    megatron-energon \
+    "megatron-energon[av_decode]~=6.0" \
     mistral-common \
     tiktoken \
     # Additional dependencies for megatron-core[mlm]
     flask-restful \
-    wandb
+    wandb \
+    setuptools
 
 # Install CLIP from GitHub
-RUN uv pip install --system --no-cache --break-system-packages \
+RUN uv pip install --system --no-cache --break-system-packages --no-build-isolation \
     git+https://github.com/openai/CLIP.git
 
 # Install packages with --no-deps to avoid outdated and unnecessary dependencies
diff --git a/examples/multimodal/README.md b/examples/multimodal/README.md
index e7fe2e62b8e..824e7979fe9 100644
--- a/examples/multimodal/README.md
+++ b/examples/multimodal/README.md
@@ -70,7 +70,7 @@ examples/multimodal/combine_lm_vision_checkpoints.sh /path/to/mistral/model /pat
     ```
     > Please enter a desired train/val/test split like "0.5, 0.2, 0.3" or "8,1,1": 9,1,0
     > Do you want to create a dataset.yaml interactively? [Y/n]: Y
-    > Please enter a number to choose a class: 10 (VQAWebdataset)
+    > Please enter a number to choose a class: 9 (VQASample)
     > Do you want to set a simple field_map[Y] (or write your own sample_loader [n])? [Y/n]: Y
     > Please enter a webdataset field name for 'image' (<class 'torch.Tensor'>): jpg
     > Please enter a webdataset field name for 'context' (<class 'str'>): json[0][value]
diff --git a/examples/multimodal/combine_state_dicts.py b/examples/multimodal/combine_state_dicts.py
index 2f7028474cd..505d5e2271d 100644
--- a/examples/multimodal/combine_state_dicts.py
+++ b/examples/multimodal/combine_state_dicts.py
@@ -27,7 +27,7 @@ def combine(input_files, module_prefixes, output_files):
             zip(current_input_files, current_module_prefixes)
         ):
             # initialize the combined state dict using the first provided input file
-            current_state_dict = torch.load(input_file)
+            current_state_dict = torch.load(input_file, weights_only=False)
             if i == 0:
                 combined_state_dict = current_state_dict.copy()
                 combined_state_dict["model"] = dict()
diff --git a/examples/multimodal/model.py b/examples/multimodal/model.py
index e5aeb221892..494a854099e 100644
--- a/examples/multimodal/model.py
+++ b/examples/multimodal/model.py
@@ -217,9 +217,7 @@ def model_provider(
         patch_dim=args.patch_dim,
         language_rotary_base=args.rotary_base,
         language_rope_scaling=args.use_rope_scaling,
-        hybrid_attention_ratio=args.hybrid_attention_ratio,
-        hybrid_mlp_ratio=args.hybrid_mlp_ratio,
-        hybrid_override_pattern=args.hybrid_override_pattern,
+        hybrid_layer_pattern=args.hybrid_layer_pattern,
         fp16_lm_cross_entropy=args.fp16_lm_cross_entropy,
         image_token_index=image_token_index,
         pixel_shuffle=args.pixel_shuffle,
diff --git a/examples/multimodal/pretrain_mistral_clip.sh b/examples/multimodal/pretrain_mistral_clip.sh
index 4afcc0f2dad..8ed3d0ecc64 100755
--- a/examples/multimodal/pretrain_mistral_clip.sh
+++ b/examples/multimodal/pretrain_mistral_clip.sh
@@ -21,7 +21,7 @@ TENSORBOARD_DIR="${OUTPUT}/tensorboard"
 
 export TRITON_CACHE_DIR="${WORKSPACE}/triton-cache/"
 # The following patch to the Triton cache manager is needed for Triton version <= 3.1
-export TRITON_CACHE_MANAGER="megatron.core.ssm.triton_cache_manager:ParallelFileCacheManager"
+# export TRITON_CACHE_MANAGER="megatron.core.ssm.triton_cache_manager:ParallelFileCacheManager"
 
 if [[ -z $LOAD_NAME ]]; then
     echo "Please set LOAD_NAME for input model name."
@@ -53,6 +53,7 @@ OPTIONS=" \
     --apply-layernorm-1p \
     --attention-softmax-in-fp32 \
     --use-checkpoint-args \
+    --no-use-tokenizer-model-from-checkpoint-args \
     --use-distributed-optimizer \
     --transformer-impl transformer_engine \
     --use-te \
@@ -129,4 +130,4 @@ OPTIONS=" \
 export NVTE_APPLY_QK_LAYER_SCALING=0
 export NVTE_ALLOW_NONDETERMINISTIC_ALGO=${NONDETERMINISTIC_ATTN}
 
-torchrun --nproc_per_node 8 examples/multimodal/train.py ${OPTIONS}
\ No newline at end of file
+torchrun --nproc_per_node 8 examples/multimodal/train.py ${OPTIONS}
diff --git a/examples/post_training/modelopt/README.md b/examples/post_training/modelopt/README.md
index 93b5022b2aa..8815e22b831 100644
--- a/examples/post_training/modelopt/README.md
+++ b/examples/post_training/modelopt/README.md
@@ -34,6 +34,7 @@ knowledge distillation, pruning, speculative decoding, and more.
 | `moonshotai/Kimi-K2-Instruct` | ✅ | ✅ | - | - |
 | `nvidia/NVIDIA-Nemotron-Nano-9B-v2` | ✅ | - | ✅ | ✅ |
 | `nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16` | ✅ | - | ✅ | ✅ |
+| `nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16` | ✅ | - | ✅ | ✅ |
 | `openai/gpt-oss-{20b, 120b}` | ✅ | **Online** | ✅ | ✅ |
 | `Qwen/Qwen3-{0.6B, 8B}` | ✅ | ✅ | ✅ | ✅ |
 | `Qwen/Qwen3-{30B-A3B, 235B-A22B}` | **WAR** | ✅ | ✅ | ✅ |
diff --git a/examples/post_training/modelopt/conf/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16.sh b/examples/post_training/modelopt/conf/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16.sh
index c294e03235c..1fa00889e99 100644
--- a/examples/post_training/modelopt/conf/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16.sh
+++ b/examples/post_training/modelopt/conf/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16.sh
@@ -30,17 +30,15 @@ MODEL_ARGS=" \
     \
     --attention-backend flash \
     --disable-gloo-process-groups \
-    --is-hybrid-model \
     --mamba-num-heads 64 \
     --mamba-head-dim 64 \
-    --hybrid-override-pattern MEMEM*EMEMEM*EMEMEM*EMEMEM*EMEMEM*EMEMEMEM*EMEMEMEME \
+    --hybrid-layer-pattern MEMEM*EMEMEM*EMEMEM*EMEMEM*EMEMEM*EMEMEMEM*EMEMEMEME \
     --use-mcore-models \
     --untie-embeddings-and-output-weights \
     --disable-bias-linear \
     --init-method-std 0.0173 \
     --position-embedding-type none \
     --squared-relu \
-    --num-layers 52 \
     --hidden-size 2688 \
     --num-attention-heads 32 \
     --group-query-attention \
diff --git a/examples/post_training/modelopt/conf/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16.sh b/examples/post_training/modelopt/conf/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16.sh
new file mode 100644
index 00000000000..977be033df0
--- /dev/null
+++ b/examples/post_training/modelopt/conf/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16.sh
@@ -0,0 +1,62 @@
+#!/bin/bash
+
+if [ -z ${HF_MODEL_CKPT} ]; then
+    HF_MODEL_CKPT=nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16
+    TOKENIZER_MODEL=nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16
+else
+    TOKENIZER_MODEL=${HF_MODEL_CKPT}
+fi
+
+
+
+MODEL_ARGS=" \
+    --trust-remote-code \
+    --save-interval 100000 \
+    --micro-batch-size 1 \
+    --enable-experimental \
+    --use-fused-weighted-squared-relu \
+    --cross-entropy-loss-fusion \
+    --cross-entropy-fusion-impl native \
+    --num-experts 512 \
+    --moe-router-score-function sigmoid \
+    --moe-grouped-gemm \
+    --moe-aux-loss-coeff 1e-4 \
+    --moe-router-topk 22 \
+    --moe-permute-fusion \
+    --moe-router-topk-scaling-factor 5.0 \
+    --moe-router-enable-expert-bias \
+    --moe-router-dtype fp32 \
+    --moe-router-load-balancing-type seq_aux_loss \
+    --moe-shared-expert-intermediate-size 5376 \
+    --moe-token-dispatcher-type allgather \
+    --moe-latent-size 1024 \
+    \
+    --attention-backend flash \
+    --disable-gloo-process-groups \
+    --is-hybrid-model \
+    --mamba-num-heads 128 \
+    --mamba-head-dim 64 \
+    --hybrid-layer-pattern MEMEMEM*EMEMEMEM*EMEMEMEM*EMEMEMEMEM*EMEMEMEMEM*EMEMEMEMEM*EMEMEMEMEM*EMEMEMEM*EMEMEMEME \
+    \
+    --use-mcore-models \
+    --untie-embeddings-and-output-weights \
+    --disable-bias-linear \
+    --init-method-std 0.014 \
+    --position-embedding-type none \
+    --squared-relu \
+    --hidden-size 4096 \
+    --num-attention-heads 32 \
+    --group-query-attention \
+    --num-query-groups 2 \
+    --ffn-hidden-size 2688 \
+    --kv-channels 128 \
+    --normalization RMSNorm \
+    --attention-dropout 0.0 \
+    --hidden-dropout 0.0 \
+    \
+    --tokenizer-type HuggingFaceTokenizer \
+    --bf16 \
+    --seq-length 8192 \
+    --max-position-embeddings 8192 \
+    --export-model-type MambaModel \
+    "
diff --git a/examples/post_training/modelopt/conf/nvidia/NVIDIA-Nemotron-Nano-9B-v2.sh b/examples/post_training/modelopt/conf/nvidia/NVIDIA-Nemotron-Nano-9B-v2.sh
index a2212483008..83867430a97 100644
--- a/examples/post_training/modelopt/conf/nvidia/NVIDIA-Nemotron-Nano-9B-v2.sh
+++ b/examples/post_training/modelopt/conf/nvidia/NVIDIA-Nemotron-Nano-9B-v2.sh
@@ -19,15 +19,13 @@ MODEL_ARGS=" \
     --no-rope-fusion \
     --normalization RMSNorm \
     --squared-relu \
-    --num-layers 56 \
     --hidden-size 4480 \
     --ffn-hidden-size 15680 \
     --num-attention-heads 40 \
     --kv-channels 128 \
     --group-query-attention \
     --num-query-groups 8 \
-    --hybrid-override-pattern M-M-M-MM-M-M-M*-M-M-M*-M-M-M-M*-M-M-M-M*-M-MM-M-M-M-M-M- \
-    --is-hybrid-model \
+    --hybrid-layer-pattern M-M-M-MM-M-M-M*-M-M-M*-M-M-M-M*-M-M-M-M*-M-MM-M-M-M-M-M- \
     --mamba-head-dim 80 \
     --mamba-num-heads 128 \
     --mamba-num-groups 8 \
diff --git a/examples/post_training/modelopt/conf/nvidia/Nemotron-H-47B-Reasoning-128K.sh b/examples/post_training/modelopt/conf/nvidia/Nemotron-H-47B-Reasoning-128K.sh
index ad07c1061c5..901e607f298 100644
--- a/examples/post_training/modelopt/conf/nvidia/Nemotron-H-47B-Reasoning-128K.sh
+++ b/examples/post_training/modelopt/conf/nvidia/Nemotron-H-47B-Reasoning-128K.sh
@@ -18,15 +18,13 @@ MODEL_ARGS=" \
     --position-embedding-type none \
     --normalization RMSNorm \
     --squared-relu \
-    --num-layers 98 \
     --hidden-size 8192 \
     --ffn-hidden-size 30720 \
     --num-attention-heads 64 \
     --kv-channels 128 \
     --group-query-attention \
     --num-query-groups 8 \
-    --hybrid-override-pattern M-M-M-M-M-M-M-M-M*-M-M-M-M-M-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M-M-M---MM---M-M*-M-M-M-M-M- \
-    --is-hybrid-model \
+    --hybrid-layer-pattern M-M-M-M-M-M-M-M-M*-M-M-M-M-M-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M-M-M---MM---M-M*-M-M-M-M-M- \
     --mamba-head-dim 64 \
     --mamba-num-heads 256 \
     --mamba-num-groups 8 \
diff --git a/examples/post_training/modelopt/conf/nvidia/Nemotron-H-4B-Instruct.sh b/examples/post_training/modelopt/conf/nvidia/Nemotron-H-4B-Instruct.sh
index 4ba91dbd8c6..084db49e0eb 100644
--- a/examples/post_training/modelopt/conf/nvidia/Nemotron-H-4B-Instruct.sh
+++ b/examples/post_training/modelopt/conf/nvidia/Nemotron-H-4B-Instruct.sh
@@ -21,14 +21,13 @@ MODEL_ARGS=" \
     --no-position-embedding \
     --normalization RMSNorm \
     --squared-relu \
-    --num-layers 52 \
     --hidden-size 3072 \
     --ffn-hidden-size 12288 \
     --kv-channels 128 \
     --num-attention-heads 32 \
     --group-query-attention \
     --num-query-groups 8 \
-    --hybrid-override-pattern M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M- \
+    --hybrid-layer-pattern M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M- \
     --mamba-head-dim 64 \
     --mamba-num-heads 112 \
     --mamba-num-groups 8 \
diff --git a/examples/post_training/modelopt/conf/nvidia/Nemotron-H-56B-Base-8K.sh b/examples/post_training/modelopt/conf/nvidia/Nemotron-H-56B-Base-8K.sh
index 8377f0f11d6..645a159d075 100644
--- a/examples/post_training/modelopt/conf/nvidia/Nemotron-H-56B-Base-8K.sh
+++ b/examples/post_training/modelopt/conf/nvidia/Nemotron-H-56B-Base-8K.sh
@@ -12,8 +12,7 @@ MODEL_ARGS=" \
     --save-interval 100000 \
     --micro-batch-size 1 \
     --attention-backend flash \
-    --is-hybrid-model \
-    --hybrid-override-pattern M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M- \
+    --hybrid-layer-pattern M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M- \
     --mamba-state-dim 256 \
     --tiktoken-pattern v2 \
     --use-mcore-models \
@@ -22,7 +21,6 @@ MODEL_ARGS=" \
     --init-method-std 0.0099 \
     --position-embedding-type none \
     --squared-relu \
-    --num-layers 118 \
     --hidden-size 8192 \
     --num-attention-heads 64 \
     --group-query-attention \
diff --git a/examples/post_training/modelopt/conf/nvidia/Nemotron-H-8B-Base-8K.sh b/examples/post_training/modelopt/conf/nvidia/Nemotron-H-8B-Base-8K.sh
index b04bf76f360..66f3ad368b4 100644
--- a/examples/post_training/modelopt/conf/nvidia/Nemotron-H-8B-Base-8K.sh
+++ b/examples/post_training/modelopt/conf/nvidia/Nemotron-H-8B-Base-8K.sh
@@ -20,14 +20,12 @@ MODEL_ARGS=" \
     --no-position-embedding \
     --normalization RMSNorm \
     --squared-relu \
-    --num-layers 52 \
     --hidden-size 4096 \
     --ffn-hidden-size 21504 \
     --num-attention-heads 32 \
     --group-query-attention \
     --num-query-groups 8 \
-    --hybrid-override-pattern M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M- \
-    --is-hybrid-model \
+    --hybrid-layer-pattern M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M- \
     --mamba-head-dim 64 \
     --mamba-num-heads 128 \
     --mamba-num-groups 8 \
diff --git a/examples/post_training/modelopt/conf/openai/gpt-oss-120b.sh b/examples/post_training/modelopt/conf/openai/gpt-oss-120b.sh
index 3c7f69c0da6..9bc6142375d 100644
--- a/examples/post_training/modelopt/conf/openai/gpt-oss-120b.sh
+++ b/examples/post_training/modelopt/conf/openai/gpt-oss-120b.sh
@@ -49,5 +49,5 @@ MODEL_ARGS=" \
     --window-attn-skip-freq 2 \
     --enable-gpt-oss \
     --activation-func-clamp-value 7.0 \
-    --window-size 128,0 \
+    --window-size 127,0 \
 "
diff --git a/examples/post_training/modelopt/conf/openai/gpt-oss-20b.sh b/examples/post_training/modelopt/conf/openai/gpt-oss-20b.sh
index a0180ec44b8..601709c847a 100644
--- a/examples/post_training/modelopt/conf/openai/gpt-oss-20b.sh
+++ b/examples/post_training/modelopt/conf/openai/gpt-oss-20b.sh
@@ -49,5 +49,5 @@ MODEL_ARGS=" \
     --window-attn-skip-freq 2 \
     --enable-gpt-oss \
     --activation-func-clamp-value 7.0 \
-    --window-size 128,0 \
+    --window-size 127,0 \
 "
diff --git a/examples/post_training/modelopt/convert_model.py b/examples/post_training/modelopt/convert_model.py
index d8acd49ee92..eaec9789e1e 100644
--- a/examples/post_training/modelopt/convert_model.py
+++ b/examples/post_training/modelopt/convert_model.py
@@ -31,7 +31,6 @@
 from model_provider import model_provider
 
 ALGO_TO_CONFIG = {
-    "eagle1": mtsp.config.EAGLE1_DEFAULT_CFG,
     "eagle3": mtsp.config.EAGLE3_DEFAULT_CFG,
     "eagle-mtp": mtsp.config.EAGLE_MTP_DEFAULT_CFG,
 }
@@ -49,7 +48,7 @@ def add_convert_args(parser):
     group.add_argument(
         '--algorithm',
         type=str,
-        choices=["medusa", "eagle1", "eagle3", "None"],
+        choices=["eagle3", "None"],
         default="None",
         help='Chosing between different speculative decoding algorithms. Default is None.',
     )
@@ -60,6 +59,12 @@ def add_convert_args(parser):
         help="EAGLE architecture config. If not given, "
         "a default config will be use. If provided, it will overwrite the default config.",
     )
+    group.add_argument(
+        "--mix-hidden-states",
+        type=bool,
+        default=False,
+        help="Whether to mix hidden states from previous TTT step.",
+    )
 
     add_modelopt_args(parser)
     return parser
@@ -149,7 +154,7 @@ def check_arguments():
     elif args.load is not None:
         _ = load_modelopt_checkpoint(model)
 
-    if args.algorithm in ("eagle1", "eagle3"):
+    if args.algorithm == "eagle3":
         mtsp_config = ALGO_TO_CONFIG[args.algorithm]
         if args.eagle_config:
             with open(args.eagle_config) as f:
@@ -158,6 +163,8 @@ def check_arguments():
 
         if args.export_offline_model:
             mtsp_config["config"]["eagle_offline"] = True
+        if args.mix_hidden_states:
+            mtsp_config["config"]["eagle_mix_hidden_states"] = True
 
         unwrapped_model = mtsp.convert(unwrapped_model, mtsp_config)
 
@@ -167,10 +174,6 @@ def check_arguments():
                 mcore_eagle_state_dict = torch.load(args.extra_model_path)
                 eagle_module.load_state_dict(mcore_eagle_state_dict, strict=False)
 
-    elif args.algorithm == "medusa":
-        config = {"medusa_num_heads": args.export_num_medusa_heads, "medusa_num_layers": 1}
-        unwrapped_model = mtsp.convert(unwrapped_model, [("medusa", config)])
-
     print_rank_0(f"Converted Model:\n {model}")
     torch.distributed.barrier()
 
diff --git a/examples/post_training/modelopt/finetune.py b/examples/post_training/modelopt/finetune.py
index 20777ba14c8..d31fbcbed0c 100755
--- a/examples/post_training/modelopt/finetune.py
+++ b/examples/post_training/modelopt/finetune.py
@@ -17,6 +17,7 @@
 from megatron.core import mpu, tensor_parallel
 from megatron.core.enums import ModelType
 from megatron.core.models.gpt import GPTModel
+from megatron.core.tokenizers.text.libraries.huggingface_tokenizer import HuggingFaceTokenizer
 from megatron.post_training.arguments import add_modelopt_args
 from megatron.post_training.loss_func import loss_func
 from megatron.post_training.model_builder import modelopt_gpt_mamba_builder
@@ -121,7 +122,7 @@ def __init__(
         self,
         num_packed_samples: int,
         hf_dataset: str,
-        tokenizer: transformers.PreTrainedTokenizerBase,
+        tokenizer: HuggingFaceTokenizer,
         seq_length: int,
         num_shards: int = 1,
         shard_index: int = 0,
@@ -142,8 +143,8 @@ def __init__(
             num_shards: number of shards for distributed training
             shard_index: shard index for distributed training
         """
-        if not isinstance(tokenizer, transformers.PreTrainedTokenizerBase):
-            raise ValueError("SFTDataset only supports transformers.PreTrainedTokenizerBase!")
+        if not isinstance(tokenizer, HuggingFaceTokenizer):
+            raise ValueError("SFTDataset only supports HuggingFaceTokenizer!")
 
         self.num_packed_samples = num_packed_samples
         self.hf_dataset = hf_dataset
@@ -283,7 +284,7 @@ def _process_example(self, example: Dict[str, Any]):
                 return None
 
         # We always add eos between samples for training purpose.
-        input_ids = self.tokenizer.apply_chat_template(example)
+        input_ids = self.tokenizer.apply_chat_template(example, self.tokenizer.chat_template)["input_ids"]
         current_loss_mask = [1] * len(input_ids)
         input_ids = input_ids + [get_eos_id()]
         current_loss_mask += [0]
@@ -343,8 +344,8 @@ def train_valid_test_sft_datasets_provider(train_val_test_num_samples):
     args = get_args()
     tokenizer = get_tokenizer()
 
-    if not isinstance(tokenizer._tokenizer, transformers.PreTrainedTokenizerBase):
-        raise ValueError("SFTDataset only supports transformers.PreTrainedTokenizerBase!")
+    if not isinstance(tokenizer._tokenizer, HuggingFaceTokenizer):
+        raise ValueError("SFTDataset only supports HuggingFaceTokenizer!")
 
     if args.micro_batch_size > 1:
         raise ValueError("SFTDataloader only supports micro_batch_size=1.")
diff --git a/examples/rl/environments/math/math_agent.py b/examples/rl/environments/math/math_agent.py
index 027ef242285..0747254d1ad 100644
--- a/examples/rl/environments/math/math_agent.py
+++ b/examples/rl/environments/math/math_agent.py
@@ -53,7 +53,7 @@ def compute_score(self, response: str, golden: dict, golden_key: str = "answer")
         """
         # Allow <answer> tags or \boxed{} tags (this is a bit of cheating in favor of deepseek distilled models I think)
         matched_format = None
-        end_tokens = ["<|end_of_text|>", "<|endoftext|>", "</s>", "<|eot_id|>"]
+        end_tokens = ["<|end_of_text|>", "<|endoftext|>", "</s>", "<|eot_id|>", "<|im_end|>"]
 
         # Only an answer immediately followed by a known end token yields 1.0 reward.
         answer_tag_pattern = r'<answer>(.*?)</answer>'
diff --git a/examples/rl/model_configs/common.sh b/examples/rl/model_configs/common.sh
index c37d88fb4df..389880d6289 100644
--- a/examples/rl/model_configs/common.sh
+++ b/examples/rl/model_configs/common.sh
@@ -16,7 +16,6 @@ COMMON_OPTIONS="\
     --transformer-impl transformer_engine \
     --${PRECISION:-bf16} \
     --te-rng-tracker \
-    --rl-offload-optimizer-during-inference \
     --inference-dynamic-batching-buffer-size-gb 20 \
     --data-parallel-random-init \
     --attention-backend flash \
@@ -39,5 +38,5 @@ else
 fi
 
 if [ ${ENABLE_CUDA_GRAPH:-true} == true ]; then
-    COMMON_OPTIONS="${COMMON_OPTIONS} --cuda-graph-impl=local"
+    COMMON_OPTIONS="${COMMON_OPTIONS} --cuda-graph-impl=local --rl-persist-cuda-graphs"
 fi
diff --git a/examples/rl/model_configs/nemotron5_56b.sh b/examples/rl/model_configs/nemotron5_56b.sh
index 741cd054b73..23b9f99a72a 100644
--- a/examples/rl/model_configs/nemotron5_56b.sh
+++ b/examples/rl/model_configs/nemotron5_56b.sh
@@ -68,7 +68,7 @@ MODEL_OPTIONS="\
     --first-last-layers-bf16 \
     \
     --fp8-recipe tensorwise \
-    --hybrid-override-pattern M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M- \
+    --hybrid-layer-pattern M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M- \
     --spec megatron.core.models.mamba.mamba_layer_specs mamba_stack_spec \
     --mamba-state-dim 256 \
     --per-split-data-args-path ${BLEND_PATH} \
@@ -82,7 +82,6 @@ MODEL_OPTIONS="\
     --init-method-std 0.0099 \
     --position-embedding-type none \
     --squared-relu \
-    --num-layers 118 \
     --hidden-size 8192 \
     --num-attention-heads 64 \
     --group-query-attention \
diff --git a/examples/rl/model_configs/nemotron5_8b.sh b/examples/rl/model_configs/nemotron5_8b.sh
index 753d4e493a2..c18149f03d6 100644
--- a/examples/rl/model_configs/nemotron5_8b.sh
+++ b/examples/rl/model_configs/nemotron5_8b.sh
@@ -60,7 +60,7 @@ MODEL_OPTIONS="\
   --inference-max-seq-length $MAX_SEQ_LENGTH \
   --inference-max-requests $MAX_INFERENCE_BS \
   --pretrained-checkpoint $CHECKPOINT \
-  --hybrid-override-pattern M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M- \
+  --hybrid-layer-pattern M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M- \
     --spec megatron.core.models.mamba.mamba_layer_specs mamba_stack_spec \
     --tiktoken-pattern v2 \
     --distributed-timeout-minutes 60 \
@@ -73,7 +73,6 @@ MODEL_OPTIONS="\
    --init-method-std 0.014 \
     --position-embedding-type none \
     --squared-relu \
-    --num-layers 52 \
     --hidden-size 4096 \
     --num-attention-heads 32 \
     --group-query-attention \
diff --git a/examples/rl/model_configs/nemotron5p5_12b_H.sh b/examples/rl/model_configs/nemotron5p5_12b_H.sh
index adbcc8d03f0..1826d57e913 100644
--- a/examples/rl/model_configs/nemotron5p5_12b_H.sh
+++ b/examples/rl/model_configs/nemotron5p5_12b_H.sh
@@ -74,9 +74,8 @@ MODEL_OPTIONS="\
   --num-layers-at-end-in-bf16 2 \
   --fp8-param-gather \
   --disable-gloo-process-groups \
-  --is-hybrid-model \
   --mamba-head-dim 80 \
-  --hybrid-override-pattern M-M-M-M*-M-M-M-M*-M-M-M-M*-M-M-M-M*-M-M-M-M*-M-M-M-M*-M-M-M-M- \
+  --hybrid-layer-pattern M-M-M-M*-M-M-M-M*-M-M-M-M*-M-M-M-M*-M-M-M-M*-M-M-M-M*-M-M-M-M- \
   --spec megatron.core.models.mamba.mamba_layer_specs mamba_stack_spec \
   --tiktoken-pattern v2 \
   --distributed-timeout-minutes 10 \
@@ -89,7 +88,6 @@ MODEL_OPTIONS="\
    --init-method-std 0.0125 \
     --position-embedding-type none \
   --squared-relu \
-    --num-layers 62 \
     --hidden-size 5120 \
     --num-attention-heads 40 \
     --group-query-attention \
diff --git a/examples/rl/model_configs/nemotron6_3b_moe.sh b/examples/rl/model_configs/nemotron6_3b_moe.sh
index 19891ad7b8b..85de0c6be0a 100644
--- a/examples/rl/model_configs/nemotron6_3b_moe.sh
+++ b/examples/rl/model_configs/nemotron6_3b_moe.sh
@@ -55,10 +55,10 @@ ENV_DEPENDENT="\
   --langrl-env-config $ENV_CONFIG "
 
 MODEL_OPTIONS="\
+  --no-use-tokenizer-model-from-checkpoint-args \
   --rl-skip-bos-token \
   --no-rl-use-sequence-packing \
   --rl-partial-rollouts \
-  --rl-offload-optimizer-during-inference \
   --moe-pad-experts-for-cuda-graph-inference \
   --inference-dynamic-batching-max-tokens 8192 \
   --inference-dynamic-batching-max-requests 128 \
@@ -102,8 +102,9 @@ MODEL_OPTIONS="\
   --weight-decay 0.01 \
   --clip-grad 1.0 \
   --tiktoken-pattern v2 \
-  --tokenizer-type TikTokenizer \
+  --tokenizer-type HuggingFaceTokenizer \
   --tokenizer-model ${TOKENIZER_MODEL} \
+  --tokenizer-hf-include-special-tokens \
   --dist-ckpt-strictness log_unexpected \
   --ckpt-format torch_dist \
   --ckpt-fully-parallel-save \
diff --git a/gpt_builders.py b/gpt_builders.py
index 6711cce356f..b273f3c12aa 100644
--- a/gpt_builders.py
+++ b/gpt_builders.py
@@ -54,7 +54,6 @@ def gpt_builder(args, pre_process, post_process, vp_stage=None, config=None, pg_
                     )
                 )
             elif args.num_experts:
-                assert not (config.transformer_impl == "inference_optimized")
                 # Define the decoder block spec
                 transformer_layer_spec = get_gpt_decoder_block_spec(
                     config,
@@ -123,46 +122,44 @@ def gpt_builder(args, pre_process, post_process, vp_stage=None, config=None, pg_
 
 def _get_transformer_layer_spec(use_te, config):
     """Get transformer layer specification based on configuration.
-    
+
     Args:
         use_te (bool): Whether to use Transformer Engine
-        args: Training arguments
         config: Model configuration
-        
+
     Returns:
         transformer_layer_spec: The transformer layer specification
     """
-    args = get_args()
     if use_te:
         return get_gpt_layer_with_transformer_engine_spec(
-            args.num_experts,
-            args.moe_grouped_gemm,
-            args.qk_layernorm,
-            args.multi_latent_attention,
-            args.experimental_attention_variant,
-            moe_use_legacy_grouped_gemm=args.moe_use_legacy_grouped_gemm,
-            qk_l2_norm=args.qk_l2_norm,
+            config.num_moe_experts,
+            config.moe_grouped_gemm,
+            config.qk_layernorm,
+            config.multi_latent_attention,
+            config.experimental_attention_variant,
+            qk_l2_norm=config.qk_l2_norm,
             use_kitchen=config.use_kitchen,
+            use_te_activation_func=config.use_te_activation_func,
             use_kitchen_attention=config.use_kitchen_attention,
             kitchen_attention_backend=config.kitchen_attention_backend,
             fallback_to_eager_attn=config.fallback_to_eager_attn,
             enable_hyper_connection=config.enable_hyper_connections,
+            mla_down_proj_fusion=getattr(config, "mla_down_proj_fusion", False),
         )
     elif config.transformer_impl == "inference_optimized":
         return get_gpt_layer_with_inference_spec(
-            args.qk_layernorm,
-            args.multi_latent_attention,
-            qk_l2_norm=args.qk_l2_norm,
+            config.qk_layernorm,
+            config.multi_latent_attention,
+            qk_l2_norm=config.qk_l2_norm,
         )
     else:
         return get_gpt_layer_local_spec(
-            args.num_experts,
-            args.moe_grouped_gemm,
-            args.qk_layernorm,
-            args.multi_latent_attention,
-            args.experimental_attention_variant,
-            moe_use_legacy_grouped_gemm=args.moe_use_legacy_grouped_gemm,
-            normalization=args.normalization,
+            config.num_moe_experts,
+            config.moe_grouped_gemm,
+            config.qk_layernorm,
+            config.multi_latent_attention,
+            config.experimental_attention_variant,
+            normalization=config.normalization,
             use_kitchen=config.use_kitchen,
             use_kitchen_attention=config.use_kitchen_attention,
             kitchen_attention_backend=config.kitchen_attention_backend,
diff --git a/mamba_builders.py b/mamba_builders.py
index 5d31af60475..650ea4a719f 100644
--- a/mamba_builders.py
+++ b/mamba_builders.py
@@ -30,10 +30,8 @@ def mamba_builder(args, pre_process, post_process, vp_stage=None, config=None, p
         mamba_stack_spec=mamba_stack_spec,
         vocab_size=args.padded_vocab_size,
         max_sequence_length=args.max_position_embeddings,
+        hybrid_layer_pattern=args.hybrid_layer_pattern,
         pre_process=pre_process,
-        hybrid_attention_ratio=args.hybrid_attention_ratio,
-        hybrid_mlp_ratio=args.hybrid_mlp_ratio,
-        hybrid_override_pattern=args.hybrid_override_pattern,
         post_process=post_process,
         fp16_lm_cross_entropy=args.fp16_lm_cross_entropy,
         parallel_output=True,
diff --git a/megatron/core/MSC_Integration.md b/megatron/core/MSC_Integration.md
index d0c93a80129..da8b5c982b8 100644
--- a/megatron/core/MSC_Integration.md
+++ b/megatron/core/MSC_Integration.md
@@ -1,4 +1,4 @@
-## Multi-Storage Client (MSC) Integration
+# Multi-Storage Client (MSC) Integration
 
 The [Multi-Storage Client](https://github.com/NVIDIA/multi-storage-client) (MSC) provides a unified interface for reading datasets and storing checkpoints from both filesystems (e.g., local disk, NFS, Lustre) and object storage providers such as S3, GCS, OCI, Azure, AIStore, and SwiftStack.
 
@@ -8,7 +8,7 @@ This guide will walk you through how to:
 2. How to train models directly using datasets in object storage
 3. How to save and load model checkpoints to/from object storage
 
-### Installation
+## Installation
 
 MSC is vended as `the multi-storage-client` package on PyPI.
 
@@ -25,7 +25,7 @@ pip install "multi-storage-client[boto3]"
 pip install "multi-storage-client[google-cloud-storage]"
 ```
 
-### Configuration File
+## Configuration File
 
 MSC uses a YAML configuration file to define how it connects to object storage systems. This design allows you to specify one or more storage profiles, each representing a different storage backend or bucket. MSC keeps your training scripts clean and portable by centralizing details in a config file. There is no need to hardcode access keys, bucket names, or other provider-specific options directly into your code.
 
@@ -58,7 +58,7 @@ To tell MSC where to find this file, set the following environment variable befo
 export MSC_CONFIG=/path/to/msc_config.yaml
 ```
 
-### MSC URL Format
+## MSC URL Format
 
 MSC uses a custom URL scheme to identify and access files across different object storage providers. This scheme makes it easy to reference data and checkpoints without worrying about the underlying storage implementation. An MSC URL has the following structure:
 
@@ -96,7 +96,7 @@ is interpreted as accessing the object with the key `dataset/train/data.bin` ins
 This abstraction allows training scripts to reference storage resources uniformly—whether they're hosted on AWS, GCP, Oracle, or Azure—just by switching profiles in the config file.
 
 
-### Train from Object Storage
+## Train from Object Storage
 
 To train with datasets stored in object storage, use an MSC URL with the `--data-path` argument. This URL references a dataset stored under a profile defined in your MSC configuration file.
 
@@ -112,7 +112,7 @@ python pretrain_gpt.py                                      \
 
 **NOTE:** All four arguments must be provided when training with datasets in object storage using MSC.
 
-### Save and Load Checkpoints from Object Storage
+## Save and Load Checkpoints from Object Storage
 
 MSC can be used to save and load model checkpoints directly from object storage by specifying MSC URLs for the `--save` and `--load` arguments. This allows you to manage checkpoints in object storage.
 
@@ -125,7 +125,7 @@ python pretrain_gpt.py                \
 
 **Notes:** Only the `torch_dist` checkpoint format is currently supported when saving to or loading from MSC URLs.
 
-### Disable MSC
+## Disable MSC
 
 By default, MSC integration is automatically enabled when the `multi-storage-client` library is installed. MSC is also used for regular filesystem paths (like `/filesystem_mountpoint/path` in `--data-path`, `--save`, or `--load`) even when not using explicit MSC URLs. MSC functions as a very thin abstraction layer with negligible performance impact when used with regular paths, so there's typically no need to disable it. If you need to disable MSC, you can do so using the `--disable-msc` flag:
 
@@ -133,7 +133,7 @@ By default, MSC integration is automatically enabled when the `multi-storage-cli
 python pretrain_gpt.py --disable-msc
 ```
 
-### Performance Considerations
+## Performance Considerations
 
 When using object storage with MSC, there are a few important performance implications to keep in mind:
 
@@ -165,7 +165,7 @@ cache:
 
 For optimal performance, configure the cache directory on a high-speed local storage device such as an NVMe SSD.
 
-### Additional Resources and Advanced Configuration
+## Additional Resources and Advanced Configuration
 
 Refer to the [MSC Configuration Documentation](https://nvidia.github.io/multi-storage-client/references/configuration.html) for complete documentation on MSC configuration options, including detailed information about supported storage providers, credentials management, and advanced caching strategies.
 
diff --git a/megatron/core/QuickStart.md b/megatron/core/QuickStart.md
index 6b38f58a2bd..2a2744cf43e 100644
--- a/megatron/core/QuickStart.md
+++ b/megatron/core/QuickStart.md
@@ -21,8 +21,8 @@ This guide for Megatron Core walks you through the following tasks:
 
     git clone https://github.com/NVIDIA/Megatron-LM.git
     cd Megatron-LM
-    pip install -U "setuptools<80.0.0,>=77.0.0" packaging
-    pip install --no-build-isolation .[dev]
+    pip install uv
+    uv pip install -e .
     ```
 
     <br>
diff --git a/megatron/core/README.md b/megatron/core/README.md
index a9134be41cd..7260a9fc4b9 100644
--- a/megatron/core/README.md
+++ b/megatron/core/README.md
@@ -9,8 +9,8 @@ Megatron Core
 ## ⚡ Quick Start
 
 ```bash
-# Install Megatron Core with required dependencies
-pip install --no-build-isolation megatron-core[dev]
+# Install Megatron Core
+uv pip install megatron-core
 
 # Distributed training example (2 GPUs, mock data)
 torchrun --nproc_per_node=2 examples/run_simple_mcore_train_loop.py
diff --git a/megatron/core/datasets/gpt_dataset.py b/megatron/core/datasets/gpt_dataset.py
index 60d5f82b441..418a02719df 100644
--- a/megatron/core/datasets/gpt_dataset.py
+++ b/megatron/core/datasets/gpt_dataset.py
@@ -50,9 +50,6 @@ class GPTDatasetConfig(BlendedMegatronDatasetConfig):
     object_storage_cache_path: Optional[str] = None
     """Path for caching indices for s3 or msc dataloading."""
 
-    context_parallel_size: int = 1
-    """Option to enable context parallelism"""
-
     data_parallel_size: int = 1
     """Option to enable data parallelism"""
 
diff --git a/megatron/core/dist_checkpointing/serialization.py b/megatron/core/dist_checkpointing/serialization.py
index 94c7a6cf663..5b13439e923 100644
--- a/megatron/core/dist_checkpointing/serialization.py
+++ b/megatron/core/dist_checkpointing/serialization.py
@@ -453,6 +453,14 @@ def get_default_save_common_strategy(
     return get_default_strategy(StrategyAction.SAVE_COMMON, backend, version)
 
 
-def get_default_load_sharded_strategy(checkpoint_dir: str) -> LoadShardedStrategy:
-    """Get default load sharded strategy."""
-    return verify_checkpoint_and_load_strategy(checkpoint_dir)[0]
+def get_default_load_sharded_strategy(
+    checkpoint_dir: str, cache_metadata: bool = False
+) -> LoadShardedStrategy:
+    """Get default load sharded strategy.
+
+    Args:
+        checkpoint_dir: Path to the checkpoint directory.
+        cache_metadata: If True and checkpoint format is torch_dist, use a strategy that caches
+            metadata (e.g. when ckpt_assume_constant_structure is enabled).
+    """
+    return verify_checkpoint_and_load_strategy(checkpoint_dir, cache_metadata=cache_metadata)[0]
diff --git a/megatron/core/dist_checkpointing/strategies/async_utils.py b/megatron/core/dist_checkpointing/strategies/async_utils.py
index 85941fd1ed6..a4e8cfca715 100644
--- a/megatron/core/dist_checkpointing/strategies/async_utils.py
+++ b/megatron/core/dist_checkpointing/strategies/async_utils.py
@@ -6,6 +6,8 @@
 """
 import gc
 import logging
+import os
+import subprocess
 from abc import ABC, abstractmethod
 from collections import deque
 from contextlib import contextmanager
@@ -23,6 +25,62 @@
 logger = logging.getLogger(__name__)
 
 
+def _set_process_qos(cpu_priority: int, io_priority: Optional[int]) -> None:
+    """
+    Set QoS (Quality of Service) for the current checkpoint writer process.
+    This ensures checkpoint writing doesn't interfere with training.
+
+    Args:
+        cpu_priority: Nice value for CPU scheduling (0-19, higher = lower priority).
+                     Default 10 is moderately deprioritized.
+        io_priority: I/O scheduling class and priority. If None, uses best-effort class.
+                    Format: class_id (0-3) where 3 = idle (lowest priority).
+
+    Note: Requires appropriate permissions. Failures are logged but not fatal.
+    """
+    pid = os.getpid()
+
+    # Set CPU priority (nice value). os.nice(increment) adds to current;
+    # get current with os.nice(0). Only increase nice (deprioritize);
+    # decreasing requires superuser.
+    if cpu_priority is not None and cpu_priority >= 0 and cpu_priority <= 19:
+        try:
+            current_nice = os.nice(0)  # 0 = no change, returns current nice value
+            increment = cpu_priority - current_nice
+            if increment <= 0:
+                logger.warning(
+                    "PID %s: Skipping CPU nice (current %s already <= target %s; "
+                    "lowering requires superuser",
+                    pid,
+                    current_nice,
+                    cpu_priority,
+                )
+            else:
+                new_nice = os.nice(increment)
+                logger.debug(
+                    "PID %s: Set CPU nice from %s to %s (target %s)",
+                    pid,
+                    current_nice,
+                    new_nice,
+                    cpu_priority,
+                )
+        except (OSError, PermissionError) as e:
+            logger.warning(f"PID {pid}: Failed to set CPU priority: {e}")
+
+    # Set I/O priority (ionice) - Linux only
+    if io_priority is not None:
+        try:
+            # ionice -c <class> -p <pid>
+            # class 3 = idle (only when no other process needs I/O)
+            # class 2 = best-effort (default, can set priority 0-7)
+            subprocess.run(
+                ["ionice", "-c", str(io_priority), "-p", str(pid)], check=True, capture_output=True
+            )
+            logger.debug(f"PID {pid}: Set I/O priority class to {io_priority}")
+        except (subprocess.CalledProcessError, FileNotFoundError, PermissionError) as e:
+            logger.warning(f"PID {pid}: Failed to set I/O priority: {e}")
+
+
 @contextmanager
 def _disable_gc():
     """Temporarily disables GC."""
@@ -58,7 +116,7 @@ class AsyncRequest(NamedTuple):
     async_fn_args: Tuple
     finalize_fns: List[Callable]
     async_fn_kwargs: Dict = {}
-    preload_fn: Callable = None
+    preload_fn: Optional[Callable] = None
     is_frozen: bool = False
     call_idx: int = 0
 
@@ -83,7 +141,7 @@ def execute_sync(self) -> None:
         """
         # preload tensors.
         async_fn_args = list(self.async_fn_args)
-        if self.preload_fn:
+        if self.preload_fn is not None:
             assert len(async_fn_args) == 3, "Expected 3 args to be passed to async function"
             # The async_fn is passed as a partial functool with pre-determined args
             # In the async_fn_args we pass the remaining positional args required by the async_fn
@@ -201,7 +259,7 @@ def schedule_async_call(self, async_req: AsyncRequest) -> None:
             return  # nothing to do
 
         async_fn_args = list(async_req.async_fn_args)
-        if async_req.preload_fn:
+        if async_req.preload_fn is not None:
             # If there's a preload_fn in `async_req`, we call this func
             # to do the defined action in `async_req.preload_fn` to
             # stage GPU tensors to its defined destination
@@ -292,20 +350,48 @@ class PersistentAsyncCaller(AsyncCaller):
     Starts process asynchronously and allows checking if all processes on all ranks are done.
     """
 
+    _persistent_process: mp.Process = None
+    _persistent_queue: mp.JoinableQueue = None
+    _persistent_preload_q: mp.JoinableQueue = None
+    _persistent_comp_q: mp.Queue = None
+
     def __init__(self):
-        self.process: mp.Process = None
+        self.process: Optional[mp.Process] = None
         self.start_time: Optional[float] = None
-        ctx = mp.get_context('spawn')
-        # main queue to deliver `AsyncRequest` from host to the ckpt worker
-        self.queue: mp.JoinableQueue = ctx.JoinableQueue()
-        # Queue used to synchronize for the completion of preloading tensors to host
-        # between a trainer and ckpt worker
-        self.preload_q: mp.JoinableQueue = ctx.JoinableQueue()
-        # Queue used to inform trainer when the saving is completed
-        self.comp_q: mp.Queue = ctx.Queue()
-        self.cur_item: int = None
+        self.cur_item: Optional[int] = None
         self.cur_idx: int = -1
 
+    @classmethod
+    def _get_process(
+        cls,
+        rank: int,
+        mp_mode: str = 'spawn',
+        cpu_priority: int = 10,
+        io_priority: Optional[int] = None,
+    ):
+        if cls._persistent_process is None:
+            ctx = mp.get_context(mp_mode)
+            logger.debug(f"PersistentAsyncCaller: {rank}, Starting Async Caller")
+            cls._persistent_queue = ctx.JoinableQueue()
+            cls._persistent_preload_q = ctx.JoinableQueue()
+            cls._persistent_comp_q = ctx.Queue()
+            cls._persistent_process = ctx.Process(
+                target=PersistentAsyncCaller.async_loop,
+                args=(
+                    rank,
+                    cls._persistent_queue,
+                    cls._persistent_preload_q,
+                    cls._persistent_comp_q,
+                    logger.getEffectiveLevel(),
+                    cpu_priority,
+                    io_priority,
+                ),
+            )
+            cls._persistent_process.daemon = True
+            cls._persistent_process.start()
+            logger.debug(f"PersistentAsyncCaller: {rank}, Started Async Caller")
+        return cls._persistent_process
+
     def schedule_async_call(self, async_req: AsyncRequest) -> None:
         """Put `AsyncRequest` to the Persistent Async Caller
 
@@ -324,34 +410,16 @@ def schedule_async_call(self, async_req: AsyncRequest) -> None:
 
         self.start_time = time()
         if self.process is None:
-            ctx = mp.get_context('spawn')
-            logger.debug(
-                f"PersistentAsyncCaller: {torch.distributed.get_rank()}, Starting Async Caller"
-            )
-            self.process: mp.Process = ctx.Process(
-                target=PersistentAsyncCaller.async_loop,
-                args=(
-                    torch.distributed.get_rank(),
-                    self.queue,
-                    self.preload_q,
-                    self.comp_q,
-                    logger.getEffectiveLevel(),
-                ),
-            )
-            self.process.start()
-            logger.debug(
-                f"PersistentAsyncCaller: {torch.distributed.get_rank()}, Started Async Caller"
-            )
-
-        if async_req.preload_fn:
-            self.preload_q.put(async_req.call_idx)
-        self.queue.put(async_req)
+            self.process = PersistentAsyncCaller._get_process(torch.distributed.get_rank())
+        if async_req.preload_fn is not None:
+            self._persistent_preload_q.put(async_req.call_idx)
+        self._persistent_queue.put(async_req)
         logger.debug(f"rank: {torch.distributed.get_rank()}, put {async_req.call_idx}")
 
-        if async_req.preload_fn:
+        if async_req.preload_fn is not None:
             start_sync = time()
             # Synchronize for pre-staging tensors
-            self.preload_q.join()
+            self._persistent_preload_q.join()
             end_sync = time()
             logger.debug(
                 f"rank: {torch.distributed.get_rank()}, "
@@ -388,7 +456,7 @@ def is_current_async_call_done(self, blocking: bool = False, no_dist: bool = Fal
             while self.cur_item is None:
                 try:
                     # Retrieve comp call_idx without waiting
-                    self.cur_item = self.comp_q.get_nowait()
+                    self.cur_item = self._persistent_comp_q.get_nowait()
                 except Empty:
                     # This method is called after any `AsyncRequest` is pushed to the main loop
                     # So, the background writing is still active
@@ -437,10 +505,14 @@ def close(self, abort=False):
                 )
                 self.process.kill()
             else:
-                self.queue.put('DONE')
-                self.queue.join()
-                self.process.join()
+                self._persistent_queue.put('DONE')
+                self._persistent_queue.join()
+                self._persistent_process.join()
             self.process = None
+            PersistentAsyncCaller._persistent_process = None
+            PersistentAsyncCaller._persistent_queue = None
+            PersistentAsyncCaller._persistent_preload_q = None
+            PersistentAsyncCaller._persistent_comp_q = None
 
     def __del__(self):
         self.close()
@@ -453,6 +525,8 @@ def async_loop(
         preload_q: mp.JoinableQueue,
         comp_q: mp.Queue,
         log_level: int = logging.INFO,
+        cpu_priority: int = 10,
+        io_priority: Optional[int] = None,
     ):
         """Main function for the persistent checkpoint worker
 
@@ -474,11 +548,16 @@ def async_loop(
                                async checkpoint request
             log_level (int, Optional): an integer to set log-level in this spawned process
                                        to get aligned with the training rank's logging level
+            cpu_priority (int): Nice value for CPU scheduling (0-19, higher = lower priority).
+                               Default 10 deprioritizes checkpoint writing vs training.
+            io_priority (int, Optional): I/O scheduling class (0-3, where 3=idle).
+                                        Default 3 ensures checkpoints don't block data loading.
 
         """
         # Set logger.
+        # Set root logger level to affect all modules in this process
+        logging.getLogger().setLevel(log_level)
         logger = logging.getLogger(__name__)
-        logger.setLevel(log_level)
         logger.debug(f"PersistentAsyncCaller: persistent ckpt worker for {rank} has started")
 
         # Set CUDA device to appropriate local_rank to ensure allocations / CUDA contexts
@@ -487,6 +566,10 @@ def async_loop(
         # this line).
         torch.cuda.set_device(rank % torch.cuda.device_count())
 
+        # Set QoS to deprioritize checkpoint writing vs training
+        # This prevents checkpoint I/O from interfering with data loader
+        _set_process_qos(cpu_priority=cpu_priority, io_priority=io_priority)
+
         # Start busy loop waiting for and executing checkpoint saves.
         while True:
             item = queue.get()
@@ -495,16 +578,20 @@ def async_loop(
                 break
             elif isinstance(item, AsyncRequest):
                 async_fn_args = list(item.async_fn_args)
-                if item.preload_fn:
+                if item.preload_fn is not None:
                     call_idx = preload_q.get()
                     # the 2nd arg is state dict
                     async_fn_args[1] = item.preload_fn()
                     logger.debug(f"{rank} has completed D2H of {call_idx}")
                     preload_q.task_done()
-                item.async_fn(*async_fn_args, **item.async_fn_kwargs)
+                if item.async_fn is not None:
+                    item.async_fn(*async_fn_args, **item.async_fn_kwargs)
                 logger.debug(f"{rank} has completed saving {item.call_idx}")
                 comp_q.put(item.call_idx)
                 queue.task_done()
+                del async_fn_args
+            del item
+            gc.collect()
 
         logger.debug(f"PersistentAsyncCaller: persistent ckpt worker for {rank}  has terminated")
 
@@ -531,18 +618,30 @@ class AsyncCallsQueue:
     active calls with `maybe_finalize_async_calls`.
     """
 
+    _persistent_caller: Optional[PersistentAsyncCaller] = None
+
     def __init__(self, persistent: bool = False):
         self.async_calls: deque[_ActiveAsyncRequest] = deque([])
         self.call_idx: int = -1
         self.persistent: bool = persistent
-        self.persistent_caller: AsyncCaller = None
 
     def _get_async_caller(self):
         if not self.persistent:
             return TemporalAsyncCaller()
-        if self.persistent_caller is None:
-            self.persistent_caller = PersistentAsyncCaller()
-        return self.persistent_caller
+        if AsyncCallsQueue._persistent_caller is None:
+            AsyncCallsQueue._persistent_caller = PersistentAsyncCaller()
+        return AsyncCallsQueue._persistent_caller
+
+    @classmethod
+    def warmup_persistent_caller(
+        cls,
+        rank: int,
+        mp_mode: str = 'spawn',
+        cpu_priority: int = 10,
+        io_priority: Optional[int] = None,
+    ):
+        """Warmup the persistent caller to avoid the overhead of creating it on the first call."""
+        PersistentAsyncCaller._get_process(rank, mp_mode, cpu_priority, io_priority)
 
     def schedule_async_request(self, async_request: AsyncRequest) -> int:
         """Start a new async call and add it to a queue of active async calls.
@@ -617,5 +716,6 @@ def close(self, abort=False):
         """
         if not abort:
             self.maybe_finalize_async_calls(blocking=True)
-        if self.persistent and self.persistent_caller:
-            self.persistent_caller.close(abort=abort)
+        if self.persistent and AsyncCallsQueue._persistent_caller:
+            AsyncCallsQueue._persistent_caller.close(abort=abort)
+            AsyncCallsQueue._persistent_caller = None
diff --git a/megatron/core/dist_checkpointing/strategies/cached_metadata_filesystem_reader.py b/megatron/core/dist_checkpointing/strategies/cached_metadata_filesystem_reader.py
index f8cb0326e26..37720a7483c 100644
--- a/megatron/core/dist_checkpointing/strategies/cached_metadata_filesystem_reader.py
+++ b/megatron/core/dist_checkpointing/strategies/cached_metadata_filesystem_reader.py
@@ -3,7 +3,7 @@
 """ FS Reader with metadata cached support. """
 
 import os
-from typing import Union
+from typing import Dict, Union
 
 from torch.distributed.checkpoint import FileSystemReader, Metadata
 
@@ -12,11 +12,16 @@ class CachedMetadataFileSystemReader(FileSystemReader):
     """
     Extends FileSystemReader to cache metadata for improved performance.
 
+    Metadata is shared across all reader instances that use the same checkpoint
+    directory (same path), since the loaded metadata is identical.
+
     Attributes:
-        _cached_metadata (Metadata or None): Cached metadata from the file system.
+        _metadata_cache (Dict[str, Metadata]): Class-level cache keyed by checkpoint path.
     """
 
-    def __init__(self, path: Union[str, os.PathLike]) -> None:
+    _metadata_cache: Dict[str, Metadata] = {}
+
+    def __init__(self, path: Union[str, os.PathLike], cache_metadata: bool = True) -> None:
         """
         Initialize with file system path.
 
@@ -24,15 +29,25 @@ def __init__(self, path: Union[str, os.PathLike]) -> None:
             path (Union[str, os.PathLike]): Path to the checkpoint directory or file.
         """
         super().__init__(path=path)
-        self._cached_metadata = None
+        self._cache_key = os.path.abspath(os.fspath(path)) if cache_metadata else None
 
     def read_metadata(self) -> Metadata:
         """
         Read metadata from file system, caching for subsequent calls.
+        Shared across instances when the checkpoint directory is the same.
 
         Returns:
             Metadata: Checkpoint metadata.
         """
-        if self._cached_metadata is None:
-            self._cached_metadata = super().read_metadata()
-        return self._cached_metadata
+        if self._cache_key not in CachedMetadataFileSystemReader._metadata_cache:
+            CachedMetadataFileSystemReader._metadata_cache[self._cache_key] = (
+                super().read_metadata()
+            )
+        return CachedMetadataFileSystemReader._metadata_cache[self._cache_key]
+
+    @classmethod
+    def clear_metadata_cache(cls):
+        """
+        Clear the metadata cache.
+        """
+        cls._metadata_cache.clear()
diff --git a/megatron/core/dist_checkpointing/strategies/filesystem_async.py b/megatron/core/dist_checkpointing/strategies/filesystem_async.py
index 6bec564d72a..f766172e910 100644
--- a/megatron/core/dist_checkpointing/strategies/filesystem_async.py
+++ b/megatron/core/dist_checkpointing/strategies/filesystem_async.py
@@ -8,6 +8,7 @@
 import os
 import pickle
 import queue
+import threading
 from functools import partial
 from heapq import heappop, heappush
 from itertools import chain
@@ -48,10 +49,19 @@
 _results_queue = None
 
 
-def _get_write_results_queue():
+@_disable_gc()
+def get_write_results_queue(mp_mode: str = 'spawn') -> mp.Queue:
+    """Get or create a multiprocessing queue for write results.
+
+    Args:
+        mp_mode (str): Multiprocessing context mode. Defaults to 'spawn'.
+
+    Returns:
+        mp.Queue: Queue for collecting write results.
+    """
     global _results_queue
     if _results_queue is None:
-        ctx = mp.get_context("spawn")
+        ctx = mp.get_context(mp_mode)
         _results_queue = ctx.Manager().Queue()
     return _results_queue
 
@@ -67,7 +77,7 @@ class FileSystemWriterAsync(FileSystemWriter):
     1. Call `write_data`
     2. Externally start an async process with `get_save_function_and_args` and its arguments.
     3. The async function `writer_proxy_func` calls `write_preloaded_data` across multiple
-        processes.
+        threads (no child processes).
     4. Once saving is finalized on all ranks, call `super().finish` with the results stored
         in `self.writer_result`.
 
@@ -83,7 +93,6 @@ def __init__(
         *args,
         separation_hint: Optional[str] = None,
         use_msc: bool = False,
-        sequential: bool = False,
         **kwargs,
     ):
         self.checkpoint_dir = path
@@ -101,7 +110,6 @@ def __init__(
         self.write_buckets: Optional[List[WriteBucket]] = None
         self.results_queue: Optional[mp.Queue] = None
         self.separation_hint = separation_hint
-        self.sequential = sequential
 
     def prepare_write_data(self, plan: SavePlan, planner: SavePlanner) -> None:
         """
@@ -152,7 +160,7 @@ def _clone_if_needed(ten: torch.Tensor):
             is_view = ten.untyped_storage().size() != ten.numel() * ten.itemsize
             return ten.clone() if is_view else ten
 
-        # Prepare bytes / tensor data in each bucket, which will be assigned to each writer process
+        # Prepare bytes / tensor data in each bucket, which will be assigned to each writer thread
         self.write_buckets = []
         for group_name, group_buckets in _split_by_separation_hint(
             item_buckets, self.separation_hint
@@ -184,7 +192,7 @@ def _clone_if_needed(ten: torch.Tensor):
                 len(self.write_buckets),
                 self.thread_count,
             )
-            self.results_queue = _get_write_results_queue()
+            self.results_queue = get_write_results_queue()
         else:
             self.results_queue = None
         end = time()
@@ -205,9 +213,7 @@ def get_save_function_and_args(self) -> Tuple[Optional[Callable], Optional[Calla
             return None, None, []
         transform_list = [self.transforms] if hasattr(self, "transforms") else []
         return (
-            partial(
-                self.write_preloaded_data_multiproc, transform_list, self.use_msc, self.sequential
-            ),
+            partial(self.write_preloaded_data_multithread, transform_list, self.use_msc),
             partial(self.preload_tensors, self.write_buckets, True),
             [torch.distributed.get_rank(), self.write_buckets, self.results_queue],
         )
@@ -239,47 +245,41 @@ def preload_tensors(write_buckets: List[WriteBucket], non_blocking=True) -> List
 
     @staticmethod
     @_disable_gc()
-    def write_preloaded_data_multiproc(
+    def write_preloaded_data_multithread(
         transform_list: List[_StorageWriterTransforms],
         use_msc: bool,
-        sequential: bool,
         rank: int,
         write_buckets: List[WriteBucket],
         global_results_queue: mp.Queue,
     ) -> None:
         """
-        Performs saving data to storage with multiple processes.
+        Performs saving data to storage with multiple threads.
 
-        Starts predefined number of processes and uses 2 queues to make sure the results
-        are complete:
-        - local_results_queue - to send the actual results
-        - count_queue - small queue to mark worker as completed
+        Uses threads (not processes) so that this can run safely inside a daemon process
+        without spawning child processes. Uses two queues:
+        - local_results_queue - to collect write results from worker threads
+        - count_queue - to signal worker completion (task_done/join).
 
-        Using just one queue disallowed proper exception handling.
-
-        This method is meant to be run in a forked subprocess.
-        Triggering GC during execution leads to CUDA errors
-        (cleaning up tensors owned by the parent process).
+        Triggering GC during execution can lead to CUDA errors when tensors are shared.
         To prevent this, we disable the GC explicitly for this function with _disable_gc.
 
         Args:
             write_buckets (List[WriteBucket]): write plan
-            global_results_queue (mp.Queue): mp.Queue to collect Dict[List[WriteResults]]
-                (or an Exception) from parallel write processes to the main training process
+            global_results_queue (mp.Queue): queue to send Dict[List[WriteResults]]
+                (or an Exception) back to the main training process
         Returns: None
         """
         logger = logging.getLogger(__name__)
         w_start = time()
         write_results_or_exc: Union[dict, Exception] = dict()
-        ctx = mp.get_context("fork")
-        local_results_queue = ctx.Queue()
-        count_queue = ctx.JoinableQueue()
-        p_list = []
+        local_results_queue: queue.Queue = queue.Queue()
+        count_queue: queue.Queue = queue.Queue()
+        thread_list: List[threading.Thread] = []
 
-        def check_local_output(local_results_or_exc, local_proc_idx):
+        def check_local_output(local_results_or_exc, local_worker_idx):
             if isinstance(local_results_or_exc, Exception):
                 err_msg = (
-                    f"Local process {local_proc_idx} encountered"
+                    f"Local worker {local_worker_idx} encountered"
                     f" an error: {local_results_or_exc}"
                 )
                 logger.error(err_msg)
@@ -296,71 +296,63 @@ def check_local_output(local_results_or_exc, local_proc_idx):
                 }
 
                 if use_msc:
-                    import inspect
-
-                    # Remove the inspect after the test_async_save.py is fixed.
                     signature = inspect.signature(FileSystemWriterAsync.write_preloaded_data)
                     if len(signature.parameters) > 6:
                         kwargs['use_msc'] = use_msc
-                # Parallel Writers are required
-                if i < len(write_buckets) - 1 and not sequential:
+                # Parallel writers: spawn threads for all but the last bucket
+                if i < len(write_buckets) - 1:
                     count_queue.put(i)
-                    p_list.append(
-                        ctx.Process(
-                            target=partial(
-                                FileSystemWriterAsync.write_preloaded_data, transform_list
-                            ),
-                            kwargs=kwargs,
-                        )
+                    t = threading.Thread(
+                        target=partial(FileSystemWriterAsync.write_preloaded_data, transform_list),
+                        kwargs=kwargs,
                     )
+                    thread_list.append(t)
                 else:
                     kwargs['count_queue'] = None
                     kwargs['results_queue'] = None
-                    logger.debug('FileSystemWriterAsync: master worker started')
+                    logger.debug('FileSystemWriterAsync: main worker started')
                     local_output = FileSystemWriterAsync.write_preloaded_data(
                         transform_list, **kwargs
                     )
                     if local_output is not None:
                         logger.debug(
-                            'FileSystemWriterAsync: master worker results successfully collected'
+                            'FileSystemWriterAsync: main worker results successfully collected'
                         )
                         check_local_output(local_output[1], local_output[0])
                         write_results_or_exc[local_output[0]] = local_output[1]
 
             except Exception as e:
-                err_msg = f"An error is caught while a proc {i} is created, error: {e}"
+                err_msg = f"An error is caught while starting worker {i}, error: {e}"
                 logger.error(err_msg)
                 write_results_or_exc = RuntimeError(err_msg)
 
-        if not isinstance(write_results_or_exc, Exception) and len(p_list) > 0 and not sequential:
-            for p in p_list:
-                p.start()
+        if not isinstance(write_results_or_exc, Exception) and len(thread_list) > 0:
+            for t in thread_list:
+                t.start()
 
             logger.debug("FileSystemWriterAsync: collecting worker results...")
 
-            # To make sure all nodes are completed
             count_queue.join()
-            # At this point, all workers completed, so the queue should have exactly
-            # `len(write_buckets)` items
-            for proc_idx in range(0, len(write_buckets) - 1):
+            for _ in range(len(write_buckets) - 1):
                 try:
                     local_proc_idx, local_results_or_exc = local_results_queue.get()
                 except queue.Empty:
                     write_results_or_exc = RuntimeError(
                         "Unexpected empty `local_results_queue`"
-                        f" (got only {proc_idx}/{len(write_buckets)} items)"
+                        f" (expected {len(write_buckets) - 1} items)"
                     )
                     break
                 else:
                     check_local_output(local_results_or_exc, local_proc_idx)
                     write_results_or_exc[local_proc_idx] = local_results_or_exc
-                    p_list[local_proc_idx].join()
+            for t in thread_list:
+                t.join()
             logger.debug('FileSystemWriterAsync: collected worker results successfully')
 
         global_results_queue.put(write_results_or_exc)
 
         w_end = time()
-        logger.debug(f"{w_end}, rank: {rank}, write(sync,parallel): {w_end - w_start}")
+        logger.debug(f"{w_end}, rank: {rank}, write(sync,threads): {w_end - w_start}")
 
     @staticmethod
     @_disable_gc()
@@ -368,23 +360,22 @@ def write_preloaded_data(
         transform_list: List[_StorageWriterTransforms],
         local_proc_idx: int,
         write_bucket: WriteBucket,
-        results_queue: mp.SimpleQueue,
-        count_queue: mp.JoinableQueue,
+        results_queue: Optional[queue.Queue],
+        count_queue: Optional[queue.Queue],
         use_fsync: bool,
         **kwargs,
     ) -> Union[Tuple[int, Exception], None]:
         """
-        Performs actual data saving to storage.
+        Performs actual data saving to storage (used by worker threads).
 
         Args:
-            local_proc_idx (int): index of a local process that performs writing
+            local_proc_idx (int): index of the worker that performs writing
             write_bucket (WriteBucket): data to write to storage
-            results_queue (mp.Queue): queue to return the write results
-                to the proxy checkpoint process.
-            count_queue (mp.JoinableQueue): queue to marks worker task as completed
+            results_queue (queue.Queue): queue to return the write results
+            count_queue (queue.Queue): queue to signal worker task completion (get + task_done)
             use_fsync (bool): if True, calls os.fsync at the end of saving
 
-        Returns: None, the write result are put into the `queue`
+        Returns: None when running in a worker (results put in queue); result tuple when main worker
         """
         logger = logging.getLogger(__name__)
         logger.debug(f"{local_proc_idx} started")
@@ -454,7 +445,7 @@ def retrieve_write_results(self) -> Union[List[WriteResult], WRAPPED_EXCEPTION]:
             into a single results lists. Includes error check.
 
         Returns (Union(List[WriteResult], WRAPPED_EXCEPTION): the list of write results
-            from all local processes performing the save, or a WRAPPED_EXCEPTION if
+            from all local workers (threads) performing the save, or a WRAPPED_EXCEPTION if
             an exception was raised during the writing process.
         """
         assert self.write_buckets is not None
diff --git a/megatron/core/dist_checkpointing/strategies/torch.py b/megatron/core/dist_checkpointing/strategies/torch.py
index 1b8306775e5..0b61ebffc3c 100644
--- a/megatron/core/dist_checkpointing/strategies/torch.py
+++ b/megatron/core/dist_checkpointing/strategies/torch.py
@@ -659,10 +659,8 @@ def async_save(
         )
         pyt_state_dict = mcore_to_pyt_state_dict(sharded_state_dict, False)
 
-        sequential = False
         if self.separation_hint is not None and self.thread_count <= 1:
             self.thread_count = 2
-            sequential = True
 
         # Use PyT saving mechanism
         writer = FileSystemWriterAsync(
@@ -670,7 +668,6 @@ def async_save(
             separation_hint=self.separation_hint,
             thread_count=self.thread_count,
             use_msc=MultiStorageClientFeature.is_enabled(),
-            sequential=sequential,
         )
         # This should be set differently if we run in a smaller process group than the default
         coordinator = 0
@@ -766,7 +763,7 @@ def _get_filesystem_reader(
         return msc.torch.MultiStorageFileSystemReader(checkpoint_dir, thread_count=2)
 
     if cache_metadata:
-        return CachedMetadataFileSystemReader(checkpoint_dir)
+        return CachedMetadataFileSystemReader(checkpoint_dir, cache_metadata=cache_metadata)
 
     return FileSystemReader(checkpoint_dir)
 
@@ -774,8 +771,9 @@ def _get_filesystem_reader(
 class TorchDistLoadShardedStrategy(LoadShardedStrategy):
     """Basic load strategy for the PyT Distributed format."""
 
-    def __init__(self):
+    def __init__(self, cache_metadata: bool = False):
         self.cached_global_metadata: Optional[Metadata] = None
+        self.cache_metadata = cache_metadata
         super().__init__()
 
     def load(self, sharded_state_dict: ShardedStateDict, checkpoint_dir: Path) -> StateDict:
@@ -806,7 +804,7 @@ def load(self, sharded_state_dict: ShardedStateDict, checkpoint_dir: Path) -> St
         )
         pyt_state_dict = mcore_to_pyt_state_dict(sharded_state_dict, True)
         # Load PyT Distributed format
-        fsr = _get_filesystem_reader(checkpoint_dir, cache_metadata=True)
+        fsr = _get_filesystem_reader(checkpoint_dir, cache_metadata=self.cache_metadata)
         checkpoint.load_state_dict(
             pyt_state_dict,
             fsr,
@@ -818,9 +816,10 @@ def load(self, sharded_state_dict: ShardedStateDict, checkpoint_dir: Path) -> St
             ),
         )
 
-        self.cached_global_metadata = (
-            fsr.read_metadata()
-        )  # no storage interaction thanks to caching
+        if self.cache_metadata:
+            self.cached_global_metadata = (
+                fsr.read_metadata()
+            )  # no storage interaction thanks to caching
 
         pyt_state_dict = cast(
             Dict[str, Union[TorchShardedTensor, List[io.BytesIO]]], pyt_state_dict
diff --git a/megatron/core/dist_checkpointing/validation.py b/megatron/core/dist_checkpointing/validation.py
index 0e5d6a011a8..b460c03e79e 100644
--- a/megatron/core/dist_checkpointing/validation.py
+++ b/megatron/core/dist_checkpointing/validation.py
@@ -203,6 +203,7 @@ def verify_checkpoint_and_load_strategy(
     checkpoint_dir: str,
     sharded_strategy: Union[LoadShardedStrategy, Tuple[str, int], None] = None,
     common_strategy: Union[LoadCommonStrategy, Tuple[str, int], None] = None,
+    cache_metadata: bool = False,
 ) -> Tuple[LoadShardedStrategy, LoadCommonStrategy]:
     """Verifies if checkpoint metadata exists and matches given strategies.
 
@@ -216,6 +217,8 @@ def verify_checkpoint_and_load_strategy(
         common_strategy (LoadCommonStrategy, Tuple[str, int], optional): common load strategy to be verified
             if compatible with the checkpoint content. If None, the default common load strategy
             for the checkpoint backend will be returned.
+        cache_metadata (bool): if True and checkpoint backend is torch_dist, use a load strategy that caches
+            metadata (e.g. when ckpt_assume_constant_structure is enabled). Ignored if sharded_strategy is set.
     """
     isdir = True
     if MultiStorageClientFeature.is_enabled():
@@ -231,11 +234,18 @@ def verify_checkpoint_and_load_strategy(
         raise CheckpointingException(f"{checkpoint_dir} is not a distributed checkpoint")
 
     if sharded_strategy is None:
-        sharded_strategy = get_default_strategy(
-            StrategyAction.LOAD_SHARDED,
-            saved_config.sharded_backend,
-            saved_config.sharded_backend_version,
-        )
+        if cache_metadata and saved_config.sharded_backend == 'torch_dist':
+            from megatron.core.dist_checkpointing.strategies.torch import (
+                TorchDistLoadShardedStrategy,
+            )
+
+            sharded_strategy = TorchDistLoadShardedStrategy(cache_metadata=True)
+        else:
+            sharded_strategy = get_default_strategy(
+                StrategyAction.LOAD_SHARDED,
+                saved_config.sharded_backend,
+                saved_config.sharded_backend_version,
+            )
     elif isinstance(sharded_strategy, tuple):
         sharded_strategy = get_default_strategy(StrategyAction.LOAD_SHARDED, *sharded_strategy)
 
diff --git a/megatron/core/distributed/distributed_data_parallel.py b/megatron/core/distributed/distributed_data_parallel.py
index 55179ff3024..dca0a8777dc 100644
--- a/megatron/core/distributed/distributed_data_parallel.py
+++ b/megatron/core/distributed/distributed_data_parallel.py
@@ -236,7 +236,10 @@ def _allocate_buffers_for_parameters(
 
             # Set `next_param_gather_bucket_group` for different bucket groups by iterating through
             # buckets in reverse order (since all-gathers happen in reverse order of buckets).
-            if self.ddp_config.use_distributed_optimizer and self.ddp_config.overlap_param_gather:
+            # Note: overlap_param_gather covers both the distributed optimizer and the
+            # layer-wise optimizer cases; the latter sets overlap_param_gather=True
+            # without use_distributed_optimizer.
+            if self.ddp_config.overlap_param_gather:
                 num_bucket_groups = len(bucket_groups)
                 for i in range(1, num_bucket_groups):
                     bucket_groups[num_bucket_groups - i].next_param_gather_bucket_group = (
@@ -344,9 +347,10 @@ def unmap_weight_tensor(m):
                     grad_acc.register_hook(self._make_backward_post_hook(param))
                     self.grad_accs.append(grad_acc)
 
-        self.use_forward_hook = (
-            self.ddp_config.use_distributed_optimizer and self.ddp_config.overlap_param_gather
-        )
+        # Note: overlap_param_gather covers both the distributed optimizer and the
+        # layer-wise optimizer cases; the latter sets overlap_param_gather=True
+        # without use_distributed_optimizer.
+        self.use_forward_hook = self.ddp_config.overlap_param_gather
         self.remove_forward_pre_hook_handles = {}
         if self.use_forward_hook:
             self.enable_forward_pre_hook()
@@ -490,10 +494,18 @@ def start_param_sync(self, *unused, force_sync: bool = False, force_dispatch: bo
                 # in "finish_param_sync" stage after zeroing the shared gardient buffers.
                 if self.ddp_config.reuse_grad_buf_for_mxfp8_param_ag:
                     for bucket in bucket_group.buckets:
+                        is_bf16_weight_bucket = False
                         for param in bucket.params:
+                            # Skip copying since bf16 weights in the mxfp8 model
+                            # are already mapped to param.data.
+                            if not is_float8tensor(param):
+                                is_bf16_weight_bucket = True
+                                break
                             param_start, param_end = bucket.param_to_index[param]
                             param_slice = bucket.param_data.view(-1)[param_start:param_end]
                             param.data.copy_(param_slice.view(param.data.shape))
+                        if is_bf16_weight_bucket:
+                            continue
                         # All-gathered params are not needed after being copied to param.data.
                         # Zero out the param buffer (shared with grad buffer) for gradient
                         # accumulation. We cannot zero out the entire grad buffer because one grad
@@ -534,6 +546,11 @@ def finish_grad_sync(self, force_all_reduce: Optional[bool] = False):
         for bucket_group in self.bucket_groups + self.expert_parallel_bucket_groups:
             bucket_group.finish_grad_sync(force_all_reduce=force_all_reduce)
 
+    def free_overlap_buffers(self):
+        """Free overlap param-gather GPU buffers across all bucket groups."""
+        for bucket_group in self.bucket_groups + self.expert_parallel_bucket_groups:
+            bucket_group.free_overlap_buffers()
+
     def scale_gradients(self, scaling_factor: float):
         """Scale all gradients inside the buffers by `scaling_factor`."""
         for buffer in self.buffers + self.expert_parallel_buffers:
diff --git a/megatron/core/distributed/distributed_data_parallel_config.py b/megatron/core/distributed/distributed_data_parallel_config.py
index 468bfafbff7..ea4cc0ddeb3 100644
--- a/megatron/core/distributed/distributed_data_parallel_config.py
+++ b/megatron/core/distributed/distributed_data_parallel_config.py
@@ -33,7 +33,10 @@ class DistributedDataParallelConfig:
     """
 
     check_for_nan_in_grad: bool = False
-    """If true, check for NaNs and Infs in gradients _before_ communication collective."""
+    """
+    If true, check for NaNs and Infs in gradients _before_ communication collective.
+    Invoked by `start_grad_sync` such as in the Megatron-LM DDP training API.
+    """
 
     check_for_large_grads: bool = False
     """If true, check for unexpectedly large gradients _before_ communication collective."""
@@ -78,7 +81,7 @@ class DistributedDataParallelConfig:
 
     data_parallel_sharding_strategy: str = 'no_shard'
     """Sharding strategy for FSDP. Valid values are 'no_shard', 'optim',
-        'optim_grads', 'optim_grads_params'."""
+      'optim_grads', 'optim_grads_params'."""
 
     gradient_reduce_div_fusion: bool = True
     """If true, perform gradient reduce and division fusion."""
@@ -91,9 +94,6 @@ class DistributedDataParallelConfig:
       disables prefetching and may degrade performance. Adjust this value
       based on your system's memory and performance requirements."""
 
-    preserve_fp32_weights: bool = True
-    """If true, preserve fp32 weights in the Megatron FSDP ParamAndGradBuffer."""
-
     keep_fp8_transpose_cache: bool = False
     """If true, keep the fp8 transpose cache when using Megatron FSDP."""
 
@@ -105,7 +105,6 @@ class DistributedDataParallelConfig:
       The follwoing will be the expected number of SM usage for various cases.
       (Note that this is just a reference number and the number of SM usage could vary 
       on message size, communication domain size and nccl version.)
-      ----------------------------------------------------------
       | Communication domain | use_sharp | SM usage of "AG/RS" |
       |----------------------|-----------|---------------------|
       | NVL                  | N/A       | 4 / 5               |
@@ -113,7 +112,6 @@ class DistributedDataParallelConfig:
       | NVL+IB               | True      | 6 / 6               |
       | IB                   | False     | 1 / 4               |
       | IB                   | True      | 1 / 1               |
-      ----------------------------------------------------------
     """
 
     fsdp_double_buffer: bool = False
@@ -130,7 +128,7 @@ class DistributedDataParallelConfig:
        allocated buffer for the bucket that does not fit, it will enable NCCL 
        user buffer with the cost of more memory usage. If false, FSDP will use
        Dynamic memory allocator, NCCL user buffer won't not enabled, which 
-       usually leads to low performance. 
+       usually leads to low performance.
     """
 
     fsdp_all_gather_in_start_param_sync: bool = True
@@ -152,8 +150,7 @@ class DistributedDataParallelConfig:
     outer_dp_sharding_strategy: str = 'no_shard'
     """
     Sharding strategy for outer data parallel group in Hybrid Sharded Data Parallel (HSDP) mode.
-    Valid values are 'no_shard', 'optim', 'optim_grads', 'optim_grads_params'.
-    This option is only effective when Hybrid FSDP is enabled.
+    Valid values are 'no_shard', 'optim'. This option is only effective when Hybrid FSDP is enabled.
     """
 
     disable_symmetric_registration: bool = False
diff --git a/megatron/core/distributed/fsdp/mcore_fsdp_adapter.py b/megatron/core/distributed/fsdp/mcore_fsdp_adapter.py
index d9af69eb41b..d92eb79c15e 100644
--- a/megatron/core/distributed/fsdp/mcore_fsdp_adapter.py
+++ b/megatron/core/distributed/fsdp/mcore_fsdp_adapter.py
@@ -45,7 +45,11 @@
 from megatron.core.utils import is_te_min_version, log_single_rank
 
 try:
-    from megatron.core.distributed.fsdp.src.megatron_fsdp import FSDPDistributedIndex, MegatronFSDP
+    from megatron.core.distributed.fsdp.src.megatron_fsdp import (
+        FSDPDistributedIndex,
+        MegatronFSDP,
+        MixedPrecisionPolicy,
+    )
 
     HAVE_MEGATRON_FSDP = True
 except ImportError as import_megatron_fsdp_error:
@@ -66,6 +70,9 @@ def __init__(
         ddp_config: DistributedDataParallelConfig,
         module: torch.nn.Module,
         fsdp_unit_modules: Optional[List[torch.nn.Module]] = None,
+        main_params_dtype: Optional[torch.dtype] = torch.float32,
+        main_grads_dtype: Optional[torch.dtype] = torch.float32,
+        grad_comm_dtype: Optional[torch.dtype] = torch.float32,
         disable_bucketing: bool = False,
         device: Optional[torch.device] = None,
         pg_collection: Optional[ProcessGroupCollection] = None,
@@ -84,6 +91,17 @@ def __init__(
             logging.INFO,
             f'Setting up DistributedDataParallel with config {self.ddp_config}',
         )
+        self.mp_policy = MixedPrecisionPolicy(
+            main_params_dtype=main_params_dtype,
+            # Grandfathered Argument: grad_reduce_in_fp32
+            main_grads_dtype=torch.float32 if ddp_config.grad_reduce_in_fp32 else main_grads_dtype,
+            grad_comm_dtype=grad_comm_dtype,
+        )
+        log_single_rank(
+            logger,
+            logging.INFO,
+            f'Setting up Megatron-FSDP MixedPrecisionPolicy with config {self.mp_policy}',
+        )
 
         self.megatron_fsdp_dist_index = self._init_dist_index(pg_collection)
 
@@ -112,6 +130,7 @@ def __init__(
             config=config,
             module=MegatronFSDP(
                 ddp_config=ddp_config,
+                mixed_precision_policy=self.mp_policy,
                 module=module,
                 fsdp_unit_modules=self.fsdp_unit_modules,
                 disable_bucketing=disable_bucketing,
diff --git a/megatron/core/distributed/fsdp/src/README.md b/megatron/core/distributed/fsdp/src/README.md
index 75cb7c45613..0b8339a7179 100644
--- a/megatron/core/distributed/fsdp/src/README.md
+++ b/megatron/core/distributed/fsdp/src/README.md
@@ -129,6 +129,7 @@ import torch
 from megatron_fsdp import (
     fully_shard_model,
     fully_shard_optimizer,
+    MixedPrecisionPolicy,
 )
 ```
 
@@ -196,10 +197,8 @@ model = fully_shard_model(
     outer_dp_sharding_strategy=1,
     # Initialize the model on devices in shards to avoid OOM. Requires device("meta")-init for model.
     init_model_with_meta_device=True,
-    # Reduce gradients in FP32.
-    grad_reduce_in_fp32=False,
-    # Store distributed optimization state in FP32.
-    preserve_fp32_weights=True,
+    # Mixed-Precision Policy for controlling compute and communication precision in Megatron-FSDP.
+    mixed_precision_policy=MixedPrecisionPolicy(),
     # Sync parameters and gradients each step. Allows for gradient transformations after backward pass,
     # and synchronizes parameters and gradients across HSDP groups, but deactivates compute-communication
     # overlap going into the subsequent training step.
@@ -285,7 +284,7 @@ Megatron-FSDP's `fully_shard_*` API has a comprehensive set of arguments for fin
 
 - `fsdp_unit_modules` is a list of sub-module classes or `str` import-paths associated with modules that you want `MegatronFSDP` to fully-shard.
   - Required if `1`, `2`, or `3` are specified as the sharding strategy. Defaults to `None`, in which case Megatron-FSDP will replicate the parameters similar to DDP.
-- `zero_dp_strategy` (and `outer_dp_sharding_strategy`) configure different degrees of zero-redundancy data parallelism as described in [ZeRO (Zero Redundancy Optimizer)](https://arxiv.org/abs/1910.02054). It reduces CUDA memory utilization during model training by distributing model parameters, gradients, and optimizer states across multiple devices in the DP `ProcessGroup`, and collectively communicating subsets of parameters and gradients to specific devices when needed for computation or differentiation. More aggressive sharding strategies will entail more communication overhead, with `no_shard` being the least memory efficient but most communication efficient, and `optim_grads_params` being the most memory efficient but least communication efficient. `outer_dp_sharding_strategy` has the same options, except for the (required) "outer" DP group (`dp_outer_dim` / `hybrid_fsdp_group`) when using [Hybrid-Sharded Data Parallelism (HSDP)](https://arxiv.org/pdf/2304.11277), and only `no_shard` (DP Replication) and `optim` (Optimizer State Hybrid Sharding, requires `zero_dp_strategy='optim_grads_params`) are supported.
+- `zero_dp_strategy` (and `outer_dp_sharding_strategy`) configure different degrees of zero-redundancy data parallelism as described in [ZeRO (Zero Redundancy Optimizer)](https://arxiv.org/abs/1910.02054). It reduces CUDA memory utilization during model training by distributing model parameters, gradients, and optimizer states across multiple devices in the DP `ProcessGroup`, and collectively communicating subsets of parameters and gradients to specific devices when needed for computation or differentiation. More aggressive sharding strategies will entail more communication overhead, with `no_shard` being the least memory efficient but most communication efficient, and `optim_grads_params` being the most memory efficient but least communication efficient. Additionally, `outer_dp_sharding_strategy` supports `no_shard` ([Hybrid-Sharded Data Parallelism (HSDP)](https://arxiv.org/pdf/2304.11277)) and `optim` (`HFSDP` = Fully-Sharded Optimizer State + _HSDP_, requires `zero_dp_strategy='optim_grads_params'`), after specifying the "outer" DP group (`dp_outer_dim` / `hybrid_fsdp_group`).
   - Default: `optim_grads_params` or `3` for `zero_dp_strategy` and `no_shard` or `0` for `outer_dp_sharding_strategy`
   - `0` or `no_shard` implies that your model is not sharded. Similar memory usage to `DDP`.
   - `1` or `optim` implies that your optimizer state is sharded for distributed optimization. Similar to optimizer state sharding in `ZeRO-DP`.
@@ -305,16 +304,25 @@ Megatron-FSDP's `fully_shard_*` API has a comprehensive set of arguments for fin
 - `init_model_with_meta_device` has `MegatronFSDP` initialize your `meta`-device model in shards on every CUDA device to avoid OOM when initializing extremely large models that cannot fit on a single device. Users can initialize their model on a [`meta`-device](https://docs.pytorch.org/docs/stable/meta.html) (`with torch.device('meta'): ...`), and ``MegatronFSDP`` will further shard and initialize the model parameters layer-by-layer adhering to the customizable `module.reset_parameters` method, which prevents the entire model from being allocated in memory at any point during runtime.
     - Defaults to `False`.
     - Note that the `device` argument which installs your model on a specific device or rank will be deactivated when `init_model_with_meta_device=True`.
-- `grad_reduce_in_fp32` will reduce gradients in `FP32` precision (in contrast to the lower `BF16` or `FP8` model training precision).
-    - Defaults to `False`.
-    - `torch.distributed.fsdp.MixedPrecisionPolicy` will be supported in the near future.
-- `preserve_fp32_weights` will preserve a `FP32` precision version of model parameters utilized for optimization.
-    - Defaults to `True`.
-    - `torch.distributed.fsdp.MixedPrecisionPolicy` will be supported in the near future.
+- `mixed_precision_policy` takes a `megatron_fsdp.MixedPrecisionPolicy` that configures mixed-precision compute and communication for Megatron-FSDP. Configuration options include:
+    - `main_params_dtype` controls the data-type for parameters used in distributed optimization or quantization. 
+        - Defaults to `torch.float32`.
+        - If set to `None`, the native model compute parameter data-type will be utilized.
+        - Requires specification (cannot be `None`) when using `FP8` parameters with Megatron-FSDP.
+    - `main_grads_dtype` controls the data-type for gradients used in distributed optimization.
+        - Defaults to `torch.float32`, which is highly-recommended for accuracy at scale.
+        - If set to `None`, the model native gradient data-type will be utilized.
+    - `grad_comm_dtype` controls the data-type for gradient communications (RS / AR) when reducing gradients. Lower precision `grad_comm_dtype` improves (communication) performance, but may increase memory utilization or sacrifice gradient precision in certain cases.
+        - Defaults to `torch.float32`.
+        - If set to `None`, the `main_grads_dtype` data-type will be utilized.
+        - If using `no_shard`, `optim`, or a `FixedPoolAllocator` (`fsdp_double_buffer`), allocating `dtype`-custom gradient communication buffers (per FSDP group) adds memory overhead.
+        - If using NCCL UBR v2.27+ (`nccl_ub=True`), gradient reduction may be performed in high-precision depending on the network domain (NVLink or IB), and can enable mixed-precision communication and accumulation, e.g. setting grad_comm_dtype to BF16 can support FP32 reduction even though we have BF16 input and output communication buffers. Otherwise, gradients will be reduced in `grad_comm_dtype` (and accumulated in `main_grads_dtype`) as usual.
 - `overlap_grad_reduce` and `overlap_param_gather` will overlap gradient [`reduce-scatter`](https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/usage/collectives.html#reducescatter) and parameter [`all-gather`](https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/usage/collectives.html#allgather) group communications with backward and forward compute with asynchronous calls and pre-fetching. (In the case of `no_shard`, parameters are not gathered but gradient [`all-reduce`](https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/usage/collectives.html#allreduce) is overlapped.)
     - Both default to `True`.
-- `sync_model_each_microbatch` will trigger a `wait` (`MegatronFSDP.finish_grad_sync()`) on gradient reduction, parameter de-allocation, and optimizer parameter / gradient installation (in preparation for `optimizer.step()`) after every forward-backward pass. When using HSDP, parameters and gradients will be all-gathered and reduced respectively on the "outer" DP group each training step instead of each optimization cycle. This behavior is desirable for a transparent and user-friendly sharded training loop where post-backward transformations on the gradient and a clean compute / memory state are necessary between training iterations, but damages performance in situations where optimization is delayed (e.g. gradient accumulation) where the communications of the previous training iteration can be overlapped with the compute of the next training iteration. Will also override `is_last_microbatch` / `microbatch_count` logic in `MegatronFSDP`.
+- `sync_model_each_microbatch` will trigger a `wait` (`MegatronFSDP.finish_grad_sync()`) on gradient reduction, parameter de-allocation, and optimizer parameter / gradient installation (in preparation for `optimizer.step()`) after every forward-backward pass. When using HSDP, parameters and gradients will be all-gathered and reduced respectively on the "outer" DP group each training step instead of each optimization cycle. This behavior is desirable for a transparent and user-friendly sharded training loop where post-backward transformations on the gradient and a clean compute / memory state are necessary within and between training iterations, but damages performance in situations where optimization is delayed (e.g. gradient accumulation) when the communications of the previous training iteration can be overlapped with the compute of the next training iteration. Will also override `is_last_microbatch` / `microbatch_count` logic in `MegatronFSDP`.
     - Defaults to `True` for `fully_shard`, but defaults to `False` when using the `MegatronFSDP` class directly.
+    - Can also be controlled with the `MegatronFSDP.sync()` context manager, or through invoking `MegatronFSDP.set_model_auto_sync(bool)`.
+    - WARNING: When this synchronization feature is activated in conjunction with `no_shard` / `0` or `optim` / `1` sharding strategies, the user is responsible for calling `MegatronFSDP.zero_grad_buffer()` or `optimizer.zero_grad()` after the subsequent forward-backward pass. This is because un-sharded gradients are all-reduced directly into the gradient accumulation buffer, and this buffer should not be all-reduced more than once per optimization cycle! Analogous to the justification for the [`no_sync()` API for PyTorch DistributedDataParallel](https://docs.pytorch.org/docs/stable/generated/torch.nn.parallel.DistributedDataParallel.html#torch.nn.parallel.DistributedDataParallel.no_sync).
 - `enable_fine_grained_param_gather` modifies FSDP to all-gather parameters with per-Module granularity instead of collectively unsharding all sub-modules of a unit module in Megatron-FSDP.
     - Defaults to `False`.
 - `keep_fp8_transpose_cache` will keep the fp8 transpose cache when using `MegatronFSDP`. This option will cause (number of parameter $\times$ 1 Byte) of memory overhead, but can skip the weight transpose operation in the backward propagation. This feature will not give any benefit from the Blackwell architecture.
@@ -362,8 +370,10 @@ mfsdp_model = fully_shard_model(
     fsdp_unit_modules=[te.pytorch.TransformerLayer],
     # Only FSDP / ZeRO-3 supports FP8 parameters.
     zero_dp_strategy=3,
-    # Needed for FP8 parameters. (Default is already True.)
-    preserve_fp32_weights=True,
+    # FP32 main weights needed for FP8 parameters.
+    mixed_precision_policy=MixedPrecisionPolicy(
+        main_params_dtype=torch.float32
+    ),
     # Needed for select FP8 recipes.
     keep_fp8_transpose_cache=True,
 )
diff --git a/megatron/core/distributed/fsdp/src/megatron_fsdp/__init__.py b/megatron/core/distributed/fsdp/src/megatron_fsdp/__init__.py
index 1dd7263c4e2..a1ae54e622d 100644
--- a/megatron/core/distributed/fsdp/src/megatron_fsdp/__init__.py
+++ b/megatron/core/distributed/fsdp/src/megatron_fsdp/__init__.py
@@ -15,6 +15,7 @@
 from .distributed_data_parallel_config import DistributedDataParallelConfig
 from .fully_shard import fully_shard, fully_shard_model, fully_shard_optimizer
 from .megatron_fsdp import MegatronFSDP
+from .mixed_precision import MixedPrecisionPolicy
 from .package_info import (
     __contact_emails__,
     __contact_names__,
@@ -34,6 +35,7 @@
     "DistributedDataParallelConfig",
     "MegatronFSDP",
     "FSDPDistributedIndex",
+    "MixedPrecisionPolicy",
     "fully_shard",
     "fully_shard_model",
     "fully_shard_optimizer",
diff --git a/megatron/core/distributed/fsdp/src/megatron_fsdp/distributed_data_parallel_config.py b/megatron/core/distributed/fsdp/src/megatron_fsdp/distributed_data_parallel_config.py
index fd61fb6ce2c..aae16b2f57d 100644
--- a/megatron/core/distributed/fsdp/src/megatron_fsdp/distributed_data_parallel_config.py
+++ b/megatron/core/distributed/fsdp/src/megatron_fsdp/distributed_data_parallel_config.py
@@ -6,10 +6,10 @@
 
 @dataclass
 class DistributedDataParallelConfig:
-    """Configuration for DistributedDataParallel."""
-
-    grad_reduce_in_fp32: bool = False
-    """If true, reduce grads in fp32."""
+    """
+    Megatron-FSDP `fully_shard` API sub-configuration
+    derived from Megatron-Core DistributedDataParallel.
+    """
 
     overlap_grad_reduce: bool = False
     """If true, overlap grad all-reduce / reduce-scatter with backward compute."""
@@ -17,38 +17,17 @@ class DistributedDataParallelConfig:
     overlap_param_gather: bool = False
     """If true, overlap param all-gather with forward compute."""
 
-    align_param_gather: bool = False
-    """If true, all PP stages will launch param all-gathers simultaneously. Otherwise, each
-    PP stage will independently launch as needed.
-    """
-
-    use_distributed_optimizer: bool = False
-    """If true, issue reduce-scatter collectives to aggregate gradients and clean up
-       originally allocated model parameters, otherwise issue all-reduce collectives.
+    check_for_nan_in_grad: bool = False
     """
-
-    num_distributed_optimizer_instances: int = 1
-    """Sets the factor by which the DP domain is sharded to have the partial DistOpt
-       enabled. Defaults to 1, which means DistOpt is across entire DP domain.
+    If True, check for NaNs and Infs in gradients _before_ communication collective.
+    Invoked by `start_grad_sync` such as in the Megatron-LM DDP training API.
     """
 
-    check_for_nan_in_grad: bool = False
-    """If true, check for NaNs and Infs in gradients _before_ communication collective."""
-
-    check_for_large_grads: bool = False
-    """If true, check for unexpectedly large gradients _before_ communication collective."""
-
     bucket_size: Optional[int] = None
     """Maximum number of parameters in each bucket. If unspecified, MCore uses a default
        value of max(40000000, 1000000 * dp_size) parameters (larger DP sizes need larger
        buckets to ensure collectives do not become latency-bound)."""
 
-    pad_buckets_for_high_nccl_busbw: bool = False
-    """If true, make sure the bucket size is divisible by a large power of 2 (2^16) to
-       ensure NCCL collectives have high bus bandwidth at large DP counts, since NCCL
-       message size (which for ring algorithms is bucket_size / dp_size) apparently needs
-       to be divisible by a power of 2 for high busbw."""
-
     average_in_collective: bool = False
     """If true, compute average in collective directly, as opposed to dividing by the
        dp_size first and then computing sum in the collective."""
@@ -57,23 +36,9 @@ class DistributedDataParallelConfig:
     """If true, keep the compute param in fp8 (do not use any other intermediate dtype) and
        perform the param all-gather in fp8."""
 
-    reuse_grad_buf_for_mxfp8_param_ag: bool = False
-    """If true, reuse the grad buffer for param AG when using mxfp8 recipe. Should be 
-       set to True only when fp8_recipe is mxfp8 and fp8_param_gather is True."""
-
-    use_megatron_fsdp: bool = False
-    """If true, use the FSDP code path for DDP."""
-
-    use_custom_fsdp: bool = False
-    """
-    NOTE: The flag `use_custom_fsdp` is deprecated and will be removed in future versions.
-    Please use `use_megatron_fsdp` instead, as all functionality will be migrated there.
-    Future updates will drop support for `use_custom_fsdp` to avoid confusion.
-    """
-
     data_parallel_sharding_strategy: str = 'no_shard'
     """Sharding strategy for FSDP. Valid values are 'no_shard', 'optim',
-        'optim_grads', 'optim_grads_params'."""
+      'optim_grads', 'optim_grads_params'."""
 
     gradient_reduce_div_fusion: bool = True
     """If true, perform gradient reduce and division fusion."""
@@ -86,9 +51,6 @@ class DistributedDataParallelConfig:
       disables prefetching and may degrade performance. Adjust this value
       based on your system's memory and performance requirements."""
 
-    preserve_fp32_weights: bool = True
-    """If true, preserve fp32 weights in the Megatron FSDP ParamAndGradBuffer."""
-
     keep_fp8_transpose_cache: bool = False
     """If true, keep the fp8 transpose cache when using Megatron FSDP."""
 
@@ -100,7 +62,6 @@ class DistributedDataParallelConfig:
       The follwoing will be the expected number of SM usage for various cases.
       (Note that this is just a reference number and the number of SM usage could vary 
       on message size, communication domain size and nccl version.)
-      ----------------------------------------------------------
       | Communication domain | use_sharp | SM usage of "AG/RS" |
       |----------------------|-----------|---------------------|
       | NVL                  | N/A       | 4 / 5               |
@@ -108,7 +69,6 @@ class DistributedDataParallelConfig:
       | NVL+IB               | True      | 6 / 6               |
       | IB                   | False     | 1 / 4               |
       | IB                   | True      | 1 / 1               |
-      ----------------------------------------------------------
     """
 
     fsdp_double_buffer: bool = False
@@ -160,9 +120,6 @@ def __post_init__(self):
         import os
 
         """Check the validity of the config."""
-        if self.reuse_grad_buf_for_mxfp8_param_ag:
-            assert self.fp8_param_gather, "Reuse grad buffer only when keeping params in MXFP8."
-
         if self.nccl_ub:
             if 'expandable_segments:True' in os.getenv('PYTORCH_CUDA_ALLOC_CONF', '').split(','):
                 raise ValueError(
diff --git a/megatron/core/distributed/fsdp/src/megatron_fsdp/fully_shard.py b/megatron/core/distributed/fsdp/src/megatron_fsdp/fully_shard.py
index 921d3d40c37..287607b4965 100644
--- a/megatron/core/distributed/fsdp/src/megatron_fsdp/fully_shard.py
+++ b/megatron/core/distributed/fsdp/src/megatron_fsdp/fully_shard.py
@@ -23,6 +23,7 @@
 from torch.distributed.tensor import DTensor
 
 from .megatron_fsdp import MegatronFSDP
+from .mixed_precision import MixedPrecisionPolicy
 from .uneven_dtensor import preprocess_state_dict_for_uneven_dtensor
 from .utils import FSDPDistributedIndex, create_updated_function_signature
 
@@ -85,13 +86,12 @@ def fully_shard_model(
     outer_dp_sharding_strategy: str | int = 0,
     device: Optional[torch.device] = None,
     init_model_with_meta_device: bool = False,
-    grad_reduce_in_fp32: bool = False,
-    preserve_fp32_weights: bool = True,
+    mixed_precision_policy: MixedPrecisionPolicy = MixedPrecisionPolicy(),
     overlap_grad_reduce: bool = True,
     overlap_param_gather: bool = True,
     sync_model_each_microbatch: bool = True,
     preproc_state_dict_for_dcp_ckpt: bool = True,
-    check_for_nan_in_grad: bool = True,
+    report_nan_in_param_grad: bool = False,
     average_in_collective: bool = False,
     disable_bucketing: bool = False,
     calculate_per_token_loss: bool = False,
@@ -182,11 +182,9 @@ class that schedules the sharding lifecycle of the model parameters and gradient
             implementing a custom Module.reset_parameters() or Module._reset_parameters() method.
             Defaults to False.
 
-        grad_reduce_in_fp32 (bool):
-            Whether to perform gradient reduction in FP32. Defaults to False.
-
-        preserve_fp32_weights (bool):
-            Whether to preserve FP32 optimization weights. Defaults to True.
+        mixed_precision_policy (megatron_fsdp.MixedPrecisionPolicy):
+            Megatron-FSDP mixed-precision config that controls compute and communication precision.
+            Defaults to FP32 for main weights, main gradients, and gradient communication buffers.
 
         overlap_grad_reduce (bool):
             Whether to overlap gradient reduce-scatter (or all-reduce) with backward compute.
@@ -209,8 +207,9 @@ class that schedules the sharding lifecycle of the model parameters and gradient
             for both the model and the optimizer.
             Defaults to True.
 
-        check_for_nan_in_grad (bool):
-            Whether to check for NaN values in gradients. Defaults to True.
+        report_nan_in_param_grad (bool):
+            Whether to precisely check for NaN values in gradients for every weight. Can
+            significantly degrade performance. Defaults to False.
 
         average_in_collective (bool):
             Whether to average gradients in collective communication. Defaults to False.
@@ -334,8 +333,6 @@ class that schedules the sharding lifecycle of the model parameters and gradient
     ddp_config = DistributedDataParallelConfig(
         data_parallel_sharding_strategy=zero_dp_strategy,
         outer_dp_sharding_strategy=outer_dp_sharding_strategy,
-        grad_reduce_in_fp32=grad_reduce_in_fp32,
-        preserve_fp32_weights=preserve_fp32_weights,
         overlap_grad_reduce=overlap_grad_reduce,
         overlap_param_gather=overlap_param_gather,
         average_in_collective=average_in_collective,
@@ -344,7 +341,6 @@ class that schedules the sharding lifecycle of the model parameters and gradient
         fsdp_double_buffer=fsdp_double_buffer or nccl_ub,
         fsdp_db_use_persist_buf_on_alloc_fail=fsdp_db_use_persist_buf_on_alloc_fail,
         disable_symmetric_registration=disable_symmetric_registration,
-        check_for_nan_in_grad=check_for_nan_in_grad,
     )
 
     # Create FSDPDistributedIndex.
@@ -373,6 +369,7 @@ class that schedules the sharding lifecycle of the model parameters and gradient
         module=module,
         dist_index=dist_index,
         ddp_config=ddp_config,
+        mixed_precision_policy=mixed_precision_policy,
         fsdp_unit_modules=fsdp_unit_modules,
         disable_bucketing=disable_bucketing,
         device=device,
@@ -380,6 +377,7 @@ class that schedules the sharding lifecycle of the model parameters and gradient
         init_model_with_meta_device=init_model_with_meta_device,
         sync_model_each_microbatch=sync_model_each_microbatch,
         enable_fine_grained_param_gather_hook=enable_fine_grained_param_gather,
+        report_nan_in_param_grad=report_nan_in_param_grad,
     )
 
     # Register a state dict post-hook to add Torch DCP metadata for writing checkpoints.
@@ -612,6 +610,7 @@ def preprocess_optimizer_state_dict_for_uneven_dtensor(optimizer, state_dict):
     return optimizer
 
 
+@experimental_api
 def fully_shard(
     module: torch.nn.Module,
     optimizer: torch.optim.Optimizer,
@@ -627,13 +626,12 @@ def fully_shard(
     outer_dp_sharding_strategy: str | int = 0,
     device: Optional[torch.device] = None,
     init_model_with_meta_device: bool = False,
-    grad_reduce_in_fp32: bool = False,
-    preserve_fp32_weights: bool = True,
+    mixed_precision_policy: MixedPrecisionPolicy = MixedPrecisionPolicy(),
     overlap_grad_reduce: bool = True,
     overlap_param_gather: bool = True,
     sync_model_each_microbatch: bool = True,
     preproc_state_dict_for_dcp_ckpt: bool = True,
-    check_for_nan_in_grad: bool = True,
+    report_nan_in_param_grad: bool = False,
     average_in_collective: bool = False,
     disable_bucketing: bool = False,
     calculate_per_token_loss: bool = False,
@@ -676,13 +674,12 @@ def fully_shard(
         outer_dp_sharding_strategy=outer_dp_sharding_strategy,
         device=device,
         init_model_with_meta_device=init_model_with_meta_device,
-        grad_reduce_in_fp32=grad_reduce_in_fp32,
-        preserve_fp32_weights=preserve_fp32_weights,
+        mixed_precision_policy=mixed_precision_policy,
         overlap_grad_reduce=overlap_grad_reduce,
         overlap_param_gather=overlap_param_gather,
         sync_model_each_microbatch=sync_model_each_microbatch,
         preproc_state_dict_for_dcp_ckpt=preproc_state_dict_for_dcp_ckpt,
-        check_for_nan_in_grad=check_for_nan_in_grad,
+        report_nan_in_param_grad=report_nan_in_param_grad,
         average_in_collective=average_in_collective,
         disable_bucketing=disable_bucketing,
         calculate_per_token_loss=calculate_per_token_loss,
diff --git a/megatron/core/distributed/fsdp/src/megatron_fsdp/megatron_fsdp.py b/megatron/core/distributed/fsdp/src/megatron_fsdp/megatron_fsdp.py
index 40198673ee7..6987729ba8f 100644
--- a/megatron/core/distributed/fsdp/src/megatron_fsdp/megatron_fsdp.py
+++ b/megatron/core/distributed/fsdp/src/megatron_fsdp/megatron_fsdp.py
@@ -24,6 +24,7 @@
 from torch.utils._pytree import tree_flatten, tree_map, tree_unflatten
 
 from .mixed_precision import (
+    MixedPrecisionPolicy,
     fp8_create_transpose_cache,
     fp8_discard_transpose_cache,
     is_float8tensor,
@@ -34,10 +35,11 @@
     GradReducePipeline,
     ParamAndGradBuffer,
     PrefetchOrder,
+    _check_nan_in_grad,
     override_sharded_param_methods_with_safety_checks,
     to_local_if_dtensor,
 )
-from .utils import FSDPDistributedIndex
+from .utils import FSDPDistributedIndex, log_single_rank
 
 logger = logging.getLogger(__name__)
 
@@ -102,9 +104,11 @@ class MegatronFSDP(torch.nn.Module):
         module (torch.nn.Module): Underlying Torch Module.
         dist_index (FSDPDistributedIndex): FSDPDistributedIndex object containing references to the
             process groups and device meshes used by Megatron-FSDP.
-        ddp_config (DistributedDataParallelConfig): FullyShardedDataParallel configuration dataclass
-            containing a variety of Megatron-derived parameters that control the behavior of
-            Megatron-FSDP.
+        ddp_config (DistributedDataParallelConfig): FullyShardedDataParallel configuration
+            dataclass containing a variety of Megatron-derived parameters that control the
+            behavior of Megatron-FSDP.
+        mixed_precision_policy (megatron_fsdp.MixedPrecisionPolicy): Configuration for
+            mixed-precision customization of compute and communications in Megatron-FSDP.
         fsdp_unit_modules (List[torch.nn.Module] | List[str]): List of modules that
             should be treated as an FSDP Unit, i.e. the minimum releasable model unit.
             It affects the granularity of the communication parameter grouping and
@@ -143,6 +147,8 @@ class MegatronFSDP(torch.nn.Module):
             userbuffer registration when nccl_ub is set.
         enable_fine_grained_param_gather (bool): Whether to enable "fine-grained" param all-gather,
             which can improve performance when using MXFP8 parameters with activation recomputation.
+        report_nan_in_param_grad (bool): Whether to enable precise NaN-checking for parameter wgrad.
+            Can significantly degrade performance. Defaults to False.
 
     Examples:
         >>> model = GPTModel(config)
@@ -167,6 +173,7 @@ def __init__(
         module: torch.nn.Module,
         dist_index: FSDPDistributedIndex,
         ddp_config: DistributedDataParallelConfig = None,
+        mixed_precision_policy: MixedPrecisionPolicy = MixedPrecisionPolicy(),
         fsdp_unit_modules: Optional[List[torch.nn.Module] | List[str]] = None,
         disable_bucketing: bool = False,
         device: Optional[torch.device] = None,
@@ -179,6 +186,7 @@ def __init__(
         fsdp_db_use_persist_buf_on_alloc_fail: bool = False,
         disable_symmetric_registration: bool = False,
         enable_fine_grained_param_gather_hook: bool = False,
+        report_nan_in_param_grad: bool = False,
     ):
         super().__init__()
         # If device is not specified, use the current device.
@@ -211,10 +219,8 @@ def __init__(
         # "optim_grads_params" is the default strategy
         if ddp_config is None:
             self.ddp_config = DistributedDataParallelConfig(
-                check_for_nan_in_grad=True,
                 data_parallel_sharding_strategy="optim_grads_params",
                 outer_dp_sharding_strategy="no_shard",
-                grad_reduce_in_fp32=True,
                 overlap_grad_reduce=True,
                 overlap_param_gather=True,
                 average_in_collective=False,
@@ -223,13 +229,21 @@ def __init__(
                 fsdp_double_buffer=fsdp_double_buffer or nccl_ub,
                 fsdp_db_use_persist_buf_on_alloc_fail=fsdp_db_use_persist_buf_on_alloc_fail,
                 disable_symmetric_registration=disable_symmetric_registration,
+                check_for_nan_in_grad=False,
             )
         else:
             self.ddp_config = ddp_config
-
+        self.data_parallel_sharding_strategy = self.ddp_config.data_parallel_sharding_strategy
+        self.mp_policy = mixed_precision_policy
         self.calculate_per_token_loss = calculate_per_token_loss
         self.init_model_with_meta_device = init_model_with_meta_device
         self.enable_fine_grained_param_gather_hook = enable_fine_grained_param_gather_hook
+        self.report_nan_in_param_grad = report_nan_in_param_grad
+
+        # FSDPDistributedIndex stores the process groups and meshes used by Megatron-FSDP.
+        # If not provided, Megatron-FSDP will default to a simple data parallel index
+        # supported by torch.distributed.group.WORLD.
+        self.dist_index = dist_index
 
         # Whether to constantly synchronize the model every training iteration,
         # which defaults to False to overlap communication with computation
@@ -242,11 +256,6 @@ def __init__(
         # Check if the module contains (Megatron-Core) expert parallel parameters or DTensors.
         has_expert_parameters = self._check_module_parameter_types()
 
-        # FSDPDistributedIndex stores the process groups and meshes used by Megatron-FSDP.
-        # If not provided, Megatron-FSDP will default to a simple data parallel index
-        # supported by torch.distributed.group.WORLD.
-        self.dist_index = dist_index
-
         # If Megatron Expert Parallelism is enabled, you need to provide an expt_dp_group.
         if (
             has_expert_parameters
@@ -272,11 +281,8 @@ def __init__(
         )
 
         # Determine if we should delay the gradient reduction.
-        self.is_delay_grad_reduce = self.ddp_config.data_parallel_sharding_strategy in [
-            "no_shard",
-            "optim",
-        ]
-        if self.ddp_config.data_parallel_sharding_strategy == "optim_grads_params":
+        self.is_delay_grad_reduce = self.data_parallel_sharding_strategy in ["no_shard", "optim"]
+        if self.data_parallel_sharding_strategy == "optim_grads_params":
             # Default to overlapped parameter gather when fully-sharding.
             self.ddp_config.overlap_param_gather = True
         if self.ddp_config.data_parallel_sharding_strategy in ["optim_grads_params", "optim_grads"]:
@@ -342,7 +348,6 @@ def _init_fsdp_param_and_grad_buffer(self):
                 expert_gradient_scaling_factor = 1.0 / data_parallel_world_size
 
         # Initialize the param and grad buffer.
-        self.data_parallel_sharding_strategy = self.ddp_config.data_parallel_sharding_strategy
         self.param_and_grad_buffer = ParamAndGradBuffer(
             self.ddp_config,
             self.module,
@@ -352,8 +357,7 @@ def _init_fsdp_param_and_grad_buffer(self):
                 data_parallel_sharding_strategy=self.data_parallel_sharding_strategy,
             ),
             dist_index=self.dist_index,
-            preserve_fp32_weights=self.ddp_config.preserve_fp32_weights,
-            grad_reduce_in_fp32=self.ddp_config.grad_reduce_in_fp32,
+            mixed_precision_policy=self.mp_policy,
             gradient_scaling_factor=gradient_scaling_factor,
             expert_gradient_scaling_factor=expert_gradient_scaling_factor,
             device=self.device,
@@ -572,18 +576,23 @@ def _grad_acc(param):
 
                     param.main_grad = param.get_main_grad()
                     if param.grad is not None:
+                        if self.report_nan_in_param_grad:
+                            _check_nan_in_grad(to_local_if_dtensor(param.grad))
                         # Copy the gradient into the allocated main gradient bucket.
                         # It will be reduce-scattered and accumulated into gbuf.
                         param.main_grad.copy_(to_local_if_dtensor(param.grad))
                         del param.grad
                     else:
+                        # Prepare for fused wgrad accumulation.
                         param.main_grad.zero_()
             # Unsharded Gradient Buffer
             else:
                 if not param.grad_added_to_main_grad:
                     if param.grad is not None:
-                        # Add the gradient into the allocated main gradient bucket.
-                        # For unsharded gradients, this is gradient accumulation.
+                        if self.report_nan_in_param_grad:
+                            _check_nan_in_grad(to_local_if_dtensor(param.grad))
+                        # Accumulate the gradient into the main gradient buffer,
+                        # because we only reduce once per optimization cycle.
                         param.main_grad = param.get_main_grad()
                         param.main_grad.add_(to_local_if_dtensor(param.grad))
                         del param.grad
@@ -608,7 +617,7 @@ def _post_backward_release_module(module, *unused):
             - Marks the module as IDLE in the training state machine.
             """
             assert isinstance(module, tuple(fsdp_unit_modules))
-            assert self.ddp_config.data_parallel_sharding_strategy == "optim_grads_params"
+            assert self.data_parallel_sharding_strategy == "optim_grads_params"
 
             # Release parameters for this module after backward.
             release_module_parameters(module, bwd=True)
@@ -656,9 +665,7 @@ def _process_post_backward_gradients(param_list):
             for param in param_list:
                 _grad_acc(param)
 
-            # Only reduce if gradients are sharded, or on the final microbatch, or when
-            # model_auto_sync is enabled.
-            grad_reduce_every_bprop = self.ddp_config.data_parallel_sharding_strategy in [
+            grad_reduce_every_bprop = self.data_parallel_sharding_strategy in [
                 "optim_grads",
                 "optim_grads_params",
             ]
@@ -672,6 +679,7 @@ def _process_post_backward_gradients(param_list):
                     param_list,
                     suggested_queue_capacity=self.suggested_RS_queue_capacity,
                     outer_fsdp_group_grad_reduce=(
+                        # HSDP all-reduce or HFSDP reduce-scatter on the DP-Outer PG.
                         self.dist_index.use_hybrid_fsdp
                         and (is_last_microbatch or self.model_auto_sync)
                     ),
@@ -775,14 +783,14 @@ def _root_post_backward(*unused):
                 _grad_acc(param)
 
             # Reduce the remaining gradients.
-            grad_reduce_every_bprop = self.ddp_config.data_parallel_sharding_strategy in [
+            grad_reduce_every_bprop = self.data_parallel_sharding_strategy in [
                 "optim_grads",
                 "optim_grads_params",
             ]
             # Only reduce if we are sharding gradients, or are on the final microbatch.
             # If is_last_microbatch is not specified, then we should reduce gradients
             # if model_auto_sync is enabled, otherwise wait until is_last_microbatch
-            # is actually specified by the user, context manager, or FW before reduction.
+            # is specified by the user, context manager, or FW before reduction.
             is_last_microbatch = getattr(self, "is_last_microbatch", False)
             if grad_reduce_every_bprop or is_last_microbatch or self.model_auto_sync:
                 self.grad_reduce_pipeline.reduce_gradients(
@@ -846,7 +854,7 @@ def _root_pre_backward(module: nn.Module, *unused):
                 return
             self._root_pre_backward_hook_issued = True
 
-            if self.ddp_config.data_parallel_sharding_strategy == "optim_grads_params":
+            if self.data_parallel_sharding_strategy == "optim_grads_params":
                 for module in root_module.modules():
                     if isinstance(module, tuple(fsdp_unit_modules)):
                         # Set PRE_BACKWARD state to skip resharding and forward pre-fetching
@@ -940,7 +948,7 @@ def _register_pre_forward_param_unshard_hook(module):
             If we are not sharding anything, we do not have a model weight buffer and thus
             have nothing to all-gather / un-shard.
             """
-            if self.ddp_config.data_parallel_sharding_strategy != "no_shard":
+            if self.data_parallel_sharding_strategy != "no_shard":
                 self.forward_pre_hooks[f"{module._get_name()} parameter unshard"] = (
                     module.register_forward_pre_hook(
                         _pre_forward_param_unshard, prepend=True, with_kwargs=True
@@ -983,7 +991,7 @@ def _register_pre_backward_param_unshard_hook(module):
                     _register_pre_backward_param_unshard_hook(module)
             elif (
                 not self.ddp_config.keep_fp8_transpose_cache
-                and self.ddp_config.data_parallel_sharding_strategy == "optim_grads_params"
+                and self.data_parallel_sharding_strategy == "optim_grads_params"
             ):
                 # Register the forward post-hook to release FP8 transpose cache
                 # after the forward pass for non-FSDP unit modules.
@@ -1097,6 +1105,23 @@ def set_model_auto_sync(self, sync_model: bool = True):
                 MegatronFSDP.model_auto_sync will be set to the value of sync_model.
                 Defaults to True. MegatronFSDP.model_auto_sync defaults to False.
         """
+        if sync_model and (
+            # Will reduce gradient buffer data in-place across DP-Shard.
+            self.data_parallel_sharding_strategy in ["no_shard", "optim"]
+            # Will reduce gradient buffer data in-place across DP-Outer.
+            or self.dist_index.use_hybrid_fsdp
+        ):
+            log_single_rank(
+                logger_=logger,
+                level=logging.WARNING,
+                msg="[Megatron-FSDP.set_model_auto_sync()] Detected Megatron-FSDP "
+                "auto-synchronization with the 'no_shard' / 'optim' sharding "
+                "strategy for either FSDP (DP-Shard) or HSDP (DP-Outer). "
+                "`MegatronFSDP.zero_grad_buffer()` or `optimizer.zero_grad()` "
+                "must be invoked every forward-backward optimization cycle to "
+                "prevent successive in-place gradient buffer reductions from "
+                "corrupting previously accumulated gradients.",
+            )
         self.model_auto_sync = sync_model
 
     def get_distributed_index(self) -> FSDPDistributedIndex:
@@ -1106,6 +1131,39 @@ def get_distributed_index(self) -> FSDPDistributedIndex:
         """
         return self.dist_index
 
+    @contextmanager
+    def mixed_precision_context(self, mixed_precision_policy: MixedPrecisionPolicy):
+        """
+        Context manager for re-configuring the MixedPrecisionPolicy
+        for MegatronFSDP / ParamAndGradBuffer.
+        """
+        mp_policy_backup = self.mp_policy
+        self.reset_mixed_precision_policy(mixed_precision_policy)
+        try:
+            yield
+        finally:
+            self.reset_mixed_precision_policy(mp_policy_backup)
+
+    def reset_mixed_precision_policy(self, mixed_precision_policy: MixedPrecisionPolicy):
+        """
+        Re-configure MixedPrecisionPolicy for MegatronFSDP / ParamAndGradBuffer.
+        """
+        mp_policy_reset = MixedPrecisionPolicy(
+            # Preserve the original main parameter + gradient data-type.
+            main_params_dtype=self.mp_policy.main_params_dtype,
+            main_grads_dtype=self.mp_policy.main_grads_dtype,
+            # Gradient communication data-type can only be reset
+            # if symmetric buffers / NCCL UB are not used, because
+            # inflates FixedPoolAllocator memory & breaks NCCL UBR.
+            grad_comm_dtype=(
+                mixed_precision_policy.grad_comm_dtype
+                if self.ddp_config.nccl_ub or self.ddp_config.fsdp_double_buffer
+                else self.mp_policy.grad_comm_dtype
+            ),
+        )
+        self.mp_policy = mp_policy_reset
+        self.param_and_grad_buffer.mp_policy = mp_policy_reset
+
     def start_param_sync(self, *unused, force_sync: bool = False, force_dispatch: bool = False):
         """
         Initiates param sync (all-gather) communication operations for all model parameters.
diff --git a/megatron/core/distributed/fsdp/src/megatron_fsdp/mixed_precision.py b/megatron/core/distributed/fsdp/src/megatron_fsdp/mixed_precision.py
index d2797d98079..26b78923590 100644
--- a/megatron/core/distributed/fsdp/src/megatron_fsdp/mixed_precision.py
+++ b/megatron/core/distributed/fsdp/src/megatron_fsdp/mixed_precision.py
@@ -14,6 +14,7 @@
 
 import logging
 from contextlib import nullcontext
+from dataclasses import dataclass
 from importlib.metadata import version
 from typing import List, Optional, Tuple
 
@@ -358,3 +359,33 @@ def get_quantized_model_init_context_cls():
             f"Verify TransformerEngine is installed (TE_INSTALLED={HAVE_TE})."
         )
     return QUANTIZED_MODEL_INIT_CLASS
+
+
+@dataclass(frozen=True)
+class MixedPrecisionPolicy:
+    """Megatron-FSDP Mixed Precision Dataclass"""
+
+    main_params_dtype: Optional[torch.dtype] = torch.float32
+    """Data type for the main weight buffer utilized for distributed optimization
+      and quantization with Megatron-FSDP. If set to None, the model compute weight
+      buffer will take the role of the main weights, or when no sharding is applied,
+      the native model weights become the main weights. Defaults to torch.float32.
+    """
+
+    main_grads_dtype: Optional[torch.dtype] = torch.float32
+    """Data type for the main gradient buffer utilized for distributed optimization with
+      Megatron-FSDP. If set to None, main gradients will match the dtype of the model
+      compute parameters specified by the user model. Defaults to torch.float32.
+    """
+
+    grad_comm_dtype: Optional[torch.dtype] = torch.float32
+    """Data type for gradient gather / scatter communications. Can be utilized to reduce
+      communication latency, but adds overhead for type-casting and copy operations.
+      If using NCCL UBR v2.27+, gradient reduction may be performed in high-precision
+      depending on the network domain (NVLink or IB), and can enable mixed-precision
+      communication and accumulation, e.g. setting grad_comm_dtype to BF16 can support
+      FP32 reduction even though we have BF16 input and output communication buffers.
+      If set to None, the main_grads_dtype is used. Defaults to torch.float32. If using
+      `no_shard`, `optim`, or a `FixedPoolAllocator` (`fsdp_double_buffer`), allocating
+      `dtype`-custom gradient communication buffers (per FSDP group) adds memory overhead.
+    """
diff --git a/megatron/core/distributed/fsdp/src/megatron_fsdp/param_and_grad_buffer.py b/megatron/core/distributed/fsdp/src/megatron_fsdp/param_and_grad_buffer.py
index d1b3e6b4a99..ca593b4c1fe 100644
--- a/megatron/core/distributed/fsdp/src/megatron_fsdp/param_and_grad_buffer.py
+++ b/megatron/core/distributed/fsdp/src/megatron_fsdp/param_and_grad_buffer.py
@@ -33,6 +33,7 @@
 from torch.distributed.tensor import DTensor, Replicate, Shard
 
 from .mixed_precision import (
+    MixedPrecisionPolicy,
     fp8_discard_transpose_cache,
     fp8_get_raw_data,
     fp8_need_transpose_data,
@@ -52,6 +53,7 @@
     get_mcore_tensor_parallel_partition_dim,
     is_mcore_tensor_model_parallel,
     is_mcore_tensor_parallel_duplicated,
+    log_single_rank,
 )
 
 logger = logging.getLogger(__name__)
@@ -63,7 +65,7 @@
         DistributedDataParallelConfig,
     )
     from megatron.core.tensor_parallel import get_cuda_rng_tracker
-    from megatron.core.utils import is_submodule, log_single_rank
+    from megatron.core.utils import is_submodule
 
     HAVE_MCORE = True
     logger.info("Detected Megatron Core, using Megatron-FSDP with Megatron.")
@@ -74,17 +76,6 @@
     from .utils import get_cuda_rng_tracker, is_submodule
 
     HAVE_MCORE = False
-
-    def log_single_rank(
-        logger_: logging.Logger, level: int, msg: str, *args, rank: int = 0, **kwargs
-    ):
-        """Fallback log_single_rank when Megatron Core is not available."""
-        if torch.distributed.is_initialized():
-            if torch.distributed.get_rank() == rank:
-                logger_.log(level, msg, *args, **kwargs)
-        else:
-            logger_.log(level, msg, *args, **kwargs)
-
     logger.info("Megatron Core is not installed, Megatron-FSDP will run without Megatron Core.")
 
 try:
@@ -952,12 +943,14 @@ def fetch_bucket(
         self, dtype: Optional[torch.dtype] = None, set_param_data: bool = False
     ) -> Bucket:
         """
-        Fetch a communication buffer for data-parallel operations.
+        Fetch a communication buffer for data-parallel operations. If the buffer
+        is not distributed and doesn't have a custom data-type, the local un-sharded
+        buffer data will be returned. Otherwise, an un-sharded bucket is allocated.
 
         The size of the bucket is defined by the `DataParallelBuffer` instance.
 
         Args:
-            dtype (Optional[torch.dtype], optional): The data type of the tensor
+            dtype (Optional[torch.dtype]): The data type of the tensor
                 to fetch a buffer for. Defaults to None.
 
         Returns:
@@ -965,9 +958,10 @@ def fetch_bucket(
         """
         if dtype is None:
             dtype = self.dtype
-        bucket_index = self.bucket_index
 
         if not self.is_data_distributed and dtype == self.dtype:
+            # Use pre-allocated un-sharded bucket data as the communication buffer.
+            bucket_index = self.bucket_index
             bucket = Bucket(
                 data=self.data[
                     bucket_index.global_data_index : bucket_index.global_data_index
@@ -975,17 +969,8 @@ def fetch_bucket(
                 ]
             )
         else:
-            # Bucket (unsharded) needs to be retrieved. If the temporary bucket cache
-            # does not have the bucket corresponding to the bucket_id, it will allocate
-            # a new Bucket with an empty tensor. Otherwise, it will simply return the
-            # pre-allocated bucket with pre-existing data.
-            bucket = self.temporary_bucket_allocator.allocate(
-                bucket_id=bucket_index.bucket_id,
-                size=bucket_index.size,
-                dtype=dtype,
-                device=self.device,
-                mem_alloc_context=self.mem_alloc_context,
-            )
+            # Sharded or dtype-custom buffers require un-sharded bucket allocation.
+            bucket = self.allocate_bucket_storage(dtype=dtype, device=self.device)
 
         # Need to set parameter data after resize model weight buffer data-storage.
         if set_param_data:
@@ -999,19 +984,72 @@ def fetch_bucket(
                     p.data = data
         return bucket
 
-    def free_bucket_storage(self):
+    def allocate_bucket_storage(
+        self,
+        shard: bool = False,
+        dtype: Optional[torch.dtype] = None,
+        device: Optional[torch.device] = None,
+        init_values: Optional[torch.Tensor] = None,
+    ) -> Bucket:
         """
-        Release the storage of a temporary communication bucket.
-        If the bucket is temporary, this method frees its storage.
+        Allocate a temporary flat communication buffer using the cached
+        allocator associated with this DataParallelBuffer. If no bucket
+        corresponding to the Bucket ID of this DataParallelBuffer has
+        been allocated yet, an empty Tensor will be allocated.
+
+        Can allocate sharded or un-sharded buckets.
+
+        Optionally, if init_values is provided, the flattened values of
+        the init_values will be copied into the newly-allocated storage.
+
+        Args:
+            shard (bool):
+                Whether to allocate a sharded or un-sharded bucket with
+                sizes defined by this DataParallelBuffer.
+            dtype (Optional[torch.dtype]):
+                Data-type of the allocated bucket.
+            device (Optional[torch.device]):
+                Device of the allocated bucket.
+            init_values (Optional[torch.Tensor]):
+                If provided, the allocated storage will be initialized
+                to the values of this (flattened) Tensor.
+
+        Returns:
+            Bucket: The communication buffer for the specified data type.
         """
-        if not self.is_data_distributed:
-            # Only free the allocated bucket if the buffer is sharded.
-            # Otherwise, the buffer contains the entire bucket.
-            return
+        # Default to buffer data-type.
+        if dtype is None:
+            dtype = self.dtype
+        if device is None:
+            device = self.device
+        # Allocate temporary storage using standardized sizes.
+        alloc_size = self.shard_bucket_index.size if shard else self.bucket_index.size
+        bucket = self.temporary_bucket_allocator.allocate(
+            bucket_id=self.bucket_index.bucket_id,
+            size=alloc_size,
+            dtype=dtype,
+            device=device,
+            mem_alloc_context=self.mem_alloc_context,
+        )
+        # Copy Tensor values into Bucket data.
+        if init_values is not None:
+            assert bucket.data.shape == init_values.flatten().shape, (
+                "[allocate_bucket_storage] Size mismatch between allocated bucket "
+                f"({bucket.data.shape}) and init_values ({init_values.shape})!"
+            )
+            bucket.data.detach().copy_(init_values.flatten())
+        return bucket
 
-        # Free the memory backing the temporarily-allocated bucket associated with this buffer.
+    def free_bucket_storage(self):
+        """
+        Release the storage of a temporarily-allocated communication bucket.
+        """
         self.temporary_bucket_allocator.free(self.bucket_index.bucket_id)
 
+    def reset_param_main_grad(self):
+        """
+        Dereference param.main_grad for grad buckets managed by this buffer.
+        """
         # Reset the main grad tensor to None to release the memory.
         for param in self.params:
             if hasattr(param, "main_grad"):
@@ -1239,7 +1277,7 @@ def get_shard_from_bucket(self, bucket: Bucket):
 
     def get_shard_from_local_buffer(self) -> torch.Tensor:
         """
-        Get the shard or virtual shard of the bucket stored in this buffer.
+        Get the shard or virtual shard of the data persistently stored in this buffer.
         """
         index = self.shard_bucket_index
         # If the buffer is sharded, return the shard stored in this buffer.
@@ -1282,10 +1320,15 @@ class ParameterGroup:
             Buffer used to store main gradients for data-parallel operations.
         hsdp_wbuf (Optional[DataParallelBuffer]):
             Buffer for weights used in Hybrid Sharded Data Parallel (HSDP).
-            Exists only if full sharding is enabled in HSDP.
+            Exists only if full sharding (HFSDP) is enabled in HSDP.
         hsdp_gbuf (Optional[DataParallelBuffer]):
             Buffer for gradients used in HSDP.
-            Exists only if full sharding is enabled in HSDP.
+            Exists only if full sharding (HFSDP) is enabled in HSDP.
+        hsdp_comm_gbuf (Optional[DataParallelBuffer]):
+            Extra buffer to allocate buffers that enable custom gradient
+            communication data-types when using HSDP or HFSDP only.
+            Only allocates memory when `MixedPrecisionPolicy.grad_comm_dtype`
+            is set to a non-`None` `torch.dtype`. Contains no local data.
     """
 
     params: List[torch.nn.Parameter]
@@ -1300,6 +1343,7 @@ class ParameterGroup:
     main_grad_buffer: Optional[DataParallelBuffer] = None
     hsdp_wbuf: Optional[DataParallelBuffer] = None
     hsdp_gbuf: Optional[DataParallelBuffer] = None
+    hsdp_comm_gbuf: Optional[DataParallelBuffer] = None
 
 
 def _get_parameter_groups(
@@ -1565,11 +1609,10 @@ class ParamAndGradBuffer:
         module (torch.nn.Module): The module whose parameters are to be grouped
             and flatten.
         bucketing_policy (BucketingPolicy): The bucketing policy.
-        data_parallel_group (torch.distributed.ProcessGroup): The data parallel group.
-        expert_data_parallel_group (Optional[torch.distributed.ProcessGroup]):
-            The expert data parallel group.
-        preserve_fp32_weights (bool): Whether to preserve FP32 weights.
-        grad_reduce_in_fp32 (bool): Whether to reduce gradients in FP32.
+        dist_index (FSDPDistributedIndex): FSDPDistributedIndex object containing references
+            to the process groups and device meshes used by Megatron-FSDP.
+        mixed_precision_policy (megatron_fsdp.MixedPrecisionPolicy): Configuration for
+            mixed-precision customization of compute and communications in Megatron-FSDP.
         gradient_scaling_factor (Optional[float]): The gradient scaling factor.
         expert_gradient_scaling_factor (Optional[float]): The expert gradient
             scaling factor.
@@ -1585,8 +1628,7 @@ def __init__(
         module: torch.nn.Module,
         bucketing_policy: BucketingPolicy,
         dist_index: FSDPDistributedIndex,
-        preserve_fp32_weights: bool = True,
-        grad_reduce_in_fp32: bool = True,
+        mixed_precision_policy: MixedPrecisionPolicy = MixedPrecisionPolicy(),
         gradient_scaling_factor: Optional[float] = None,
         expert_gradient_scaling_factor: Optional[float] = None,
         device: torch.device = torch.device("cuda"),
@@ -1603,8 +1645,7 @@ def __init__(
         self.module = module
         self.bucketing_policy = bucketing_policy
         self.param_to_name = {p: name for name, p in self.module.named_parameters()}
-        self.preserve_fp32_weights = preserve_fp32_weights
-        self.grad_reduce_in_fp32 = grad_reduce_in_fp32
+        self.mp_policy = mixed_precision_policy
         self.dist_index = dist_index
         self.params = list(module.parameters())
         self.gradient_scaling_factor = gradient_scaling_factor
@@ -1900,6 +1941,26 @@ def _init_each_parameter_group_buffers(self, meta_device_init_fp8_params):
             raise ValueError(
                 f"Invalid data_parallel_sharding_strategy: {data_parallel_sharding_strategy}"
             )
+
+        # Only create HSDP buffers if sharding on DP-Outer. Otherwise, no need to all-gather
+        # parameters on DP-Outer, but still need to all-reduce gradients on DP-Outer.
+        should_create_hfsdp_wbuf_and_gbuf = (
+            self.dist_index.use_hybrid_fsdp
+            and self.ddp_config.outer_dp_sharding_strategy != "no_shard"
+        )
+        # DP-Outer sharding is only supported for fully-sharded DP-Shard.
+        # NOTE(@cspades): Important guard for HFSDP functionality!
+        if (
+            should_create_hfsdp_wbuf_and_gbuf
+            and self.ddp_config.data_parallel_sharding_strategy != "optim_grads_params"
+        ):
+            raise NotImplementedError(
+                "[Megatron-FSDP] Optimizer fully-sharded HFSDP is only supported "
+                "with full-sharding on DP-Shard.\nMegatron-FSDP DP-Shard Strategy: "
+                f"{self.ddp_config.data_parallel_sharding_strategy}\nMegatron-FSDP "
+                f"DP-Outer Strategy: {self.ddp_config.outer_dp_sharding_strategy}"
+            )
+
         if self.ddp_config.nccl_ub:
             assert self.ddp_config.fsdp_double_buffer, (
                 "NCCL UB is only supported with FSDP double buffer. "
@@ -1922,35 +1983,47 @@ def _init_each_parameter_group_buffers(self, meta_device_init_fp8_params):
                 name="fsdp_grads",
                 fsdp_param_groups=self.parameter_groups,
                 size=UB_BUFFER_NUM,
-                fallback_to_persistent_buffer=self.ddp_config.fsdp_db_use_persist_buf_on_alloc_fail,
+                fallback_to_persistent_buffer=(
+                    self.ddp_config.fsdp_db_use_persist_buf_on_alloc_fail
+                ),
             )
+            if self.dist_index.use_hybrid_fsdp:
+                # Only required for custom communication dtype buffer allocation
+                # to leverage NCCL UBR for high-precision gradient reduction with
+                # low-precision gradient communication over DP-Outer for H(F)SDP.
+                # Otherwise, this allocator will never be used.
+                self.hsdp_grad_comm_alloc = FixedPoolAllocator(
+                    name="hsdp_grad_comm",
+                    fsdp_param_groups=self.parameter_groups,
+                    size=UB_BUFFER_NUM,
+                    fallback_to_persistent_buffer=(
+                        self.ddp_config.fsdp_db_use_persist_buf_on_alloc_fail
+                    ),
+                )
             self.double_buf_units = self.weight_alloc.fsdp_double_buffer_units
         else:
             self.weight_alloc = StorageResizeBasedBucketAllocator()
             self.transpose_weight_alloc = StorageResizeBasedBucketAllocator()
             self.main_grad_alloc = None
+            if self.dist_index.use_hybrid_fsdp:
+                # Only required for custom communication dtype buffer allocation
+                # for low-precision gradient communication over DP-Outer for H(F)SDP.
+                # Otherwise, this allocator will never be used.
+                self.hsdp_grad_comm_alloc = None
             self.double_buf_units = []
 
         self.buffer_all_in_one = True
-
-        preserve_fp32_weights = self.preserve_fp32_weights
-        grad_reduce_in_fp32 = self.grad_reduce_in_fp32
         buffer_size = {torch.float32: 0, torch.float16: 0, torch.bfloat16: 0, "float8": 0}
 
-        # Only create HSDP buffers if sharding on DP-Outer. Otherwise, no need to all-gather
-        # parameters on DP-Outer, but still need to all-reduce gradients on DP-Outer.
-        should_create_hfsdp_wbuf_and_gbuf = (
-            self.dist_index.use_hybrid_fsdp
-            and self.ddp_config.outer_dp_sharding_strategy != "no_shard"
-        )
-
         # For all bucket groups (partitioned parameter groups)...
         for group_id, group in enumerate(self.parameter_groups):
             main_buf_extra_kwargs = {}
             if should_create_hfsdp_wbuf_and_gbuf:
+                # DP-Outer + DP-Shard
                 main_buf_dp_group = self.dist_index.get_dp_group(
                     is_expert_parallel=group.is_expert_param
                 )
+                # DP-Shard
                 hsdp_buf_dp_group = self.dist_index.get_fsdp_group(
                     is_expert_parallel=group.is_expert_param
                 )
@@ -1958,6 +2031,7 @@ def _init_each_parameter_group_buffers(self, meta_device_init_fp8_params):
                     is_expert_parallel=group.is_expert_param
                 )
             else:
+                # DP-Shard only, since we're not sharding on DP-Outer.
                 main_buf_dp_group = self.dist_index.get_fsdp_group(
                     is_expert_parallel=group.is_expert_param
                 )
@@ -1985,12 +2059,17 @@ def _init_each_parameter_group_buffers(self, meta_device_init_fp8_params):
                 is_float8tensor(one_param)
                 or meta_device_init_fp8_params.get(self.param_to_name[one_param], (False, False))[0]
             )
+
+            # Designate buffer data-types for compute parameters and main gradients.
             if is_dtype_float8:
                 param_dtype = torch.uint8
-                grad_dtype = torch.bfloat16
+                main_grads_dtype = torch.bfloat16
             else:
                 param_dtype = group.params[0].dtype
-                grad_dtype = param_dtype
+                main_grads_dtype = param_dtype
+            # Use a custom main gradient data-type.
+            if self.mp_policy.main_grads_dtype is not None:
+                main_grads_dtype = self.mp_policy.main_grads_dtype
 
             # Check if the parameter group needs a transpose buffer for model weights.
             # Currently, only mxfp8 needs it.
@@ -2017,6 +2096,9 @@ def _init_each_parameter_group_buffers(self, meta_device_init_fp8_params):
                     and model_wbuf_dp_group.size() > 1,
                     dtype=param_dtype,
                     device=self.device,
+                    # Note: This will be DP-Outer + DP-Shard when sharding
+                    # the optimizer state in HFSDP, else just DP-Shard when
+                    # using basic HSDP or FSDP.
                     data_parallel_group=model_wbuf_dp_group,
                     is_transpose_buffer=False,
                     temporary_bucket_allocator=self.weight_alloc,
@@ -2042,14 +2124,18 @@ def _init_each_parameter_group_buffers(self, meta_device_init_fp8_params):
                         **main_buf_extra_kwargs,
                     )
 
-            # Initialize the main weight buffer.
-            if should_create_grad_buffer_or_main_weight_buffer and preserve_fp32_weights:
+            # Initialize the main weight buffer if a main weight data-type is specified.
+            # Otherwise, don't create this buffer, and use the model compute weight buffer instead.
+            if (
+                should_create_grad_buffer_or_main_weight_buffer
+                and self.mp_policy.main_params_dtype is not None
+            ):
                 group.main_weight_buffer = DataParallelBuffer(
                     self.ddp_config,
                     group.params,
                     is_data_distributed=is_main_weight_buffer_distributed
                     and main_buf_dp_group.size() > 1,
-                    dtype=torch.float32,
+                    dtype=self.mp_policy.main_params_dtype,
                     device=self.device,
                     data_parallel_group=main_buf_dp_group,
                     bucket_id=group_id,
@@ -2061,16 +2147,19 @@ def _init_each_parameter_group_buffers(self, meta_device_init_fp8_params):
             # Initialize the main grad buffer.
             if should_create_grad_buffer_or_main_weight_buffer:
                 assert (
-                    grad_reduce_in_fp32 or grad_dtype is not torch.uint8
-                ), "Gradients can not be reduced in FP8. "
+                    main_grads_dtype.is_floating_point
+                ), f"Main gradient dtype ({main_grads_dtype}) must be Float."
                 group.main_grad_buffer = DataParallelBuffer(
                     self.ddp_config,
                     # Proxy because the number of gradient parameters is the same
                     # as the number of model parameters.
                     group.params,
                     is_data_distributed=is_grad_buffer_distributed and main_buf_dp_group.size() > 1,
-                    dtype=torch.float32 if grad_reduce_in_fp32 else grad_dtype,
+                    dtype=main_grads_dtype,
                     device=self.device,
+                    # Note: This will be DP-Outer + DP-Shard when sharding
+                    # the optimizer state in HFSDP, else just DP-Shard when
+                    # using basic HSDP or FSDP.
                     data_parallel_group=main_buf_dp_group,
                     is_transpose_buffer=False,
                     temporary_bucket_allocator=self.main_grad_alloc,
@@ -2113,7 +2202,13 @@ def _init_each_parameter_group_buffers(self, meta_device_init_fp8_params):
                 )
 
                 if group.transpose_weight_buffer is not None:
-                    raise NotImplementedError("HSDP for transpose buffer is not implemented yet")
+                    # TODO(@kunlunl, @cspades): Create a hybrid-sharded transpose buffer
+                    # to map fully-sharded transpose weights to partially-sharded transpose
+                    # weights before and after fully-distributed optimization.
+                    raise NotImplementedError(
+                        "HFSDP (HSDP + fully-sharded optimizer state) doesn't "
+                        "support FP8 recipes that require a transpose buffer."
+                    )
 
                 if should_create_grad_buffer_or_main_weight_buffer:
                     # Initialize the HSDP grad buffer.
@@ -2145,6 +2240,44 @@ def _init_each_parameter_group_buffers(self, meta_device_init_fp8_params):
                     buffer_size[group.main_grad_buffer.dtype] -= group.main_grad_buffer.data_size
                     buffer_size[group.main_grad_buffer.dtype] += group.hsdp_gbuf.data_size
 
+            # Only create an extra grad comm buffer for HSDP.
+            if should_create_grad_buffer_or_main_weight_buffer and self.dist_index.use_hybrid_fsdp:
+                # Create a (DP-Shard)-sized gradient communication buffer that manages
+                # allocation for custom gradient communication data-types during runtime.
+                # If gradient communication data-type customization is not used, the
+                # allocator associated with this buffer will never allocate memory.
+                # This buffer will never initialize local data, i.e. self.data = None.
+                gbuf = group.main_grad_buffer
+                fsdp_group = self.dist_index.get_fsdp_group(
+                    is_expert_parallel=group.is_expert_param
+                )
+                hfsdp_kwargs = {}
+                if should_create_hfsdp_wbuf_and_gbuf:
+                    hfsdp_kwargs["item_index_map"] = gbuf.item_index_map
+                    hfsdp_kwargs["bucket_index"] = gbuf.bucket_index
+                    hfsdp_kwargs["shard_bucket_index"] = _get_dp_buffer_shard_bucket_index(
+                        gbuf.bucket_index,
+                        is_data_distributed=is_grad_buffer_distributed and fsdp_group.size() > 1,
+                        data_parallel_world_size=fsdp_group.size(),
+                        data_parallel_rank=fsdp_group.rank(),
+                    )
+                group.hsdp_comm_gbuf = DataParallelBuffer(
+                    self.ddp_config,
+                    group.params,
+                    is_data_distributed=is_grad_buffer_distributed and fsdp_group.size() > 1,
+                    # Set allocation to grad_comm_dtype, or default to param(.grad).dtype.
+                    dtype=self.mp_policy.grad_comm_dtype,
+                    device=gbuf.device,
+                    data_parallel_group=fsdp_group,
+                    is_transpose_buffer=False,
+                    # Use the HSDP gradient communication allocator!
+                    temporary_bucket_allocator=self.hsdp_grad_comm_alloc,
+                    bucket_id=group_id,
+                    chunk_size_factor=group.chunk_size_factor,
+                    mem_alloc_context=self.mem_alloc_context,
+                    **hfsdp_kwargs,
+                )
+
         reset_context_args = {"init_param_with_fp8": self.ddp_config.fp8_param_gather}
         module_reset_flag = {}
         if self.reset_parameters_for_meta_device_init_module:
@@ -2197,6 +2330,7 @@ def _init_each_parameter_group_buffers(self, meta_device_init_fp8_params):
                         )
                         outer_fsdp_group = self.dist_index.get_outer_fsdp_group()
                         wbuf_data = hsdp_wbuf.data[
+                            # Requires FSDP sharding for (DP-Shard, DP-Outer) to cover DP-Shard.
                             wbuf.data_size
                             * outer_fsdp_group.rank() : wbuf.data_size
                             * (outer_fsdp_group.rank() + 1)
@@ -2214,9 +2348,11 @@ def _init_each_parameter_group_buffers(self, meta_device_init_fp8_params):
                 with self.mem_alloc_context():
                     if group.hsdp_wbuf:
                         raise NotImplementedError(
-                            "HSDP for transpose buffer is not implemented yet"
+                            "HFSDP (HSDP + fully-sharded optimizer state) doesn't "
+                            "support FP8 recipes that require a transpose buffer."
                         )
                     else:
+                        # Initialize the transpose buffer.
                         tbuf.init_data(
                             torch.empty(tbuf.data_size, dtype=tbuf.dtype, device=self.device)
                         )
@@ -2299,18 +2435,24 @@ def _init_each_parameter_group_buffers(self, meta_device_init_fp8_params):
                         new_transpose_data = None
 
                     if is_float8tensor(p_local):
+                        # Attach FP8 row-wise data in the FP8 parameter
+                        # to slice of the model compute weight bucket.
                         old_param_data = fp8_get_raw_data(p_local)
                         assert old_param_data._base is None
                         new_param_data.detach().copy_(old_param_data)
                         fp8_set_raw_data(p_local, new_param_data)
                         del old_param_data
                         if new_transpose_data is not None:
+                            # Attach FP8 col-wise data in the FP8 parameter
+                            # to slice of the FP8 transpose bucket.
                             old_transpose_data = fp8_get_raw_data(p_local, True)
                             assert old_transpose_data._base is None
                             new_transpose_data.detach().copy_(old_transpose_data)
                             fp8_set_raw_data(p_local, new_transpose_data, True)
                             del old_transpose_data
                     elif isinstance(p, DTensor):
+                        # Same as Tensor case, except for DTensor parameters
+                        # in the original model. Tensor = DTensor.to_local().
                         old_param_data = p._local_tensor.data
                         p._local_tensor.data = new_param_data
                         assert old_param_data._base is None
@@ -2423,6 +2565,7 @@ def _alloc(dtype, size):
                     hsdp_gbuf.init_data(_alloc(hsdp_gbuf.dtype, hsdp_gbuf.data_size))
                     outer_fsdp_group = self.dist_index.get_outer_fsdp_group()
                     gbuf_data = hsdp_gbuf.data[
+                        # Requires FSDP sharding for (DP-Shard, DP-Outer) to cover DP-Shard.
                         gbuf.data_size
                         * outer_fsdp_group.rank() : gbuf.data_size
                         * (outer_fsdp_group.rank() + 1)
@@ -2441,7 +2584,15 @@ def _alloc(dtype, size):
 
                 def main_grad_getter(p):
                     # Make sure main_grad memory is allocated when initially accessed.
-                    bucket = p._gbuf.fetch_bucket()
+                    # When gradients are sharded, we can pre-allocate a communication
+                    # bucket to avoid casting to a communication data-type. Otherwise,
+                    # return the item backed by the main gradient buffer required to
+                    # support un-sharded gradient accumulation at high precision.
+                    bucket = p._gbuf.fetch_bucket(
+                        dtype=(
+                            self.mp_policy.grad_comm_dtype if p._gbuf.is_data_distributed else None
+                        )
+                    )
                     gbuf = p._gbuf
                     item_id = p._item_id
                     # View it as p.shape so you can insert the param.grad into
@@ -2453,6 +2604,8 @@ def main_grad_getter(p):
                 # Patch the parameter class to include a main_grad property.
                 # Utilized in the gradient reduction pipeline to save computed
                 # data-parallel gradients on every rank and reduce-scatter them.
+                # Enables TransformerEngine's fuse_wgrad_accumulation=True feature
+                # which dumps gradients into param.main_grad with zero-copy.
                 p.get_main_grad = main_grad_getter.__get__(p)
 
         # Clean up deallocated memory.
@@ -2534,8 +2687,8 @@ def _init_distributed_params(self):
         Register model training and high-precision parameters as optimizer
         named parameters and DTensor(s). Specifically, we utilize the highest
         precision weights available for optimization using fall-back logic
-        on mbuf -> wbuf -> orig_param depending on if preserve_fp32_weights
-        or "no_shard" is utilized.
+        on mbuf -> wbuf -> orig_param depending on if main_params_dtype is
+        specified or "no_shard" is utilized.
         """
         dist_main_weight = {}
         for pg in self.parameter_groups:
@@ -2627,6 +2780,7 @@ def set_param_attribute():
                             "partition_dim",
                             "partition_stride",
                             "is_embedding_or_output_parameter",
+                            "is_embedding_parameter",
                             "_mcore_tp",
                             "_tp_duplicated",
                             "_tp_partition_dim",
@@ -2965,6 +3119,8 @@ def reduce_scatter_gradients(self, async_op: bool = True):
             if gbuf is None:
                 continue
             scaling_factor = gbuf.gradient_scaling_factor
+            if self.ddp_config.check_for_nan_in_grad:
+                _check_nan_in_grad(gbuf.data)
             reduce_op = gradient_reduce_preprocessing(gbuf.data, scaling_factor, self.ddp_config)
             reduce_scatter_handler = torch.distributed.reduce_scatter_tensor(
                 output=gbuf.get_shard_from_local_buffer(),
@@ -3003,6 +3159,8 @@ def all_reduce_gradients(self, async_op: bool = False):
             if gbuf is not None:
                 continue
             scaling_factor = gbuf.gradient_scaling_factor
+            if self.ddp_config.check_for_nan_in_grad:
+                _check_nan_in_grad(gbuf.data)
             reduce_op = gradient_reduce_preprocessing(gbuf.data, scaling_factor, self.ddp_config)
             all_reduce_handler = torch.distributed.all_reduce(
                 gbuf.data, op=reduce_op, group=gbuf.data_parallel_group, async_op=async_op
@@ -3086,6 +3244,7 @@ def reset(self):
         for bucket_id, _ in self.bucket_status.items():
             gbuf = self.get_fsdp_buffer(bucket_id)
             gbuf.free_bucket_storage()
+            gbuf.reset_param_main_grad()
             self.bucket_status[bucket_id] = BucketStatus.EMPTY
 
     def reduce_gradients(
@@ -3239,6 +3398,7 @@ def _bucket_group_gradient_reduce(
         # released ensures that our double buffer will not explode due to too
         # many empty bucket requests.
         ddp_config = self.buffer.ddp_config
+        mp_policy = self.buffer.mp_policy
         if ddp_config.fsdp_double_buffer:
             self._enforce_double_buffer_limit(bucket_group)
 
@@ -3248,105 +3408,188 @@ def _bucket_group_gradient_reduce(
         )
         reduce_scatter_stream.wait_stream(current_stream)
 
+        # DP-Shard Gradient Reduction
         dp_group = self.get_fsdp_buffer(bucket_group[0]).data_parallel_group
         with torch.cuda.stream(reduce_scatter_stream):
             with _coalescing_manager(dp_group):
-                grad_buffer = []
-                reduced_grad = []
+                # List of gradient accumulation closure tasks.
+                # (grad_buffer, reduced_grad)
+                grad_accum_closure = []
                 for bucket_id in bucket_group:
-                    # Fetch pre-allocated main gradient bucket.
+
+                    # Get the DP-Shard gradient buffer associated with this bucket ID.
                     gbuf = self.get_fsdp_buffer(bucket_id)
-                    bucket = gbuf.fetch_bucket()
-                    # Scale gradients.
+
+                    # Get the unreduced gradients associated with the gradient buffer.
+                    unreduced_grad_bucket = gbuf.fetch_bucket(
+                        dtype=mp_policy.grad_comm_dtype if gbuf.is_data_distributed else None
+                    )
+                    # NOTE(@cspades): `no_shard` or `optim`
+                    # Un-sharded gradient buffers accumulate un-reduced gradients locally
+                    # without allocating an un-sharded buffer. For custom communication
+                    # data-type(s), an extra un-sharded buffer needs to be allocated!
+                    custom_grad_comm_dtype = (
+                        mp_policy.grad_comm_dtype is not None
+                        and unreduced_grad_bucket.data.dtype != mp_policy.grad_comm_dtype
+                    )
+                    if not gbuf.is_data_distributed and custom_grad_comm_dtype:
+                        # Create a custom communication buffer with gbuf.
+                        # Introduces copy and memory overhead.
+                        unreduced_grad_bucket = gbuf.allocate_bucket_storage(
+                            dtype=mp_policy.grad_comm_dtype,
+                            device=unreduced_grad_bucket.data.device,
+                            init_values=unreduced_grad_bucket.data,
+                        )
+                    unreduced_grad = unreduced_grad_bucket.data
+
+                    # Pre-scale unsharded bucket gradient and prepare the ReduceOp.
                     scaling_factor = gbuf.gradient_scaling_factor
                     reduce_op = gradient_reduce_preprocessing(
-                        bucket.data, scaling_factor, gbuf.ddp_config
+                        unreduced_grad, scaling_factor, ddp_config
                     )
+
+                    # Reduce-scatter or all-reduce the unsharded gradient.
                     if ddp_config.data_parallel_sharding_strategy == "no_shard":
-                        # All-reduce the gradients on every rank. No scattering
-                        # or sharding necessary.
+                        # All-reduce un-sharded gradients from every rank.
                         torch.distributed.all_reduce(
-                            bucket.data, op=reduce_op, group=gbuf.data_parallel_group
+                            unreduced_grad, op=reduce_op, group=gbuf.data_parallel_group
                         )
+                        if custom_grad_comm_dtype:
+                            # Reduction used a temporary communication buffer.
+                            grad_accum_closure.append(
+                                # Un-sharded buffer data.
+                                (gbuf.data, unreduced_grad)
+                            )
                     else:
-                        # Get the shard of the gradient from the pre-allocated bucket.
-                        # The reduced gradient will be scattered into this shard of the
-                        # bucket managed by the sharded buffer on this rank.
-                        grad_shard = gbuf.get_shard_from_bucket(bucket)
-                        # pylint: disable=C0301
-                        # The `grad_shard`` is part of `bucket.data`` and the following
-                        # new empty is important for memory safety, when using
-                        # TORCH_NCCL_AVOID_RECORD_STREAMS=1.
-                        # For reference: https://dev-discuss.pytorch.org/t/fsdp-cudacachingallocator-an-outsider-newb-perspective/1486
-                        if not self.buffer.ddp_config.fsdp_double_buffer:
-                            grad_shard = torch.empty_like(grad_shard)
-                        # Reduce-scatter gradients on the FSDP group.
+                        # Slice a gradient shard from the communication bucket.
+                        grad_shard = gbuf.get_shard_from_bucket(unreduced_grad_bucket)
+
+                        # Execute the reduce-scatter collective.
                         torch.distributed.reduce_scatter_tensor(
                             output=grad_shard,
-                            input=bucket.data,
+                            input=unreduced_grad,
                             op=reduce_op,
                             group=gbuf.data_parallel_group,
                         )
-                        reduced_grad.append(grad_shard)
-                        grad_buffer.append(gbuf.get_shard_from_local_buffer())
+
+                        # Track closure tasks to accumulate the reduced gradient shard.
+                        # NOTE: If the gradient buffer is unsharded and no communication
+                        # bucket is allocated, then the output bucket shard is backed by
+                        # the unsharded gradient buffer and the reduce-scatter result
+                        # has already been installed into the gradient buffer.
+                        if gbuf.is_data_distributed or custom_grad_comm_dtype:
+                            grad_accum_closure.append(
+                                # Target for sharded or un-sharded gradient buffers.
+                                (gbuf.get_shard_from_local_buffer(), grad_shard)
+                            )
+
+                    # Mark bucket ID as CUDA work-in-progress.
                     self.bucket_status[bucket_id] = BucketStatus.COMMUNICATING
-            for local_grad, reduced_grad in zip(grad_buffer, reduced_grad):
-                # Accumulate the reduced gradient shard into the local gradient buffer,
-                # when ZeRO-2 (gradient sharding) is enabled. Otherwise, bucket.data
-                # is equivalent to the buffer data and will have been all-reduced.
-                local_grad += reduced_grad
+
+            for local_grad, reduced_grad in grad_accum_closure:
+                if ddp_config.data_parallel_sharding_strategy in ["no_shard", "optim"]:
+                    # Copy the reduced gradient into the main gradient buffer.
+                    local_grad.copy_(reduced_grad)
+                else:
+                    # Accumulate the reduced gradient into the local gradient buffer.
+                    # Accumulation data-type is type-promoted with respect to the
+                    # accumulated gradient and the buffer main_grads_dtype.
+                    local_grad += reduced_grad
+
             # Record a checkpoint for the event to synchronize against the reduce-scatter stream.
             reduce_scatter_view_out_event = reduce_scatter_stream.record_event()
 
-        # Outer-DP group gradient reduction.
+        # DP-Outer Gradient Reduction
         if outer_fsdp_group_grad_reduce:
+            # Wait on the DP-Shard reduction before further reduction.
             self.outer_fsdp_group_grad_reduce_stream.wait_stream(reduce_scatter_stream)
             outer_fsdp_group = self.buffer.dist_index.get_outer_fsdp_group()
             with torch.cuda.stream(self.outer_fsdp_group_grad_reduce_stream):
                 with _coalescing_manager(outer_fsdp_group):
-                    reduced_grad = []
+                    # List of gradient accumulation closure tasks.
+                    # (grad_buffer, reduced_grad)
+                    grad_accum_closure = []
                     for bucket_id in bucket_group:
+                        # Skip gradient scaling for DP-Outer, because the
+                        # (DP-Shard, DP-Outer) scaling is already applied.
                         if ddp_config.average_in_collective:
                             reduce_op = torch.distributed.ReduceOp.AVG
                         else:
                             reduce_op = torch.distributed.ReduceOp.SUM
 
-                        # All-reduce/reduce-scatter the gradients on every rank
-                        # in the outer-DP group.
-                        gbuf = self.get_fsdp_buffer(bucket_id)
+                        # (DP-Shard, DP-Outer) if HFSDP, otherwise just DP-Shard for HSDP
+                        main_grad_buffer = self.buffer.parameter_groups[bucket_id].main_grad_buffer
+
+                        # FSDP buffer can be un-sharded or sharded for HSDP, but sharded for HFSDP.
+                        # TODO(@cspades): For `optim`, we don't need to reduce the local un-sharded
+                        # gradient, just the shard updated via reduce-scatter.
+                        fsdp_grad_buffer = self.get_fsdp_buffer(bucket_id)
+                        unreduced_grad = fsdp_grad_buffer.data
+                        assert (
+                            main_grad_buffer.dtype == fsdp_grad_buffer.dtype
+                        ), "Main and DP-Shard gradient buffer must share the exact same dtype."
+
+                        # Cast DP-Shard gradient to communication dtype if specified and necessary.
+                        custom_grad_comm_dtype = (
+                            mp_policy.grad_comm_dtype is not None
+                            and unreduced_grad.dtype != mp_policy.grad_comm_dtype
+                        )
+                        if custom_grad_comm_dtype:
+                            # Allocate a custom communication buffer with the HSDP gradient
+                            # communication buffer. Introduces copy and memory overhead.
+                            hsdp_comm_gbuf = self.buffer.parameter_groups[bucket_id].hsdp_comm_gbuf
+                            unreduced_grad = hsdp_comm_gbuf.allocate_bucket_storage(
+                                # Allocate memory for the sharded or un-sharded
+                                # gradient reduced over DP-Shard.
+                                shard=fsdp_grad_buffer.is_data_distributed,
+                                dtype=mp_policy.grad_comm_dtype,
+                                device=unreduced_grad.device,
+                                init_values=unreduced_grad,
+                            ).data
+
+                        # All-reduce or reduce-scatter the DP-Shard gradients across DP-Outer.
                         if ddp_config.outer_dp_sharding_strategy != "no_shard":
-                            # Outer-DP sharding is only supported for fully-sharded inner-DP.
-                            assert ddp_config.data_parallel_sharding_strategy != "no_shard"
                             # Retrieve the (DP-Outer, DP-Shard) gradient shard from the
                             # main gradient buffer which shards across the entire DP group,
                             # i.e. across all DP-Shard and DP-Outer ranks.
-                            grad_full_shard = self.buffer.parameter_groups[
-                                bucket_id
-                            ].main_grad_buffer.get_shard_from_local_buffer()
-                            # NOTE: This is a fix for convergence, needed to make
-                            # sure NCCL reduce-scatter inplace didn't seem
-                            # to work correctly
-                            grad_full_shard = torch.empty_like(grad_full_shard)
-                            reduced_grad.append(grad_full_shard)
+                            main_grad_shard = main_grad_buffer.get_shard_from_local_buffer()
+                            if custom_grad_comm_dtype:
+                                # Scatter back into communication buffer.
+                                dp_outer_rank = outer_fsdp_group.rank()
+                                output_buffer = unreduced_grad[
+                                    dp_outer_rank
+                                    * main_grad_shard.numel() : (dp_outer_rank + 1)
+                                    * main_grad_shard.numel()
+                                ]
+                            else:
+                                # Scatter directly into the main gradient buffer.
+                                output_buffer = main_grad_shard
                             # Reduce-scatter the FSDP gradient buffer shard further
                             # into the (DP-Outer, DP-Shard) gradient shard.
                             torch.distributed.reduce_scatter_tensor(
-                                output=grad_full_shard,
-                                input=gbuf.data,
+                                output=output_buffer,
+                                input=unreduced_grad,
                                 op=reduce_op,
                                 group=outer_fsdp_group,
                             )
-                        else:
-                            # No outer-DP sharding, so just all-reduce the FSDP gradient
-                            # buffer shard into itself.
+                            if custom_grad_comm_dtype:
+                                # Reduce-scatter output was a temporary communication buffer.
+                                grad_accum_closure.append((main_grad_shard, output_buffer))
+                        else:  # HSDP -> main_grad_buffer = (DP-Shard,)
+                            # No DP-Outer sharding, so all-reduce FSDP gradients across DP-Outer.
+                            # All FSDP buffers will have reduced un-sharded or sharded gradients.
                             torch.distributed.all_reduce(
-                                gbuf.data, group=outer_fsdp_group, op=reduce_op
+                                unreduced_grad, group=outer_fsdp_group, op=reduce_op
                             )
-                for bucket_id, grad_full_shard in zip(bucket_group, reduced_grad):
+                            if custom_grad_comm_dtype:
+                                # Reduction used a temporary communication buffer.
+                                grad_accum_closure.append((main_grad_buffer.data, unreduced_grad))
+
+                for main_grad_buffer, reduced_grad in grad_accum_closure:
                     # Update the (DP-Outer, DP-Shard) gradient shard in the main gradient buffer.
-                    self.buffer.parameter_groups[
-                        bucket_id
-                    ].main_grad_buffer.get_shard_from_local_buffer().copy_(grad_full_shard)
+                    # No accumulation should happen in the (DP-Shard, DP-Outer) gradient buffer.
+                    main_grad_buffer.copy_(reduced_grad)
+
             reduce_scatter_view_out_event = self.outer_fsdp_group_grad_reduce_stream.record_event()
 
         free_up_grad_bucket_func = {}
@@ -3357,10 +3600,16 @@ def free_up_grad_bucket():
                     # Empty the set of parameters that are ready for gradient reduction.
                     self.bucket_grad_ready_params[bucket_id] = set()
                     gbuf = self.get_fsdp_buffer(bucket_id)
-                    if gbuf.is_data_distributed:
-                        # Free the memory backing the temporarily-allocated bucket
-                        # associated with this buffer.
-                        gbuf.free_bucket_storage()
+                    # Free the memory backing the temporarily-allocated communication
+                    # bucket associated with this buffer. Only exists for sharded
+                    # gradient buffers, or if a custom gradient data-type is used!
+                    gbuf.free_bucket_storage()
+                    # Gradient reduction completed, can de-reference param.main_grad.
+                    gbuf.reset_param_main_grad()
+                    hsdp_comm_gbuf = self.buffer.parameter_groups[bucket_id].hsdp_comm_gbuf
+                    if hsdp_comm_gbuf is not None:
+                        # Also de-allocate any communication buffers used for H(F)SDP.
+                        hsdp_comm_gbuf.free_bucket_storage()
                     # Mark the bucket as deallocated / empty.
                     self.bucket_status[bucket_id] = BucketStatus.EMPTY
 
@@ -3505,7 +3754,10 @@ def all_gather_params(
             suggested_AG_prefetch_size (Optional[int], optional):
                 The suggested prefetch size for all-gathering. Defaults to None.
             outer_fsdp_group_param_gather (bool, optional):
-                Whether to all-gather parameters across outer-DP groups. Defaults to False.
+                Whether to all-gather parameters across DP-Outer. Defaults to False.
+            bwd (bool, optional):
+                Whether to all-gather column-wise parameters instead of row-wise parameters
+                for the backward pass for formats that require a transpose buffer like MXFP8.
         """
         if len(params) == 0:
             return
@@ -3622,7 +3874,7 @@ def need_skip_prefetch(bucket_id):
                 self.ag_stream if self.ag_stream is not None else torch.cuda.current_stream()
             )
             if outer_fsdp_group_param_gather:
-                # TODO(mxfp8): Support hsdp
+                # TODO(@kunlunl): Support MXFP8 with HFSDP. Requires an HFSDP transpose buffer.
                 self.outer_fsdp_group_param_gather_stream.wait_stream(torch.cuda.current_stream())
                 with torch.cuda.stream(self.outer_fsdp_group_param_gather_stream):
                     outer_fsdp_group = self.buffer.dist_index.get_outer_fsdp_group()
@@ -3637,7 +3889,7 @@ def need_skip_prefetch(bucket_id):
                                 input_tensor=wbuf.data,
                                 group=outer_fsdp_group,
                             )
-                # Wait for the outer-DP group all-gather to finish.
+                # Wait for the DP-Outer group all-gather to finish.
                 all_gather_stream.wait_stream(self.outer_fsdp_group_param_gather_stream)
 
             # Coalesce the asynchronous NCCL operations in this context.
@@ -3712,6 +3964,11 @@ def release_bucket(self, bucket_id, bwd, lazy: bool = False):
             they are no longer needed.
             - If the bucket has a transpose weight buffer (used in FP8 backward passes),
             this buffer is freed; otherwise, the model weight buffer is released.
+            - This function should NOT be invoked on buckets associated with modules not
+            identified as FSDP unit modules, even when weights are sharded in the case of
+            `optim_grads_params`. Non-unit modules should remain persistently allocated
+            because they do not satisfy FSDP unit module state requirements, e.g. their
+            parameters are simultaneously modified or shared with other modules.
         """
         bucket_key = self.get_bucket_key(bucket_id, bwd)
         if self.bucket_status[bucket_key] == BucketStatus.EMPTY:
@@ -3743,7 +4000,10 @@ def recycle_unused_buckets(self):
                 self.bucket_can_be_released[bucket_key] = False
 
     def get_fsdp_buffer(self, bucket_id: int, bwd=False) -> DataParallelBuffer:
-        """Get the FSDP buffer with the given bucket ID."""
+        """
+        Get the FSDP / DP-Shard buffer with the given bucket ID.
+        If bwd=True, return the FSDP transpose buffer instead.
+        """
         param_group = self.buffer.parameter_groups[bucket_id]
         if self.buffer.ddp_config.outer_dp_sharding_strategy != "no_shard":
             if bwd and param_group.transpose_weight_buffer is not None:
@@ -3766,12 +4026,16 @@ def async_bucket_gather(self, bucket_id, bwd) -> None:
 
         self.bucket_status[bucket_key] = BucketStatus.COMMUNICATING
 
+        # Retrieve the buffer associated with the DP-Shard PG
+        # that backs the model compute weights.
         wbuf = self.get_fsdp_buffer(bucket_id, bwd)
 
         # Lazy release the unused buckets.
         self.recycle_unused_buckets()
+
         # Allocate an empty bucket to store the module weights.
         bucket = wbuf.fetch_bucket(set_param_data=True)
+
         # All-gather the module weights in each buffer shard into the allocated bucket.
         # Now each rank will have a copy of this FSDP unit module's weights.
         param_gather_event = torch.distributed.all_gather_into_tensor(
@@ -3804,19 +4068,38 @@ def gradient_reduce_preprocessing(grad_data, scaling_factor, ddp_config):
     Gradient reduce preprocessing for gradient averaging and gradient scaling.
     """
 
+    # TODO(@cspades): Clean up this logic in conjunction with
+    # gradient reduction arguments: calculate_per_token_loss,
+    # and average_in_collective.
     if scaling_factor is None:
+        # No scaling - use SUM reduction.
         reduce_op = torch.distributed.ReduceOp.SUM
     elif ddp_config.average_in_collective:
+        # Scaling overridden by AVG reduction.
         reduce_op = torch.distributed.ReduceOp.AVG
     elif ddp_config.gradient_reduce_div_fusion and grad_data.dtype != torch.bfloat16:
+        # Fused SUM reduction.
         reduce_op = torch.distributed._make_nccl_premul_sum(scaling_factor)
     else:
+        # Scale gradients with SUM reduction.
         grad_data.mul_(scaling_factor)
         reduce_op = torch.distributed.ReduceOp.SUM
 
     return reduce_op
 
 
+def _check_nan_in_grad(grad: torch.Tensor):
+    """
+    Check if there are any NaN or Inf in grad.
+    """
+    # Compute gradient norm.
+    grad_norm = torch.linalg.norm(grad)
+    if torch.isnan(grad_norm) or not torch.isfinite(grad_norm):
+        raise ValueError(
+            f"[Megatron-FSDP](check_for_nan_in_grad=True) Detected NaN or Inf in wgrad: {grad}"
+        )
+
+
 def check_gpu_memory(threshold=0.9):
     """
     Check if the GPU memory is over the threshold.
@@ -3936,24 +4219,33 @@ def override_sharded_param_cpu_function(*args, **kwargs):
 
 def _dtype_size(dtype: torch.dtype) -> int:
     """
-    Get the size of the dtype.
+    Get the size of the dtype. Note that many data-types un-common to ML
+    or not supported by NCCL communication (e.g. CFloat) are listed here
+    for mixed-precision coverage and to avoid allocating a dummy Tensor.
+
     Args:
         dtype (torch.dtype): The dtype to get the size of.
     Returns:
         int: The size of the dtype.
     """
-    if dtype == torch.float16 or dtype == torch.bfloat16:
+    if dtype == torch.float16 or dtype == torch.bfloat16 or dtype == torch.int16:
         return 2
-    elif dtype == torch.float32 or dtype == torch.int32:
+    elif dtype == torch.float32 or dtype == torch.int32 or torch.complex32:
         return 4
-    elif dtype == torch.int64:
+    elif dtype == torch.float64 or dtype == torch.int64 or torch.complex64:
         return 8
-    elif dtype == torch.uint8:
+    elif dtype == torch.uint8 or dtype == torch.int8:
         return 1
     elif dtype == "float8":
         return 1
     else:
-        raise ValueError(f"Unsupported dtype: {dtype}")
+        try:
+            # Allocate an empty Tensor on-the-fly to check the size.
+            # Non-ideal fall-back option before sizing the new dtype.
+            # Why does torch.dtype not support this without alloc?
+            return torch.empty((), dtype=dtype).element_size()
+        except:
+            raise ValueError(f"Unsupported dtype: {dtype}")
 
 
 def to_local_if_dtensor(tensor):
diff --git a/megatron/core/distributed/fsdp/src/megatron_fsdp/utils.py b/megatron/core/distributed/fsdp/src/megatron_fsdp/utils.py
index 20aee12e394..6dd5ab6d342 100644
--- a/megatron/core/distributed/fsdp/src/megatron_fsdp/utils.py
+++ b/megatron/core/distributed/fsdp/src/megatron_fsdp/utils.py
@@ -434,6 +434,34 @@ def get_cuda_rng_tracker(
     return _CUDA_RNG_STATE_TRACKER
 
 
+def safe_get_rank() -> int:
+    """Safely get the rank of the current process.
+
+    Returns the rank from torch.distributed if initialized, otherwise falls back
+    to the RANK environment variable, defaulting to 0.
+
+    Returns:
+        int: The rank of the current process.
+    """
+    if torch.distributed.is_initialized():
+        return torch.distributed.get_rank()
+
+    # If torch.distributed is not initialized, try to read environment variables.
+    try:
+        return int(os.environ.get("RANK", 0))
+    except (ValueError, TypeError):
+        # Return rank 0 regardless of the actual rank.
+        return 0
+
+
+def log_single_rank(logger_: logging.Logger, level: int, msg: str, *args, rank: int = 0, **kwargs):
+    """Log on a single rank."""
+    if safe_get_rank() == rank:
+        logger_.log(level, msg, *args, **kwargs)
+
+
+# TODO(@cspades): Migrate this to a new module: fsdp_dist_index.py.
+# Needs more visibility and is easily refactored / standalone.
 class FSDPDistributedIndex:
     """
     Class containing references to the process groups utilized by Megatron-FSDP.
diff --git a/megatron/core/distributed/param_and_grad_buffer.py b/megatron/core/distributed/param_and_grad_buffer.py
index 85b9d98a3be..278a15d1a7c 100644
--- a/megatron/core/distributed/param_and_grad_buffer.py
+++ b/megatron/core/distributed/param_and_grad_buffer.py
@@ -10,6 +10,7 @@
 from typing import Dict, List, Optional
 
 import torch
+from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
 from torch.distributed import _coalescing_manager
 
 import megatron.core.nccl_allocator as nccl_allocator
@@ -112,6 +113,42 @@ def __init__(
             global_start, global_end, _ = param_index_map[param]
             self.param_to_index[param] = (global_start - offset, global_end - offset)
 
+        # Layer-wise optimizer attributes for async param gather.
+        self.layerwise_params_list = None
+        self.layerwise_param_flat_sizes = None
+        self.layerwise_gather_list = None
+        self._layerwise_src_buffer = None
+
+    def set_layerwise_params_list(self, layerwise_params_list: List[List[torch.nn.Parameter]]):
+        """Set per-rank parameter lists for layer-wise async all-gather.
+
+        Args:
+            layerwise_params_list: List of param lists, one per rank in the DP group.
+                Each inner list contains the parameters owned by that rank's
+                layer-wise optimizer that also belong to this bucket.
+        """
+        self.layerwise_params_list = layerwise_params_list
+        self.layerwise_param_flat_sizes = [
+            sum([p.numel() for p in param_list]) for param_list in layerwise_params_list
+        ]
+
+
+class _LayerwiseAllGatherHandle:
+    """Handle wrapping multiple async all-gather work objects.
+
+    NCCL guarantees in-order completion on the same communicator, so waiting
+    on only the last handle is sufficient.
+    """
+
+    def __init__(self, handles):
+        self.handles = handles
+
+    def wait(self):
+        """Wait on the last handle and clear all handles."""
+        if self.handles:
+            self.handles[-1].wait()
+        self.handles = None
+
 
 class _ParamAndGradBucketGroup:
     """
@@ -138,11 +175,13 @@ def __init__(
         self.buckets = buckets
         self.ddp_config = ddp_config
 
-        if self.ddp_config.use_distributed_optimizer:
+        # overlap_param_gather covers the layer-wise optimizer case, which sets
+        # overlap_param_gather=True without use_distributed_optimizer.
+        if self.ddp_config.use_distributed_optimizer or self.ddp_config.overlap_param_gather:
             self.intra_distributed_optimizer_instance_group = collective_group
             self.intra_distributed_optimizer_instance_size = collective_group_size
             self.intra_distributed_optimizer_instance_rank = collective_group.rank()
-        else:
+        if not self.ddp_config.use_distributed_optimizer:
             self.data_parallel_group = collective_group
 
         # State for bookkeeping: params is the set of parameters this bucket group is
@@ -262,7 +301,9 @@ def start_param_sync(self, force_sync: bool = False):
             force_sync (bool, optional): force synchronous collective regardless of
                 other settings if true.
         """
-        assert self.ddp_config.use_distributed_optimizer
+        # overlap_param_gather covers the layer-wise optimizer case, which sets
+        # overlap_param_gather=True without use_distributed_optimizer.
+        assert self.ddp_config.use_distributed_optimizer or self.ddp_config.overlap_param_gather
 
         if force_sync:
             if self.param_gather_handle is not None:
@@ -273,33 +314,114 @@ def start_param_sync(self, force_sync: bool = False):
             assert self.param_gather_handle is None
 
         async_op = self.ddp_config.overlap_param_gather and not force_sync
-        # Coalesce communication kernels across buckets in the bucket group.
-        with _coalescing_manager(
-            self.intra_distributed_optimizer_instance_group, async_ops=async_op
-        ) as cm:
-            for idx, bucket in enumerate(self.buckets):
-                if self.cached_param_buffer_shard_list[idx] is None:
-                    self.cached_param_buffer_shard_list[idx] = shard_buffer(
-                        bucket.param_data, self.intra_distributed_optimizer_instance_size
+
+        if not self.ddp_config.use_distributed_optimizer:
+            # Layer-wise optimizer path: use all_gather for variable-size
+            # param gather.
+            #
+            # Each rank may own a different number of params per bucket, so
+            # layerwise_param_flat_sizes can vary across ranks.  PyTorch's NCCL
+            # backend handles uneven tensor sizes in torch.distributed.all_gather
+            # (falling back to grouped send/recv internally when sizes differ),
+            # so no manual padding is needed.
+            dp_size = self.intra_distributed_optimizer_instance_size
+            local_rank = self.intra_distributed_optimizer_instance_rank
+            group = self.intra_distributed_optimizer_instance_group
+            layerwise_work_handles = []
+            for bucket in self.buckets:
+                # Use param dtype (e.g., bf16), NOT grad dtype (which may be
+                # fp32 when grad_reduce_in_fp32 is enabled).
+                param_dtype = bucket.params_list[0].dtype
+
+                if max(bucket.layerwise_param_flat_sizes) == 0:
+                    # All ranks have empty params for this bucket — skip.
+                    bucket.layerwise_gather_list = None
+                    continue
+
+                # Flatten local params.  Detach from the autograd graph because
+                # start_param_sync can be called during the forward pass (where
+                # autograd is active) and all_gather will write into gather_list
+                # entries in-place.
+                local_size = bucket.layerwise_param_flat_sizes[local_rank]
+                if local_size > 0:
+                    flat_local_params = _flatten_dense_tensors(
+                        bucket.layerwise_params_list[local_rank]
+                    ).detach()
+                else:
+                    flat_local_params = torch.empty(
+                        0, device=bucket.grad_data.device, dtype=param_dtype
                     )
-                local_data_view = self.cached_param_buffer_shard_list[idx][
-                    self.intra_distributed_optimizer_instance_rank
-                ]
-                dist_all_gather_func(
-                    bucket.param_data,
-                    local_data_view,
-                    group=self.intra_distributed_optimizer_instance_group,
-                    async_op=async_op,
+                # Keep flat_local_params alive until the async operation completes.
+                bucket._layerwise_src_buffer = flat_local_params
+
+                # Allocate per-rank receive buffers with actual sizes (no padding).
+                # Reuse flat_local_params for local_rank's slot to avoid an extra allocation.
+                gather_list = []
+                for i in range(dp_size):
+                    if i == local_rank:
+                        gather_list.append(flat_local_params)
+                    else:
+                        gather_list.append(
+                            torch.empty(
+                                bucket.layerwise_param_flat_sizes[i],
+                                device=flat_local_params.device,
+                                dtype=flat_local_params.dtype,
+                            )
+                        )
+                bucket.layerwise_gather_list = gather_list
+
+                work = torch.distributed.all_gather(
+                    gather_list, flat_local_params, group=group, async_op=async_op
                 )
-        if async_op:
-            self.param_gather_handle = cm
+                if async_op and work is not None:
+                    layerwise_work_handles.append(work)
+
+            if async_op:
+                self.param_gather_handle = _LayerwiseAllGatherHandle(layerwise_work_handles)
+            else:
+                # Synchronous: unflatten and copy gathered params immediately.
+                for bucket in self.buckets:
+                    if bucket.layerwise_gather_list is None:
+                        continue
+                    for idx, params in enumerate(bucket.layerwise_params_list):
+                        if len(params) == 0 or idx == local_rank:
+                            continue
+                        updated_params = _unflatten_dense_tensors(
+                            bucket.layerwise_gather_list[idx], params
+                        )
+                        for updated_p, model_p in zip(updated_params, params):
+                            model_p.data.copy_(updated_p)
+                    bucket.layerwise_gather_list = None
+                    bucket._layerwise_src_buffer = None
+                self.param_gather_handle = None
         else:
-            # When using `_coalescing_manager`, even if a synchronous op (async_op=False) is used,
-            # `cm` is not None, which is different from when `_coalescing_manager` is not used in
-            # which case the torch.distributed._all_gather_base() will return None. In order to
-            # maintain consistency with prior code, we need to manually set communication handle to
-            # None.
-            self.param_gather_handle = None
+            # Standard distributed optimizer path: use _coalescing_manager.
+            # all_gather_into_tensor writes directly into a contiguous output buffer and
+            # does not need a copy-back step, so coalescing works correctly.
+            with _coalescing_manager(
+                self.intra_distributed_optimizer_instance_group, async_ops=async_op
+            ) as cm:
+                for idx, bucket in enumerate(self.buckets):
+                    if self.cached_param_buffer_shard_list[idx] is None:
+                        self.cached_param_buffer_shard_list[idx] = shard_buffer(
+                            bucket.param_data, self.intra_distributed_optimizer_instance_size
+                        )
+                    local_data_view = self.cached_param_buffer_shard_list[idx][
+                        self.intra_distributed_optimizer_instance_rank
+                    ]
+                    dist_all_gather_func(
+                        bucket.param_data,
+                        local_data_view,
+                        group=self.intra_distributed_optimizer_instance_group,
+                        async_op=async_op,
+                    )
+            if async_op:
+                self.param_gather_handle = cm
+            else:
+                # When using `_coalescing_manager`, even if a synchronous op
+                # (async_op=False) is used, `cm` is not None. Manually set to None for
+                # consistency with prior code.
+                self.param_gather_handle = None
         self.param_gather_dispatched = True
 
     def finish_param_sync(self, skip_next_bucket_dispatch: bool = False):
@@ -317,7 +439,6 @@ def finish_param_sync(self, skip_next_bucket_dispatch: bool = False):
             skip_next_bucket_dispatch (bool, optional): if true, dispatch next
                 bucket's communication if available.
         """
-        assert self.ddp_config.use_distributed_optimizer
         assert self.ddp_config.overlap_param_gather
 
         # If current bucket's param AG has not been dispatched, dispatch it now (e.g., first
@@ -345,16 +466,43 @@ def finish_param_sync(self, skip_next_bucket_dispatch: bool = False):
             # after the param all-gather.
             if self.ddp_config.reuse_grad_buf_for_mxfp8_param_ag:
                 for bucket in self.buckets:
+                    is_bf16_weight_bucket = False
                     for param in bucket.params:
+                        # Skip copying since bf16 weights in the mxfp8 model
+                        # are already mapped to param.data.
+                        if not is_float8tensor(param):
+                            is_bf16_weight_bucket = True
+                            break
                         param_start, param_end = bucket.param_to_index[param]
                         param_slice = bucket.param_data.view(-1)[param_start:param_end]
                         param.data.copy_(param_slice.view(param.data.shape))
+                    if is_bf16_weight_bucket:
+                        continue
                     # All-gathered params are not needed after being copied to param.data.
                     # Zero out the param buffer (shared with grad buffer) for gradient accumulation.
                     # We cannot zero out the entire grad buffer because one grad buffer may
                     # correspond to multiple param buffers. If we zero out the entire grad buffer,
                     # it would clear the data of those param buffers that have not yet completed AG.
                     bucket.param_data.zero_()
+            elif not self.ddp_config.use_distributed_optimizer:
+                for bucket in self.buckets:
+                    if bucket.layerwise_gather_list is None:
+                        continue
+                    # Unflatten and copy gathered params for each rank.
+                    for idx, params in enumerate(bucket.layerwise_params_list):
+                        # Skip local params and empty tensors.
+                        if (
+                            len(params) == 0
+                            or idx == self.intra_distributed_optimizer_instance_rank
+                        ):
+                            continue
+                        updated_params = _unflatten_dense_tensors(
+                            bucket.layerwise_gather_list[idx], params
+                        )
+                        for updated_p, model_p in zip(updated_params, params):
+                            model_p.data.copy_(updated_p)
+                    bucket.layerwise_gather_list = None
+                    bucket._layerwise_src_buffer = None
             else:
                 fp8_params = []
                 for bucket in self.buckets:
@@ -539,6 +687,21 @@ def finish_grad_sync(self, force_all_reduce: Optional[bool] = False):
         self.grad_reduce_handle.wait()
         self.grad_reduce_handle = None
 
+    def free_overlap_buffers(self):
+        """Free GPU buffers used by overlap param gather.
+
+        Waits on any pending param all-gather handle, then releases the
+        per-bucket temporary buffers so that the CUDA memory allocator can
+        reclaim them.  Called before async checkpoint saves to avoid OOM in
+        the persistent checkpoint worker process.
+        """
+        if self.param_gather_handle is not None:
+            self.param_gather_handle.wait()
+            self.param_gather_handle = None
+        for bucket in self.buckets:
+            bucket.layerwise_gather_list = None
+            bucket._layerwise_src_buffer = None
+
     def register_grad_ready(
         self, param: torch.nn.Parameter, force_all_reduce: Optional[bool] = False
     ):
@@ -820,9 +983,9 @@ def _does_param_require_new_bucket(param):
         cur_bucket_id = 0
         for param in params[::-1]:
             param_start_index, param_end_index, bucket_id = self.param_index_map[param]
-            # For MXFP8 param: we only need to map weight gradients to the buffer.
-            if not self.ddp_config.reuse_grad_buf_for_mxfp8_param_ag:
-                # Assign param.data to appropriate segment of self.param_data.
+            # For MXFP8 param:
+            # we only need to map bf16 weights (layernorm, embedding, etc) to the buffer.
+            if not self.ddp_config.reuse_grad_buf_for_mxfp8_param_ag or not is_mxfp8tensor(param):
                 if self.param_data is not None:
                     new_param_data = self._get(
                         param.data.shape, param_start_index, buffer_type=BufferType.PARAM
diff --git a/megatron/core/extensions/transformer_engine.py b/megatron/core/extensions/transformer_engine.py
index 20ab554382e..42f081c473c 100644
--- a/megatron/core/extensions/transformer_engine.py
+++ b/megatron/core/extensions/transformer_engine.py
@@ -433,41 +433,227 @@ def __new__(cls, config: TransformerConfig):
     TEActivationOp = None
 
 
+if HAVE_TE and is_te_min_version("1.13.0"):
+
+    class TEFusedResidualRMSNorm(te.pytorch.RMSNorm):
+        """
+        RMSNorm with fused residual output for Megatron Core.
+
+        Inherits from te.pytorch.RMSNorm to maintain all parameter management,
+        checkpoint compatibility, and Megatron-specific features. Creates a fused
+        implementation using TE's ops API that shares the base class parameters.
+
+        The fused implementation uses:
+        - MakeExtraOutput: Forks the residual connection
+        - RMSNorm: Normalizes the main path
+
+        Forward pass returns: (normalized_output, residual)
+        """
+
+        def __init__(self, *args, **kwargs):
+            super().__init__(*args, **kwargs)
+            # Fused implementation (stored in tuple to avoid submodule registration)
+            self._fused_impl: Optional[Tuple[te.pytorch.ops.Sequential]] = None
+
+        def _make_fused_impl(self) -> te.pytorch.ops.Sequential:
+            """
+            Construct fused ops pipeline that shares parameters with base RMSNorm.
+
+            Creates MakeExtraOutput + RMSNorm ops, where the RMSNorm op shares
+            the weight parameter with self.weight from the base class.
+            """
+
+            fused_impl = te.pytorch.ops.Sequential()
+
+            # Op 1: MakeExtraOutput - forks the residual
+            fused_impl.append(te.pytorch.ops.MakeExtraOutput())
+
+            # Op 2: RMSNorm - shares weight parameter with self
+            kwargs = {
+                "eps": self.eps,
+                "device": "meta",  # Already initialized
+                "dtype": self.weight.dtype,
+                "zero_centered_gamma": self.zero_centered_gamma,
+            }
+
+            # Add sm_margin if available (TE 2.5+)
+            if hasattr(self, '_sm_margins'):
+                kwargs["sm_margin"] = self._sm_margins
+
+            rmsnorm_op = te.pytorch.ops.RMSNorm(self.weight.shape, **kwargs)
+
+            rmsnorm_op.weight = self.weight
+
+            fused_impl.append(rmsnorm_op)
+
+            self._register_hooks_on_fused_impl(fused_impl)
+
+            return fused_impl
+
+        def _register_hooks_on_fused_impl(self, fused_impl: torch.nn.Module) -> None:
+
+            forward_pre_hooks = []
+            forward_post_hooks = []
+            backward_pre_hooks = []
+            backward_post_hooks = []
+
+            for submodule in self.modules():
+                for hook in submodule._forward_pre_hooks.values():
+                    forward_pre_hooks.append((submodule, hook))
+                for hook in submodule._forward_hooks.values():
+                    forward_post_hooks.append((submodule, hook))
+                for hook in submodule._backward_pre_hooks.values():
+                    backward_pre_hooks.append((submodule, hook))
+                for hook in submodule._backward_hooks.values():
+                    backward_post_hooks.append((submodule, hook))
+
+            # Pre-forward hooks
+            # Note: DDP pre-forward hooks are safe since they do not
+            # interact with input tensor.
+            if forward_pre_hooks:
+                from megatron.core.distributed import distributed_data_parallel
+
+                if any(
+                    inspect.getmodule(hook) != distributed_data_parallel
+                    for _, hook in forward_pre_hooks
+                ):
+                    warnings.warn(
+                        "TEFusedResidualRMSNorm module has a submodule with a pre-forward hook. "
+                        "TEFusedResidualRMSNorm module does not expose intermediate tensors, "
+                        "so the hook may have incorrect behavior if it attempts to "
+                        "access the input tensor."
+                    )
+
+                def forward_pre_hook(module, *_) -> None:
+                    for submodule, hook in forward_pre_hooks:
+                        # Assume that hook does not interact with input
+                        ret = hook(submodule, None)
+                        if ret is not None:
+                            raise RuntimeError(
+                                "TEFusedResidualRMSNorm module does not expose "
+                                "intermediate tensors, but submodule has "
+                                "pre-forward hook that modifies input tensor."
+                            )
+
+                fused_impl.register_forward_pre_hook(forward_pre_hook)
+
+            # Post-forward hooks
+            if forward_post_hooks:
+                warnings.warn(
+                    "TEFusedResidualRMSNorm module has a submodule with a post-forward hook. "
+                    "TEFusedResidualRMSNorm module does not expose intermediate tensors, "
+                    "so the hook may have incorrect behavior if it attempts to "
+                    "access the input or output tensors."
+                )
+
+                def forward_post_hook(module, *_) -> None:
+                    for submodule, hook in forward_post_hooks:
+                        # Assume that hook does not interact with input or output
+                        ret = hook(submodule, None, None)
+                        if ret is not None:
+                            raise RuntimeError(
+                                "TEFusedResidualRMSNorm module does not expose "
+                                "intermediate tensors, but submodule has "
+                                "post-forward hook that modifies output tensor."
+                            )
+
+                fused_impl.register_forward_hook(forward_post_hook)
+
+            # Backward hooks
+            if backward_pre_hooks:
+                raise RuntimeError(
+                    "TEFusedResidualRMSNorm module does not support "
+                    "submodules with pre-backward hooks"
+                )
+            if backward_post_hooks:
+                raise RuntimeError(
+                    "TEFusedResidualRMSNorm module does not support "
+                    "submodules with post-backward hooks"
+                )
+
+        def forward(self, hidden_states: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+            """
+            Forward pass with fused residual output.
+
+            Args:
+                hidden_states: Input tensor [s, b, h]
+
+            Returns:
+                Tuple of (normalized_output, residual), both [s, b, h]
+
+            Note:
+                Sequential.forward() automatically returns (output, extra_outputs...)
+                when MakeExtraOutput is present, so we don't need manual unpacking.
+            """
+
+            # Construct fused impl lazily on first forward
+            # (in case parameters are modified after __init__)
+            if self._fused_impl is None:
+                self._fused_impl = (self._make_fused_impl(),)
+
+            # Apply fused implementation
+            # Sequential returns (normalized_output, residual) automatically
+            return self._fused_impl[0](hidden_states)
+
+else:
+    TEFusedResidualRMSNorm = None  # type: ignore[assignment, misc]
+
+
 class TENorm:
     """A conditional wrapper to initialize an instance of
-    Transformer-Engine's `LayerNorm` or `RMSNorm` based on input."""
+    Transformer-Engine's `LayerNorm` or `RMSNorm` based on input.
+
+    Residual fusion is a two-level opt-in mechanism:
+
+    1. Global capability: config.fused_residual_rmsnorm must be True (enables the feature)
+    2. Local intent: has_residual=True must be passed at build site (declares this specific
+       norm is followed by a residual connection)
+
+    Fusion only happens when BOTH conditions are met.
+
+    """
 
     # TODO should we ditch normalization config and just use spec to choose LayerNorm vs RMSNorm?
     def __new__(
-        cls, config: TransformerConfig, hidden_size: int, eps: float = 1e-5
-    ) -> LayerNormInterface:
+        cls,
+        config: TransformerConfig,
+        hidden_size: int,
+        eps: float = 1e-5,
+        has_residual: bool = False,
+    ):
         if not HAVE_TE:
             raise ImportError(
                 "Transformer Engine is not installed. "
                 "Please install it with `pip install transformer-engine`."
             )
 
+        use_fused_residual = config.fused_residual_rmsnorm and has_residual
+        if use_fused_residual and config.normalization != "RMSNorm":
+            raise ValueError("Fused residual is only supported " "for RMSNorm normalization")
+
         if config.normalization == "LayerNorm":
-            instance = te.pytorch.LayerNorm(
-                hidden_size=hidden_size,
-                eps=eps,
-                sequence_parallel=config.sequence_parallel,
-                zero_centered_gamma=config.layernorm_zero_centered_gamma,
-                **_get_extra_te_kwargs(config),
-            )
+            norm_module = te.pytorch.LayerNorm
         elif config.normalization == "RMSNorm":
             assert hasattr(
                 te.pytorch, "RMSNorm"
             ), "Transformer-Engine >= v0.11 required to use this feature"
-            instance = te.pytorch.RMSNorm(
-                hidden_size=hidden_size,
-                eps=eps,
-                sequence_parallel=config.sequence_parallel,
-                zero_centered_gamma=config.layernorm_zero_centered_gamma,
-                **_get_extra_te_kwargs(config),
-            )
+            if use_fused_residual:
+                assert (
+                    TEFusedResidualRMSNorm is not None
+                ), "TEFusedResidualRMSNorm requires Transformer-Engine >= v1.13.0"
+                norm_module = TEFusedResidualRMSNorm
+            else:
+                norm_module = te.pytorch.RMSNorm
         else:
-            raise Exception("Only LayerNorm and RMSNorm are curently supported")
+            raise Exception("Only LayerNorm and RMSNorm are currently supported")
+
+        instance = norm_module(
+            normalized_shape=hidden_size,
+            eps=eps,
+            sequence_parallel=config.sequence_parallel,
+            zero_centered_gamma=config.layernorm_zero_centered_gamma,
+            **_get_extra_te_kwargs(config),
+        )
 
         return cast(LayerNormInterface, instance)
 
@@ -1324,6 +1510,10 @@ def __init__(
             self.kept_packed_seq_params.discard("cu_seqlens_q_padded")
             self.kept_packed_seq_params.discard("cu_seqlens_kv_padded")
 
+        # total_tokens and seq_idx are only for Mamba and should not be forwarded to TE attention.
+        self.kept_packed_seq_params.discard("total_tokens")
+        self.kept_packed_seq_params.discard("seq_idx")
+
         if config.qk_clip or config.log_max_attention_logit:
             # qk-clip is only supported in TE 2.9.0 and later
             assert is_te_min_version("2.9.0"), "qk-clip is only supported in TE 2.9.0 and later"
@@ -2485,7 +2675,13 @@ def te_parallel_cross_entropy(
 
 try:
     from transformer_engine.pytorch.cpp_extensions import general_gemm
-    from transformer_engine.pytorch.module.base import get_workspace
+
+    try:
+        from transformer_engine.pytorch.module.base import get_workspace
+
+        _get_workspace = get_workspace
+    except ImportError:
+        _get_workspace = None
 
     def te_general_gemm(
         A: torch.Tensor,
@@ -2503,10 +2699,7 @@ def te_general_gemm(
         Note: not all combinations of these settings are supported. If not supported,
         cublaslt will throw an error.
         """
-        return general_gemm(
-            A,
-            B,
-            workspace=get_workspace(),
+        kwargs = dict(
             out_dtype=out_dtype,
             quantization_params=None,
             gelu=None,
@@ -2522,6 +2715,9 @@ def te_general_gemm(
             extra_output=None,
             bulk_overlap=False,
         )
+        if _get_workspace is not None:
+            kwargs["workspace"] = _get_workspace()
+        return general_gemm(A, B, **kwargs)
 
 except ImportError:
     te_general_gemm = None  # type: ignore[assignment, misc]
diff --git a/megatron/core/extensions/transformer_engine_spec_provider.py b/megatron/core/extensions/transformer_engine_spec_provider.py
index ab87f8760b5..08bb025f3b9 100644
--- a/megatron/core/extensions/transformer_engine_spec_provider.py
+++ b/megatron/core/extensions/transformer_engine_spec_provider.py
@@ -21,7 +21,6 @@
 from megatron.core.transformer.dot_product_attention import DotProductAttention
 from megatron.core.transformer.mlp import MLPSubmodules, TEActivationFunctionBuilder
 from megatron.core.transformer.moe.experts import (
-    GroupedMLP,
     SequentialMLP,
     TEGroupedMLP,
     TEGroupedMLPSubmodules,
@@ -30,6 +29,13 @@
 from megatron.core.utils import get_te_version, is_te_min_version
 
 
+class _TENormWithResidual:
+    """Class adapter for TENorm with residual fusion enabled."""
+
+    def __new__(cls, *args, **kwargs):
+        return TENorm(*args, has_residual=True, **kwargs)
+
+
 class TESpecProvider(BackendSpecProvider):
     """A protocol for providing the submodules used in Spec building."""
 
@@ -57,14 +63,17 @@ def column_parallel_layer_norm_linear(self) -> Optional[type]:
         """Which module for sequential layernorm and linear"""
         return TELayerNormColumnParallelLinear
 
-    def layer_norm(self, rms_norm: bool = False, for_qk: bool = False) -> LayerNormBuilder:
+    def layer_norm(
+        self, rms_norm: bool = False, for_qk: bool = False, has_residual: bool = False
+    ) -> LayerNormBuilder:
         """Which module to use for layer norm"""
         if for_qk and not is_te_min_version("1.9.0"):
             # TENorm significantly harms convergence when used
             # for QKLayerNorm if TE Version < 1.9;
             # we instead use the Apex implementation.
             return FusedLayerNorm
-        return TENorm
+        # Keep returning a class so this path stays aligned with build_module's class handling.
+        return _TENormWithResidual if has_residual else TENorm
 
     def core_attention(self) -> type:
         """Which module to use for attention"""
@@ -73,27 +82,16 @@ def core_attention(self) -> type:
         return TEDotProductAttention
 
     def grouped_mlp_modules(
-        self, moe_use_grouped_gemm: bool, moe_use_legacy_grouped_gemm: bool
+        self, moe_use_grouped_gemm: bool
     ) -> (
         tuple[type[TEGroupedMLP], TEGroupedMLPSubmodules]
         | tuple[type[SequentialMLP], MLPSubmodules]
-        | tuple[type[GroupedMLP], None]
     ):
         """Which module and submodules to use for grouped mlp"""
-        if (
-            moe_use_grouped_gemm
-            and TEColumnParallelGroupedLinear is not None
-            and not moe_use_legacy_grouped_gemm
-        ):
+        if moe_use_grouped_gemm and TEColumnParallelGroupedLinear is not None:
             return TEGroupedMLP, TEGroupedMLPSubmodules(
                 linear_fc1=TEColumnParallelGroupedLinear, linear_fc2=TERowParallelGroupedLinear
             )
-        elif moe_use_grouped_gemm:
-            warnings.warn(
-                'The legacy GroupedMLP will be deprecated in Megatron-Core v0.12.0. '
-                'Please update the TransformerEngine to version>=1.7.0 and use TEGroupedMLP.'
-            )
-            return GroupedMLP, None
         else:
             if not is_te_min_version("1.7.0.dev0"):
                 warnings.warn(
diff --git a/megatron/core/inference/batch_dimensions_utils.py b/megatron/core/inference/batch_dimensions_utils.py
index 1a202c35af5..c6bf8c79e78 100644
--- a/megatron/core/inference/batch_dimensions_utils.py
+++ b/megatron/core/inference/batch_dimensions_utils.py
@@ -73,7 +73,9 @@ def is_applicable_for_batch_dim(
                 >= real_batch_dim.prefill_req_count + real_batch_dim.decode_req_count
             )
 
-    def is_valid(self, max_requests: int, max_sequence_length: int) -> bool:
+    def is_valid(
+        self, max_requests: int, max_sequence_length: int, num_speculative_tokens: int
+    ) -> bool:
         """
         Checks if the batch dimension is valid based on resource constraints.
 
@@ -92,11 +94,17 @@ def is_valid(self, max_requests: int, max_sequence_length: int) -> bool:
             return False
 
         # Check if token count is sufficient for requests
-        if self.token_count < self.prefill_req_count + self.decode_req_count:
+        if self.token_count < self.prefill_req_count + self.decode_req_count * (
+            num_speculative_tokens + 1
+        ):
             return False
 
         # Check if the prefill requests are shorter than the max sequence length
-        if self.token_count > self.prefill_req_count * max_sequence_length + self.decode_req_count:
+        if (
+            self.token_count
+            > self.prefill_req_count * max_sequence_length
+            + self.decode_req_count * (num_speculative_tokens + 1)
+        ):
             return False
 
         return True
@@ -133,6 +141,7 @@ def adjust_batch_dims_for_expert_parallelism(
         strict: bool,
         decode_only_cuda_graphs: bool,
         explicit_chunked_prefill: bool,
+        smallest_non_decode_cuda_graph_size: int,
         ep_group: Optional[torch.distributed.ProcessGroup] = None,
     ) -> Optional["InferenceBatchDimensions"]:
         """Adjusted cuda graph batch dimensions for expert parallelism.
@@ -157,6 +166,7 @@ def adjust_batch_dims_for_expert_parallelism(
         # all reduce local work across expert model parallel group
 
         is_non_decode = local_batch_dims.prefill_req_count > 0
+
         sync_tensor = torch.tensor(
             [
                 local_batch_dims.token_count,
@@ -174,9 +184,9 @@ def adjust_batch_dims_for_expert_parallelism(
         is_any_ep_rank_in_non_decode = sync_tensor[1].item() == 1
 
         # We force eager mode for scenarios where some ranks will run with CUDA graphs
-        # while others will not. Without this check, the all-to-all communication in the
+        # while others will not. Without this check, communication in the
         # expert routing layer would pad up to the maximum capacity only for the ranks that
-        # are using CUDA graphs in this step, leading to a NCCL hang.
+        # are using CUDA graphs in this step, leading to a hang.
         # This can happen in the following cases:
         #   1. If we only allow decode CUDA graphs but some ranks are running non-decode batches
         #   2. Some ranks are running explicit chunked prefill requests
@@ -193,12 +203,22 @@ def adjust_batch_dims_for_expert_parallelism(
         adjusted_decode_req_count = (
             int(sync_tensor[3].item()) if strict else local_batch_dims.decode_req_count
         )
+        adjusted_token_count = int(sync_tensor[0].item())
+
+        # When any EP rank has prefill requests (non-strict mode), elevate
+        # the token count to be >= the smallest prefill/mixed cuda graph.
+        # This ensures decode-only ranks don't match a fine-grained decode
+        # graph while prefill ranks match a coarser mixed graph, which would
+        # produce inconsistent token counts across EP ranks.
+        if is_any_ep_rank_in_non_decode and not strict:
+            adjusted_token_count = max(adjusted_token_count, smallest_non_decode_cuda_graph_size)
 
         adjusted_batch_dim = InferenceBatchDimensions(
-            token_count=int(sync_tensor[0].item()),
+            token_count=adjusted_token_count,
             prefill_req_count=adjusted_prefill_req_count,
             decode_req_count=adjusted_decode_req_count,
         )
+
         return adjusted_batch_dim
 
 
@@ -291,11 +311,12 @@ def generate_cuda_graph_batch_dimensions_list(
         tp_size: int,
         num_cuda_graphs: Optional[int],
         cuda_graph_max_tokens: int,
-        cuda_graph_mixed_prefill_count: Optional[int],
+        cuda_graph_mixed_prefill_request_count: Optional[int],
         max_requests: int,
         max_tokens: int,
         max_sequence_length: int,
         use_cuda_graphs_for_non_decode_steps: bool,
+        num_speculative_tokens: int = 0,
     ) -> Tuple[List[InferenceBatchDimensions], Optional[List[int]]]:
         """
         Generate CUDA graph batch dimensions.
@@ -327,11 +348,12 @@ def generate_cuda_graph_batch_dimensions_list(
             tp_size: Tensor parallel size
             num_cuda_graphs: Number of CUDA graphs to generate
             cuda_graph_max_tokens: Maximum tokens for CUDA graphs
-            cuda_graph_mixed_prefill_count: Number of mixed prefill requests for CUDA graphs
+            cuda_graph_mixed_prefill_request_count: Number of mixed prefill requests for CUDA graphs
             max_requests: Maximum number of requests
             max_tokens: Maximum total tokens
             max_sequence_length: Maximum sequence length
             use_cuda_graphs_for_non_decode_steps: Whether to use CUDA graphs for non-decode steps
+            num_speculative_tokens: Number of speculative tokens
 
         Returns:
             Tuple containing:
@@ -343,7 +365,7 @@ def generate_cuda_graph_batch_dimensions_list(
         def add_if_valid(token_count: int, prefill_req_count: int, decode_req_count: int) -> None:
             """Helper to create and append batch dimension to list only if it's valid."""
             batch_dim = InferenceBatchDimensions(token_count, prefill_req_count, decode_req_count)
-            if batch_dim.is_valid(max_requests, max_sequence_length):
+            if batch_dim.is_valid(max_requests, max_sequence_length, num_speculative_tokens):
                 cuda_graph_batch_dimensions_list.append(batch_dim)
 
         # Cuda graph token-counts
@@ -360,6 +382,13 @@ def add_if_valid(token_count: int, prefill_req_count: int, decode_req_count: int
             ):
                 cuda_graph_max_tokens = max_tokens
 
+            assert cuda_graph_max_tokens == max_requests * (num_speculative_tokens + 1), (
+                f"cuda_graph_max_tokens ({cuda_graph_max_tokens}) must equal max_requests *"
+                f"(num_speculative_tokens + 1) ({max_requests * (num_speculative_tokens + 1)}). "
+                "This is required for correctly syncing EP ranks: "
+                f"prefill and decode graph pools must have the same token count granularity."
+            )
+
             if num_cuda_graphs != -1:
                 # if -1, no need to adjust. This will be taken care of in
                 # the _calculate_cuda_graph_token_counts function where we will generate
@@ -377,8 +406,9 @@ def add_if_valid(token_count: int, prefill_req_count: int, decode_req_count: int
             )
 
             # Calculate separate token counts for decode-only graphs.
-            # Decode graphs can be more conservative since each request uses exactly 1 token.
-            cuda_graph_max_tokens_decode = min(cuda_graph_max_tokens, max_requests)
+            cuda_graph_max_tokens_decode = min(
+                cuda_graph_max_tokens, max_requests * (num_speculative_tokens + 1)
+            )
             cuda_graph_decode_token_counts = (
                 CUDAGraphBatchDimensionBuilder._calculate_cuda_graph_token_counts(
                     tp_size=tp_size,
@@ -391,31 +421,40 @@ def add_if_valid(token_count: int, prefill_req_count: int, decode_req_count: int
         if num_cuda_graphs is None:
             cuda_graph_batch_dimensions_list = []
         elif (
-            not cuda_graph_mixed_prefill_count
-            or cuda_graph_mixed_prefill_count <= 0
+            not cuda_graph_mixed_prefill_request_count
+            or cuda_graph_mixed_prefill_request_count <= 0
             or not use_cuda_graphs_for_non_decode_steps
         ):  # decode only
             # Use decode-specific token counts for decode-only graphs
             for size in cuda_graph_decode_token_counts:
+                decode_req_count = min(size // (num_speculative_tokens + 1), max_requests)
+                token_count = decode_req_count * (num_speculative_tokens + 1)
+                token_count = token_count // tp_size * tp_size
                 add_if_valid(
-                    token_count=min(size, max_requests),
-                    prefill_req_count=0,
-                    decode_req_count=min(size, max_requests),
+                    token_count=token_count, prefill_req_count=0, decode_req_count=decode_req_count
                 )
         else:
             # Mixed prefill and decode mode
             # Create prefill and mixed dimensions with full token counts
             for size in cuda_graph_prefill_token_counts:
+                assert size % tp_size == 0
+                prefill_req_count = min(cuda_graph_mixed_prefill_request_count, max_requests)
+                decode_req_count = max(
+                    0,
+                    min(
+                        (size - prefill_req_count) // (num_speculative_tokens + 1),
+                        max_requests - prefill_req_count,
+                    ),
+                )
                 add_if_valid(
                     token_count=size,
-                    prefill_req_count=min(cuda_graph_mixed_prefill_count, max_requests),
-                    decode_req_count=min(size, max_requests)
-                    - min(cuda_graph_mixed_prefill_count, max_requests),
+                    prefill_req_count=prefill_req_count,
+                    decode_req_count=decode_req_count,
                 )
                 # We need to ensure the prefill requests are shorter than the max sequence length,
                 # considering the one decode token is used for prefill request construction
                 prefill_only_minimal_num = max(
-                    cuda_graph_mixed_prefill_count,
+                    cuda_graph_mixed_prefill_request_count,
                     math.ceil(size / max(1, max_sequence_length - 1)),
                 )
                 if prefill_only_minimal_num < max_requests:
@@ -427,16 +466,21 @@ def add_if_valid(token_count: int, prefill_req_count: int, decode_req_count: int
 
             # Create decode-only dimensions with optimized token counts
             for size in cuda_graph_decode_token_counts:
+                decode_req_count = min(size // (num_speculative_tokens + 1), max_requests)
+                token_count = decode_req_count * (num_speculative_tokens + 1)
+                token_count = token_count // tp_size * tp_size
                 add_if_valid(
-                    token_count=min(size, max_requests),
-                    prefill_req_count=0,
-                    decode_req_count=min(size, max_requests),
+                    token_count=token_count, prefill_req_count=0, decode_req_count=decode_req_count
                 )
 
         # Remove duplicates and sort by prefill token count
         cuda_graph_batch_dimensions_list = list(set(cuda_graph_batch_dimensions_list))
         cuda_graph_batch_dimensions_list.sort(
-            key=lambda x: ((x.token_count - x.decode_req_count), x.decode_req_count), reverse=True
+            key=lambda x: (
+                (x.token_count - x.decode_req_count * (num_speculative_tokens + 1)),
+                x.decode_req_count,
+            ),
+            reverse=True,
         )
 
         # Collect actual token counts from batch dimensions, then unique and sort
@@ -456,6 +500,7 @@ def add_if_valid(token_count: int, prefill_req_count: int, decode_req_count: int
     def match_graph_config(
         real_batch_dim: InferenceBatchDimensions,
         cuda_graph_batch_dimensions_list: List[InferenceBatchDimensions],
+        smallest_non_decode_cuda_graph_size: int,
         strict: bool = False,
         decode_only_cuda_graphs: bool = False,
         explicit_chunked_prefill: bool = False,
@@ -490,6 +535,7 @@ def match_graph_config(
             decode_only_cuda_graphs=decode_only_cuda_graphs,
             explicit_chunked_prefill=explicit_chunked_prefill,
             ep_group=ep_group,
+            smallest_non_decode_cuda_graph_size=smallest_non_decode_cuda_graph_size,
         )
 
         if adjusted_batch_dim is None:
@@ -512,4 +558,5 @@ def match_graph_config(
             return None
         # then find the best batch dimension
         best_batch_dim = min(graph_batch_dims_applicable)
+
         return best_batch_dim
diff --git a/megatron/core/inference/communication/torch_symm_triton/__init__.py b/megatron/core/inference/communication/torch_symm_triton/__init__.py
index ca58663d9ec..967dc8329f1 100644
--- a/megatron/core/inference/communication/torch_symm_triton/__init__.py
+++ b/megatron/core/inference/communication/torch_symm_triton/__init__.py
@@ -1,4 +1,5 @@
 # Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 
-from .collectives import multimem_all_gather, multimem_reduce_scatter
+from .collectives import multimem_all_gather, multimem_all_gather_fused, multimem_reduce_scatter
 from .fused_collectives import fused_multimem_rs_add_norm_ag
+from .utils import are_tensors_nvls_eligible, is_device_nvls_capable
diff --git a/megatron/core/inference/communication/torch_symm_triton/collectives.py b/megatron/core/inference/communication/torch_symm_triton/collectives.py
index 4bc4dbde42b..cf2003c8595 100644
--- a/megatron/core/inference/communication/torch_symm_triton/collectives.py
+++ b/megatron/core/inference/communication/torch_symm_triton/collectives.py
@@ -23,43 +23,39 @@
 
 from .barrier import symm_mem_sync
 from .multimem_asm import ld_128, st_128
-from .utils import get_flat_tid, sync_threads
+from .utils import are_tensors_nvls_eligible, get_flat_tid, sync_threads
 
 
 @triton.jit
-def _multimem_all_gather_kernel(
-    local_ptr,
-    multicast_ptr,
-    signal_pad_ptrs,
-    numel,
-    BLOCK_SIZE: tl.constexpr,
-    NUMEL_PER_THREAD: tl.constexpr,
-    RANK: tl.constexpr,
-    WORLD_SIZE: tl.constexpr,
+def _ag_phase(
+    local_ptr, multicast_ptr, byte_offset, numel, BLOCK_SIZE, NUMEL_PER_THREAD, RANK, WORLD_SIZE
 ):
     """
-    Triton kernel to perform multicast all-gather over nvlink using multimem instructions.
-    """
-    # an all-gather is simply a multicast store operation
-    # we only need a barrier at the end to ensure visibility of writes
+    Core all-gather phase: load from local memory, multicast-store to symmetric buffer.
+    This is the building block for both single-tensor and fused multi-tensor all-gathers.
 
+    Each thread handles 128-bit (NUMEL_PER_THREAD elements) at a time.
+    byte_offset locates the tensor within the multicast buffer.
+
+    """
     pid = tl.program_id(axis=0)
     tid = get_flat_tid()
 
-    # From this point on, we pretend each element is 128-bit
-    numel = numel // NUMEL_PER_THREAD
-    numel_per_rank = tl.cdiv(numel, WORLD_SIZE)
+    numel_128 = numel // NUMEL_PER_THREAD
+    numel_per_rank = tl.cdiv(numel_128, WORLD_SIZE)
     block_start = pid * BLOCK_SIZE
 
     while block_start < numel_per_rank:
         offsets = block_start + tid
         mask = offsets < numel_per_rank
 
-        # Each pointer points to a 128-bit bit pack
-        # RANK * numel_per_rank -> brings us to the start of our rank's segment
-        # offsets -> brings us to the right offset within our rank's segment
+        # byte_offset // 8 -> converts byte offset to uint64 offset
+        # RANK * numel_per_rank -> start of our rank's segment
+        # * 2 -> each 128-bit pack is 2 uint64s
         multicast_ptrs = (
-            multicast_ptr.to(tl.pointer_type(tl.uint64)) + (RANK * numel_per_rank + offsets) * 2
+            multicast_ptr.to(tl.pointer_type(tl.uint64))
+            + byte_offset // 8
+            + (RANK * numel_per_rank + offsets) * 2
         )
         local_ptrs = local_ptr.to(tl.pointer_type(tl.uint64)) + offsets * 2
         (x, y, z, w) = ld_128(local_ptrs, mask=mask, multicast_op=False)
@@ -67,6 +63,23 @@ def _multimem_all_gather_kernel(
 
         block_start += tl.num_programs(axis=0) * BLOCK_SIZE
 
+
+@triton.jit
+def _multimem_all_gather_kernel(
+    local_ptr,
+    multicast_ptr,
+    signal_pad_ptrs,
+    numel,
+    byte_offset,
+    BLOCK_SIZE: tl.constexpr,
+    NUMEL_PER_THREAD: tl.constexpr,
+    RANK: tl.constexpr,
+    WORLD_SIZE: tl.constexpr,
+):
+    """Single-tensor multicast all-gather kernel."""
+    _ag_phase(
+        local_ptr, multicast_ptr, byte_offset, numel, BLOCK_SIZE, NUMEL_PER_THREAD, RANK, WORLD_SIZE
+    )
     sync_threads()
     symm_mem_sync(
         signal_pad_ptrs,
@@ -78,54 +91,68 @@ def _multimem_all_gather_kernel(
     )
 
 
-def multimem_all_gather(
-    output_tensor: torch.Tensor,
-    input_tensor: torch.Tensor,
-    symm_mem_hdl: _SymmetricMemory,
-    **kwargs,
-) -> torch.Tensor:
+@triton.jit
+def _multimem_all_gather_3_kernel(
+    local_ptr_0,
+    local_ptr_1,
+    local_ptr_2,
+    multicast_ptr,
+    signal_pad_ptrs,
+    numel_0,
+    byte_offset_0,
+    numel_1,
+    byte_offset_1,
+    numel_2,
+    byte_offset_2,
+    BLOCK_SIZE: tl.constexpr,
+    NUMEL_PER_THREAD: tl.constexpr,
+    RANK: tl.constexpr,
+    WORLD_SIZE: tl.constexpr,
+):
     """
-    Calls a multicast all-gather triton kernel on the given tensor.
-    Output tensor must be a symmetric memory buffer.
-    Input tensor can be a regular torch tensor
-    Arguments:
-        output_tensor: torch.Tensor - output tensor to be all-gathered into
-        input_tensor: torch.Tensor - input tensor to be all-gathered from
-        symm_mem_hdl: _SymmetricMemory - handle to the symmetric memory buffer for output_tensor
-    Returns:
-        torch.Tensor - all-gathered tensor, which is output_tensor
+    Fused 3-tensor multicast all-gather. Processes three tensors in sequence
+    then synchronizes once, eliminating 2 kernel launches and 2 barriers
+    compared to three separate multimem_all_gather calls.
     """
-    assert HAVE_TRITON, "Triton is required for multimem all-gather."
-
-    config = {
-        "max_num_blocks": kwargs.get("max_num_blocks", 24),
-        "num_warps": kwargs.get("num_warps", 32),
-        "BLOCK_SIZE": kwargs.get("BLOCK_SIZE", 1024),
-    }
-    assert input_tensor.dtype == torch.bfloat16, "Only bfloat16 is supported for now."
-    assert output_tensor.dtype == torch.bfloat16, "Only bfloat16 is supported for now."
-    numel_per_thread = 128 // (input_tensor.element_size() * 8)
-
-    assert (
-        output_tensor.numel() % numel_per_thread == 0
-    ), "The number of elements must be 128-bit aligned."
-
-    num_threads = triton.cdiv(output_tensor.numel() // numel_per_thread, symm_mem_hdl.world_size)
-    num_blocks = min(triton.cdiv(num_threads, config["BLOCK_SIZE"]), config["max_num_blocks"])
-
-    _multimem_all_gather_kernel[(num_blocks, 1, 1)](
-        input_tensor.data_ptr(),
-        symm_mem_hdl.multicast_ptr,
-        symm_mem_hdl.signal_pad_ptrs_dev,
-        numel=output_tensor.numel(),
-        BLOCK_SIZE=config["BLOCK_SIZE"],
-        NUMEL_PER_THREAD=numel_per_thread,
-        RANK=symm_mem_hdl.rank,
-        WORLD_SIZE=symm_mem_hdl.world_size,
-        num_warps=config["num_warps"],
+    _ag_phase(
+        local_ptr_0,
+        multicast_ptr,
+        byte_offset_0,
+        numel_0,
+        BLOCK_SIZE,
+        NUMEL_PER_THREAD,
+        RANK,
+        WORLD_SIZE,
+    )
+    _ag_phase(
+        local_ptr_1,
+        multicast_ptr,
+        byte_offset_1,
+        numel_1,
+        BLOCK_SIZE,
+        NUMEL_PER_THREAD,
+        RANK,
+        WORLD_SIZE,
+    )
+    _ag_phase(
+        local_ptr_2,
+        multicast_ptr,
+        byte_offset_2,
+        numel_2,
+        BLOCK_SIZE,
+        NUMEL_PER_THREAD,
+        RANK,
+        WORLD_SIZE,
+    )
+    sync_threads()
+    symm_mem_sync(
+        signal_pad_ptrs,
+        None,
+        RANK,
+        WORLD_SIZE,
+        hasPreviousMemAccess=True,
+        hasSubsequentMemAccess=True,
     )
-
-    return output_tensor
 
 
 @triton.jit
@@ -175,47 +202,147 @@ def _multimem_reduce_scatter_kernel(
         block_start += tl.num_programs(axis=0) * BLOCK_SIZE
 
 
-def multimem_reduce_scatter(
+# ── Python wrappers ─────────────────────────────────────────────────────────
+
+_DEFAULT_KERNEL_CONFIG = {"max_num_blocks": 128, "num_warps": 32, "BLOCK_SIZE": 1024}
+
+
+def _kernel_launch_config(element_size: int, max_numel: int, world_size: int, **kwargs):
+    """Compute kernel launch config shared by all collective wrappers.
+
+    Args:
+        element_size: bytes per element (e.g. 2 for bf16).
+        max_numel: largest tensor numel (determines grid size).
+        world_size: number of ranks.
+
+    Returns:
+        (numel_per_thread, num_blocks, config) tuple.
+    """
+    config = {k: kwargs.get(k, v) for k, v in _DEFAULT_KERNEL_CONFIG.items()}
+    numel_per_thread = 128 // (element_size * 8)
+    num_threads = triton.cdiv(max_numel // numel_per_thread, world_size)
+    num_blocks = min(triton.cdiv(num_threads, config["BLOCK_SIZE"]), config["max_num_blocks"])
+    return numel_per_thread, num_blocks, config
+
+
+def multimem_all_gather(
     output_tensor: torch.Tensor,
     input_tensor: torch.Tensor,
     symm_mem_hdl: _SymmetricMemory,
+    byte_offset: int = 0,
     **kwargs,
 ) -> torch.Tensor:
     """
-    Calls a multicast reduce-scatter triton kernel on the given tensor.
-    Input tensor must be a symmetric memory buffer.
-    Output tensor can be a regular torch tensor
-    Arguments:
-        output_tensor: torch.Tensor - output tensor to be reduce-scattered into
-        input_tensor: torch.Tensor - input tensor to be reduce-scattered from
-        symm_mem_hdl: _SymmetricMemory - handle to the symmetric memory buffer for input_tensor
-        **kwargs: Additional keyword arguments for kernel configuration:
-            max_num_blocks (int, optional): The maximum number of blocks to launch.
-            num_warps (int, optional): The number of warps per block.
-            BLOCK_SIZE (int, optional): The BLOCK_SIZE parameter for the kernel.
-    Returns:
-        torch.Tensor - reduce-scattered tensor, which is output_tensor
+    Multicast all-gather for a single tensor.
+    Output tensor must be a symmetric memory buffer.
+    Input tensor can be a regular torch tensor.
     """
+    assert HAVE_TRITON, "Triton is required for multimem all-gather."
+    assert are_tensors_nvls_eligible(
+        input_tensor
+    ), "Input tensor must be 16-byte divisible on Hopper+ for NVLS."
+    assert (
+        output_tensor.numel() % input_tensor.numel() == 0
+        and output_tensor.numel() // input_tensor.numel() == symm_mem_hdl.world_size
+    ), "Output numel must be exactly world_size * input numel for all-gather."
 
-    assert HAVE_TRITON, "Triton is required for multimem reduce-scatter."
+    numel_per_thread, num_blocks, config = _kernel_launch_config(
+        input_tensor.element_size(), output_tensor.numel(), symm_mem_hdl.world_size, **kwargs
+    )
+    _multimem_all_gather_kernel[(num_blocks, 1, 1)](
+        input_tensor.data_ptr(),
+        symm_mem_hdl.multicast_ptr,
+        symm_mem_hdl.signal_pad_ptrs_dev,
+        numel=output_tensor.numel(),
+        byte_offset=byte_offset,
+        BLOCK_SIZE=config["BLOCK_SIZE"],
+        NUMEL_PER_THREAD=numel_per_thread,
+        RANK=symm_mem_hdl.rank,
+        WORLD_SIZE=symm_mem_hdl.world_size,
+        num_warps=config["num_warps"],
+    )
+
+    return output_tensor
+
+
+def multimem_all_gather_fused(
+    output_0: torch.Tensor,
+    input_0: torch.Tensor,
+    byte_offset_0: int,
+    output_1: torch.Tensor,
+    input_1: torch.Tensor,
+    byte_offset_1: int,
+    output_2: torch.Tensor,
+    input_2: torch.Tensor,
+    byte_offset_2: int,
+    symm_mem_hdl: _SymmetricMemory,
+    **kwargs,
+) -> None:
+    """
+    Fused 3-tensor multicast all-gather. Equivalent to calling multimem_all_gather
+    three times but with a single kernel launch and a single barrier.
+
+    All tensors must share the same symmetric memory handle.
+    """
+    assert HAVE_TRITON, "Triton is required for multimem all-gather."
+    assert are_tensors_nvls_eligible(
+        input_0, input_1, input_2
+    ), "All input tensors must be 16-byte divisible on Hopper+ for NVLS."
+    for inp, out in [(input_0, output_0), (input_1, output_1), (input_2, output_2)]:
+        assert (
+            out.numel() % inp.numel() == 0 and out.numel() // inp.numel() == symm_mem_hdl.world_size
+        ), "Output numel must be exactly world_size * input numel for all-gather."
+
+    max_numel = max(output_0.numel(), output_1.numel(), output_2.numel())
+
+    numel_per_thread, num_blocks, config = _kernel_launch_config(
+        input_0.element_size(), max_numel, symm_mem_hdl.world_size, **kwargs
+    )
+    _multimem_all_gather_3_kernel[(num_blocks, 1, 1)](
+        input_0.data_ptr(),
+        input_1.data_ptr(),
+        input_2.data_ptr(),
+        symm_mem_hdl.multicast_ptr,
+        symm_mem_hdl.signal_pad_ptrs_dev,
+        numel_0=output_0.numel(),
+        byte_offset_0=byte_offset_0,
+        numel_1=output_1.numel(),
+        byte_offset_1=byte_offset_1,
+        numel_2=output_2.numel(),
+        byte_offset_2=byte_offset_2,
+        BLOCK_SIZE=config["BLOCK_SIZE"],
+        NUMEL_PER_THREAD=numel_per_thread,
+        RANK=symm_mem_hdl.rank,
+        WORLD_SIZE=symm_mem_hdl.world_size,
+        num_warps=config["num_warps"],
+    )
 
-    config = {
-        "max_num_blocks": kwargs.get("max_num_blocks", 24),
-        "num_warps": kwargs.get("num_warps", 32),
-        "BLOCK_SIZE": kwargs.get("BLOCK_SIZE", 1024),
-    }
 
+def multimem_reduce_scatter(
+    output_tensor: torch.Tensor,
+    input_tensor: torch.Tensor,
+    symm_mem_hdl: _SymmetricMemory,
+    **kwargs,
+) -> torch.Tensor:
+    """
+    Multicast reduce-scatter for a single tensor.
+    Input tensor must be a symmetric memory buffer.
+    Output tensor can be a regular torch tensor.
+    """
+    assert HAVE_TRITON, "Triton is required for multimem reduce-scatter."
     assert input_tensor.dtype == torch.bfloat16, "Only bfloat16 is supported for now."
     assert output_tensor.dtype == torch.bfloat16, "Only bfloat16 is supported for now."
-    numel_per_thread = 128 // (output_tensor.element_size() * 8)
-
+    assert are_tensors_nvls_eligible(
+        output_tensor
+    ), "Output tensor must be 16-byte divisible on Hopper+ for NVLS."
     assert (
-        input_tensor.numel() % numel_per_thread == 0
-    ), "The number of elements must be 128-bit aligned."
-
-    num_threads = triton.cdiv(input_tensor.numel() // numel_per_thread, symm_mem_hdl.world_size)
-    num_blocks = min(triton.cdiv(num_threads, config["BLOCK_SIZE"]), config["max_num_blocks"])
+        input_tensor.numel() % output_tensor.numel() == 0
+        and input_tensor.numel() // output_tensor.numel() == symm_mem_hdl.world_size
+    ), "Input numel must be exactly world_size * output numel for reduce-scatter."
 
+    numel_per_thread, num_blocks, config = _kernel_launch_config(
+        output_tensor.element_size(), input_tensor.numel(), symm_mem_hdl.world_size, **kwargs
+    )
     _multimem_reduce_scatter_kernel[(num_blocks, 1, 1)](
         output_tensor.data_ptr(),
         symm_mem_hdl.multicast_ptr,
diff --git a/megatron/core/inference/communication/torch_symm_triton/utils.py b/megatron/core/inference/communication/torch_symm_triton/utils.py
index 785481dfba6..3cc6dd8dcc0 100644
--- a/megatron/core/inference/communication/torch_symm_triton/utils.py
+++ b/megatron/core/inference/communication/torch_symm_triton/utils.py
@@ -4,6 +4,8 @@
 
 from unittest.mock import MagicMock
 
+import torch
+
 from megatron.core.utils import null_decorator
 
 try:
@@ -15,6 +17,27 @@
     triton.jit = null_decorator
 
 
+def is_device_nvls_capable(device: torch.device) -> bool:
+    """Check if the device supports NVLS (multicast) collectives.
+    Requires CUDA Hopper+ (SM >= 9)."""
+    return device.type == "cuda" and torch.cuda.get_device_properties(device).major >= 9
+
+
+def are_tensors_nvls_eligible(*tensors: torch.Tensor) -> bool:
+    """Check if tensors are eligible for NVLS (multicast) collectives.
+
+    Requirements:
+    - Hopper+ GPU (SM >= 9)
+    - All tensor byte sizes are divisible by 16 (128-bit), since NVLS
+      kernels process data in 128-bit chunks.
+    """
+    if not tensors:
+        return False
+    return is_device_nvls_capable(tensors[0].device) and all(
+        t.element_size() * t.numel() % 16 == 0 for t in tensors
+    )
+
+
 @triton.jit
 def get_tid():
     """
diff --git a/megatron/core/inference/config.py b/megatron/core/inference/config.py
index 54800a3fbff..bc0770d450d 100644
--- a/megatron/core/inference/config.py
+++ b/megatron/core/inference/config.py
@@ -27,14 +27,25 @@ class MambaInferenceStateConfig:
     See `megatron/core/ssm/mamba_hybrid_layer_allocation.py` for the list of symbols.
     """
 
-    mamba_conv_states_shape: Tuple[int]
+    conv_states_shape: Tuple[int]
     """Mamba conv states shape per request."""
 
-    mamba_ssm_states_shape: Tuple[int]
-    """Mamba ssm states shape per request."""
+    ssm_states_shape: Tuple[int]
+    """Mamba SSM states shape per request."""
+
+    conv_states_dtype: torch.dtype
+    """The dtype to use for the Mamba conv state tensor. Defaults to the model dtype."""
+
+    ssm_states_dtype: torch.dtype
+    """The dtype to use for the Mamba SSM state tensor. Defaults to the model dtype."""
 
     @classmethod
-    def from_model(cls, model: MegatronModule) -> Optional["MambaInferenceStateConfig"]:
+    def from_model(
+        cls,
+        model: MegatronModule,
+        conv_states_dtype: Optional[torch.dtype] = None,
+        ssm_states_dtype: Optional[torch.dtype] = None,
+    ) -> Optional["MambaInferenceStateConfig"]:
         """Returns Mamba inference state config from the model if it is a hybrid model."""
         from megatron.core.ssm.mamba_hybrid_layer_allocation import Symbols
 
@@ -44,14 +55,46 @@ def from_model(cls, model: MegatronModule) -> Optional["MambaInferenceStateConfi
             (mamba_conv_states_shape, mamba_ssm_states_shape) = (
                 decoder.mamba_state_shapes_per_request()
             )
+            if conv_states_dtype is None:
+                conv_states_dtype = model.config.params_dtype
+            if ssm_states_dtype is None:
+                ssm_states_dtype = model.config.params_dtype
             return cls(
                 layer_type_list=layer_type_list,
-                mamba_conv_states_shape=mamba_conv_states_shape,
-                mamba_ssm_states_shape=mamba_ssm_states_shape,
+                conv_states_shape=mamba_conv_states_shape,
+                ssm_states_shape=mamba_ssm_states_shape,
+                conv_states_dtype=conv_states_dtype,
+                ssm_states_dtype=ssm_states_dtype,
             )
         return None
 
 
+class PrefixCachingEvictionPolicy(str, Enum):
+    """Eviction policy for prefix caching blocks.
+
+    Only applies when enable_prefix_caching is True.
+    """
+
+    REF_ZERO = "ref_zero"
+    """Deregister blocks immediately when ref_count hits 0. No caching after release."""
+
+    LRU = "lru"
+    """Keep released blocks in hash table. Evict oldest ref=0 blocks when space is needed."""
+
+
+class PrefixCachingCoordinatorPolicy(str, Enum):
+    """Routing policy for the DP inference coordinator with prefix caching."""
+
+    LONGEST_PREFIX = "longest_prefix"
+    """Route to the rank with the longest consecutive prefix match."""
+
+    FIRST_PREFIX_BLOCK = "first_prefix_block"
+    """Route to the rank that has the first block hash cached. O(ranks) check."""
+
+    ROUND_ROBIN = "round_robin"
+    """Route requests to ranks in round-robin order, ignoring prefix affinity."""
+
+
 class KVCacheManagementMode(str, Enum):
     """Mode for handling large tensors (KV cache, Mamba states) during suspend/resume."""
 
@@ -74,7 +117,7 @@ class InferenceConfig:
     """
 
     # =================================
-    # KV cache config
+    # KV cache and Mamba states config
     # =================================
     block_size_tokens: int = 256
     """Size of KV cache block size."""
@@ -95,6 +138,9 @@ class InferenceConfig:
         - uvm 1: buffer_size_gb + paused_buffer_size_gb
     """
 
+    mamba_inference_state_config: Optional[MambaInferenceStateConfig] = None
+    """The Mamba inference state config if the model is a hybrid model."""
+
     mamba_memory_ratio: Optional[float] = None
     """
     Percentage of memory buffer to allocate for Mamba states. If not specified, allocates Mamba
@@ -161,9 +207,6 @@ class InferenceConfig:
     max_sequence_length: int = 2560
     """Max possible sequence length (prompt + output) that will occur."""
 
-    mamba_inference_state_config: Optional[MambaInferenceStateConfig] = None
-    """The Mamba inference state config if the model is a hybrid model."""
-
     pg_collection: Optional[ProcessGroupCollection] = None
     """A `ProcessGroupCollection` for distributed execution."""
 
@@ -185,6 +228,29 @@ class InferenceConfig:
     enable_chunked_prefill: bool = False
     """Whether to enable chunked prefill."""
 
+    num_speculative_tokens: int = 0
+    """The number of speculative tokens to generate for decode steps."""
+
+    enable_prefix_caching: bool = False
+    """Whether to enable prefix caching for KV cache block sharing."""
+
+    prefix_caching_eviction_policy: PrefixCachingEvictionPolicy = (
+        PrefixCachingEvictionPolicy.REF_ZERO
+    )
+    """Eviction policy for prefix caching blocks. See `PrefixCachingEvictionPolicy` for options.
+
+    Only applies when enable_prefix_caching is True.
+    """
+
+    prefix_caching_coordinator_policy: PrefixCachingCoordinatorPolicy = (
+        PrefixCachingCoordinatorPolicy.FIRST_PREFIX_BLOCK
+    )
+    """Routing policy for the DP inference coordinator. See
+    `PrefixCachingCoordinatorPolicy` for options.
+
+    Only applies when enable_prefix_caching is True and using a coordinator.
+    """
+
     # =================================
     # Logging config
     # =================================
diff --git a/megatron/core/inference/contexts/attention_context/mamba_metadata.py b/megatron/core/inference/contexts/attention_context/mamba_metadata.py
index d7fcf7436a2..34a19cf0394 100644
--- a/megatron/core/inference/contexts/attention_context/mamba_metadata.py
+++ b/megatron/core/inference/contexts/attention_context/mamba_metadata.py
@@ -259,12 +259,17 @@ def update(
             self.cu_seqlens = self._cu_seqlens_buffer[: padded_prefill_count + 1]
 
         if padded_decode_count > 0 and padded_prefill_count > 0:
-            self._device_decode_prefill_buffer[0] = real_decode_count
+            self._device_decode_prefill_buffer[0] = cu_seqlens[real_decode_count]
             # This describes the number of items in the prefill tensor relative to the
             # decode tensor. If chunked prefill is present, it is included in the
             # "prefill" part of the main split.
-            self._device_decode_prefill_buffer[1] = regular_prefill_count + (
-                1 if has_chunked_prefill_req else 0
+            self._device_decode_prefill_buffer[1] = (
+                cu_seqlens[
+                    real_decode_count
+                    + regular_prefill_count
+                    + (1 if has_chunked_prefill_req else 0)
+                ]
+                - cu_seqlens[real_decode_count]
             )
             self.device_decode_prefill = self._device_decode_prefill_buffer
 
@@ -305,6 +310,25 @@ def allocate_slot(self) -> Optional[int]:
 
         return mamba_idx
 
+    def batch_allocate_slots(self, num_slots: int) -> Optional[torch.Tensor]:
+        """
+        Allocates new slots for the given number of requests in the Mamba state buffers.
+
+        Returns:
+            torch.Tensor: The indices of the allocated slots.
+            Returns None if not enough slots are available.
+        """
+        if self.mamba_state_free_slot_count < num_slots:
+            return None
+
+        # Get free slots
+        self.mamba_state_free_slot_count -= num_slots
+        mamba_idx = self.mamba_state_free_slots[
+            self.mamba_state_free_slot_count : self.mamba_state_free_slot_count + num_slots
+        ]
+
+        return mamba_idx
+
     def free_slots(self, request_indices: torch.Tensor) -> None:
         """
         Frees the Mamba state slots associated with the given request indices.
diff --git a/megatron/core/inference/contexts/attention_context/mha_metadata.py b/megatron/core/inference/contexts/attention_context/mha_metadata.py
index 1b6e8020275..07f8a349b51 100644
--- a/megatron/core/inference/contexts/attention_context/mha_metadata.py
+++ b/megatron/core/inference/contexts/attention_context/mha_metadata.py
@@ -41,6 +41,7 @@ def update(
         request_to_kv_block_ids: torch.Tensor,
         batch_dimensions: InferenceBatchDimensions,
         padded_batch_dimensions: InferenceBatchDimensions,
+        num_speculative_tokens: int = 0,
     ):
         """
         Args:
@@ -49,6 +50,7 @@ def update(
             request_to_kv_block_ids: (>real_batch_size, max_kv_blocks)
             batch_dimensions: Configuration object containing real batch settings
             padded_batch_dimensions: Configuration object containing padded batch settings
+            num_speculative_tokens: Number of speculative tokens
         """
         # Extract values from configs
         real_batch_size = batch_dimensions.req_count
@@ -99,7 +101,7 @@ def update(
         )
 
         if padded_batch_dimensions.prefill_req_count == 0:
-            self._max_seqlen_q = 1
+            self._max_seqlen_q = num_speculative_tokens + 1
         else:
             # Make sure we will launch the prefill kernel for prefill graphs
             self._max_seqlen_q = max(2, padded_batch_dimensions.token_count)
@@ -150,6 +152,7 @@ def update(
         request_to_kv_block_ids: torch.Tensor,
         batch_dimensions: InferenceBatchDimensions,
         padded_batch_dimensions: InferenceBatchDimensions,
+        num_speculative_tokens: int = 0,
     ):
         """
         Args:
@@ -158,6 +161,7 @@ def update(
             request_to_kv_block_ids: (>real_batch_size, max_kv_blocks)
             batch_dimensions: Configuration object containing real batch settings
             padded_batch_dimensions: Configuration object containing padded batch settings
+            num_speculative_tokens: Number of speculative tokens
         """
         super().update(
             request_query_lengths,
@@ -165,6 +169,7 @@ def update(
             request_to_kv_block_ids,
             batch_dimensions,
             padded_batch_dimensions,
+            num_speculative_tokens,
         )
 
     def reset(self):
@@ -183,6 +188,7 @@ def update(
         request_to_kv_block_ids: torch.Tensor,
         batch_dimensions: InferenceBatchDimensions,
         padded_batch_dimensions: InferenceBatchDimensions,
+        num_speculative_tokens: int = 0,
     ):
         """
         Args:
@@ -191,6 +197,7 @@ def update(
             request_to_kv_block_ids: (>real_batch_size, max_kv_blocks)
             batch_dimensions: Configuration object containing real batch settings
             padded_batch_dimensions: Configuration object containing padded batch settings
+            num_speculative_tokens: Number of speculative tokens
         """
         super().update(
             request_query_lengths,
@@ -198,10 +205,11 @@ def update(
             request_to_kv_block_ids,
             batch_dimensions,
             padded_batch_dimensions,
+            num_speculative_tokens,
         )
         if len(self.state_data["query_lengths"]) > 0:
             self.state_data["max_seqlen_q"] = torch.max(self.state_data["query_lengths"]).item()
             self.state_data["max_seqlen_k"] = torch.max(self.state_data["kv_seq_lengths"]).item()
         else:
-            self.state_data["max_seqlen_q"] = 1
+            self.state_data["max_seqlen_q"] = num_speculative_tokens + 1
             self.state_data["max_seqlen_k"] = 1
diff --git a/megatron/core/inference/contexts/dynamic_block_allocator.py b/megatron/core/inference/contexts/dynamic_block_allocator.py
index 8207a17550c..5bbf7001094 100644
--- a/megatron/core/inference/contexts/dynamic_block_allocator.py
+++ b/megatron/core/inference/contexts/dynamic_block_allocator.py
@@ -1,10 +1,13 @@
 # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
 
-from typing import Optional
+from collections import deque
+from typing import Dict, Optional
 
 import torch
 from torch import Tensor
 
+from megatron.core.inference.config import PrefixCachingEvictionPolicy
+
 
 class BlockAllocator:
     """Allocator that manages blocks of memory for the KV cache.
@@ -21,9 +24,20 @@ class BlockAllocator:
             than `total_count`.
     """
 
-    def __init__(self, context: "DynamicInferenceContext", total_count: int, paused_count: int):
+    def __init__(
+        self,
+        context: "DynamicInferenceContext",
+        total_count: int,
+        paused_count: int,
+        enable_prefix_caching: bool = False,
+        prefix_caching_eviction_policy: PrefixCachingEvictionPolicy = (
+            PrefixCachingEvictionPolicy.REF_ZERO
+        ),
+    ):
 
         self.context = context
+        self.enable_prefix_caching = enable_prefix_caching
+        self.prefix_caching_eviction_policy = prefix_caching_eviction_policy
 
         self.total_count = total_count
         self.total_avail = total_count - 1  # -1 for dummy_block_idx (see below)
@@ -37,6 +51,27 @@ def __init__(self, context: "DynamicInferenceContext", total_count: int, paused_
             self.total_count, dtype=torch.int32, device=torch.cuda.current_device()
         )
 
+        if self.enable_prefix_caching:
+            # Block hash tracking for prefix caching: -1 = uncomputed, positive = valid hash
+            self.block_hashes = torch.full(
+                (self.total_count,), -1, dtype=torch.int64, device=torch.cuda.current_device()
+            )
+
+            # Hash-to-block mapping for O(1) prefix lookup
+            self.hash_to_block_id: Dict[int, int] = {}
+
+            # Reference count per block: 0 = cached (evictable), >0 = actively used
+            self.block_ref_counts = torch.zeros(
+                (self.total_count,), dtype=torch.int32, device=torch.cuda.current_device()
+            )
+
+            # LRU timestamps for eviction ordering (higher = more recently used)
+            # Only needed in LRU mode; RZ mode evicts immediately on ref_count==0
+            if self.prefix_caching_eviction_policy == PrefixCachingEvictionPolicy.LRU:
+                self.block_timestamps = torch.zeros(
+                    (self.total_count,), dtype=torch.int64, device=torch.cuda.current_device()
+                )
+
     def __str__(self):
         return (
             f"using: total {self.get_total_used()}/{self.total_count - 1}"
@@ -50,19 +85,39 @@ def get_total_used(self):
 
     def get_active_used(self):
         """Compute number of active blocks used."""
-        return (
-            self.context.request_kv_block_counts[
-                self.context.paused_request_count : self.context.total_request_count
-            ]
-            .sum()
-            .item()
-        )
+        if not self.enable_prefix_caching:
+            return (
+                self.context.request_kv_block_counts[
+                    self.context.paused_request_count : self.context.total_request_count
+                ]
+                .sum()
+                .item()
+            )
+
+        active_start = self.context.paused_request_count
+        active_end = self.context.total_request_count
+        if active_end > active_start:
+            active_rows = self.context.request_to_kv_block_ids[active_start:active_end]
+            valid_ids = active_rows[active_rows >= 0]
+            if valid_ids.numel() > 0:
+                return int(torch.unique(valid_ids).numel())
+        return 0
 
     def get_paused_used(self):
         """Compute number of paused blocks used."""
-        return (
-            self.context.request_kv_block_counts[: self.context.paused_request_count].sum().item()
-        )
+        if not self.enable_prefix_caching:
+            return (
+                self.context.request_kv_block_counts[: self.context.paused_request_count]
+                .sum()
+                .item()
+            )
+
+        if self.context.paused_request_count > 0:
+            paused_rows = self.context.request_to_kv_block_ids[: self.context.paused_request_count]
+            valid_ids = paused_rows[paused_rows >= 0]
+            if valid_ids.numel() > 0:
+                return int(torch.unique(valid_ids).numel())
+        return 0
 
     def get_active_avail(self):
         """Compute number of active blocks available."""
@@ -75,33 +130,65 @@ def get_paused_avail(self):
     def is_memory_available(self, num_blocks: int) -> bool:
         """Check if memory blocks are available.
 
+        Includes both free pool blocks and evictable cached blocks (ref_count == 0).
+
         Args:
             num_blocks (int): Number of blocks to check.
 
         Return:
             (bool) Is memory available?
         """
-        return self.total_avail >= num_blocks
+        # Fast path: avoid expensive evictable count computation when free pool suffices
+        if self.total_avail >= num_blocks:
+            return True
+        if not self.enable_prefix_caching:
+            return False
+        if self.prefix_caching_eviction_policy == PrefixCachingEvictionPolicy.REF_ZERO:
+            return False  # RZ: no cached blocks to evict
+        # Also count evictable cached blocks
+        evictable_count = self.get_evictable_block_count()
+        return (self.total_avail + evictable_count) >= num_blocks
 
     def allocate_memory_blocks(self, num_blocks: int) -> Optional[Tensor]:
         """Allocate memory blocks if available, else return None.
 
+        Will attempt LRU eviction of cached blocks if the free pool is insufficient.
+
         Args:
             num_blocks (int): Number of blocks to allocate.
 
         Return:
             (Optional[Tensor]) Allocated block IDs.
         """
-        if self.is_memory_available(num_blocks):
-            self.total_avail -= num_blocks
-            block_ids = self.block_bag[self.total_avail : (self.total_avail + num_blocks)]
-            assert num_blocks == block_ids.numel()
-            return block_ids
-        else:
-            return None
+        # Try to evict cached blocks if free pool is insufficient
+        if self.total_avail < num_blocks:
+            if (
+                not self.enable_prefix_caching
+                or self.prefix_caching_eviction_policy == PrefixCachingEvictionPolicy.REF_ZERO
+            ):
+                return None  # RZ: no eviction path; disabled: no cached blocks
+            blocks_needed_from_eviction = num_blocks - self.total_avail
+            if not self.evict_lru_blocks(blocks_needed_from_eviction):
+                return None  # Not enough blocks even after eviction
+
+        # Now allocate from the free pool
+        self.total_avail -= num_blocks
+        block_ids = self.block_bag[self.total_avail : (self.total_avail + num_blocks)]
+        assert num_blocks == block_ids.numel()
+
+        if self.enable_prefix_caching:
+            # Initialize ref counts for newly allocated blocks
+            self.block_ref_counts[block_ids] = 1
+            if self.prefix_caching_eviction_policy == PrefixCachingEvictionPolicy.LRU:
+                self.update_timestamps(block_ids)
+
+        return block_ids
 
     def release_memory_blocks(self, blocks: Tensor) -> None:
-        """Release memory blocks.
+        """Release memory blocks by decrementing reference counts.
+
+        Blocks with ref_count == 0 remain cached (in hash map) for potential reuse.
+        They will be evicted via LRU when space is needed.
 
         Args:
             blocks (Tensor): Block IDs to release.
@@ -109,9 +196,19 @@ def release_memory_blocks(self, blocks: Tensor) -> None:
         Return:
             None
         """
-        num_blocks = blocks.size(dim=0)
-        self.block_bag[self.total_avail : (self.total_avail + num_blocks)] = blocks
-        self.total_avail += num_blocks
+        if blocks.numel() == 0:
+            return
+
+        if self.enable_prefix_caching:
+            self.block_ref_counts[blocks] -= 1
+            if self.prefix_caching_eviction_policy == PrefixCachingEvictionPolicy.REF_ZERO:
+                zero_mask = self.block_ref_counts[blocks] == 0
+                if zero_mask.any():
+                    self._deregister_blocks(blocks[zero_mask])
+        else:
+            num_blocks = blocks.numel()
+            self.block_bag[self.total_avail : self.total_avail + num_blocks] = blocks
+            self.total_avail += num_blocks
 
     def reset(self) -> None:
         """Reset the allocator to initial state.
@@ -133,3 +230,112 @@ def reset(self) -> None:
         )
 
         self.total_avail = self.total_count - 1
+
+        if self.enable_prefix_caching:
+            # Reset all block hashes
+            self.block_hashes.fill_(-1)
+
+            # Reset prefix caching state
+            self.hash_to_block_id.clear()
+            self.block_ref_counts.fill_(0)
+            if self.prefix_caching_eviction_policy == PrefixCachingEvictionPolicy.LRU:
+                self.block_timestamps.fill_(0)
+
+    # =========================================================================
+    # Prefix caching methods
+    # =========================================================================
+
+    def register_block_hashes(self, block_ids: list[int], block_hashes: list[int]) -> None:
+        """Register blocks in the hash-to-block mapping for discovery (batch).
+
+        Args:
+            block_ids: List of block IDs.
+            block_hashes: List of computed hash values (same length as block_ids).
+        """
+        if not block_ids:
+            return
+        id_tensor = torch.tensor(block_ids, dtype=torch.int64, device=self.block_hashes.device)
+        hash_tensor = torch.tensor(block_hashes, dtype=torch.int64, device=self.block_hashes.device)
+        self.block_hashes[id_tensor] = hash_tensor
+        self.hash_to_block_id.update(zip(block_hashes, block_ids))
+
+    def _deregister_blocks(self, block_ids: Tensor) -> None:
+        """Remove blocks from prefix caching state and return to free pool.
+
+        Shared cleanup logic for both LRU eviction and RZ proactive eviction.
+
+        Args:
+            block_ids: Tensor of block IDs to deregister.
+        """
+        num_blocks = block_ids.numel()
+        if num_blocks == 0:
+            return
+
+        # Gather hashes via batched tensor indexing
+        block_ids_i64 = block_ids.to(torch.int64)
+        hashes = self.block_hashes[block_ids_i64].tolist()
+
+        # Remove from hash_to_block_id dict (set ops + C-level map, no Python loop)
+        keys_to_delete = set(hashes) - {-1}
+        deque(
+            map(self.hash_to_block_id.pop, keys_to_delete & self.hash_to_block_id.keys()), maxlen=0
+        )
+
+        # Reset block state (batched tensor ops)
+        self.block_hashes[block_ids] = -1
+        self.block_ref_counts[block_ids] = 0
+        if self.prefix_caching_eviction_policy == PrefixCachingEvictionPolicy.LRU:
+            self.block_timestamps[block_ids] = 0
+
+        # Return blocks to free pool
+        self.block_bag[self.total_avail : self.total_avail + num_blocks] = block_ids
+        self.total_avail += num_blocks
+
+    def update_timestamps(self, block_ids: Tensor) -> None:
+        """Update LRU timestamps for accessed blocks. No-op in RZ mode.
+
+        Args:
+            block_ids: Tensor of block IDs that were accessed.
+        """
+        if (
+            self.prefix_caching_eviction_policy != PrefixCachingEvictionPolicy.LRU
+            or block_ids.numel() == 0
+        ):
+            return
+        self.block_timestamps[block_ids] = self.context.step_count
+
+    def get_evictable_block_count(self) -> Tensor:
+        """Get count of cached blocks that can be evicted (ref_count == 0, hash set).
+
+        Returns:
+            Scalar tensor with the number of evictable cached blocks.
+        """
+        cached_mask = (self.block_ref_counts == 0) & (self.block_hashes != -1)
+        return cached_mask.sum()
+
+    def evict_lru_blocks(self, num_blocks_needed: int) -> bool:
+        """Evict LRU cached blocks to free up space in the pool.
+
+        Evicts blocks with ref_count == 0, starting with oldest timestamps.
+
+        Args:
+            num_blocks_needed: Number of blocks to evict.
+
+        Returns:
+            True if enough blocks were evicted, False otherwise.
+        """
+        # Find all cached blocks (ref_count == 0, hash != -1)
+        cached_mask = (self.block_ref_counts == 0) & (self.block_hashes != -1)
+        cached_block_ids = torch.nonzero(cached_mask, as_tuple=True)[0]
+
+        if cached_block_ids.numel() < num_blocks_needed:
+            return False  # Not enough cached blocks to evict
+
+        # Sort by timestamp (ascending = oldest first)
+        cached_timestamps = self.block_timestamps[cached_block_ids]
+        sorted_indices = torch.argsort(cached_timestamps)
+        blocks_to_evict = cached_block_ids[sorted_indices[:num_blocks_needed]]
+
+        self._deregister_blocks(blocks_to_evict)
+
+        return True
diff --git a/megatron/core/inference/contexts/dynamic_context.py b/megatron/core/inference/contexts/dynamic_context.py
index 23e05bad92c..92ced8a0fc7 100644
--- a/megatron/core/inference/contexts/dynamic_context.py
+++ b/megatron/core/inference/contexts/dynamic_context.py
@@ -15,14 +15,18 @@
     CUDAGraphBatchDimensionBuilder,
     InferenceBatchDimensions,
 )
-from megatron.core.inference.config import InferenceConfig, KVCacheManagementMode
+from megatron.core.inference.config import (
+    InferenceConfig,
+    KVCacheManagementMode,
+    PrefixCachingEvictionPolicy,
+)
 from megatron.core.inference.inference_request import DynamicInferenceRequest
 from megatron.core.inference.sampling_params import SamplingParams
 from megatron.core.inference.unified_memory import (
     UnifiedMemoryUnsupportedError,
     create_unified_mempool,
 )
-from megatron.core.inference.utils import tensor_swap
+from megatron.core.inference.utils import device_memory_summary, tensor_swap
 from megatron.core.models.common.embeddings.rope_utils import apply_rotary_pos_emb
 from megatron.core.package_info import __version__ as mcore_version
 from megatron.core.ssm.mamba_hybrid_layer_allocation import get_layer_maps_from_layer_type_list
@@ -231,6 +235,7 @@ class DynamicInferenceContext(BaseInferenceContext):
     DEFAULT_MAX_TOKENS = 16384
     TOKEN_ROUNDER = 64
     REQUEST_ROUNDER = 4
+    TMS_TAG = "inference_context"
 
     @deprecate_args(
         *DEPRECATED_ARGS,
@@ -242,6 +247,14 @@ class DynamicInferenceContext(BaseInferenceContext):
     def __init__(self, model_config: TransformerConfig, inference_config: InferenceConfig):
         super().__init__(inference_config=inference_config)
 
+        # Prefix caching configuration
+        self.enable_prefix_caching = inference_config.enable_prefix_caching
+        self.prefix_caching_eviction_policy = inference_config.prefix_caching_eviction_policy
+        self.prefix_caching_coordinator_policy = inference_config.prefix_caching_coordinator_policy
+
+        # Step counter (used for LRU timestamps in prefix caching)
+        self.step_count = 0
+
         self.cache_mla_latent = (
             isinstance(model_config, MLATransformerConfig) and model_config.cache_mla_latents
         )
@@ -261,7 +274,16 @@ def __init__(self, model_config: TransformerConfig, inference_config: InferenceC
             tp_size = model_config.tensor_model_parallel_size
             pp_size = model_config.pipeline_model_parallel_size
         self.hidden_size_per_attention_head = core_divide(projection_size, num_attention_heads)
-        self.num_attention_heads_per_partition = core_divide(num_attention_heads, tp_size)
+        if num_attention_heads >= tp_size:
+            self.num_attention_heads_per_partition = core_divide(num_attention_heads, tp_size)
+        else:
+            self.num_attention_heads_per_partition = 1
+
+        self.num_speculative_tokens = inference_config.num_speculative_tokens
+        assert self.num_speculative_tokens < inference_config.block_size_tokens, (
+            f"num_speculative_tokens ({self.num_speculative_tokens}) must be < "
+            f"block_size_tokens ({inference_config.block_size_tokens})"
+        )
 
         # Cache the PP group we should use for PP collectives inside the context.
         # If the model provides a pg_collection with a pp group, prefer it.
@@ -286,14 +308,10 @@ def __init__(self, model_config: TransformerConfig, inference_config: InferenceC
         mamba_inference_state_config = inference_config.mamba_inference_state_config
         self.is_hybrid_model = mamba_inference_state_config is not None
         if self.is_hybrid_model:
-            mamba_conv_states_shape = mamba_inference_state_config.mamba_conv_states_shape
-            mamba_ssm_states_shape = mamba_inference_state_config.mamba_ssm_states_shape
-            assert (
-                mamba_conv_states_shape is not None
-            ), "`mamba_conv_states_shape` must be specified for hybrid models"
-            assert (
-                mamba_ssm_states_shape is not None
-            ), "`mamba_ssm_states_shape` must be specified for hybrid models"
+            self.mamba_conv_states_shape = mamba_inference_state_config.conv_states_shape
+            self.mamba_ssm_states_shape = mamba_inference_state_config.ssm_states_shape
+            self.mamba_conv_states_dtype = mamba_inference_state_config.conv_states_dtype
+            self.mamba_ssm_states_dtype = mamba_inference_state_config.ssm_states_dtype
 
             # For hybrid models, the layer map converts the global layer index to the
             # corresponding attention layer index or Mamba layer index depending on the
@@ -303,8 +321,6 @@ def __init__(self, model_config: TransformerConfig, inference_config: InferenceC
             )
             self.num_attention_layers = len(attention_layer_map)
             self.num_mamba_layers = len(mamba_layer_map)
-            self.mamba_conv_states_shape = mamba_conv_states_shape
-            self.mamba_ssm_states_shape = mamba_ssm_states_shape
             self.layer_map = attention_layer_map | mamba_layer_map
         else:
             # The layer map is the identity function for pure Transformer models.
@@ -319,20 +335,20 @@ def __init__(self, model_config: TransformerConfig, inference_config: InferenceC
             )
 
         # Block size tokens, bytes.
-        dtype_size_bytes = model_config.params_dtype.itemsize
+        kv_dtype_size_bytes = model_config.params_dtype.itemsize
         self.block_size_tokens = inference_config.block_size_tokens
         if self.cache_mla_latent:
             #   one vector  c_t  (rank)  +  optional RoPE phase slice
             self.kv_reduced_dim = model_config.kv_lora_rank + model_config.qk_pos_emb_head_dim
             self.block_size_bytes = (
-                dtype_size_bytes
+                kv_dtype_size_bytes
                 * self.num_attention_layers
                 * self.block_size_tokens
                 * self.kv_reduced_dim
             )
         else:
             self.block_size_bytes = (
-                dtype_size_bytes
+                kv_dtype_size_bytes
                 * 2  # key, value
                 * self.num_attention_layers
                 * self.block_size_tokens
@@ -343,10 +359,22 @@ def __init__(self, model_config: TransformerConfig, inference_config: InferenceC
 
         mamba_states_memory_per_request = 0
         if self.is_hybrid_model:
-            mamba_states_memory_per_request += math.prod(self.mamba_conv_states_shape)
-            mamba_states_memory_per_request += math.prod(self.mamba_ssm_states_shape)
+            mamba_states_memory_per_request += (
+                math.prod(self.mamba_conv_states_shape) * self.mamba_conv_states_dtype.itemsize
+            )
+            mamba_states_memory_per_request += (
+                math.prod(self.mamba_ssm_states_shape) * self.mamba_ssm_states_dtype.itemsize
+            )
             mamba_states_memory_per_request *= self.num_mamba_layers
-            mamba_states_memory_per_request *= dtype_size_bytes
+            if self.num_speculative_tokens > 0:
+                # Add memory for intermediate conv and SSM states
+                intermediate_memory_per_request = (
+                    math.prod(self.mamba_conv_states_shape) * self.mamba_conv_states_dtype.itemsize
+                    + math.prod(self.mamba_ssm_states_shape) * self.mamba_ssm_states_dtype.itemsize
+                )
+                intermediate_memory_per_request *= self.num_mamba_layers
+                intermediate_memory_per_request *= self.num_speculative_tokens + 1
+                mamba_states_memory_per_request += intermediate_memory_per_request
 
         # Unified memory and general tensor management.
         self.unified_memory_level = inference_config.unified_memory_level
@@ -439,6 +467,8 @@ def __init__(self, model_config: TransformerConfig, inference_config: InferenceC
                 block_count if self.unified_memory_level == 0 else block_count + paused_block_count
             ),
             paused_count=paused_block_count,
+            enable_prefix_caching=self.enable_prefix_caching,
+            prefix_caching_eviction_policy=self.prefix_caching_eviction_policy,
         )
 
         # Track request metadata.
@@ -451,20 +481,6 @@ def __init__(self, model_config: TransformerConfig, inference_config: InferenceC
         self.params_dtype = model_config.params_dtype
         self.max_sequence_length = inference_config.max_sequence_length
 
-        # Request and token counts.
-        self.total_request_count = 0
-        self.active_token_count = 0
-        self.paused_request_count = 0
-        self.batch_dimensions = InferenceBatchDimensions(
-            token_count=0, prefill_req_count=0, decode_req_count=0
-        )
-        self.padded_batch_dimensions = InferenceBatchDimensions(
-            token_count=0, prefill_req_count=0, decode_req_count=0
-        )
-        self.padded_active_token_count = 0
-        self.padded_active_request_count = 0
-        self.paused_tokens = None
-
         # Block ids.
         self.max_kv_block_count = math.ceil(self.max_sequence_length / self.block_size_tokens)
 
@@ -497,11 +513,8 @@ def __init__(self, model_config: TransformerConfig, inference_config: InferenceC
 
         # Attention metadata initialization (tensors are now handled by MHAMetadata classes)
 
-        self.num_prefill_requests = 0
         self.graph_attn_metadata = {}
         self.non_graph_attn_metadata = {}
-        self.active_attn_metadata = None
-        self.is_creating_cuda_graphs = False
 
         self.graph_attn_metadata["mha_metadata"] = GraphedMHAMetadata(
             block_count_total=self.block_allocator.total_count,
@@ -534,19 +547,22 @@ def __init__(self, model_config: TransformerConfig, inference_config: InferenceC
             CUDAGraphBatchDimensionBuilder.generate_cuda_graph_batch_dimensions_list(
                 tp_size=tp_size,
                 num_cuda_graphs=inference_config.num_cuda_graphs,
-                cuda_graph_max_tokens=self.max_requests,
-                cuda_graph_mixed_prefill_count=inference_config.cuda_graph_mixed_prefill_count,
+                cuda_graph_max_tokens=self.max_requests * (self.num_speculative_tokens + 1),
+                cuda_graph_mixed_prefill_request_count=inference_config.cuda_graph_mixed_prefill_count,
                 max_requests=self.max_requests,
                 max_tokens=self.max_tokens,
                 max_sequence_length=self.max_sequence_length,
                 use_cuda_graphs_for_non_decode_steps=self.use_cuda_graphs_for_non_decode_steps,
+                num_speculative_tokens=self.num_speculative_tokens,
             )
         )
 
-        self._using_cuda_graph_this_step = False
+        self.smallest_non_decode_cuda_graph_size = min(
+            inference_config.cuda_graph_mixed_prefill_count, self.max_requests
+        )
+
         # Deal with chunked prefill
         self.enable_chunked_prefill = inference_config.enable_chunked_prefill
-        self.chunked_prefill_request_id = -1
 
         # FlashInfer.
         if inference_config.use_flashinfer_fused_rope is True:
@@ -613,14 +629,35 @@ def _allocate_mamba_states(self):
             )
             self.mamba_conv_states = torch.empty(
                 (self.num_mamba_layers, self.max_requests) + self.mamba_conv_states_shape,
-                dtype=self.params_dtype,
+                dtype=self.mamba_conv_states_dtype,
                 device=torch.cuda.current_device(),
             )
             self.mamba_ssm_states = torch.empty(
                 (self.num_mamba_layers, self.max_requests) + self.mamba_ssm_states_shape,
-                dtype=self.params_dtype,
+                dtype=self.mamba_ssm_states_dtype,
                 device=torch.cuda.current_device(),
             )
+            if self.num_speculative_tokens > 0:
+                self.mamba_intermediate_conv_states = torch.empty(
+                    (
+                        self.num_mamba_layers,
+                        self.max_requests,
+                        self.num_speculative_tokens + 1,
+                        *self.mamba_conv_states_shape,
+                    ),
+                    dtype=self.mamba_conv_states_dtype,
+                    device=torch.cuda.current_device(),
+                )
+                self.mamba_intermediate_ssm_states = torch.empty(
+                    (
+                        self.num_mamba_layers,
+                        self.max_requests,
+                        self.num_speculative_tokens + 1,
+                        *self.mamba_ssm_states_shape,
+                    ),
+                    dtype=self.mamba_ssm_states_dtype,
+                    device=torch.cuda.current_device(),
+                )
             if (
                 self.kv_cache_management_mode == KVCacheManagementMode.OFFLOAD
                 and not self._uses_torch_memory_saver
@@ -634,6 +671,19 @@ def _allocate_mamba_states(self):
                 self._offloadable_cpu_backups["mamba_ssm_states"] = torch.empty_like(
                     self.mamba_ssm_states, device="cpu"
                 ).pin_memory()
+                if self.num_speculative_tokens > 0:
+                    self._offloadable_tensor_names.add("mamba_intermediate_conv_states")
+                    self._offloadable_cpu_backups["mamba_intermediate_conv_states"] = (
+                        torch.empty_like(
+                            self.mamba_intermediate_conv_states, device="cpu"
+                        ).pin_memory()
+                    )
+                    self._offloadable_tensor_names.add("mamba_intermediate_ssm_states")
+                    self._offloadable_cpu_backups["mamba_intermediate_ssm_states"] = (
+                        torch.empty_like(
+                            self.mamba_intermediate_ssm_states, device="cpu"
+                        ).pin_memory()
+                    )
         else:
             self.mamba_metadata = None
 
@@ -658,6 +708,8 @@ def initialize_all_tensors(self) -> None:
         )
         # request_query_lengths is the input prompt tokens length during prefill phase (1st step) and then 1 for the decode phase (i.e During generation)
         self.request_query_lengths = torch.empty_like(self.request_ids)
+        # True only for a new request , then after a forward pass it is set to False
+        self.request_in_prefill_status_tensor = torch.empty_like(self.request_ids)
         # request_output_lengths is len(input_prompt_tokens) + num_tokens_to_generate
         self.request_output_lengths = torch.empty_like(self.request_ids)
         # request_kv_length_offsets is the same as query length during prefill phase (1st step) and then 1 for the decode phase (i.e During generation)
@@ -694,6 +746,12 @@ def initialize_all_tensors(self) -> None:
         self.token_to_position_in_request = torch.empty_like(self.token_to_input_ids)
         self.token_to_local_position_within_kv_block = torch.empty_like(self.token_to_input_ids)
 
+        # NOTE: Need to build this outside the UVM / TMS context to avoid IMA.
+        if self.is_hybrid_model:
+            self.mamba_metadata = MambaMetadata(
+                max_requests=self.max_requests, max_tokens=self.max_tokens
+            )
+
         # Allocate large non-graphed buffers.
         need_static_addr = (
             self.static_kv_memory_pointers
@@ -705,7 +763,7 @@ def initialize_all_tensors(self) -> None:
             ctx_manager = torch.cuda.use_mem_pool(self.unified_memory_mempool)
         elif HAVE_TORCH_MEMORY_SAVER and need_static_addr:
             ctx_manager = torch_memory_saver.region(
-                tag="inference_context",
+                tag=self.TMS_TAG,
                 enable_cpu_backup=(self.kv_cache_management_mode == KVCacheManagementMode.OFFLOAD),
             )
             self._uses_torch_memory_saver = True
@@ -713,9 +771,8 @@ def initialize_all_tensors(self) -> None:
             self._allocate_memory_buffer()
             self._allocate_mamba_states()
 
-        # Reset attention and Mamba state.
-        self.reset_attention_state()
-        self.reset_mamba_state()
+        # Reset tensor-related metadata.
+        self.reset_metadata()
 
     def reinitialize_inference_state_buffers(self):
         """Restore large tensors (KV cache, Mamba states) after a suspend.
@@ -730,10 +787,20 @@ def reinitialize_inference_state_buffers(self):
             return
 
         if self.unified_memory_level != 0 or self._uses_torch_memory_saver:
-            if self.kv_cache_management_mode == KVCacheManagementMode.RECOMPUTE:
-                self.reset()
+            # Need to bring back the memory block before we reset it.
             if self._uses_torch_memory_saver:
-                torch_memory_saver.resume("inference_context")
+                tag = self.TMS_TAG
+                if torch.distributed.get_rank() == 0:
+                    logging.info(
+                        "torch_memory_saver: resuming %s, before: %s", tag, device_memory_summary()
+                    )
+                torch_memory_saver.resume(tag)
+                if torch.distributed.get_rank() == 0:
+                    logging.info(
+                        "torch_memory_saver: resumed  %s, after:  %s", tag, device_memory_summary()
+                    )
+            if self.kv_cache_management_mode == KVCacheManagementMode.RECOMPUTE:
+                self.reset_metadata()
             return
 
         if self.kv_cache_management_mode == KVCacheManagementMode.OFFLOAD:
@@ -760,7 +827,16 @@ def deallocate_inference_state_buffers(self):
             return
 
         if self._uses_torch_memory_saver:
-            torch_memory_saver.pause("inference_context")
+            tag = self.TMS_TAG
+            if torch.distributed.get_rank() == 0:
+                logging.info(
+                    "torch_memory_saver: pausing %s, before: %s", tag, device_memory_summary()
+                )
+            torch_memory_saver.pause(tag)
+            if torch.distributed.get_rank() == 0:
+                logging.info(
+                    "torch_memory_saver: paused  %s, after:  %s", tag, device_memory_summary()
+                )
             return
 
         if self.kv_cache_management_mode == KVCacheManagementMode.OFFLOAD:
@@ -928,13 +1004,19 @@ def key_value_cache(self, layer_number: int) -> Tuple[Tensor, Optional[Tensor],
                 self.active_attn_metadata["mha_metadata"].state_data["block_table"],
             )
 
-    def mamba_states_cache(self, layer_number: int) -> Tuple[Tensor, Tensor]:
+    def mamba_states_cache(
+        self, layer_number: int, intermediate: bool = False
+    ) -> Tuple[Tensor, Tensor]:
         """Returns the Mamba state tensors for the given layer."""
         assert self.is_hybrid_model, "Only hybrid models have Mamba state tensors"
 
         mamba_layer_number = self.layer_map[layer_number - 1]
-        conv_state = self.mamba_conv_states[mamba_layer_number]
-        ssm_state = self.mamba_ssm_states[mamba_layer_number]
+        if intermediate:
+            conv_state = self.mamba_intermediate_conv_states[mamba_layer_number]
+            ssm_state = self.mamba_intermediate_ssm_states[mamba_layer_number]
+        else:
+            conv_state = self.mamba_conv_states[mamba_layer_number]
+            ssm_state = self.mamba_ssm_states[mamba_layer_number]
 
         return (conv_state, ssm_state)
 
@@ -1136,6 +1218,7 @@ def add_dummy_requests_parallel(
 
         self.request_ids[request_slice] = request_ids_tensor
         self.request_query_lengths[request_slice] = lengths_tensor
+        self.request_in_prefill_status_tensor[request_slice] = 1
         self.request_output_lengths[request_slice] = lengths_tensor + tokens_to_generate_tensor
         self.request_kv_length_offsets[request_slice] = 0
         self.request_kv_block_counts[request_slice] = block_counts
@@ -1216,11 +1299,15 @@ def add_dummy_requests_for_cudagraph_capture(
         Adds dummy requests to reflect the number of prefill and decode requests in the graph config.
         These are using during cuda graph captures.
         """
-        prefill_tokens = graph_dimensions.token_count - graph_dimensions.decode_req_count
+        prefill_tokens = graph_dimensions.token_count - (
+            graph_dimensions.decode_req_count * (self.num_speculative_tokens + 1)
+        )
 
         # Pre-construct shared objects (safe due to deep copy in DynamicInferenceRequest.__post_init__)
         shared_sampling_params = SamplingParams(num_tokens_to_generate=1, termination_id=-1)
-        shared_decode_tokens = torch.zeros(1, dtype=torch.long, device=torch.cuda.current_device())
+        shared_decode_tokens = torch.zeros(
+            self.num_speculative_tokens + 1, dtype=torch.long, device=torch.cuda.current_device()
+        )
 
         decode_requests = [
             DynamicInferenceRequest(
@@ -1271,20 +1358,87 @@ def num_decode_requests(self) -> int:
         """
         return self.total_request_count - self.paused_request_count - self.num_prefill_requests
 
+    def add_dummy_requests_for_expert_parallel_step(self) -> None:
+        """Minimal context setup so an EP rank with no real requests can replay
+        an already-captured cuda graph without crashing or corrupting memory.
+
+        This is the fast alternative to add_dummy_requests_for_cudagraph_capture
+        (which goes through the heavyweight add_dummy_requests_parallel path).
+
+        We setup minimal state such the initialize_attention_state and the forward
+        pass can run without error.
+
+        """
+        smallest_cuda_graph_dimensions = min(
+            [x for x in self.cuda_graph_batch_dimensions_list if x.prefill_req_count == 0]
+        )
+        # the smallest cuda graph is decode only.
+        assert smallest_cuda_graph_dimensions.prefill_req_count == 0
+
+        N = smallest_cuda_graph_dimensions.decode_req_count
+        tokens_per_request = self.num_speculative_tokens + 1
+        T = smallest_cuda_graph_dimensions.token_count  # N * tokens_per_request
+        dummy_block_idx = self.block_allocator.dummy_block_idx
+
+        # 1. Request counts and token count.
+        #    With speculative decoding each decode request has (num_speculative_tokens + 1) tokens.
+        self.total_request_count = N
+        self.active_token_count = T
+        self.num_prefill_requests = 0
+
+        # 2. Per-request state consumed by mha_metadata.update().
+        self.request_query_lengths[0:N].fill_(tokens_per_request)
+        self.request_kv_length_offsets[0:N].fill_(0)
+        self.request_to_kv_block_ids[0:N, 0] = dummy_block_idx
+
+        # 3. Token-level state consumed by the triton KV append kernel.
+        self.token_to_block_idx[0:T] = dummy_block_idx
+        self.token_to_local_position_within_kv_block[0:T] = (
+            torch.arange(T, device=self.token_to_block_idx.device) % tokens_per_request
+        )
+
+        if self.is_hybrid_model:
+            # 4. token_to_request_idx: needed by mamba_metadata.update() for hybrid models.
+            self.token_to_request_idx[0:T] = torch.repeat_interleave(
+                torch.arange(
+                    0,
+                    N,
+                    device=self.token_to_request_idx.device,
+                    dtype=self.token_to_request_idx.dtype,
+                ),
+                tokens_per_request,
+            )
+
+            # 5. Mamba state: allocate slots for dummy requests.
+            self.mamba_metadata.request_to_mamba_state_idx[0:N] = (
+                self.mamba_metadata.batch_allocate_slots(N)
+            )
+
     def initialize_attention_state(
-        self, *, construct_graph_dimensions: Optional[InferenceBatchDimensions] = None
+        self,
+        *,
+        construct_graph_dimensions: Optional[InferenceBatchDimensions] = None,
+        is_expert_parallel_dummy_cuda_graph_step: bool = False,
     ) -> None:
         """Initialize attention state so that every layer can use it.
 
         Args:
-            construct_graph_dimensions (Optional[InferenceBatchDimensions]): The graph config to use for constructing the cuda graphs.
+            construct_graph_dimensions (Optional[InferenceBatchDimensions]):
+                The graph config to use for constructing the cuda graphs.
+            is_expert_parallel_dummy_cuda_graph_step (bool):
+                Whether this is a dummy expert model parallel step.
         Return:
             None.
         """
         self.is_creating_cuda_graphs = construct_graph_dimensions is not None
+        assert not (
+            self.is_creating_cuda_graphs and is_expert_parallel_dummy_cuda_graph_step
+        ), "Dummy expert model parallel steps should not be creating cuda graphs."
 
         # If in CUDA graph creation mode, add dummy requests for CUDA graph capture
-        if self.is_creating_cuda_graphs:
+        if is_expert_parallel_dummy_cuda_graph_step:
+            self.add_dummy_requests_for_expert_parallel_step()
+        elif self.is_creating_cuda_graphs:
             self.add_dummy_requests_for_cudagraph_capture(construct_graph_dimensions)
 
         batch_dimensions = InferenceBatchDimensions(
@@ -1292,10 +1446,13 @@ def initialize_attention_state(
             prefill_req_count=self.num_prefill_requests,
             decode_req_count=self.num_decode_requests,
         )
+
         self.batch_dimensions = batch_dimensions
+
         best_graph = CUDAGraphBatchDimensionBuilder.match_graph_config(
             batch_dimensions,
             self.cuda_graph_batch_dimensions_list,
+            smallest_non_decode_cuda_graph_size=self.smallest_non_decode_cuda_graph_size,
             strict=self.is_hybrid_model,
             decode_only_cuda_graphs=(not self.use_cuda_graphs_for_non_decode_steps),
             explicit_chunked_prefill=self.is_chunked_prefill_enabled() and self.is_hybrid_model,
@@ -1303,19 +1460,35 @@ def initialize_attention_state(
         )
         self._using_cuda_graph_this_step = best_graph is not None
 
+        if construct_graph_dimensions is not None:
+            assert self._using_cuda_graph_this_step
+
+        if is_expert_parallel_dummy_cuda_graph_step and not self.using_cuda_graph_this_step():
+            # If we are here, this means that CUDAGraphBatchDimensionBuilder.match_graph_config
+            # could not find a compatible cuda graph for the dummy forward step.
+            # Now, we need not do the remaining setup. The controller
+            # will directly call the model forward pass with a single token.
+            return
+
         if self.using_cuda_graph_this_step():
             self.padded_batch_dimensions = best_graph
         else:
-            padded_token_count = self.round_up_tokens(self.active_token_count)
             if self.is_decode_only():
-                padded_token_count = min(
-                    self.max_tokens,
-                    self.max_requests,
-                    self.round_up_tokens(self.active_token_count),
-                )
-                padded_decode_req_count = padded_token_count
+                if self.num_speculative_tokens > 0:
+                    padded_decode_req_count = min(
+                        self.max_requests, self.round_up_requests(self.num_decode_requests)
+                    )
+                    padded_token_count = padded_decode_req_count * (self.num_speculative_tokens + 1)
+                else:
+                    padded_token_count = min(
+                        self.max_tokens,
+                        self.max_requests,
+                        self.round_up_tokens(self.active_token_count),
+                    )
+                    padded_decode_req_count = padded_token_count
                 padded_prefill_req_count = 0
             else:
+                padded_token_count = self.round_up_tokens(self.active_token_count)
                 target_padding_req_count = min(
                     self.max_requests,
                     self.round_up_requests(self.total_request_count - self.paused_request_count),
@@ -1374,6 +1547,7 @@ def initialize_attention_state(
             request_to_kv_block_ids=request_to_kv_block_ids_view,
             batch_dimensions=attn_dimensions,
             padded_batch_dimensions=self.padded_batch_dimensions,
+            num_speculative_tokens=self.num_speculative_tokens,
         )
 
         if self.is_hybrid_model:
@@ -1397,29 +1571,8 @@ def initialize_attention_state(
             else:
                 self.moe_routing_metadata.disable_static_buffer_recording()
 
-    def reset(self) -> None:
-        """Reset entire context.
-
-        This method does:
-        - Reset active/paused request/token counts to zero.
-        - Reset available blocks to entire memory.
-        - Reset other tensors to zeros (unncessary, just or sanity checking).
-
-        This method is useful after cuda graph warmup iterations, where the
-        context's memory buffer is referenced by the cuda graph system and
-        cannot be deallocated.
-        """
-
-        # Reset request/token counts.
-        self.total_request_count = 0
-        self.active_token_count = 0
-        self.paused_request_count = 0
-        self.batch_dimensions = InferenceBatchDimensions(
-            token_count=0, prefill_req_count=0, decode_req_count=0
-        )
-        self.padded_active_token_count = 0
-        self.padded_active_request_count = 0
-        self.paused_tokens = None
+    def reset_tensors(self) -> None:
+        """Fill all GPU tensors with sentinel values."""
 
         # Reset request indexes.
         self.request_ids.fill_(-1)
@@ -1430,6 +1583,7 @@ def reset(self) -> None:
         self.request_last_kv_block_id.fill_(-1)
         self.request_last_kv_block_offset.fill_(0)
         self.request_to_kv_block_ids.fill_(-1)
+        self.request_in_prefill_status_tensor.fill_(-1)
 
         # Reset request metadata.
         for metadata_tensor in self.request_metadata.values():
@@ -1443,12 +1597,38 @@ def reset(self) -> None:
         self.token_to_block_idx.fill_(-1)
         self.token_to_local_position_within_kv_block.fill_(0)
 
-        # Reset available block count.
+    def reset_metadata(self) -> None:
+        """Reset all bookkeeping state: counters, block allocator, attention/mamba state.
+
+        This must be called after ``initialize_all_tensors()`` and after any
+        suspend/resume cycle to bring the context back to a clean state.
+        """
+
+        # Reset request/token counts.
+        self.total_request_count = 0
+        self.active_token_count = 0
+        self.lifetime_prefill_token_count = 0
+        self.paused_request_count = 0
+        self.batch_dimensions = InferenceBatchDimensions(
+            token_count=0, prefill_req_count=0, decode_req_count=0
+        )
+        self.padded_batch_dimensions = InferenceBatchDimensions(
+            token_count=0, prefill_req_count=0, decode_req_count=0
+        )
+        self.padded_active_token_count = 0
+        self.padded_active_request_count = 0
+        self.paused_tokens = None
+        self.paused_speculative_tokens = None
+
+        # Reset attention, mamba, and block allocator state.
         self.reset_attention_state()
         self.reset_mamba_state()
         self.block_allocator.reset()
         self.request_to_kv_block_ids.fill_(-1)
 
+        # Reset step counter
+        self.step_count = 0
+
         # Reset chunked prefill state
         self.chunked_prefill_request_id = -1
         self.num_prefill_requests = 0
@@ -1458,6 +1638,21 @@ def reset(self) -> None:
             token_count=0, prefill_req_count=0, decode_req_count=0
         )
 
+    def reset(self) -> None:
+        """Reset entire context.
+
+        This method does:
+        - Fill all GPU tensors with sentinel values.
+        - Reset active/paused request/token counts to zero.
+        - Reset available blocks to entire memory.
+
+        This method is useful after cuda graph warmup iterations, where the
+        context's memory buffer is referenced by the cuda graph system and
+        cannot be deallocated.
+        """
+        self.reset_tensors()
+        self.reset_metadata()
+
     def current_input_and_position_ids(
         self, *, num_warmup_tokens: Optional[int] = None
     ) -> Tuple[Tensor, Tensor]:
@@ -1472,6 +1667,9 @@ def current_input_and_position_ids(
             (Tuple[Tensor, Tensor]) Flattened active input and position IDs.
         """
         num_tokens = num_warmup_tokens or self.padded_active_token_count
+        assert num_tokens >= self.padded_batch_dimensions.decode_req_count * (
+            self.num_speculative_tokens + 1
+        )
         return (
             self.token_to_input_ids[:num_tokens].unsqueeze(0),
             self.token_to_pos_ids[:num_tokens].unsqueeze(0),
@@ -1486,7 +1684,6 @@ def last_token_logits(self, logits: Tensor) -> Tensor:
         Return:
             (Tensor) Last token logits.
         """
-
         # todo: @lmcafee, remove these asserts?
         assert logits.size(0) == 1, f"logits.size(0) ({tuple(logits.shape)}) != 1"
         assert logits.size(1) == self.padded_active_token_count, (
@@ -1507,6 +1704,66 @@ def last_token_logits(self, logits: Tensor) -> Tensor:
 
         return last_token_logits
 
+    def _compute_prefix_match(
+        self, req: DynamicInferenceRequest, chunk_length: int
+    ) -> Tuple[list, int, int, int, int, int]:
+        """Compute prefix match results and skip counts for a request chunk.
+
+        Shared by check_availability (budget checks) and add_request (execution).
+
+        Returns:
+            Tuple of (matched_block_ids, num_blocks_from_pool,
+                      already_allocated_blocks, overall_required_blocks,
+                      prefix_skip_tokens, effective_chunk_length).
+        """
+        finished = req.finished_chunk_token_count
+        already_allocated_blocks = (finished + self.block_size_tokens - 1) // self.block_size_tokens
+        overall_required_blocks = (
+            finished + chunk_length + self.block_size_tokens - 1
+        ) // self.block_size_tokens
+
+        # Fast path: skip all prefix matching when disabled.
+        if not self.enable_prefix_caching:
+            num_blocks_from_pool = max(0, overall_required_blocks - already_allocated_blocks)
+            return (
+                [],
+                num_blocks_from_pool,
+                already_allocated_blocks,
+                overall_required_blocks,
+                0,
+                chunk_length,
+            )
+
+        matched_block_ids, _ = self._find_matching_prefix_blocks(
+            req, already_allocated_blocks, overall_required_blocks
+        )
+        num_matched = len(matched_block_ids)
+
+        block_aligned = finished % self.block_size_tokens == 0
+        if num_matched > 0 and block_aligned:
+            prefix_skip_tokens = min(num_matched * self.block_size_tokens, chunk_length - 1)
+        else:
+            prefix_skip_tokens = 0
+
+        # Hybrid models: disable prefill skipping (no Mamba states per block),
+        # but keep matched blocks for memory sharing.
+        if self.is_hybrid_model:
+            prefix_skip_tokens = 0
+
+        effective_chunk_length = chunk_length - prefix_skip_tokens
+        num_blocks_from_pool = max(
+            0, overall_required_blocks - already_allocated_blocks - num_matched
+        )
+
+        return (
+            matched_block_ids,
+            num_blocks_from_pool,
+            already_allocated_blocks,
+            overall_required_blocks,
+            prefix_skip_tokens,
+            effective_chunk_length,
+        )
+
     def check_availability(self, req: DynamicInferenceRequest) -> Tuple[bool, bool, bool]:
         """
         Check if the request can be added to the context.
@@ -1517,17 +1774,71 @@ def check_availability(self, req: DynamicInferenceRequest) -> Tuple[bool, bool,
             self.total_request_count < self.max_requests and self.paused_request_count == 0
         )
 
+        (_, num_blocks_from_pool, _, _, _, effective_chunk_length) = self._compute_prefix_match(
+            req, req.remaining_prompt_length
+        )
+
         request_tokens_can_be_added = (
-            self.active_token_count + req.remaining_prompt_length <= self.max_tokens
+            self.active_token_count + effective_chunk_length <= self.max_tokens
         )
-        blocks = math.ceil(
-            (req.remaining_prompt_length + req.finished_chunk_token_count) / self.block_size_tokens
-        ) - math.ceil(req.finished_chunk_token_count / self.block_size_tokens)
-        kv_cache_available = self.block_allocator.is_memory_available(blocks)
+        kv_cache_available = self.block_allocator.is_memory_available(num_blocks_from_pool)
         return request_can_be_added, request_tokens_can_be_added, kv_cache_available
 
+    def _find_matching_prefix_blocks(
+        self, req: DynamicInferenceRequest, start_block: int, end_block: int
+    ) -> tuple[list[int], int]:
+        """Find cached blocks matching a range of the prompt using precomputed hashes.
+
+        Looks up hashes in req.precomputed_block_hashes[start_block:end_block] against
+        the block allocator's hash-to-block mapping. Stops at the first non-match.
+
+        Args:
+            req: The inference request with precomputed_block_hashes set.
+            start_block: First block index to match (inclusive).
+            end_block: Last block index to match (exclusive); clamped to hash count.
+
+        Returns:
+            Tuple of:
+            - List of matched block IDs (consecutive from start_block)
+            - Parent hash of the last matched block (0 if no matches)
+        """
+        # Early return if prefix caching is disabled
+        if not self.enable_prefix_caching:
+            return [], 0
+
+        # Early return if request has no precomputed hashes
+        if not req.precomputed_block_hashes:
+            return [], 0
+
+        # Clamp end_block to the number of precomputed hashes (the trailing
+        # partial block has no hash).
+        end_block = min(end_block, len(req.precomputed_block_hashes))
+        if start_block >= end_block:
+            return [], 0
+
+        hashes = req.precomputed_block_hashes[start_block:end_block]
+        hash_to_block = self.block_allocator.hash_to_block_id
+
+        # Batch dict lookups via C-level map() — faster than Python for loop
+        block_ids = list(map(hash_to_block.get, hashes))
+
+        # Find prefix length (first None = first miss)
+        try:
+            prefix_len = block_ids.index(None)
+        except ValueError:
+            prefix_len = len(block_ids)
+
+        if prefix_len == 0:
+            return [], 0
+
+        matched_blocks = block_ids[:prefix_len]
+        parent_hash = hashes[prefix_len - 1]
+        return matched_blocks, parent_hash
+
     def add_request(self, req: DynamicInferenceRequest, chunk_length: Optional[int] = None) -> None:
-        """Add request to context. At this stage, we assume that the request is valid and can be added, as the checks are done in the schedule function.
+        """
+        Add request to context. At this stage, we assume that the request is valid and can be added,
+        as the checks are done in the schedule function.
 
         Args:
             req (DynamicInferenceRequest): Request to add.
@@ -1552,35 +1863,47 @@ def add_request(self, req: DynamicInferenceRequest, chunk_length: Optional[int]
         assert (
             chunk_length <= req.remaining_prompt_length
         ), "Chunk length is greater than remaining prompt length"
-        if self.active_token_count + chunk_length > self.max_tokens:
-            raise TokenOverflowError(req.request_id)
-
-        # Use the remaining prompt tokens for this chunk
-        this_round_tokens = req.remaining_prompt_tokens[:chunk_length]
-
-        # only allocate new blocks
-        already_allocated_blocks = (
-            req.finished_chunk_token_count + self.block_size_tokens - 1
-        ) // self.block_size_tokens  # ceiling division
-        overall_required_blocks = (
-            req.finished_chunk_token_count + chunk_length + self.block_size_tokens - 1
-        ) // self.block_size_tokens  # ceiling division
-
-        num_blocks_needed = overall_required_blocks - already_allocated_blocks
 
-        if num_blocks_needed > 0:
-            new_block_ids = self.block_allocator.allocate_memory_blocks(num_blocks_needed)
-            if new_block_ids is None or len(new_block_ids) != num_blocks_needed:
+        # =========================================================================
+        # Block allocation + prefix matching + prefill skipping
+        # =========================================================================
+        (
+            matched_block_ids,
+            num_blocks_from_pool,
+            already_allocated_blocks,
+            overall_required_blocks,
+            prefix_skip_tokens,
+            effective_chunk_length,
+        ) = self._compute_prefix_match(req, chunk_length)
+        num_matched_blocks = len(matched_block_ids)
+        effective_kv_offset = req.finished_chunk_token_count + prefix_skip_tokens
+
+        # Slice tokens to skip matched prefix
+        this_round_tokens = req.remaining_prompt_tokens[prefix_skip_tokens:chunk_length]
+
+        new_block_ids = None
+        if num_blocks_from_pool > 0:
+            new_block_ids = self.block_allocator.allocate_memory_blocks(num_blocks_from_pool)
+            if new_block_ids is None or len(new_block_ids) != num_blocks_from_pool:
                 raise BlockOverflowError(req.request_id)
 
+        # Increment ref counts and update timestamps for matched (shared) blocks
+        if num_matched_blocks > 0:
+            matched_tensor = torch.tensor(
+                matched_block_ids, dtype=torch.int32, device=torch.cuda.current_device()
+            )
+            self.block_allocator.block_ref_counts[matched_tensor] += 1
+            if self.prefix_caching_eviction_policy == PrefixCachingEvictionPolicy.LRU:
+                self.block_allocator.update_timestamps(matched_tensor)
+
         # when a request already starts chunked prefill, it is exactly the last request in the current system
         # (see dynamic_engine.py, schedule_chunked_prefill invariants)
         # no need to update count, as it is already here
         if is_chunked_prefill:
             current_id = self.total_request_count - 1
-            self.active_token_count -= (
-                1  # Overwrite the last token, which is the useless token from chunked prefill
-            )
+            # Overwrite the last token, which is the useless token from chunked prefill
+            chunked_prefill_offset = 1 + self.num_speculative_tokens
+            self.active_token_count -= chunked_prefill_offset
             assert (
                 self.request_ids[current_id] == req.request_id
             ), "Continuation current_id mismatch"
@@ -1590,7 +1913,7 @@ def add_request(self, req: DynamicInferenceRequest, chunk_length: Optional[int]
         if current_id >= self.max_requests:
             raise RequestOverflowError(req.request_id)
 
-        if self.active_token_count + chunk_length > self.max_tokens:
+        if self.active_token_count + effective_chunk_length > self.max_tokens:
             raise TokenOverflowError(req.request_id)
 
         self.request_ids[current_id] = req.request_id
@@ -1613,17 +1936,28 @@ def add_request(self, req: DynamicInferenceRequest, chunk_length: Optional[int]
             self.request_metadata[label][current_id] = m
 
         # Handle length and block assignments.
-        self.request_query_lengths[current_id] = chunk_length
+        self.request_query_lengths[current_id] = effective_chunk_length
+        self.request_in_prefill_status_tensor[current_id] = 1
         self.request_output_lengths[current_id] = (
             req.finished_chunk_token_count
             + chunk_length
             + req.sampling_params.num_tokens_to_generate
         )
-        if num_blocks_needed > 0:
+
+        # Assign blocks: matched blocks at [already_allocated, already_allocated + num_matched),
+        # then newly allocated blocks after that.
+        match_start = already_allocated_blocks
+        new_block_start = already_allocated_blocks + num_matched_blocks
+        if num_matched_blocks > 0:
             self.request_to_kv_block_ids[current_id][
-                already_allocated_blocks:overall_required_blocks
+                match_start : match_start + num_matched_blocks
+            ] = matched_tensor
+        if new_block_ids is not None:
+            self.request_to_kv_block_ids[current_id][
+                new_block_start : new_block_start + len(new_block_ids)
             ] = new_block_ids
-        self.request_kv_length_offsets[current_id] = req.finished_chunk_token_count
+
+        self.request_kv_length_offsets[current_id] = effective_kv_offset
         self.request_kv_block_counts[current_id] = overall_required_blocks
         self.request_last_kv_block_id[current_id] = self.request_to_kv_block_ids[current_id][
             overall_required_blocks - 1
@@ -1633,29 +1967,52 @@ def add_request(self, req: DynamicInferenceRequest, chunk_length: Optional[int]
         ) % self.block_size_tokens
 
         token_offset_range = torch.arange(
-            req.finished_chunk_token_count,
-            req.finished_chunk_token_count + chunk_length,
+            effective_kv_offset,
+            effective_kv_offset + effective_chunk_length,
             device=self.token_to_pos_ids.device,
         )
-        self.token_to_pos_ids[self.active_token_count : self.active_token_count + chunk_length] = (
-            token_offset_range
-        )
+        self.token_to_pos_ids[
+            self.active_token_count : self.active_token_count + effective_chunk_length
+        ] = token_offset_range
         self.token_to_input_ids[
-            self.active_token_count : self.active_token_count + chunk_length
+            self.active_token_count : self.active_token_count + effective_chunk_length
         ] = this_round_tokens
         self.token_to_request_idx[
-            self.active_token_count : self.active_token_count + chunk_length
+            self.active_token_count : self.active_token_count + effective_chunk_length
         ] = current_id
         self.token_to_position_in_request[
-            self.active_token_count : self.active_token_count + chunk_length
+            self.active_token_count : self.active_token_count + effective_chunk_length
         ] = token_offset_range
         self.token_to_block_idx[
-            self.active_token_count : self.active_token_count + chunk_length
+            self.active_token_count : self.active_token_count + effective_chunk_length
         ] = self.request_to_kv_block_ids[current_id][token_offset_range // self.block_size_tokens]
         self.token_to_local_position_within_kv_block[
-            self.active_token_count : self.active_token_count + chunk_length
+            self.active_token_count : self.active_token_count + effective_chunk_length
         ] = (token_offset_range % self.block_size_tokens)
 
+        # Register hashes for completely filled blocks (skip matched blocks).
+        # Two disjoint ranges may need registration:
+        #   Range 1: [previously_complete, min(already_allocated_blocks, num_complete_blocks))
+        #       — the partial block from a prior chunk that this chunk's tokens completed
+        #   Range 2: [already_allocated_blocks + num_matched_blocks, num_complete_blocks)
+        #       — newly allocated blocks that are now complete
+        if self.enable_prefix_caching and req.precomputed_block_hashes:
+            total_tokens_after = req.finished_chunk_token_count + chunk_length
+            num_complete_blocks = total_tokens_after // self.block_size_tokens
+            previously_complete = req.finished_chunk_token_count // self.block_size_tokens
+
+            def _register_range(start: int, end: int):
+                if start >= end:
+                    return
+                block_ids_to_hash = self.request_to_kv_block_ids[current_id][start:end].tolist()
+                block_hashes_slice = req.precomputed_block_hashes[start:end]
+                self.block_allocator.register_block_hashes(block_ids_to_hash, block_hashes_slice)
+
+            # Range 1: prior-chunk partial block that this chunk just completed
+            _register_range(previously_complete, min(already_allocated_blocks, num_complete_blocks))
+            # Range 2: newly allocated (non-matched) blocks that are now complete
+            _register_range(already_allocated_blocks + num_matched_blocks, num_complete_blocks)
+
         if self.is_hybrid_model and not is_chunked_prefill:
             # Allocate a slot for Mamba states
             mamba_idx = self.mamba_metadata.allocate_slot()
@@ -1667,20 +2024,27 @@ def add_request(self, req: DynamicInferenceRequest, chunk_length: Optional[int]
             self.mamba_ssm_states[:, mamba_idx] = 0.0
             self.mamba_metadata.request_to_mamba_state_idx[self.total_request_count] = mamba_idx
 
-        self.active_token_count += chunk_length
+        self.active_token_count += effective_chunk_length
+        self.lifetime_prefill_token_count += effective_chunk_length
         self.total_request_count += 0 if req.finished_chunk_token_count > 0 else 1
         self.num_prefill_requests += 1
 
-    def _move_book_keeping_tensors(self, src_idxs, dst_idxs, next_tokens):
+    def _move_book_keeping_tensors(
+        self, src_idxs, dst_idxs, next_tokens, new_speculative_tokens=None
+    ):
         """
         Move all the relevent booking tensors with src idxs to dst idxs
         """
         self.request_kv_length_offsets[dst_idxs] = self.request_kv_length_offsets[src_idxs]
+        self.request_in_prefill_status_tensor[dst_idxs] = self.request_in_prefill_status_tensor[
+            src_idxs
+        ]
         self.request_query_lengths[dst_idxs] = self.request_query_lengths[src_idxs]
         self.request_output_lengths[dst_idxs] = self.request_output_lengths[src_idxs]
         self.request_ids[dst_idxs] = self.request_ids[src_idxs]
-        next_tokens[dst_idxs] = next_tokens[src_idxs]
-
+        next_tokens[dst_idxs] = next_tokens[src_idxs]  # num tokens sames as num samples
+        if new_speculative_tokens is not None:
+            new_speculative_tokens[:, dst_idxs] = new_speculative_tokens[:, src_idxs]
         self.request_to_kv_block_ids[dst_idxs] = self.request_to_kv_block_ids[src_idxs]
         self.request_kv_block_counts[dst_idxs] = self.request_kv_block_counts[src_idxs]
         self.request_last_kv_block_id[dst_idxs] = self.request_last_kv_block_id[src_idxs]
@@ -1694,12 +2058,15 @@ def _move_book_keeping_tensors(self, src_idxs, dst_idxs, next_tokens):
                 self.mamba_metadata.request_to_mamba_state_idx[src_idxs]
             )
 
-    def _swap_book_keeping_tensors(self, src_idxs, dst_idxs, next_tokens):
+    def _swap_book_keeping_tensors(
+        self, src_idxs, dst_idxs, next_tokens, new_speculative_tokens=None
+    ):
         """
         Swaps all the relevent booking tensors with src idxs to dst idxs
         """
         tensor_swap(self.request_kv_length_offsets, src_idxs, dst_idxs)
         tensor_swap(self.request_query_lengths, src_idxs, dst_idxs)
+        tensor_swap(self.request_in_prefill_status_tensor, src_idxs, dst_idxs)
         tensor_swap(self.request_output_lengths, src_idxs, dst_idxs)
         tensor_swap(self.request_ids, src_idxs, dst_idxs)
         tensor_swap(next_tokens, src_idxs, dst_idxs)
@@ -1708,6 +2075,11 @@ def _swap_book_keeping_tensors(self, src_idxs, dst_idxs, next_tokens):
         tensor_swap(self.request_last_kv_block_id, src_idxs, dst_idxs)
         tensor_swap(self.request_last_kv_block_offset, src_idxs, dst_idxs)
 
+        if new_speculative_tokens is not None:
+            # new_speculative_tokens has request dimension as second dimension,
+            # so swap on transposed view
+            tensor_swap(new_speculative_tokens.t(), src_idxs, dst_idxs)
+
         for metadata_tensor in self.request_metadata.values():
             tensor_swap(metadata_tensor, src_idxs, dst_idxs)
 
@@ -1751,10 +2123,7 @@ def release_memory_blocks_from_request_indexes(self, request_indexes) -> None:
             self.mamba_metadata.free_slots(request_indexes)
 
     def resume_paused_requests(
-        self,
-        active_request_count: int,
-        newly_paused_request_ids: torch.Tensor,
-        next_tokens: torch.Tensor,
+        self, active_request_count: int, newly_paused_request_ids: torch.Tensor
     ) -> tuple[int, torch.Tensor]:
         """Resume as many paused requests as we have space for in the active buffer.
 
@@ -1772,55 +2141,60 @@ def resume_paused_requests(
         resume_request_count = 0
         if self.paused_request_count > 0:
             active_block_count_avail = self.block_allocator.get_active_avail()
+            # Clone not needed: flip() makes a copy.
             paused_block_counts = self.request_kv_block_counts[: self.paused_request_count]
             # Flip counts before cumsum, since paused requests are resumed from
             # the right-most index, so we must count resumed blocks starting from
             # the right side.
             paused_block_counts = paused_block_counts.flip(dims=[0])
-            # Add +1 to all block counts, since any time a paused request is
-            # resumed, it will be starting a new memory block. For background,
-            # pausing happens after a request has generated the final token of a
-            # memory block (i.e., token 256 of that block), which means the very
-            # next token (whenever that request gets unpaused) will be in a new
-            # block. So, when we resume a paused request, we have to account for
-            # the fact that it will need an extra block beyond the ones that it
-            # has already used.
-            paused_block_counts += 1  # +1 for newly added block
+
+            # Check which paused requests will actually need a new block upon resuming
+            offsets = self.request_last_kv_block_offset[: self.paused_request_count]
+            needs_new_block = (
+                offsets >= self.block_size_tokens - 1 - self.num_speculative_tokens
+            ).to(paused_block_counts.dtype)
+            needs_new_block = needs_new_block.flip(dims=[0])
+
+            # Add +1 ONLY to the block counts of requests that finished their previous memory block
+            paused_block_counts += needs_new_block
             paused_block_counts_cumsum = paused_block_counts.cumsum(dim=0)
             resume_request_count = min(
                 torch.nonzero(paused_block_counts_cumsum <= active_block_count_avail).numel(),
                 self.block_allocator.total_avail,
             )
 
+            # Constrain resumptions by the maximum allowed active requests and tokens
+            max_allowed_active = min(
+                self.max_requests, self.max_tokens // (self.num_speculative_tokens + 1)
+            )
+            allowed_to_resume = max(0, max_allowed_active - active_request_count)
+            resume_request_count = min(resume_request_count, allowed_to_resume)
+
         self.paused_request_count -= resume_request_count
         active_request_count += resume_request_count
 
         # Resume requests by assigning blocks and updating bookkeeping tensors.
         if resume_request_count > 0:
-            assert torch.all(
-                self.request_last_kv_block_offset[
-                    self.paused_request_count : (self.paused_request_count + resume_request_count)
-                ]
-                == self.block_size_tokens - 1
-            ), "The request_last_kv_block_offset should be 0 for the requests that just got resumed this step."
+            resume_start = self.paused_request_count
+            resume_end = self.paused_request_count + resume_request_count
 
-            assert resume_request_count <= self.block_allocator.total_avail
-            block_ids = self.block_allocator.allocate_memory_blocks(resume_request_count)
-            row_idx = torch.arange(
-                self.paused_request_count,
-                self.paused_request_count + resume_request_count,
-                device=torch.cuda.current_device(),
-            )
-            col_idx = self.request_kv_block_counts[
-                self.paused_request_count : (self.paused_request_count + resume_request_count)
-            ]
-            self.request_to_kv_block_ids[row_idx, col_idx] = block_ids
-            self.request_kv_block_counts[
-                self.paused_request_count : (self.paused_request_count + resume_request_count)
-            ] += 1
-            self.request_last_kv_block_id[
-                self.paused_request_count : (self.paused_request_count + resume_request_count)
-            ] = block_ids
+            # Check which resumed requests actually need a new block
+            offsets = self.request_last_kv_block_offset[resume_start:resume_end]
+            needs_new_block = offsets >= (self.block_size_tokens - 1 - self.num_speculative_tokens)
+            num_new_blocks = needs_new_block.sum().item()
+
+            if num_new_blocks > 0:
+                assert num_new_blocks <= self.block_allocator.total_avail
+                block_ids = self.block_allocator.allocate_memory_blocks(num_new_blocks)
+
+                # Apply updates only to the requests that required a new block
+                relative_row_idx = torch.nonzero(needs_new_block).squeeze(1)
+                row_idx = resume_start + relative_row_idx
+                col_idx = self.request_kv_block_counts[row_idx]
+
+                self.request_to_kv_block_ids[row_idx, col_idx] = block_ids
+                self.request_kv_block_counts[row_idx] += 1
+                self.request_last_kv_block_id[row_idx] = block_ids
 
         # Remove resumed requests from newly_paused_request_ids. We do this by
         # truncating the end of newly_paused_request_ids, which works because we
@@ -1833,7 +2207,10 @@ def resume_paused_requests(
         return active_request_count, newly_paused_request_ids
 
     def evict_overflow_paused_requests(
-        self, active_request_count: int, next_tokens: torch.Tensor
+        self,
+        active_request_count: int,
+        next_tokens: torch.Tensor,
+        new_speculative_tokens: Optional[torch.Tensor] = None,
     ) -> Optional[tuple[torch.Tensor, torch.Tensor]]:
         """Evict requests that overflow the paused buffer.
 
@@ -1888,6 +2265,8 @@ def evict_overflow_paused_requests(
         evict_request_idxs = torch.arange(
             evict_start_idx, evict_end_idx, device=torch.cuda.current_device()
         )
+        # Clone needed: subsequent release_memory_blocks_from_request_indexes and
+        # _swap_book_keeping_tensors calls mutate self.request_ids in place.
         evict_request_ids = self.request_ids[evict_start_idx:evict_end_idx].clone()
 
         # Release memory.
@@ -1922,7 +2301,10 @@ def evict_overflow_paused_requests(
 
         # Swap evicted and active requests.
         self._swap_book_keeping_tensors(
-            src_idxs=src_idxs, dst_idxs=dst_idxs, next_tokens=next_tokens
+            src_idxs=src_idxs,
+            dst_idxs=dst_idxs,
+            next_tokens=next_tokens,
+            new_speculative_tokens=new_speculative_tokens,
         )
 
         # Update tracking vars.
@@ -1939,7 +2321,12 @@ def evict_overflow_paused_requests(
 
         return evict_request_ids
 
-    def update_requests(self, active_requests_mask: Tensor, new_tokens: Tensor) -> Tensor:
+    def update_requests(
+        self,
+        active_requests_mask: Tensor,
+        new_tokens: Tensor,
+        new_speculative_tokens: Tensor = None,
+    ) -> Tensor:
         """Update context state after calling engine.step().
 
         This method is responsible for:
@@ -1972,8 +2359,11 @@ def update_requests(self, active_requests_mask: Tensor, new_tokens: Tensor) -> T
         8. We make relevant changes to the token bookkeeping tensors
 
         Args:
-            active_requests_mask (Tensor): 1D Mask tensor marking active requests.
-            new_tokens (Tensor): Newly sampled tokens, with one token per active request.
+            active_requests_mask (Tensor): 1D Mask tensor marking active requests. (Active request length)
+            new_tokens (Tensor): Newly sampled tokens, with one token per active request. (Active request length)
+            new_speculative_tokens (Tensor): Newly sampled speculative tokens,
+                with num_speculative tokens per active request.
+                (num_speculative_tokens, active_request_length)
 
         Return:
             (Tensor) Newly paused request IDs.
@@ -1984,6 +2374,10 @@ def update_requests(self, active_requests_mask: Tensor, new_tokens: Tensor) -> T
         # finished_request_count are requests that have reached the termination criterion
 
         self.num_prefill_requests = 0  # all turns to decode
+        # All request that were in prefill become decode requests
+        self.request_in_prefill_status_tensor[self.request_in_prefill_status_tensor == 1] = (
+            0  # TODO : Check how this works with chunked prefill
+        )
         if self.chunked_prefill_request_id != -1:
             active_requests_mask[-1] = (
                 1  # must keep this, next iteration will add a new chunk to it
@@ -2024,6 +2418,10 @@ def update_requests(self, active_requests_mask: Tensor, new_tokens: Tensor) -> T
         if self.paused_request_count != 0:
             assert self.paused_tokens is not None
             next_tokens = torch.cat((self.paused_tokens, new_tokens))
+            if new_speculative_tokens is not None and self.paused_speculative_tokens is not None:
+                new_speculative_tokens = torch.cat(
+                    (self.paused_speculative_tokens, new_speculative_tokens), dim=1
+                )
         else:
             next_tokens = new_tokens
 
@@ -2054,6 +2452,7 @@ def update_requests(self, active_requests_mask: Tensor, new_tokens: Tensor) -> T
                     src_idxs=active_idxs_on_right,
                     dst_idxs=finished_idxs_on_left,
                     next_tokens=next_tokens,
+                    new_speculative_tokens=new_speculative_tokens,
                 )
 
                 # Reset chunk ids for recently moved requests.
@@ -2071,7 +2470,7 @@ def update_requests(self, active_requests_mask: Tensor, new_tokens: Tensor) -> T
                 self.paused_request_count : (active_request_count + self.paused_request_count)
             ]
             active_requests_requiring_new_block = (
-                num_tokens_in_last_block == self.block_size_tokens - 1
+                num_tokens_in_last_block >= self.block_size_tokens - 1 - self.num_speculative_tokens
             ).byte()
 
             if self.chunked_prefill_request_id != -1:
@@ -2079,6 +2478,13 @@ def update_requests(self, active_requests_mask: Tensor, new_tokens: Tensor) -> T
                 active_requests_requiring_new_block[
                     self.get_index_of_chunked_prefill_request() - self.paused_request_count
                 ] = 0  # chunked prefill should not be paused
+            else:
+                max_allowed_active = min(
+                    self.max_requests, self.max_tokens // (self.num_speculative_tokens + 1)
+                )
+                if active_request_count > max_allowed_active:
+                    # Force-pause excess requests in a decode-only batch
+                    active_requests_requiring_new_block[max_allowed_active:] = 1
 
             active_requests_requiring_new_block_count = (
                 (active_requests_requiring_new_block == 1).sum().item()
@@ -2119,7 +2525,10 @@ def update_requests(self, active_requests_mask: Tensor, new_tokens: Tensor) -> T
                 dst_idxs = torch.cat((active_request_ids_on_left, paused_requests_idxs_on_right))
                 src_idxs = torch.cat((paused_requests_idxs_on_right, active_request_ids_on_left))
                 self._move_book_keeping_tensors(
-                    src_idxs=src_idxs, dst_idxs=dst_idxs, next_tokens=next_tokens
+                    src_idxs=src_idxs,
+                    dst_idxs=dst_idxs,
+                    next_tokens=next_tokens,
+                    new_speculative_tokens=new_speculative_tokens,
                 )
 
             self.paused_request_count += active_requests_requiring_new_block_count
@@ -2128,17 +2537,30 @@ def update_requests(self, active_requests_mask: Tensor, new_tokens: Tensor) -> T
         # 6. Now that we have the requests in following order [Paused, Active, Finished]
         # We determine how many requests we can resume and resume them
 
+        # For multi-token generation: store previous block IDs BEFORE resume allocates new blocks.
+        # This allows us to know which block tokens should go to if they don't cross the boundary.
+        # After resume_paused_requests, request_last_kv_block_id will be updated to the NEW block
+        # for resumed requests, but we need the OLD block for tokens that don't cross.
+        prev_last_block_ids = None
+        if self.num_speculative_tokens > 0:
+            # Clone needed: resume_paused_requests mutates request_last_kv_block_id
+            # (assigns new block IDs), but we need the old values later to determine
+            # which block tokens should go to when they don't cross a block boundary.
+            prev_last_block_ids = self.request_last_kv_block_id.clone()
+
         # 6.a. First, resume temporarily paused requests.
         active_request_count, newly_paused_request_ids = self.resume_paused_requests(
-            active_request_count, newly_paused_request_ids, next_tokens
+            active_request_count, newly_paused_request_ids
         )
 
         # 6.b. Evict requests that overflow the paused buffer.
-        evict_request_ids = self.evict_overflow_paused_requests(active_request_count, next_tokens)
+        evict_request_ids = self.evict_overflow_paused_requests(
+            active_request_count, next_tokens, new_speculative_tokens
+        )
 
         # 6.c. Resume any additional requests.
         active_request_count, newly_paused_request_ids = self.resume_paused_requests(
-            active_request_count, newly_paused_request_ids, next_tokens
+            active_request_count, newly_paused_request_ids
         )
 
         assert active_request_count > 0, "active_request_count == %d." % active_request_count
@@ -2150,50 +2572,159 @@ def update_requests(self, active_requests_mask: Tensor, new_tokens: Tensor) -> T
                 src_idxs=torch.tensor([self.get_index_of_chunked_prefill_request()]),
                 dst_idxs=torch.tensor([self.total_request_count - 1]),
                 next_tokens=next_tokens,
+                new_speculative_tokens=new_speculative_tokens,
             )
 
         # 7. We make changes to the request book keeping tesnsors and setup the tokens for next iteration
         assert self.total_request_count == active_request_count + self.paused_request_count
 
-        # All these active requests are in decode phase, so they need only 1 token per request
-        self.active_token_count = active_request_count
-        # Always the first section of token input ids are only used.
-        self.token_to_input_ids[: self.active_token_count] = next_tokens[
-            self.paused_request_count : self.total_request_count
-        ]
-
         if self.paused_request_count > 0:
-            self.paused_tokens = next_tokens[: self.paused_request_count]
-
-        # add_ and fill_ calls seems to work as intended with sliced indexing (i.e. x[3:5].add(...) or x[3:5].fill_)
-        # but when another tensor is used for indexing, it does not work as expected (i.e. x[y] if x and y are torch tensors)
+            # Clone needed: next_tokens is a shared buffer that will be overwritten in
+            # the next iteration; paused_tokens must persist independently.
+            self.paused_tokens = next_tokens[: self.paused_request_count].clone()
+            if new_speculative_tokens is not None:
+                # Clone needed: same reason as paused_tokens above.
+                self.paused_speculative_tokens = new_speculative_tokens[
+                    :, : self.paused_request_count
+                ].clone()
+
+        # add_ and fill_ calls seems to work as intended with sliced indexing
+        # (i.e. x[3:5].add(...) or x[3:5].fill_) but when another tensor is used
+        # for indexing, it does not work as expected (i.e. x[y] if x and y are torch tensors)
         self.request_kv_length_offsets[self.paused_request_count : self.total_request_count].add_(
             self.request_query_lengths[self.paused_request_count : self.total_request_count]
         )
-        self.request_query_lengths[self.paused_request_count : self.total_request_count].fill_(1)
-        self.token_to_pos_ids[: self.active_token_count] = self.request_kv_length_offsets[
+
+        num_generated_tokens = 1 + self.num_speculative_tokens
+        self.request_query_lengths[self.paused_request_count : self.total_request_count].fill_(
+            num_generated_tokens
+        )
+
+        # Clone needed: old_offsets is reused later to compute raw_positions
+        # for block-boundary detection. The write-back on the next line overwrites the
+        # underlying tensor, so without clone the boundary-crossing logic would see the
+        # new offsets instead of the pre-update values.
+        old_offsets = self.request_last_kv_block_offset[
             self.paused_request_count : self.total_request_count
-        ]
+        ].clone()
 
         self.request_last_kv_block_offset[self.paused_request_count : self.total_request_count] = (
-            self.request_last_kv_block_offset[self.paused_request_count : self.total_request_count]
-            + 1
+            old_offsets + num_generated_tokens
         ) % self.block_size_tokens
 
-        # 8. We make relevant changes to the token bookkeeping tensors
+        self.active_token_count = active_request_count * num_generated_tokens
+        sampled_tokens = next_tokens[self.paused_request_count : self.total_request_count]
+
+        if self.num_speculative_tokens > 0:
+            # new_speculative_tokens has shape [num_spec_tokens, num_requests],
+            # slice the request dimension (dim 1)
+            sampled_speculative_tokens = new_speculative_tokens[
+                :, self.paused_request_count : self.total_request_count
+            ]
+            # This will become [sampled, spec1, spec2, sampled, spec1, spec2 ...]
+            # For every request we will have the sampled token followed by the
+            # speculative tokens (i.e next indices)
+            next_tokens = torch.vstack(
+                [sampled_tokens.unsqueeze(0), sampled_speculative_tokens]
+            ).T.reshape(-1)
+        else:
+            next_tokens = sampled_tokens
+
+        self.token_to_input_ids[: self.active_token_count] = next_tokens
+
+        # Req kv length offsets : [0, 5, 10 ... ]
+        # For num spec tokens = 2 , this will become [0, 1, 2, 5, 6, 7 10, 11, 12 ...]
+        self.token_to_pos_ids[: self.active_token_count] = self.request_kv_length_offsets[
+            self.paused_request_count : self.total_request_count
+        ].repeat_interleave(num_generated_tokens) + torch.arange(
+            num_generated_tokens, device=torch.cuda.current_device()
+        ).repeat(
+            active_request_count
+        )
+        #
+        # Token to request idx : [0, 0, 0, 1, 1, 1, 2, 2, 2 ...]
         self.token_to_request_idx[: self.active_token_count] = torch.arange(
             self.paused_request_count, self.total_request_count, device=torch.cuda.current_device()
-        )
-        self.token_to_position_in_request[: self.active_token_count] = (
-            self.request_kv_length_offsets[self.paused_request_count : self.total_request_count]
+        ).repeat_interleave(num_generated_tokens)
+
+        self.token_to_position_in_request[: self.active_token_count] = self.token_to_pos_ids[
+            : self.active_token_count
+        ]
+
+        self.token_to_local_position_within_kv_block[: self.active_token_count] = (
+            self.token_to_pos_ids[: self.active_token_count] % self.block_size_tokens
         )
 
-        self.token_to_block_idx[: self.active_token_count] = self.request_last_kv_block_id[
+        current_block_ids = self.request_last_kv_block_id[
             self.paused_request_count : self.total_request_count
         ]
-        self.token_to_local_position_within_kv_block[: self.active_token_count] = (
-            self.request_last_kv_block_offset[self.paused_request_count : self.total_request_count]
+
+        # raw positions shape : [active_request_count, num_generated_tokens]
+        # e.g block size 6, old_offsets = [1,5,2] , num_generated_tokens = 3
+        # raw_positions = [[1, 2, 3], [5, 6, 7], [2, 3, 4]]
+        # crosses_boundary = [[False, False, False], [False, True, True], [False, False, False]]
+        raw_positions = (
+            old_offsets[:, None]
+            + 1  # Offset by 1 because old_offsets points to the LAST token
+            + torch.arange(num_generated_tokens, device=torch.cuda.current_device())[None, :]
         )
+        #
+        # A token crosses to the next block if its raw_position >= block_size
+        crosses_boundary = raw_positions >= self.block_size_tokens
+
+        if not crosses_boundary.any() or self.num_speculative_tokens == 0:
+            # Fast path: no tokens cross block boundary, all use current block
+            self.token_to_block_idx[: self.active_token_count] = self.request_last_kv_block_id[
+                self.paused_request_count : self.total_request_count
+            ].repeat_interleave(num_generated_tokens)
+        else:
+
+            # Some tokens cross to the next block (this happens for resumed requests)
+            #
+            # When a request is paused and resumed:
+            # 1. It was paused because remaining_space < num_tokens_per_step
+            # 2. A NEW block is allocated in resume_paused_requests
+            # 3. request_last_kv_block_id is updated to the NEW block
+            # 4. The old offset is preserved (wasn't reset)
+            #
+            # So for resumed requests:
+            # - Tokens before the boundary (raw_pos < block_size): go to PREVIOUS block
+            # - Tokens at/after the boundary (raw_pos >= block_size): go to CURRENT (new) block
+            #
+            # For non-resumed requests (no boundary crossing): all go to current block
+            #
+            # We use prev_last_block_ids which was stored BEFORE resume_paused_requests
+            # was called, so it contains the OLD block IDs before new blocks were allocated.
+
+            # Get previous block IDs (stored before resume_paused_requests)
+            prev_block_ids = prev_last_block_ids[
+                self.paused_request_count : self.total_request_count
+            ]  # [active_count]
+
+            # For each request, check if ANY token crosses (i.e., request was resumed)
+            request_has_crossing = crosses_boundary.any(dim=1)  # [active_count]
+
+            # Build block_idx: [active_count, N]
+            # Start with current (new) block for all
+            # Lets say current block ids is [a1, a2 , a3] and num generated_tokens is 3
+            # This will be [[a1, a1, a1], [a2, a2, a2], [a3, a3, a3]]
+            # No clone needed: expand() returns a read-only view, and downstream
+            # torch.where() and .flatten() both return new tensors without in-place mutation.
+            block_idx = current_block_ids[:, None].expand(
+                -1, num_generated_tokens
+            )  # [active_count, N]
+
+            # For requests that have crossing, tokens BEFORE boundary use prev block
+            # crosses_boundary is False for tokens before boundary
+            # So: where request_has_crossing AND NOT crosses_boundary, use prev_block
+            use_prev_block = request_has_crossing[:, None] & ~crosses_boundary  # [active_count, N]
+
+            # Apply previous block IDs where needed
+            prev_block_ids_expanded = prev_block_ids[:, None].expand(-1, num_generated_tokens)
+            block_idx = torch.where(use_prev_block, prev_block_ids_expanded, block_idx)
+
+            # Convert back to 1d tensor
+            self.token_to_block_idx[: self.active_token_count] = block_idx.flatten()
 
         return {
             "newly_paused_request_ids": newly_paused_request_ids,
diff --git a/megatron/core/inference/data_parallel_inference_coordinator.py b/megatron/core/inference/data_parallel_inference_coordinator.py
index d5cf1605d90..5239b219c9e 100644
--- a/megatron/core/inference/data_parallel_inference_coordinator.py
+++ b/megatron/core/inference/data_parallel_inference_coordinator.py
@@ -2,17 +2,20 @@
 
 import errno
 import faulthandler
+import json
 import logging
 import signal
 import socket
 from collections import deque
-from itertools import cycle
+from enum import Enum, auto
 from multiprocessing import Event
 from multiprocessing.connection import Connection
 
 import torch
 
+from megatron.core.inference.config import PrefixCachingCoordinatorPolicy
 from megatron.core.inference.headers import Headers, UnknownHeaderError
+from megatron.core.inference.inference_request import compute_block_hashes_batched
 
 try:
     import zmq
@@ -68,6 +71,14 @@ class DataParallelInferenceCoordinator:
         next_request_id (int): A counter for generating unique server-side request IDs.
     """
 
+    class CoordinatorState(Enum):
+        """State machine for the coordinator."""
+
+        RUNNING = auto()
+        PAUSED = auto()
+        SUSPENDED = auto()
+        STOPPING = auto()
+
     def __init__(
         self,
         pipe_connection: Connection,
@@ -75,6 +86,12 @@ def __init__(
         tokenizer,
         inference_coordinator_port: int | None = None,
         deterministic_mode: bool = False,
+        block_size_tokens: int | None = None,
+        enable_prefix_caching: bool = False,
+        prefix_caching_coordinator_policy: PrefixCachingCoordinatorPolicy = (
+            PrefixCachingCoordinatorPolicy.FIRST_PREFIX_BLOCK
+        ),
+        schedule_output_path: str | None = None,
     ):
         """
         Initializes the inference coordinator.
@@ -113,6 +130,8 @@ def __init__(
         local_ip = socket.gethostname()
 
         self.router_socket = self.context.socket(zmq.ROUTER)
+        # Raise error if the other side of the connection has dropped.
+        self.router_socket.setsockopt(zmq.ROUTER_MANDATORY, 1)
         is_bound = False
         if inference_coordinator_port is not None:
             try:
@@ -152,15 +171,31 @@ def __init__(
             self.identities_of_data_parallel_ranks = deque(
                 sorted(self.identities_of_data_parallel_ranks)
             )
-        self.data_parallel_rank_iterator = cycle(self.identities_of_data_parallel_ranks)
-        self.data_parallel_pause_acks = set()
-        self.data_parallel_stop_acks = set()
+        self._round_robin_idx = 0
 
         self.request_id_to_client_id = {}
         self.request_id_to_client_request_id = {}
 
         self.next_request_id = 0
         self.tokenizer = tokenizer
+        self.state = self.CoordinatorState.RUNNING
+
+        # Prefix caching state for routing.
+        self.block_size_tokens = block_size_tokens
+        self.enable_prefix_caching = enable_prefix_caching
+        self.prefix_caching_coordinator_policy = prefix_caching_coordinator_policy
+        self.hash_to_rank_info = {}  # Dict[int, Dict[bytes, int]]: hash → {rank → timestamp}
+        self._assignment_counter = 0
+
+        # Schedule recording.
+        self.schedule_output_path = schedule_output_path
+        self.schedule_records = [] if schedule_output_path else None
+
+        # Deterministic rank index mapping (sorted identity -> 0-based index).
+        sorted_identities = sorted(self.identities_of_data_parallel_ranks)
+        self.identity_to_rank_index = {
+            identity: idx for idx, identity in enumerate(sorted_identities)
+        }
 
     def get_next_data_parallel_rank(self):
         """
@@ -169,7 +204,99 @@ def get_next_data_parallel_rank(self):
         Returns:
             bytes: The ZMQ identity of the next data parallel rank to receive a request.
         """
-        return next(self.data_parallel_rank_iterator)
+        identities = self.identities_of_data_parallel_ranks
+        if not identities:
+            raise RuntimeError("No engines connected")
+        idx = self._round_robin_idx % len(identities)
+        self._round_robin_idx = idx + 1
+        return identities[idx]
+
+    def _remove_engine(self, identity):
+        """Remove a disconnected engine from the routing pool."""
+        self.identities_of_data_parallel_ranks.remove(identity)
+        logging.warning(
+            "Coordinator: removed engine %s (now %d engines)",
+            identity,
+            len(self.identities_of_data_parallel_ranks),
+        )
+
+    def _send_to_engine(self, identity, payload):
+        """Send payload to an engine, removing it from the pool if unreachable.
+
+        Returns:
+            True if the send succeeded, False if the engine was unreachable and removed.
+        """
+        try:
+            self.router_socket.send_multipart([identity, payload])
+            return True
+        except zmq.error.ZMQError as e:
+            if e.errno == zmq.EHOSTUNREACH:
+                self._remove_engine(identity)
+                return False
+            raise
+
+    def compute_request_hashes(self, prompt):
+        """Compute block hashes for a prompt on CPU.
+
+        Args:
+            prompt: Either a string (to be tokenized) or a list of token IDs.
+
+        Returns:
+            List of integer block hashes, or empty list if prefix caching is disabled.
+        """
+        if not self.enable_prefix_caching or self.block_size_tokens is None:
+            return []
+        if isinstance(prompt, str):
+            tokens = self.tokenizer.tokenize(prompt)
+        else:
+            tokens = list(prompt)
+        token_tensor = torch.tensor(tokens, dtype=torch.int64)
+        return compute_block_hashes_batched(token_tensor, self.block_size_tokens)
+
+    def get_best_data_parallel_rank(self, request_hashes):
+        """Select the best DP rank based on prefix cache affinity.
+
+        Iterates request hashes in reverse order and picks the rank that cached
+        the longest matching prefix (the furthest hash found). Since hashes are
+        parent-chained, finding hash[i] in a rank guarantees hash[0..i-1] are
+        also present. Among ranks that share the longest match, the most recently
+        assigned rank (highest timestamp) is preferred. Falls back to round-robin
+        when no rank matches.
+
+        Args:
+            request_hashes: List of block hashes for the request.
+
+        Returns:
+            bytes: The ZMQ identity of the selected data parallel rank.
+        """
+        if (
+            not self.enable_prefix_caching
+            or not request_hashes
+            or self.prefix_caching_coordinator_policy == PrefixCachingCoordinatorPolicy.ROUND_ROBIN
+        ):
+            return self.get_next_data_parallel_rank()
+
+        # Reverse scan: first match is the longest prefix (parent-chained hashes).
+        for h in reversed(request_hashes):
+            rank_info = self.hash_to_rank_info.get(h)
+            if rank_info:
+                # Pick the most recently assigned rank.
+                best_rank = max(rank_info, key=rank_info.get)
+                return best_rank
+
+        return self.get_next_data_parallel_rank()
+
+    def _update_rank_hashes(self, rank_identity, request_hashes):
+        """Record that a rank owns the given hashes.
+
+        Args:
+            rank_identity: ZMQ identity of the target rank.
+            request_hashes: List of block hashes assigned to this rank.
+        """
+        self._assignment_counter += 1
+        ts = self._assignment_counter
+        for h in request_hashes:
+            self.hash_to_rank_info.setdefault(h, {})[rank_identity] = ts
 
     def start(self):
         """
@@ -185,6 +312,13 @@ def start(self):
         known_clients = set()
         while True:
             sender_identity, serialized_payload = self.router_socket.recv_multipart()
+
+            # Allow for re-registration if connecting to a running coordinator.
+            if serialized_payload == b"":
+                if sender_identity not in self.identities_of_data_parallel_ranks:
+                    self.identities_of_data_parallel_ranks.append(sender_identity)
+                continue
+
             deserialized_payload = msgpack.unpackb(serialized_payload, raw=False)
             header = Headers(deserialized_payload[0])
 
@@ -230,85 +364,103 @@ def start(self):
                 else:
                     raise Exception("specialize for <%s> prompt." % type(prompt).__name__)
 
-                next_data_parallel_rank_identity = self.get_next_data_parallel_rank()
-                self.router_socket.send_multipart(
-                    [
-                        next_data_parallel_rank_identity,
-                        msgpack.packb(
-                            [Headers.SUBMIT_REQUEST.value, request_id, prompt, sampling_params],
-                            use_bin_type=True,
-                        ),
-                    ]
+                payload = msgpack.packb(
+                    [Headers.SUBMIT_REQUEST.value, request_id, prompt, sampling_params],
+                    use_bin_type=True,
                 )
-            elif header in [
+
+                request_hashes = self.compute_request_hashes(prompt)
+                if (
+                    self.prefix_caching_coordinator_policy
+                    == PrefixCachingCoordinatorPolicy.FIRST_PREFIX_BLOCK
+                ):
+                    request_hashes = request_hashes[:1]
+
+                # Account for the fact that some engines may have died.
+                for _ in range(len(self.identities_of_data_parallel_ranks)):
+                    next_identity = self.get_best_data_parallel_rank(request_hashes)
+                    if self._send_to_engine(next_identity, payload):
+                        break
+                else:
+                    # If all engines have died, we are in an abnormal state, and must exit cleanly.
+                    logging.error("Coordinator: no reachable engines for request %d", request_id)
+                    del self.request_id_to_client_id[request_id]
+                    del self.request_id_to_client_request_id[request_id]
+                    return
+
+                if request_hashes:
+                    self._update_rank_hashes(next_identity, request_hashes)
+                if self.schedule_records is not None:
+                    self.schedule_records.append(
+                        {
+                            "request_id": request_id,
+                            "rank_index": self.identity_to_rank_index[next_identity],
+                            "num_hashes": len(request_hashes),
+                        }
+                    )
+
+            elif header in (
                 Headers.PAUSE,
                 Headers.UNPAUSE,
                 Headers.SUSPEND,
                 Headers.RESUME,
                 Headers.INCREMENT_STALENESS,
                 Headers.STOP,
-            ]:
-                # control signals for the engine
-                # broadcast to all data parallel ranks
+            ):
+                # Start by checking the current state against the control signal.
                 if sender_identity not in known_clients:
+                    logging.warning("Coordinator: ignoring signal from unknown client.")
                     continue
-                for data_parallel_rank_id in self.identities_of_data_parallel_ranks:
-                    self.router_socket.send_multipart(
-                        [data_parallel_rank_id, msgpack.packb([header.value], use_bin_type=True)]
-                    )
-                if header == Headers.UNPAUSE:
-                    self.data_parallel_pause_acks = set()
-            elif header == Headers.PAUSE_ACK:
-                # control signal ack from the engine
-                assert sender_identity in self.identities_of_data_parallel_ranks
-                assert sender_identity not in self.data_parallel_pause_acks
-                self.data_parallel_pause_acks.add(sender_identity)
-                # route to all clients only once we have gotten an ack from all data parallel ranks
-                if len(self.data_parallel_pause_acks) == self.data_parallel_size:
-                    for client_id in known_clients:
-                        self.router_socket.send_multipart(
-                            [
-                                client_id,
-                                msgpack.packb([header.value, sender_identity], use_bin_type=True),
-                            ]
-                        )
-                    for data_parallel_rank_id in self.identities_of_data_parallel_ranks:
-                        self.router_socket.send_multipart(
-                            [
-                                data_parallel_rank_id,
-                                msgpack.packb([Headers.PAUSE_ACK.value], use_bin_type=True),
-                            ]
-                        )
-            elif header == Headers.STOP_ACK:
-                # control signal ack from the engine
-                assert sender_identity in self.identities_of_data_parallel_ranks
-                assert sender_identity not in self.data_parallel_stop_acks
-                self.data_parallel_stop_acks.add(sender_identity)
-                # route to all clients only once we have gotten an ack from all data parallel ranks
-                if len(self.data_parallel_stop_acks) == self.data_parallel_size:
-                    for client_id in known_clients:
-                        self.router_socket.send_multipart(
-                            [
-                                client_id,
-                                msgpack.packb([header.value, sender_identity], use_bin_type=True),
-                            ]
-                        )
-                    for data_parallel_rank_id in self.identities_of_data_parallel_ranks:
-                        self.router_socket.send_multipart(
-                            [
-                                data_parallel_rank_id,
-                                msgpack.packb([Headers.STOP_ACK.value], use_bin_type=True),
-                            ]
-                        )
-                    break  # Exit the main loop after STOP_ACKs have been processed.
+
+                if header == Headers.PAUSE:
+                    idem_states = (self.CoordinatorState.PAUSED, self.CoordinatorState.SUSPENDED)
+                    if self.state == self.CoordinatorState.RUNNING:
+                        self.state = self.CoordinatorState.PAUSED
+                    elif self.state in idem_states:
+                        # Already paused/suspended, ignore redundant PAUSE.
+                        continue
+                    else:
+                        logging.warning("Coordinator: ignoring PAUSE in state %s", self.state)
+                        continue
+                elif header == Headers.UNPAUSE:
+                    if self.state != self.CoordinatorState.PAUSED:
+                        logging.warning("Coordinator: ignoring UNPAUSE in state %s", self.state)
+                        continue
+                    self.state = self.CoordinatorState.RUNNING
+                elif header == Headers.SUSPEND:
+                    if self.state != self.CoordinatorState.PAUSED:
+                        logging.warning("Coordinator: ignoring SUSPEND in state %s", self.state)
+                        continue
+                    self.state = self.CoordinatorState.SUSPENDED
+                elif header == Headers.RESUME:
+                    if self.state != self.CoordinatorState.SUSPENDED:
+                        logging.warning("Coordinator: ignoring RESUME in state %s", self.state)
+                        continue
+                    self.state = self.CoordinatorState.PAUSED
+                elif header == Headers.STOP:
+                    good_states = (self.CoordinatorState.PAUSED, self.CoordinatorState.SUSPENDED)
+                    if self.state not in good_states:
+                        logging.warning("Coordinator: ignoring STOP in state %s", self.state)
+                        continue
+                    self.state = self.CoordinatorState.STOPPING
+
+                # Broadcast the control signal if we're in a good state.
+                broadcast_payload = msgpack.packb([header.value], use_bin_type=True)
+                for data_parallel_rank_id in list(self.identities_of_data_parallel_ranks):
+                    self._send_to_engine(data_parallel_rank_id, broadcast_payload)
+
+                # STOP affects engines; reset coordinator to RUNNING to allow future engines.
+                if header == Headers.STOP:
+                    self.state = self.CoordinatorState.RUNNING
+
             elif header == Headers.ENGINE_REPLY:
                 # This is the output of a single engine step on some data parallel rank.
                 assert sender_identity in self.identities_of_data_parallel_ranks
-                finished_request_records = deserialized_payload[1]
+                finished_requests = deserialized_payload[1]
 
-                for finished_request_record in finished_request_records:
-                    self.detokenize(finished_request_record)
-                    fid = finished_request_record["requests"][0]["request_id"]
+                for finished_request in finished_requests:
+                    self.detokenize(finished_request)
+                    fid = finished_request["request_id"]
                     client_identity = self.request_id_to_client_id[fid]
                     client_request_identity = self.request_id_to_client_request_id[fid]
                     del self.request_id_to_client_id[fid]
@@ -318,30 +470,43 @@ def start(self):
                         [
                             client_identity,
                             msgpack.packb(
-                                [header.value, client_request_identity, finished_request_record],
+                                [header.value, client_request_identity, finished_request],
                                 use_bin_type=True,
                             ),
                         ]
                     )
 
+            elif header == Headers.SHUTDOWN:
+                if sender_identity not in known_clients:
+                    logging.warning("Coordinator: ignoring signal from unknown client.")
+                    continue
+                break
+
+            elif header == Headers.DISCONNECT:
+                if sender_identity in self.identities_of_data_parallel_ranks:
+                    self._remove_engine(sender_identity)
+
             else:
                 raise UnknownHeaderError(header)
 
-    def detokenize(self, finished_request_record):
+    def detokenize(self, finished_request):
         """
-        Detokenizes the generated tokens in the finished request record.
+        Detokenizes the generated tokens in the finished request.
 
         This method uses the coordinator's tokenizer to convert the list of
         generated token IDs back into human-readable text.
 
         Args:
-            finished_request_record (dict): The record containing the generated
-                tokens to be detokenized. It is modified in place.
+            finished_request (dict): The serialized merged request containing the
+                generated tokens to be detokenized. It is modified in place.
         """
-        for request in finished_request_record["requests"]:
-            if request["prompt"] is None:
-                request["prompt"] = self.tokenizer.detokenize(request["prompt_tokens"][1])
-            request["generated_text"] = self.tokenizer.detokenize(request["generated_tokens"])
+        if finished_request["prompt"] is None:
+            finished_request["prompt"] = self.tokenizer.detokenize(
+                finished_request["prompt_tokens"][1]
+            )
+        finished_request["generated_text"] = self.tokenizer.detokenize(
+            finished_request["generated_tokens"]
+        )
 
     @classmethod
     def entrypoint(
@@ -352,6 +517,12 @@ def entrypoint(
         tokenizer,
         inference_coordinator_port: int | None = None,
         deterministic_mode: bool = False,
+        block_size_tokens: int | None = None,
+        enable_prefix_caching: bool = False,
+        prefix_caching_coordinator_policy: PrefixCachingCoordinatorPolicy = (
+            PrefixCachingCoordinatorPolicy.FIRST_PREFIX_BLOCK
+        ),
+        schedule_output_path: str | None = None,
     ):
         """
         Class method to instantiate and run the coordinator, for use in a separate process.
@@ -366,6 +537,10 @@ def entrypoint(
             inference_coordinator_port (int): The port to bind to.
             data_parallel_size (int): The number of expected TP-coordinators.
             deterministic_mode (bool): Whether to enable deterministic scheduling.
+            block_size_tokens (Optional[int]): Token block size for prefix caching hashing.
+            enable_prefix_caching (bool): Whether prefix caching is enabled.
+            prefix_caching_coordinator_policy (PrefixCachingCoordinatorPolicy): Routing policy.
+            schedule_output_path (Optional[str]): Path to write scheduling decisions JSON.
         """
         coordinator = cls(
             pipe_connection,
@@ -373,6 +548,10 @@ def entrypoint(
             tokenizer,
             inference_coordinator_port,
             deterministic_mode=deterministic_mode,
+            block_size_tokens=block_size_tokens,
+            enable_prefix_caching=enable_prefix_caching,
+            prefix_caching_coordinator_policy=prefix_caching_coordinator_policy,
+            schedule_output_path=schedule_output_path,
         )
         ready_event.set()
         try:
@@ -386,5 +565,14 @@ def stop(self):
         """
         Stops the inference coordinator, performing any necessary cleanup operations.
         """
+        if self.schedule_output_path and self.schedule_records:
+            schedule_data = {
+                "policy": self.prefix_caching_coordinator_policy.value,
+                "data_parallel_size": self.data_parallel_size,
+                "num_requests": len(self.schedule_records),
+                "records": self.schedule_records,
+            }
+            with open(self.schedule_output_path, "w") as f:
+                json.dump(schedule_data, f, indent=2)
         self.router_socket.close()
         self.context.term()
diff --git a/megatron/core/inference/engines/async_zmq_communicator.py b/megatron/core/inference/engines/async_zmq_communicator.py
index 7076bb283bd..124f2d46932 100644
--- a/megatron/core/inference/engines/async_zmq_communicator.py
+++ b/megatron/core/inference/engines/async_zmq_communicator.py
@@ -65,46 +65,54 @@ def __init__(self, zmq_context: zmq.Context, process_group: dist.ProcessGroup):
             self.bcast_sock.connect(bcast_socket_addr)
             self.bcast_sock.setsockopt_string(zmq.SUBSCRIBE, "")
 
-    async def all_reduce_max(self, local_val: int) -> int:
-        """
-        Asyncio friendly all reduce max operation. Gathers on rank 0, computes max,
-        and broadcasts the result.
+    async def all_reduce_max(self, *local_vals: int) -> int | tuple[int, ...]:
+        """Element-wise all-reduce max of one or more integers.
+
+        Packs all values into a single message so the communication cost
+        is independent of the number of values.
+
+        Returns a single int when called with one argument, otherwise a tuple.
         """
+        n = len(local_vals)
+        if n == 0:
+            raise ValueError("all_reduce_max requires at least one value")
+
         if self.world_size <= 1:
-            return local_val
+            return local_vals[0] if n == 1 else local_vals
 
-        payload = struct.pack('!i', local_val)
+        fmt = f'!{n}i'
+        payload = struct.pack(fmt, *local_vals)
 
         if self.is_leader:
-            # Rank 0: Gather -> Max -> Broadcast
-            values = [local_val]
+            rows = [local_vals]
 
-            # Non-blocking gather from N-1 peers
-            while len(values) < self.world_size:
+            while len(rows) < self.world_size:
                 try:
                     msg = self.gather_sock.recv(flags=zmq.NOBLOCK)
-                    values.append(struct.unpack('!i', msg)[0])
+                    rows.append(struct.unpack(fmt, msg))
                 except zmq.Again:
-                    await asyncio.sleep(0.001)  # Yield to event loop
+                    await asyncio.sleep(0.001)
 
-            max_val = max(values)
-            self.bcast_sock.send(struct.pack('!i', max_val))
-            return max_val
+            maxes = tuple(max(row[i] for row in rows) for i in range(n))
+            self.bcast_sock.send(struct.pack(fmt, *maxes))
+            return maxes[0] if n == 1 else maxes
 
         else:
-            # Worker: Send -> Wait for Broadcast
             self.gather_sock.send(payload)
 
             while True:
                 try:
                     msg = self.bcast_sock.recv(flags=zmq.NOBLOCK)
-                    return struct.unpack('!i', msg)[0]
+                    result = struct.unpack(fmt, msg)
+                    return result[0] if n == 1 else result
                 except zmq.Again:
-                    await asyncio.sleep(0.001)  # Yield to event loop
+                    await asyncio.sleep(0.001)
 
     def close(self):
         """
         Close the ZMQ sockets.
         """
-        self.gather_sock.close()
-        self.bcast_sock.close()
+        # linger=0: discard unsent messages immediately on close rather than blocking until sent.
+        # The ZMQ default is to not allow `close` until all messages have been successfully sent.
+        self.gather_sock.close(linger=0)
+        self.bcast_sock.close(linger=0)
diff --git a/megatron/core/inference/engines/dynamic_engine.py b/megatron/core/inference/engines/dynamic_engine.py
index a10b7e3d60f..dbd6f2efc86 100644
--- a/megatron/core/inference/engines/dynamic_engine.py
+++ b/megatron/core/inference/engines/dynamic_engine.py
@@ -12,6 +12,7 @@
 from contextlib import contextmanager
 from dataclasses import dataclass
 from datetime import datetime
+from enum import Enum, auto
 from itertools import repeat
 from typing import Dict, List, Optional, Tuple, Union
 
@@ -41,7 +42,12 @@
 from megatron.core.inference.text_generation_controllers.text_generation_controller import (
     TextGenerationController,
 )
-from megatron.core.inference.utils import Counter, await_process_call
+from megatron.core.inference.utils import (
+    Counter,
+    await_process_call,
+    set_inference_cuda_graphed_iteration_for_ep_inference,
+    unset_inference_cuda_graphed_iteration_for_ep_inference,
+)
 from megatron.core.process_groups_config import ProcessGroupCollection
 from megatron.core.transformer.cuda_graphs import delete_cuda_graphs
 from megatron.core.transformer.enums import CudaGraphScope
@@ -105,6 +111,21 @@
 ]
 
 
+class EngineState(Enum):
+    """State machine for the inference engine."""
+
+    RUNNING = auto()  # Processing requests
+    PAUSING = auto()  # PAUSE received; waiting for EP consensus + world barrier
+    PAUSED = auto()  # Globally confirmed idle
+    UNPAUSING = auto()  # UNPAUSE received; waiting for world barrier
+    SUSPENDING = auto()  # SUSPEND received; offloading GPU; waiting for world barrier
+    SUSPENDED = auto()  # GPU offloaded, all ranks confirmed
+    RESUMING = auto()  # RESUME received; onloading GPU; waiting for world barrier
+    RESUMED = auto()  # GPU onloaded, all ranks confirmed; cleared on next SUSPEND
+    STOPPING = auto()  # STOP received; futures cancelled; waiting for world barrier
+    STOPPED = auto()  # All ranks confirmed; teardown complete
+
+
 class EngineSuspendedError(Exception):
     """Engine is currently suspended and not performing steps."""
 
@@ -148,6 +169,15 @@ class DynamicInferenceEngine(AbstractEngine):
             batching and a dynamic block-level KV cache (similar to paged attention).
     """
 
+    # Map stable states to their corresponding asyncio events.
+    _STATE_EVENTS = (
+        EngineState.RUNNING,
+        EngineState.PAUSED,
+        EngineState.SUSPENDED,
+        EngineState.RESUMED,
+        EngineState.STOPPED,
+    )
+
     @deprecate_args(
         *DEPRECATED_ARGS,
         message="Argument `{name}` has been deprecated. Only pass `controller` and `context`",
@@ -172,15 +202,28 @@ def __init__(self, controller: TextGenerationController, context: DynamicInferen
         # Initialization options.
         self.controller = controller
         self.context = context
+
+        self.num_speculative_tokens = inference_config.num_speculative_tokens
+        self.materialize_only_last_token_logits = (
+            inference_config.materialize_only_last_token_logits
+        )
+
+        assert self.num_speculative_tokens >= 0, "Number of speculative tokens must be non-negative"
+
+        if self.num_speculative_tokens > 0:
+            assert (
+                self.num_speculative_tokens <= self.controller.num_mtp_heads
+            ), f"Number of speculative tokens {self.num_speculative_tokens} must be less than or equal to number of MTP heads {self.controller.num_mtp_heads}"
+            assert (
+                not self.materialize_only_last_token_logits
+            ), "materialize_only_last_token_logits must be False when num_speculative_tokens > 0"
+
         self.track_paused_request_events = inference_config.track_paused_request_events
         self.track_generated_token_events = inference_config.track_generated_token_events
         self.enable_chunked_prefill = inference_config.enable_chunked_prefill
         self.metrics_writer = inference_config.metrics_writer
         self.logging_step_interval = inference_config.logging_step_interval
         self.unified_memory_level = inference_config.unified_memory_level
-        self.materialize_only_last_token_logits = (
-            inference_config.materialize_only_last_token_logits
-        )
         self.cuda_graph_impl = model_config.cuda_graph_impl
         self.cuda_graph_scope = model_config.cuda_graph_scope
         # Initialize engine.
@@ -239,7 +282,6 @@ def reset(self) -> None:
 
         # Timing and logging variables.
         self.rank = torch.distributed.get_rank()
-        self.step_count = 0
         self.step_start_event = torch.cuda.Event(enable_timing=True)
         self.step_end_event = torch.cuda.Event(enable_timing=True)
         self.capture_stats = None
@@ -247,18 +289,36 @@ def reset(self) -> None:
         # Runtime state.
         self._loop = get_asyncio_loop(getattr(self, "_loop", None))
         self._cond = asyncio.Condition()
-        self.running = asyncio.Event()
-        self.paused = asyncio.Event()
-        self.stopped = asyncio.Event()
-        self.received_pause: bool = False
-        self.received_stop: bool = False
-        self.suspend_signal = False
-        self.is_suspended = False
+        self._state_events = {k: asyncio.Event() for k in self._STATE_EVENTS}
+        self.state = EngineState.RUNNING
+        self._state_events[EngineState.RUNNING].set()
+        self._pending_signals = deque()
+
         self.resume_request_ids = None
 
+        # Speculative decoding acceptance tracking.
+        self._spec_tokens_proposed = 0
+        self._spec_tokens_accepted = 0
+        self._spec_steps = 0
+
+        # Prefix caching coordination state.
+        self._prefix_coordination_waits = 0
+
         # Coordinator state.
         self.use_coordinator = False
 
+    async def wait_until(self, state: EngineState):
+        """Wait until the engine reaches the given state.
+
+        Only stable states (RUNNING, PAUSED, SUSPENDED, RESUMED,
+        STOPPED) are supported.  Transient states (PAUSING, SUSPENDING,
+        RESUMING, STOPPING) are not directly waitable.
+        """
+        event = self._state_events.get(state)
+        if event is None:
+            raise ValueError(f"Cannot wait for transient state {state}")
+        await event.wait()
+
     def create_cuda_graphs(self, reset_context: bool = True):
         """Create cuda graphs.
 
@@ -291,6 +351,16 @@ def create_cuda_graphs(self, reset_context: bool = True):
         for graph in context.cuda_graph_batch_dimensions_list:
             logging.info(graph)
 
+        # Enable inference dispatcher for EP during graph capture
+        model_config = controller.inference_wrapped_model.model.config
+        is_inference_optimized_ep = (
+            model_config.transformer_impl == "inference_optimized"
+            and model_config.expert_model_parallel_size > 1
+        )
+        if is_inference_optimized_ep:
+            unwrapped_model = controller.inference_wrapped_model.model
+            set_inference_cuda_graphed_iteration_for_ep_inference(unwrapped_model)
+
         tbar = enumerate(context.cuda_graph_batch_dimensions_list)
         if HAVE_TQDM:
             tbar = tqdm(tbar, total=len(context.cuda_graph_batch_dimensions_list))
@@ -318,6 +388,10 @@ def create_cuda_graphs(self, reset_context: bool = True):
 
             context.reset()
 
+        # Disable inference dispatcher after graph capture
+        if is_inference_optimized_ep:
+            unset_inference_cuda_graphed_iteration_for_ep_inference(unwrapped_model)
+
         # Memory usage.
         time_end = time.time()
         mem_stats_end = torch.cuda.memory_stats()
@@ -348,6 +422,7 @@ async def start_listening_to_data_parallel_coordinator(
         inference_coordinator_port: int | None = None,
         launch_inference_coordinator: bool = True,
         *,
+        coordinator_schedule_output_path: str | None = None,
         loop: Optional[asyncio.AbstractEventLoop] = None,
     ):
         """Initializes ZMQ communication to connect the engine with an inference coordinator.
@@ -399,7 +474,7 @@ async def start_listening_to_data_parallel_coordinator(
             "pip install msgpack"
         )
 
-        self.zmq_context = zmq.Context().instance()
+        self.zmq_context = zmq.Context.instance()
         self.zmq_sockets = []  # keep track of all sockets created by this engine
 
         # Get world info.
@@ -433,6 +508,10 @@ async def start_listening_to_data_parallel_coordinator(
                     self.controller.tokenizer,
                     inference_coordinator_port,
                     deterministic_mode,
+                    self.context.block_size_tokens,
+                    self.context.enable_prefix_caching,
+                    self.context.prefix_caching_coordinator_policy,
+                    coordinator_schedule_output_path,
                 ),
             )
             self.inference_coordinator_process.start()
@@ -521,6 +600,11 @@ async def start_listening_to_data_parallel_coordinator(
                 self.zmq_context, process_group=self.pg_collection.ep
             )
 
+        # initialize zmq-based world communicator for consensus barriers
+        total_world_size = torch.distributed.get_world_size()
+        if total_world_size > 1:
+            self.world_zmq_communicator = AsyncZMQCommunicator(self.zmq_context, process_group=None)
+
         if launch_inference_coordinator and self.is_dp_coordinator:
             await await_process_call(
                 coordinator_ready_event.wait, self.inference_coordinator_process
@@ -600,11 +684,9 @@ def suspend_resume_ctx(key: str, *, unified_memory_level: int) -> None:
     def suspend(self):
         """Suspend engine by deallocating context's GPU state."""
 
-        # Skip if already suspended, which can happen when using the inference
-        # coordinator.
-        if self.is_suspended:
+        # Skip if already suspended or in the process of suspending.
+        if self.state in (EngineState.SUSPENDED, EngineState.SUSPENDING):
             return
-        self.is_suspended = True
 
         # Deallocate context tensors.
         with self.__class__.suspend_resume_ctx(
@@ -634,14 +716,16 @@ def suspend(self):
         for request_id in recompute_active_ids:
             self.requests[request_id].record.checkpoint()
 
+        # If we are not using the inference coordinator, we need to manually handle state.
+        if not self.use_coordinator:
+            self.state = EngineState.SUSPENDED
+
     def resume(self):
         """Resume engine by reallocating context's GPU state."""
 
-        # Skip if not suspended, which can happen when using the inference
-        # coordinator.
-        if not self.is_suspended:
+        # Skip if not suspended or in the process of suspending.
+        if self.state not in (EngineState.SUSPENDED, EngineState.SUSPENDING):
             return
-        self.is_suspended = False
 
         # Resume.
         with self.__class__.suspend_resume_ctx(
@@ -683,8 +767,13 @@ def resume(self):
             )
         )
 
-        # Notify event loop.
-        self._loop.call_soon_threadsafe(asyncio.create_task, self._notify_cond_for_new_request())
+        # If we are not using the inference coordinator, we need to manually handle state.
+        if not self.use_coordinator:
+            self.state = EngineState.RUNNING
+            # Notify the condition variable that run_engine() waits on.
+            self._loop.call_soon_threadsafe(
+                asyncio.create_task, self._notify_cond_for_new_request()
+            )
 
     @trace_async_exceptions
     async def _notify_cond_for_new_request(self):
@@ -768,10 +857,16 @@ def _add_request(
             len(request.prompt_tokens) + request.sampling_params.num_tokens_to_generate
             > self.context.max_sequence_length
         ) or (request.sampling_params.num_tokens_to_generate < 0):
+            logging.error(
+                f"{request_id=} Invalid number of tokens to generate. Prompt len: {len(request.prompt_tokens)}, tokens to generate: {request.sampling_params.num_tokens_to_generate}, max seq len: {self.context.max_sequence_length}."
+            )
             request.status = Status.FAILED
             request.add_event_error_nontransient(MaxSequenceLengthOverflowError(request_id))
 
         if len(request.prompt_tokens) > self.context.max_tokens and not self.enable_chunked_prefill:
+            logging.error(
+                f"{request_id=} Prompt is longer than context.max_tokens. Prompt tokens: {len(request.prompt_tokens)}, context.max_tokens: {self.context.max_tokens}, chunked_prefill: {self.enable_chunked_prefill}"
+            )
             request.status = Status.FAILED
             request.add_event_error_nontransient(TokenOverflowError(request_id))
 
@@ -789,7 +884,10 @@ def _add_request(
             self.failed_request_ids.append(request_id)
             if self.rank == 0:
                 warnings.warn(
-                    f"Request {request_id} failed to be added to the engine due to errors."
+                    f"Request {request_id} failed to be added to the engine due to errors. "
+                    f"Prompt Tokens: {len(request.prompt_tokens)} "
+                    f"Tokens to generate: {request.sampling_params.num_tokens_to_generate} "
+                    f"Max sequence length: {self.context.max_sequence_length} "
                 )
 
         return self.requests[request_id].future
@@ -842,6 +940,8 @@ def add_request(
             prompt=prompt_str,
             prompt_tokens=tokens,
             sampling_params=sampling_params,
+            block_size_tokens=self.context.block_size_tokens,
+            enable_prefix_caching=self.context.enable_prefix_caching,
         )
 
         # Add request.
@@ -854,6 +954,7 @@ def post_process_requests(
         evict_request_ids: torch.Tensor,
         step_time: float,
         sample: torch.Tensor,
+        accepted_tokens: torch.Tensor,
         log_probs: torch.Tensor,
         top_n_logprobs: Optional[Dict[int, List[Tuple[torch.Tensor, torch.Tensor]]]] = None,
         routing_indices_per_request: Optional[Dict[int, torch.Tensor]] = None,
@@ -866,7 +967,8 @@ def post_process_requests(
             finished_request_ids (torch.Tensor): A list of finished request ids
             evict_request_ids (torch.Tensor): A list of evicted request ids.
             step_time (float): The latency of the last step
-            sample: (torch.Tensor): The newly generated tokens for each request
+            sample: Tensor: The newly generated token for each request
+            accepted_tokens: Tensor: The additional accepted tokens for each request
             log_probs: (List): Log probs for each request
             top_n_logprobs: (Dict): Top-n log probs for each request. Maps request_idx to
                 list of (top_n_logprobs, top_n_indices) tuples.
@@ -885,36 +987,112 @@ def post_process_requests(
             self.evicted_request_count += evict_request_ids.numel()
 
         log_probs_iter = log_probs if log_probs else repeat(None)
+        block_allocator = self.context.block_allocator
+
+        # Pre-compute step-level block stats (before the per-request loop)
+        if self.track_generated_token_events:
+            blocks_allocated = block_allocator.total_count - block_allocator.total_avail
+            if block_allocator.enable_prefix_caching:
+                blocks_hashed_active = int((block_allocator.block_ref_counts > 0).sum().item())
+                blocks_ref_count = block_allocator.block_ref_counts.sum().item()
+            else:
+                blocks_hashed_active = blocks_allocated
+                blocks_ref_count = None
+
+        # When accepted_tokens is None (no speculative decoding), use repeat([]) to provide
+        # empty lists for each request, so the zip produces the correct number of iterations
+        accepted_tokens_iter = repeat([]) if accepted_tokens is None else accepted_tokens.tolist()
 
-        for req_idx, (request_id, token, request_log_probs) in enumerate(
-            zip(request_ids.tolist(), sample.tolist(), log_probs_iter)
+        if self.num_speculative_tokens > 0 and accepted_tokens is not None:
+            self._spec_steps += 1
+
+        for req_idx, (request_id, tokens, accepted_tokens_list, request_log_probs) in enumerate(
+            zip(request_ids.tolist(), sample.tolist(), accepted_tokens_iter, log_probs_iter)
         ):
+
+            # Ensure tokens is always a list for consistent handling
+            if not isinstance(tokens, list):
+                tokens = [tokens]
+
             request: DynamicInferenceRequest = self.get_request(request_id)
+
+            if self.num_speculative_tokens > 0:
+                accepted_tokens = list(filter(lambda tok: tok != -1, accepted_tokens_list))
+
+                # Track acceptance statistics for logging (decode requests only).
+                # Prefill requests don't propose speculative tokens, so including
+                # them would inflate the proposed count and deflate the rate.
+                # A request in its first generation step (empty generated_tokens)
+                # was in prefill this step.
+                if len(request.generated_tokens) > 0:
+                    self._spec_tokens_proposed += self.num_speculative_tokens
+                    self._spec_tokens_accepted += len(accepted_tokens)
+
+                # The order `accepted_tokens + tokens` is correct here.
+                # `accepted_tokens` contains the sequence of
+                # successfully verified draft tokens. `tokens` (from `sample`) is the
+                # brand new token generated by the target model based on that accepted prefix.
+                # Therefore, the newly sampled token must go at the end of the sequence.
+                tokens = accepted_tokens + tokens
+
+            num_stop_word_trim = 0
             if request_id != self.context.chunked_prefill_request_id:
                 # Skip appending token for requests being finished due to stop words
                 # (they already have their final token from the previous step)
+                # If the request already has more tokens, then we only append as much as is necessary
+                if (
+                    len(request.generated_tokens) + len(tokens)
+                    >= request.sampling_params.num_tokens_to_generate
+                ):
+                    tokens = tokens[
+                        : request.sampling_params.num_tokens_to_generate
+                        - len(request.generated_tokens)
+                    ]
                 if request_id not in self.stop_word_being_finished_ids:
                     is_first_token = len(request.generated_tokens) == 0
-                    request.generated_tokens.append(token)
+                    request.generated_tokens += tokens
+                    first_token_event = None
                     if self.track_generated_token_events:
-                        event_generated_token = request.add_event_generated_token(token)
+                        for token in tokens:
+                            if block_allocator.enable_prefix_caching:
+                                event = request.add_event_generated_token(
+                                    token,
+                                    blocks_total=block_allocator.total_count,
+                                    blocks_hashed_total=blocks_allocated,
+                                    blocks_hashed_active=blocks_hashed_active,
+                                    blocks_ref_count=blocks_ref_count,
+                                )
+                            else:
+                                event = request.add_event_generated_token(
+                                    token,
+                                    blocks_total=block_allocator.total_count,
+                                    blocks_hashed_total=blocks_allocated,
+                                    blocks_hashed_active=blocks_hashed_active,
+                                )
+                            if first_token_event is None:
+                                first_token_event = event
                     if is_first_token:
-                        if self.track_generated_token_events:
-                            first_token_event = event_generated_token
-                        else:
+                        if not self.track_generated_token_events:
                             first_token_event = DynamicInferenceEvent(
                                 type=DynamicInferenceEventType.GENERATED_TOKEN,
-                                payload={"token_id": token},
+                                payload={"token_id": tokens[0]},
                             )
                         request.ttft = (
                             first_token_event.timestamp - request.event_add_engine.timestamp
                         )
                     if request.tpot is None:
                         request.tpot = []
-                    request.tpot.append(step_time)
-
-                # Check for stop words (after token is appended)
-                stop_word_hit = self._check_stop_words_for_request_post_append(request)
+                    per_token_step_time = step_time / len(tokens)
+                    request.tpot.extend([per_token_step_time] * len(tokens))
+
+                # Check for stop words (after token is appended).
+                # With speculative decoding, a stop word may end before the last
+                # appended token. The check truncates generated_tokens in-place and
+                # returns how many trailing tokens were removed so we can also trim
+                # the corresponding log probs below.
+                stop_word_hit, num_stop_word_trim = self._check_stop_words_for_request_post_append(
+                    request
+                )
 
                 if request_id in finished_request_ids:
                     # Request finished by normal means (termination_id, max_length, or stop word from previous step)
@@ -939,6 +1117,14 @@ def post_process_requests(
                 # Additionally, chunked prefill request do not finish.
                 active_request_ids.append(request_id)
 
+            # When a stop word was found mid-speculative-batch, trim log probs
+            # and top_n_logprobs to match the truncated generated_tokens.
+            if num_stop_word_trim > 0:
+                if request_log_probs is not None:
+                    request_log_probs = request_log_probs[:-num_stop_word_trim]
+                if top_n_logprobs is not None and req_idx in top_n_logprobs:
+                    top_n_logprobs[req_idx] = top_n_logprobs[req_idx][:-num_stop_word_trim]
+
             # Process log_probs if available (unified for both regular and chunked prefill)
             if request_log_probs is not None:
                 # Initialize lists if they don't exist
@@ -947,40 +1133,30 @@ def post_process_requests(
                 if not request.generated_log_probs:
                     request.generated_log_probs = []
 
-                # For chunked prefill with materialize_only_last_token_logits, discard intermediate log probs
-                if (
-                    request_id == self.context.chunked_prefill_request_id
-                    and self.materialize_only_last_token_logits
-                ):
-                    request.prompt_log_probs = []
-                    request.generated_log_probs = []
+                is_chunked_prefill = request_id == self.context.chunked_prefill_request_id
+                is_prefill = len(request.generated_log_probs) == 0
+
+                if request.sampling_params.skip_prompt_log_probs:
+                    # We only want decode log probs.
+                    if is_chunked_prefill:
+                        pass
+                    elif is_prefill:
+                        request.generated_log_probs.append(request_log_probs[-1])
+                    else:
+                        request.generated_log_probs.extend(request_log_probs)
                 else:
+                    # Split log probs between prompt and generated based on remaining prompt slots.
                     prompt_length = len(request.prompt_tokens)
                     total_accumulated = len(request.prompt_log_probs) + len(
                         request.generated_log_probs
                     )
+                    remaining_prompt_slots = max(0, prompt_length - 1 - total_accumulated)
+                    split_idx = min(remaining_prompt_slots, len(request_log_probs))
 
-                    # Handle skip_prompt_log_probs during prefill
-                    # If skip_prompt_log_probs is True and we have multiple log probs (prefill),
-                    # only process the last one (first generated token)
-                    if request.sampling_params.skip_prompt_log_probs and len(request_log_probs) > 1:
-                        # Only append the last log prob (first generated token) to generated_log_probs
-                        request.generated_log_probs.append(request_log_probs[-1])
-                    else:
-                        # Vectorized approach: calculate split point and use list slicing
-                        if not request.sampling_params.skip_prompt_log_probs:
-                            # Calculate how many log probs go to prompt vs generated
-                            remaining_prompt_slots = max(0, prompt_length - 1 - total_accumulated)
-                            split_idx = min(remaining_prompt_slots, len(request_log_probs))
-
-                            # Batch extend instead of individual appends
-                            if split_idx > 0:
-                                request.prompt_log_probs.extend(request_log_probs[:split_idx])
-                            if split_idx < len(request_log_probs):
-                                request.generated_log_probs.extend(request_log_probs[split_idx:])
-                        else:
-                            # All log probs go to generated
-                            request.generated_log_probs.extend(request_log_probs)
+                    if split_idx > 0:
+                        request.prompt_log_probs.extend(request_log_probs[:split_idx])
+                    if split_idx < len(request_log_probs):
+                        request.generated_log_probs.extend(request_log_probs[split_idx:])
 
             # Process top_n_logprobs if available (unified for both regular and chunked prefill)
             if top_n_logprobs is not None and req_idx in top_n_logprobs:
@@ -1079,33 +1255,58 @@ def _get_and_clear_stop_word_finished_ids(self, active_request_ids: list[int]) -
         self.stop_word_finished_request_ids -= result
         return result
 
-    def _check_stop_words_for_request_post_append(self, request: DynamicInferenceRequest) -> bool:
+    def _check_stop_words_for_request_post_append(
+        self, request: DynamicInferenceRequest
+    ) -> Tuple[bool, int]:
         """Check if a request should stop due to stop words (after token is appended).
 
         This method is called from post_process_requests after the token has already
-        been appended to request.generated_tokens.
+        been appended to request.generated_tokens. In the speculative decoding case,
+        multiple tokens may have been appended at once. If a stop word is found in the
+        middle of the speculative tokens, the trailing tokens after the stop word are
+        truncated from generated_tokens.
+
+        With speculative decoding, multiple tokens are appended at once. The stop word
+        may end before the last appended token, leaving extra tokens that must be
+        trimmed. When this happens, generated_tokens is truncated in-place and the
+        number of trimmed tokens is returned so the caller can also trim log probs.
 
         Args:
             request: The request to check.
 
         Returns:
-            bool: True if the generated sequence ends with a stop word, False otherwise.
+            Tuple of (stop_word_hit, num_tokens_trimmed):
+                stop_word_hit: True if the generated sequence contains a stop word.
+                num_tokens_trimmed: Number of tokens removed from the end of
+                    generated_tokens (0 when the stop word is at the very end
+                    or when no stop word was found).
         """
-        # Check if request has stop words configured
         if request.stop_word_ids is None or len(request.stop_word_ids) == 0:
-            return False
+            return False, 0
 
         generated_tokens = request.generated_tokens
 
-        # Check if the sequence ends with any stop word
         for stop_word_ids in request.stop_word_ids:
             stop_len = len(stop_word_ids)
             if len(generated_tokens) >= stop_len:
-                # Check if the last stop_len tokens match the stop word
-                if list(generated_tokens[-stop_len:]) == stop_word_ids:
-                    return True
+                # Check the last stop_len tokens shifting by 1 up to num_speculative_tokens.
+                # Speculative decoding can append multiple tokens at once, so the stop
+                # word might end at any position within the newly appended tokens.
+                for i in range(self.num_speculative_tokens + 1):
+                    end_idx = -i if i > 0 else None
+                    if list(generated_tokens[-stop_len - i : end_idx]) == stop_word_ids:
+                        if i > 0:
+                            request.generated_tokens = request.generated_tokens[:-i]
+                        return True, i
+        return False, 0
+
+    def get_prefix_coordination_metrics(self) -> dict:
+        """Return prefix caching coordination metrics.
 
-        return False
+        Returns:
+            Dict with coordination stats including the number of scheduling waits.
+        """
+        return {"waits": self._prefix_coordination_waits}
 
     def schedule_waiting_requests(self):
         """Tries to schedule any requests in the waiting pool."""
@@ -1118,12 +1319,34 @@ def schedule_non_chunked_prefill(self):
         """
         Perform the same original scheduling logic for non-chunked runs
         """
+        prefix_caching_enabled = self.context.enable_prefix_caching
+        if prefix_caching_enabled:
+            pending_block_hashes = set()
+            pending_request_ids = []
         while self.waiting_request_ids:
             req = self.get_request(self.waiting_request_ids[0])
+
+            # Check for conflicting block hashes.
+            if prefix_caching_enabled:
+                has_pending_hash = False
+                for block_hash in req.precomputed_block_hashes:
+                    if block_hash in pending_block_hashes:
+                        has_pending_hash = True
+                        break
+                if has_pending_hash:
+                    self._prefix_coordination_waits += 1
+                    pending_request_ids.append(self.waiting_request_ids.popleft())
+                    continue
+
             request_can_be_added, request_tokens_can_be_added, kv_cache_available = (
                 self.context.check_availability(req)
             )
             if request_can_be_added and request_tokens_can_be_added and kv_cache_available:
+                # Add these hashes to pending.
+                if prefix_caching_enabled:
+                    for block_hash in req.precomputed_block_hashes:
+                        if block_hash not in self.context.block_allocator.hash_to_block_id:
+                            pending_block_hashes.add(block_hash)
                 self.context.add_request(req)
                 self._loop.call_soon_threadsafe(
                     self._loop.create_task, self._notify_cond_for_new_request()
@@ -1134,6 +1357,10 @@ def schedule_non_chunked_prefill(self):
             else:
                 break
 
+        # Prepend pending request ids to waiting queue.
+        if prefix_caching_enabled and pending_request_ids:
+            self.waiting_request_ids.extendleft(reversed(pending_request_ids))
+
     def schedule_chunked_prefill(self):
         """
         This function schedules chunked prefill requests.
@@ -1149,6 +1376,10 @@ def schedule_chunked_prefill(self):
                 it is during a chunked prefill
             - For each request, remaining_prompt_tokens holds the **unprefilled** prompt tokens
         """
+        prefix_caching_enabled = self.context.enable_prefix_caching
+        if prefix_caching_enabled:
+            pending_block_hashes = set()
+            pending_request_ids = []
         can_schedule = True
         while self.waiting_request_ids and can_schedule:
             can_schedule = False
@@ -1158,6 +1389,21 @@ def schedule_chunked_prefill(self):
             # chunk of a existing chunked prefill request
             is_continuing_chunked_prefill = self.context.chunked_prefill_request_id >= 0
 
+            # Check for conflicting block hashes.
+            if prefix_caching_enabled and not is_continuing_chunked_prefill:
+                has_pending_hash = False
+                for block_hash in req.precomputed_block_hashes:
+                    # pylint: disable-next=possibly-used-before-assignment
+                    if block_hash in pending_block_hashes:
+                        has_pending_hash = True
+                        break
+                if has_pending_hash:
+                    self._prefix_coordination_waits += 1
+                    pending_request_ids.append(  # pylint: disable=possibly-used-before-assignment
+                        self.waiting_request_ids.popleft()
+                    )
+                    continue
+
             # Use remaining prompt tokens for scheduling decisions
             remaining_len = len(req.remaining_prompt_tokens)
             token_fully_can_be_added = (
@@ -1169,6 +1415,11 @@ def schedule_chunked_prefill(self):
 
             if request_can_be_added and kv_cache_available:
                 if token_fully_can_be_added:
+                    # Add these hashes to pending.
+                    if prefix_caching_enabled:
+                        for block_hash in req.precomputed_block_hashes:
+                            if block_hash not in self.context.block_allocator.hash_to_block_id:
+                                pending_block_hashes.add(block_hash)
                     self.context.chunked_prefill_request_id = -1
                     self.context.add_request(req)
                     self._loop.call_soon_threadsafe(
@@ -1181,6 +1432,11 @@ def schedule_chunked_prefill(self):
                     # Only this case we keep checking the rest of the waiting queue
                     can_schedule = True
                 elif token_partially_can_be_added:
+                    # Add these hashes to pending.
+                    if prefix_caching_enabled:
+                        for block_hash in req.precomputed_block_hashes:
+                            if block_hash not in self.context.block_allocator.hash_to_block_id:
+                                pending_block_hashes.add(block_hash)
                     chunk_length = self.context.max_tokens - self.context.active_token_count
 
                     # If this chunk would leave exactly 1 token for the final chunk, reduce this
@@ -1201,7 +1457,17 @@ def schedule_chunked_prefill(self):
                     # chunked prefill request at the head of the waiting queue
                     # Note that we do not need to continue check the queue, as the tokens are full
 
-    async def async_forward(self) -> Tuple[Dict, Dict, float, int]:
+        # Prepend pending request ids to waiting queue.
+        if prefix_caching_enabled and pending_request_ids:
+            is_continuing_chunked_prefill = self.context.chunked_prefill_request_id >= 0
+            if is_continuing_chunked_prefill:
+                chunked_request_id = self.waiting_request_ids.popleft()
+                self.waiting_request_ids.extendleft(reversed(pending_request_ids))
+                self.waiting_request_ids.appendleft(chunked_request_id)
+            else:
+                self.waiting_request_ids.extendleft(reversed(pending_request_ids))
+
+    async def async_forward(self) -> Tuple[Dict, Dict, float]:
         """Uses `asyncio` for continuous generation.
         Sleeps when no requests are available, until new requests have been added.
 
@@ -1214,8 +1480,8 @@ async def async_forward(self) -> Tuple[Dict, Dict, float, int]:
         """
 
         # If suspended, no stepping.
-        if self.is_suspended:
-            raise EngineSuspendedError(self.step_count)
+        if self.state in (EngineState.SUSPENDED, EngineState.SUSPENDING):
+            raise EngineSuspendedError(self.context.step_count)
 
         # schedule requests
         self.schedule_waiting_requests()
@@ -1240,14 +1506,14 @@ async def async_forward(self) -> Tuple[Dict, Dict, float, int]:
         self.step_end_event.record()
         self.step_end_event.synchronize()
         step_time = self.step_start_event.elapsed_time(self.step_end_event) / 1e3
-        self.step_count += 1
+        self.context.step_count += 1
 
         range_pop()
 
         if (
             self.logging_step_interval > 0
-            and self.step_count > 0
-            and self.step_count % self.logging_step_interval == 0
+            and self.context.step_count > 0
+            and self.context.step_count % self.logging_step_interval == 0
             and self.metrics_writer is not None
         ):
             kvcache_util_stats = self.context.get_kvcache_utilization_stats()
@@ -1269,10 +1535,10 @@ async def async_forward(self) -> Tuple[Dict, Dict, float, int]:
 
         context_state = {**pre_step_context_state, **post_step_context_state}
 
-        return result, context_state, step_time, self.step_count
+        return result, context_state, step_time
 
     async def async_bookkeep(
-        self, step_result: Optional[Dict], context_state: Dict, step_time: float, step_count: int
+        self, step_result: Optional[Dict], context_state: Dict, step_time: float
     ):
         """Uses `asyncio` for continuous bookkeeping.
 
@@ -1280,7 +1546,6 @@ async def async_bookkeep(
             step_result (Optional[Dict]): The result of the step.
             context_state (Dict): is_decode_only, total/paused request count, active token count.
             step_time (float): How long this step took.
-            step_count (int): The count of the step.
 
         Returns:
             A dictionary containing:
@@ -1299,6 +1564,7 @@ async def async_bookkeep(
             newly_paused_request_ids = step_result.get("newly_paused_request_ids")
             evict_request_ids = step_result.get("evict_request_ids")
             sample = step_result["sample"]
+            accepted_tokens = step_result["accepted_tokens"]
             log_probs = step_result["log_probs"]
             top_n_logprobs = step_result.get("top_n_logprobs", None)
             routing_indices_per_request = step_result.get("routing_indices_per_request", None)
@@ -1316,6 +1582,7 @@ async def async_bookkeep(
                 evict_request_ids,
                 step_time,
                 sample,
+                accepted_tokens,
                 log_probs,
                 top_n_logprobs,
                 routing_indices_per_request,
@@ -1356,7 +1623,10 @@ async def async_bookkeep(
         if self.use_coordinator and self.is_mp_coordinator and finished_request_records:
             range_push("coordinator_communication")
             payload = msgpack.packb(
-                [Headers.ENGINE_REPLY.value, [r.serialize() for r in finished_request_records]],
+                [
+                    Headers.ENGINE_REPLY.value,
+                    [r.merge().serialize() for r in finished_request_records],
+                ],
                 use_bin_type=True,
             )
             self.socket_for_receiving_requests.send(payload)
@@ -1367,7 +1637,9 @@ async def async_bookkeep(
             # Prepare metrics dictionary with all stats
             # Use 'inference/' prefix for all metrics to separate from training metrics
             metrics = {
-                'inference/inference_step': int(self.inference_step_offset + int(step_count)),
+                'inference/inference_step': int(
+                    self.inference_step_offset + int(self.context.step_count)
+                ),
                 'inference/step_time_s': float(step_time),
                 'inference/waiting_queue_len': int(len(self.waiting_request_ids)),
                 'inference/total_requests_dict_size': int(len(self.requests)),
@@ -1381,25 +1653,36 @@ async def async_bookkeep(
                 else:
                     metrics[f'inference/{key}'] = value
 
+            # Add speculative decoding acceptance metrics.
+            if self.num_speculative_tokens > 0 and self._spec_tokens_proposed > 0:
+                acceptance_rate = self._spec_tokens_accepted / self._spec_tokens_proposed
+                metrics['inference/spec_decode_acceptance_rate'] = float(acceptance_rate * 100.0)
+                metrics['inference/spec_decode_tokens_proposed'] = int(self._spec_tokens_proposed)
+                metrics['inference/spec_decode_tokens_accepted'] = int(self._spec_tokens_accepted)
+                metrics['inference/spec_decode_num_steps'] = int(self._spec_steps)
+
             if HAVE_WANDB and self.metrics_writer.__name__ == "wandb":
                 self.metrics_writer.log(metrics, commit=True)
             else:
                 raise ValueError(f"Unsupported metrics writer type: {type(self.metrics_writer)}")
 
         # Print context state.
-        if self.logging_step_interval > 0 and step_count % self.logging_step_interval == 0:
+        if (
+            self.logging_step_interval > 0
+            and self.context.step_count % self.logging_step_interval == 0
+        ):
             mem = torch.cuda.memory_stats()
             step_type = "decode" if context_state["is_decode_only"] else "non-decode"
             output_str = (
-                "* rank %d | step %d | %s ... time: %.3f%s ... "
+                "* rank %d | step %d | %s ... time: %.3f ms%s ... "
                 "reqs: a %d/%d, p %d, w %d, f %d, e %d ... "
                 "blocks: a %d/%d, p %d/%d ... "
                 "mem: tensors %d, alloc %.1f gb, res %.1f gb."
                 % (
                     self.rank,
-                    step_count,
+                    self.context.step_count,
                     datetime.now().strftime("%H:%M:%S"),
-                    step_time,
+                    step_time * 1000,
                     (
                         " [%s + real config %s + cuda graph %s]"
                         % (
@@ -1427,10 +1710,24 @@ async def async_bookkeep(
                     mem["reserved_bytes.all.current"] / (1024**3),
                 )
             )
+            if self.num_speculative_tokens > 0 and self._spec_tokens_proposed > 0:
+                spec_rate = self._spec_tokens_accepted / self._spec_tokens_proposed * 100.0
+                output_str += " ... spec: accept %.1f%% (%d/%d in %d steps)" % (
+                    spec_rate,
+                    self._spec_tokens_accepted,
+                    self._spec_tokens_proposed,
+                    self._spec_steps,
+                )
             if context_state["is_decode_only"]:
                 output_str = f"\033[94m{output_str}\033[0m"
             logging.info(output_str)
 
+            # Reset speculative decoding accumulators after both wandb and console logging.
+            if self.num_speculative_tokens > 0:
+                self._spec_tokens_proposed = 0
+                self._spec_tokens_accepted = 0
+                self._spec_steps = 0
+
         return {
             "active_request_ids": active_request_ids,
             "finished_request_records": finished_request_records,
@@ -1583,77 +1880,105 @@ def schedule_requests(self) -> int:
                 all_messages = []
 
         range_pop()
+
+        # First pass: add all requests and detect staleness increments.
+        # Control signals are queued for the second pass.
+        has_staleness_increment = False
         for message in all_messages:
             data = msgpack.unpackb(message, raw=False)
             header = Headers(data[0])
-
-            if self.received_stop:
-                assert (
-                    header == Headers.STOP_ACK
-                ), "Engine is shutting down. No other messages allowed except STOP_ACK."
-
             if header == Headers.SUBMIT_REQUEST:
                 request_id, prompt, sampling_params = data[1:]
                 sampling_params = SamplingParams.deserialize(sampling_params)
                 range_push("add_request")
                 self.add_request(request_id, prompt, sampling_params)
                 range_pop()
-            elif header == Headers.PAUSE:
-                # Pause thyself.
-                self.received_pause = True
-                self.running.clear()
-                # Send PAUSE_ACK back to coordinator.
-                if self.is_mp_coordinator:
-                    payload = msgpack.packb([Headers.PAUSE_ACK.value], use_bin_type=True)
-                    self.socket_for_receiving_requests.send(payload)
-            elif header == Headers.STOP:
-                # Stop thyself.
-                self.received_stop = True
-                self.running.clear()
-                # Send STOP_ACK back to coordinator.
-                if self.is_mp_coordinator:
-                    payload = msgpack.packb([Headers.STOP_ACK.value], use_bin_type=True)
-                    self.socket_for_receiving_requests.send(payload)
-            elif header == Headers.PAUSE_ACK:
-                self.paused.set()
-                self.received_pause = False
-            elif header == Headers.STOP_ACK:
-                self.stopped.set()
-                self.received_stop = False
+            elif header == Headers.INCREMENT_STALENESS:
+                has_staleness_increment = True
+            else:
+                # Control signal: queue for second pass.
+                self._pending_signals.append(message)
+
+        if has_staleness_increment:
+            waiting = set(self.waiting_request_ids)
+            for request_id, entry in self.requests.items():
+                entry.record.increment_staleness(policy_only=request_id in waiting)
+
+        # Second pass: apply at most one control signal (the engine loop
+        # processes one state transition per iteration).
+        if self._pending_signals:
+            message = self._pending_signals.popleft()
+            data = msgpack.unpackb(message, raw=False)
+            header = Headers(data[0])
+
+            if header == Headers.PAUSE:
+                if self.state == EngineState.RUNNING:
+                    self.state = EngineState.PAUSING
+                    self._state_events[EngineState.RUNNING].clear()
+                # Any other state can safely ignore PAUSE.
+
             elif header == Headers.UNPAUSE:
-                self.paused.clear()
-                self.running.set()
+                assert self.state == EngineState.PAUSED, f"Received UNPAUSE in state {self.state}"
+                self.state = EngineState.UNPAUSING
+
             elif header == Headers.SUSPEND:
-                self.suspend_signal = True
+                assert self.state == EngineState.PAUSED, f"Received SUSPEND in state {self.state}"
+                self._state_events[EngineState.RESUMED].clear()
+                self.suspend()
+                self.state = EngineState.SUSPENDING
+
             elif header == Headers.RESUME:
-                self.suspend_signal = False
-            elif header == Headers.INCREMENT_STALENESS:
-                waiting = set(self.waiting_request_ids)
-                for request_id, entry in self.requests.items():
-                    entry.record.increment_staleness(policy_only=request_id in waiting)
+                assert self.state == EngineState.SUSPENDED, f"Received RESUME in state {self.state}"
+                self._state_events[EngineState.SUSPENDED].clear()
+                self.resume()
+                self.state = EngineState.RESUMING
+
             elif header == Headers.STOP:
-                self.received_stop = True
+                assert self.state in (
+                    EngineState.PAUSED,
+                    EngineState.SUSPENDED,
+                ), f"Received STOP in state {self.state}"
+                if self.state == EngineState.SUSPENDED:
+                    self._state_events[EngineState.SUSPENDED].clear()
+                self.state = EngineState.STOPPING
+
             else:
                 raise UnknownHeaderError(header)
 
         return len(all_messages)
 
-    def stop(self):
-        """
-        Stops the inference engine by terminating the inference coordinator process
-        if it exists, and destroys the model parallel state.
-        This method ensures that any running inference coordinator subprocess
-        is properly terminated, and cleans up resources associated with
-        model parallelism.
+    async def shutdown(self):
+        """Shut down the engine and clean up ZMQ resources.
+
+        Called from the engine loop's finally block after the loop exits.
         """
+        self.state = EngineState.STOPPED
+
+        # Cleanup the request futures.
+        for entry in self.requests.values():
+            if not entry.future.done():
+                entry.future.cancel()
 
-        if hasattr(self, "inference_coordinator_process"):
-            self.inference_coordinator_process.join()
-        for socket in self.zmq_sockets:
-            socket.close()
+        # ZMQ cleanup; designed to be idempotent.
+        sock = getattr(self, 'socket_for_receiving_requests', None)
+        if sock is not None and not sock.closed:
+            try:
+                sock.send(msgpack.packb([Headers.DISCONNECT.value], use_bin_type=True))
+            except Exception:
+                pass
+        for socket in getattr(self, 'zmq_sockets', []):
+            socket.close(linger=0)
+        if hasattr(self, 'zmq_sockets'):
+            self.zmq_sockets.clear()
         if hasattr(self, "expert_parallel_zmq_communicator"):
             self.expert_parallel_zmq_communicator.close()
-        self.zmq_context.term()
+        if hasattr(self, "world_zmq_communicator"):
+            self.world_zmq_communicator.close()
+        if not self.zmq_context.closed:
+            self.zmq_context.term()
+
+        # Set the stopped state at the very end.
+        self._state_events[EngineState.STOPPED].set()
 
     @trace_async_exceptions
     async def run_engine(self, *, loop: Optional[asyncio.AbstractEventLoop] = None):
@@ -1666,7 +1991,7 @@ async def run_engine(self, *, loop: Optional[asyncio.AbstractEventLoop] = None):
                 async with self._cond:
                     await self._cond.wait_for(
                         lambda: (
-                            not self.is_suspended
+                            self.state not in (EngineState.SUSPENDED, EngineState.SUSPENDING)
                             and (
                                 self.context.get_active_request_count() > 0
                                 or self.waiting_request_ids
@@ -1677,118 +2002,144 @@ async def run_engine(self, *, loop: Optional[asyncio.AbstractEventLoop] = None):
         except asyncio.CancelledError:
             pass
 
-    async def _ep_group_has_work(self, local_work: int) -> bool:
-        """Determines if there are some pending requests in the expert parallel group this
-        rank is a part of.
+    async def _ep_establish_consensus(
+        self, local_work: int, signal_consensus: bool
+    ) -> tuple[int, bool]:
+        """EP all-reduce to share work counts and pause consensus.
+
+        All-reduces two integers at once:
+        - local_work: actual pending request count (always >= 0).
+        - consensus flag: -1 if this rank wants to pause, 0 otherwise.
+
+        Using max for both:
+        - max(work) > 0 means at least one EP peer has real work.
+        - max(consensus) == -1 means ALL peers signaled -1 (all PAUSING).
+          Any RUNNING peer contributes 0, pulling the max to 0.
+
         Args:
-            local_work (int): The local work count for this rank. This is a sum of active
-            and waiting requests.
+            local_work: Pending request count for this rank.
+            signal_consensus: True if this rank is ready to pause.
         Returns:
-            bool: True if there is some work in the EP group, False otherwise.
+            (global_work, all_pausing): max work across EP, and whether
+            all peers signaled consensus.
         """
-        range_push("_ep_group_has_work")
-
-        is_stopped = self.stopped.is_set() or self.received_stop
-        is_paused = self.paused.is_set() or self.received_pause
-        is_suspended = self.suspend_signal
-        if is_stopped or is_paused or is_suspended:
-            # Signals can be received asynchronously on EP ranks.
-            # We do not want a rank to pause/stop/suspend prematurely if one of it's peers
-            # is yet to receive the signal.
-            # So this is an *attempt* to process the signal. This rank has received the signal
-            # and passes 0 to the all-reduce. If any other rank in the EP group has not received the signal yet,
-            # it will pass a non-zero value to the all-reduce, and hence the global work will be non-zero,
-            # and we will defer processing the signal.
-            # When all ranks receive the signal, global work will be zero, and we can process the signal safely.
-            local_work = 0
+        range_push("_ep_establish_consensus")
+
+        consensus_val = -1 if signal_consensus else 0
+
+        # Signals can be received asynchronously on EP ranks.
+        # We do not want a rank to pause prematurely if its peers have yet to receive the signal.
+        # So this is an *attempt* to process the signal. This rank has received the signal
+        # and passes -1 to the all-reduce. If any other rank in the EP group has not received
+        # the signal yet, it will pass a zero value to the all-reduce, hence the global consensus
+        # will be zero and we will defer processing the signal.
+        # When all ranks receive the signal, global consensus will be -1 and we can process.
 
         if self.ep_world_size > 1:
-            # Perform all-reduce to get max global work across EP group.
             # Note that it is important to use a non-blocking asyncio-friendly all-reduce here.
             # The user may have other tasks running in the event loop that need to be serviced.
             # Do not using a torch.distributed blocking all-reduce here using nccl/gloo.
-            # We have tried that and it blocks the event loop is megatron-rl.
-            max_global_work = await self.expert_parallel_zmq_communicator.all_reduce_max(local_work)
+            # We have tried that and it blocks the event loop in megatron-rl.
+            global_work, global_consensus = (
+                await self.expert_parallel_zmq_communicator.all_reduce_max(
+                    local_work, consensus_val
+                )
+            )
         else:
-            max_global_work = local_work
+            global_work, global_consensus = local_work, consensus_val
 
         range_pop()
-        return max_global_work > 0
+        return global_work, global_consensus == -1
+
+    async def _world_barrier(self):
+        """World-wide ZMQ all-reduce barrier for global rank consensus.
+
+        Used for all state transitions that require global synchronization:
+        PAUSING → PAUSED, UNPAUSING → RUNNING, SUSPENDING → SUSPENDED,
+        RESUMING → PAUSED, and STOPPING → STOPPED.
+
+        No-op when world_size == 1 (communicator is not created).
+        """
+        range_push("world_barrier")
+        if hasattr(self, 'world_zmq_communicator'):
+            await self.world_zmq_communicator.all_reduce_max(1)
+        range_pop()
 
     @trace_async_exceptions
     async def run_engine_with_coordinator(
         self, *, loop: Optional[asyncio.AbstractEventLoop] = None
     ):
-        """Continually steps the engine asynchronously."""
+        """Continually steps the engine asynchronously.
+
+        State-dependent behavior:
+        - RUNNING: EP all-reduce to check for work, then step or idle.
+        - PAUSING: EP all-reduce to reach consensus, then world barrier.
+        - PAUSED / SUSPENDED: Idle-sleep, wait for signals via schedule_requests().
+        - UNPAUSING / SUSPENDING / RESUMING / STOPPING: World barrier, then transition.
+        - STOPPED: Teardown and exit.
+        """
         self._loop = get_asyncio_loop(loop)
         self.use_coordinator = True
+
         try:
             while True:
                 self.schedule_requests()
 
-                # for the cases below (no active requests, or undergoing a state-change)
-                # do not use asyncio.sleep(0)
-                # as tp-rank=0 will flood the num_messages publisher
-                # with "0" repeatedly. This causes some packets to drop.
-                # Instead be nice, and sleep
-                # for a short time.
-                # The minimum sleep time needed is ~100us i.e. the time
-                # needed to send one message on an IPC socket. However
-                # just to be safe, we use 20ms here.
-
-                local_pending_requests = self.context.get_active_request_count() + len(
-                    self.waiting_request_ids
-                )
-                # 1. Check for work availability (Consensus Step)
-                ep_group_has_work = await self._ep_group_has_work(local_pending_requests)
-
-                # 2. Dummy Work Logic (Keep group alive if peers have work)
-                if ep_group_has_work and local_pending_requests == 0:
-                    # run dummy forward pass if EP group as a whole has work,
-                    # but this rank does not have any work.
-                    self.step_start_event.record()
-                    self.controller.dummy_forward()
-                    self.step_end_event.record()
-                    self.step_end_event.synchronize()
-                    self.step_count += 1
-                    continue
-
-                # 3. No work in EP group
-                # We handle control signals (PAUSE/STOP/SUSPEND) only when
-                # the entire EP group has received the signal. It is important to
-                # not process these signals immediately upon receipt, because
-                # other ranks in the EP group may not have received them yet.
-                # If we exit prematurely, other ranks will deadlock at the all-to-all.
-                # We use self._ep_group_has_work() to build consensus across the EP group
-                # as to when it is safe to process these signals. The function returns False
-                # when all ranks have received the signal.
-                if not ep_group_has_work:
-                    # Priority A: STOP
-                    if self.stopped.is_set():
-                        if self.rank == 0:
-                            logging.info("Stopping engine.")
-                        self.stop()
-                        break
+                if self.state in (EngineState.RUNNING, EngineState.PAUSING):
+                    local_pending = self.context.get_active_request_count() + len(
+                        self.waiting_request_ids
+                    )
+                    global_work, all_pausing = await self._ep_establish_consensus(
+                        local_pending, signal_consensus=(self.state == EngineState.PAUSING)
+                    )
 
-                    # Priority B: SUSPEND
-                    if self.suspend_signal:
-                        self.suspend()
+                    if all_pausing:
+                        # All EP peers are PAUSING: pause immediately.
+                        await self._world_barrier()
+                        self.state = EngineState.PAUSED
+                        self._state_events[EngineState.PAUSED].set()
+                    elif global_work > 0:
+                        # At least one EP peer has work: all must participate.
+                        if local_pending > 0:
+                            await self.async_step()
+                        else:
+                            # Dummy forward to participate in the EP collective.
+                            self.step_start_event.record()
+                            self.controller.dummy_forward()
+                            self.step_end_event.record()
+                            self.step_end_event.synchronize()
+                            self.context.step_count += 1
                     else:
-                        self.resume()
-
-                    # Priority C: PAUSE or no work - nothing needs to be done
-                    # To avoid flooding the TP publisher socket with packets,
-                    # we sleep for 20 ms here.
-                    # todo [Siddharth]: Can this hardcoded sleep be avoided
-                    # with asyncio zmq sockets?
-                    await asyncio.sleep(0.02)  # Yield to event loop
-                    continue
+                        # No work, but not all pausing: idle.
+                        await asyncio.sleep(0.02)
 
-                try:
-                    await self.async_step()
-                except EngineSuspendedError:
+                elif self.state == EngineState.PAUSED:
                     await asyncio.sleep(0.02)
-                    continue
 
-        except asyncio.CancelledError:
-            pass
+                elif self.state == EngineState.UNPAUSING:
+                    await self._world_barrier()
+                    self.state = EngineState.RUNNING
+                    self._state_events[EngineState.PAUSED].clear()
+                    self._state_events[EngineState.RUNNING].set()
+
+                elif self.state == EngineState.SUSPENDING:
+                    await self._world_barrier()
+                    self.state = EngineState.SUSPENDED
+                    self._state_events[EngineState.SUSPENDED].set()
+
+                elif self.state == EngineState.SUSPENDED:
+                    await asyncio.sleep(0.02)
+
+                elif self.state == EngineState.RESUMING:
+                    await self._world_barrier()
+                    self.state = EngineState.PAUSED
+                    self._state_events[EngineState.RESUMED].set()
+
+                elif self.state == EngineState.STOPPING:
+                    await self._world_barrier()
+                    if self.rank == 0:
+                        logging.info("Stopping engine.")
+                    break
+
+        finally:
+            await self.shutdown()
diff --git a/megatron/core/inference/headers.py b/megatron/core/inference/headers.py
index 2551bc54f53..03c290ea925 100644
--- a/megatron/core/inference/headers.py
+++ b/megatron/core/inference/headers.py
@@ -13,17 +13,17 @@ class Headers(Enum):
     SUBMIT_REQUEST = auto()
     ENGINE_REPLY = auto()
     PAUSE = auto()
-    PAUSE_ACK = auto()
     UNPAUSE = auto()
     SUSPEND = auto()
     RESUME = auto()
     INCREMENT_STALENESS = auto()
     STOP = auto()
-    STOP_ACK = auto()
+    DISCONNECT = auto()
+    SHUTDOWN = auto()
 
 
 class UnknownHeaderError(Exception):
     """A signal with an unrecognized header was received by the coordinator."""
 
-    def __init_(self, header):
+    def __init__(self, header):
         super().__init__(f"specialize for {header}.")
diff --git a/megatron/core/inference/inference_client.py b/megatron/core/inference/inference_client.py
index 5a4ed24bebb..8f6717ae7fd 100644
--- a/megatron/core/inference/inference_client.py
+++ b/megatron/core/inference/inference_client.py
@@ -3,9 +3,9 @@
 import asyncio
 import logging
 import time
-from typing import Awaitable, List, Optional, Union
+from typing import List, Optional, Union
 
-from megatron.core.inference.inference_request import DynamicInferenceRequestRecord
+from megatron.core.inference.inference_request import DynamicInferenceRequest
 from megatron.core.inference.sampling_params import SamplingParams
 from megatron.core.utils import get_asyncio_loop, trace_async_exceptions
 
@@ -25,8 +25,6 @@
 except:
     HAVE_MSGPACK = False
 
-from .headers import Headers
-
 
 class InferenceClient:
     """
@@ -53,13 +51,16 @@ class InferenceClient:
             completed requests.
     """
 
-    def __init__(self, inference_coordinator_address: str):
+    def __init__(self, inference_coordinator_address: str, deserialize: bool = False):
         """
         Initializes the InferenceClient.
 
         Args:
             inference_coordinator_address (str): The address on which the
                 inference coordinator is listening.
+            deserialize (bool): If True, deserialize completed requests
+                into DynamicInferenceRequest objects. If False (default), return
+                the raw serialized dict for lower overhead.
         """
         assert (
             HAVE_ZMQ
@@ -69,14 +70,16 @@ def __init__(self, inference_coordinator_address: str):
         ), "please install the messagepack library to use InferenceClient - pip install msgpack"
         self.context = zmq.Context()
         socket = self.context.socket(zmq.DEALER)
+
+        # Prevent socket.send() from thread-blocking at >1000 concurrent requests
+        socket.setsockopt(zmq.SNDHWM, 0)
+        socket.setsockopt(zmq.RCVHWM, 0)
+
         socket.connect(inference_coordinator_address)
 
         self._loop = None
-        self.running = asyncio.Event()
-        self.paused = asyncio.Event()
-        self.stopped = asyncio.Event()
-
         self.socket = socket
+        self.deserialize = deserialize
         self.completion_futures = {}
         self.request_submission_times = {}
         self.next_request_id = 0
@@ -99,10 +102,9 @@ def add_request(
 
         Returns:
             asyncio.Future: A future that will be resolved with a
-            `DynamicInferenceRequestRecord` object containing the completed result.
+            `DynamicInferenceRequest` object (if deserialize=True) or a raw
+            serialized dict (if deserialize=False) containing the completed result.
         """
-        if not self.running.is_set():
-            raise RuntimeError("InferenceClient is not currently running.")
         request_id = self.next_request_id
         self.next_request_id += 1
         payload = [Headers.SUBMIT_REQUEST.value, request_id, prompt, sampling_params.serialize()]
@@ -139,14 +141,10 @@ async def _recv_task(self):
                     if completion_future.done():
                         logging.warning(f"Client: The future for {request_id} has been cancelled!")
                         continue
-                    completed_request = DynamicInferenceRequestRecord.deserialize(reply)
-                    completion_future.get_loop().call_soon_threadsafe(
-                        completion_future.set_result, completed_request
+                    completed_request = (
+                        DynamicInferenceRequest.deserialize(reply) if self.deserialize else reply
                     )
-                elif header == Headers.PAUSE_ACK:
-                    self.paused.set()
-                elif header == Headers.STOP_ACK:
-                    self.stopped.set()
+                    completion_future.set_result(completed_request)
             except zmq.Again:
                 await asyncio.sleep(0.005)
                 continue
@@ -165,19 +163,16 @@ def _connect_with_inference_coordinator(self):
         reply = msgpack.unpackb(self.socket.recv(), raw=False)[0]
         assert Headers(reply) == Headers.CONNECT_ACK
 
-    async def start(self, loop: Optional[asyncio.AbstractEventLoop] = None):
+    def start(self, loop: Optional[asyncio.AbstractEventLoop] = None):
         """
         Connects to the coordinator and starts the background listener task.
 
-        This method must be awaited before submitting any requests. It handles
+        This must be called before submitting any requests. It handles
         the initial handshake and spawns the `listen_for_completed_requests`
         coroutine.
         """
         logging.info("Client: Connecting to InferenceCoordinator...")
         self._loop = get_asyncio_loop(loop)
-        self.running.set()
-        self.paused.clear()
-        self.stopped.clear()
         self._connect_with_inference_coordinator()
         self.listener_task = self._loop.create_task(self._recv_task())
 
@@ -192,58 +187,51 @@ def _send_signal_to_engines(self, signal):
         payload_serialized = msgpack.packb(payload, use_bin_type=True)
         self.socket.send(payload_serialized)
 
-    def pause_engines(self) -> Awaitable:
-        """Sends a signal to pause all inference engines.
+    def pause_engines(self):
+        """Sends PAUSE to all engines via coordinator.
 
-        The signal first propagates thru the coordinator to all engines.
-        All engines acknowledge this signal and clear their `running` flags.
-        The coordinator awaits all acknowledgements before forwarding the ACK
-            back to the client, as well as to the engines.
-        The engines set their `paused` flags upon seeing the ACK.
-
-        Returns:
-            Awaitable: An awaitable that resolves when all engines have paused.
+        The coordinator broadcasts PAUSE. Each engine reaches EP consensus,
+        then synchronizes via a world-wide barrier before transitioning to
+        PAUSED. Callers should await engine.paused for confirmation.
         """
         self._send_signal_to_engines(Headers.PAUSE)
-        return self.paused.wait()
 
     def unpause_engines(self) -> None:
-        """Sends a signal to unpause all inference engines."""
-        self.paused.clear()
-        self.running.set()
+        """Sends UNPAUSE to all engines. No synchronization needed."""
         self._send_signal_to_engines(Headers.UNPAUSE)
 
     def increment_staleness(self):
         """Sends a signal to increment staleness on all in-flight requests."""
-        assert self.paused.is_set(), "Can only increment staleness while engines are paused."
         self._send_signal_to_engines(Headers.INCREMENT_STALENESS)
 
     def suspend_engines(self):
-        """Sends a signal to pause all inference engines."""
-        self._send_signal_to_engines(Headers.PAUSE)
+        """Sends SUSPEND to all engines via coordinator. Requires PAUSED.
+
+        Callers should await engine.suspended for confirmation.
+        """
         self._send_signal_to_engines(Headers.SUSPEND)
 
     def resume_engines(self):
-        """Sends a signal to unpause all inference engines."""
-        self.paused.clear()
-        self._send_signal_to_engines(Headers.RESUME)
-        self._send_signal_to_engines(Headers.UNPAUSE)
+        """Sends RESUME to all engines via coordinator. Requires SUSPENDED.
 
-    def stop_engines(self) -> Awaitable:
-        """Sends a signal to gracefully stop all inference engines.
+        Callers should await engine.paused (or engine.running after UNPAUSE) for confirmation.
+        """
+        self._send_signal_to_engines(Headers.RESUME)
 
-        The signal first propagates thru the coordinator to all engines.
-        All engines acknowledge this signal and clear their `running` flags.
-        The coordinator awaits all acknowledgements before forwarding the ACK
-            back to the client, as well as to the engines.
-        The engines set their `stopped` flags upon seeing the ACK.
+    def stop_engines(self):
+        """Sends STOP to all engines via coordinator. Requires PAUSED or SUSPENDED.
 
-        Returns:
-            Awaitable: An awaitable that resolves when all engines have stopped.
+        Callers should await engine.stopped for confirmation.
+        Does not affect the coordinator.
         """
         self._send_signal_to_engines(Headers.STOP)
-        self.running.clear()
-        return self.stopped.wait()
+
+    def shutdown_coordinator(self):
+        """Tells the coordinator process to exit its main loop.
+
+        Does not affect the engines.
+        """
+        self._send_signal_to_engines(Headers.SHUTDOWN)
 
     def stop(self):
         """
@@ -253,6 +241,12 @@ def stop(self):
         and terminates the ZMQ context. It should be called when the client is
         no longer needed to ensure a graceful shutdown.
         """
-        self.listener_task.cancel()
-        self.socket.close()
+        if hasattr(self, 'listener_task') and not self.listener_task.done():
+            self.listener_task.cancel()
+        # Wake up any listeners.
+        for future in self.completion_futures.values():
+            if not future.done():
+                future.cancel()
+        self.completion_futures.clear()
+        self.socket.close(linger=0)
         self.context.term()
diff --git a/megatron/core/inference/inference_request.py b/megatron/core/inference/inference_request.py
index 328a77aeed0..fe2381cf611 100644
--- a/megatron/core/inference/inference_request.py
+++ b/megatron/core/inference/inference_request.py
@@ -5,6 +5,7 @@
 import warnings
 from dataclasses import asdict, dataclass, field
 from enum import Enum, auto
+from itertools import accumulate
 from typing import Any, Dict, List, Optional, Tuple
 
 import torch
@@ -45,6 +46,21 @@ def deserialize_tensor(tensor_as_list: List) -> torch.Tensor:
     return tensor
 
 
+def unwrap_serialized_tensors(serialized_request: dict) -> dict:
+    """Unwrap ("tensor", [...]) tuples produced by serialize() into plain lists.
+
+    Args:
+        serialized_request (dict): A dict produced by `serialize()`.
+
+    Returns:
+        dict: A shallow copy with tensor wrapper tuples replaced by their inner lists.
+    """
+    return {
+        k: v[1] if isinstance(v, (list, tuple)) and len(v) == 2 and v[0] == "tensor" else v
+        for k, v in serialized_request.items()
+    }
+
+
 # class syntax
 class Status(Enum):
     """Enum for status"""
@@ -56,6 +72,61 @@ class Status(Enum):
     FAILED = 5
 
 
+# =========================================================================
+# Hash computation for prefix caching
+# =========================================================================
+
+# Constants for hash computation
+# Using 2^61 - 1 (Mersenne prime) for ~10^18 hash space, reducing collision probability
+# from ~10^-9 to ~10^-18 compared to the previous prime (1000000007).
+HASH_PRIME = 2305843009213693951
+HASH_BASE = 31
+
+_hash_powers: Optional[torch.Tensor] = None
+
+
+def compute_block_hashes_batched(prompt_tokens: torch.Tensor, block_size: int) -> List[int]:
+    """Compute hashes for all complete blocks in a prompt in one batched operation.
+
+    Reshapes prompt tokens into [num_blocks, block_size], computes all per-block
+    token hashes via a single GPU matmul, transfers results with one .tolist() call,
+    and chains parent hashes on CPU.
+
+    Args:
+        prompt_tokens: All prompt token IDs, shape [seq_len].
+        block_size: Number of tokens per block.
+
+    Returns:
+        List of positive integer hash values (1 to HASH_PRIME), one per complete block.
+    """
+    num_complete_blocks = len(prompt_tokens) // block_size
+    if num_complete_blocks == 0:
+        return []
+
+    global _hash_powers
+    if _hash_powers is None or _hash_powers.shape[0] != block_size:
+        positions = torch.arange(block_size, device=prompt_tokens.device, dtype=torch.int64)
+        _hash_powers = torch.pow(HASH_BASE, positions).to(torch.int64) % HASH_PRIME
+
+    # Reshape to [num_blocks, block_size] (zero-copy view) and compute all token hashes
+    blocks = prompt_tokens[: num_complete_blocks * block_size].view(num_complete_blocks, block_size)
+    token_hashes = (blocks.to(torch.int64) * _hash_powers).sum(dim=1) % HASH_PRIME
+
+    # Single GPU→CPU transfer
+    token_hashes_list = token_hashes.tolist()
+
+    # Chain parent hashes on CPU (C-level accumulate, no Python loop)
+    hashes = list(
+        accumulate(
+            token_hashes_list,
+            lambda parent, th: (parent * HASH_BASE + th) % HASH_PRIME + 1,
+            initial=0,
+        )
+    )[1:]
+
+    return hashes
+
+
 @dataclass(kw_only=True)
 class InferenceRequest:
     """Class for one inference request
@@ -296,11 +367,37 @@ class DynamicInferenceRequest(InferenceRequest):
     finished_chunk_token_count: int = 0
     stop_word_ids: Optional[List[List[int]]] = None  # Tokenized stop words (populated internally)
 
+    # Prefix caching fields
+    block_size_tokens: Optional[int] = None  # Block size for hash computation
+    enable_prefix_caching: bool = False  # Whether prefix caching is enabled
+
+    # Computed field - not passed by caller
+    precomputed_block_hashes: List[int] = field(default_factory=list)
+
     def __post_init__(self):
         self.sampling_params = copy.deepcopy(self.sampling_params)
         if self.prompt_tokens is not None:
             self.remaining_prompt_tokens = copy.deepcopy(self.prompt_tokens)
 
+        # Compute block hashes for prefix matching
+        if (
+            self.enable_prefix_caching
+            and self.block_size_tokens is not None
+            and self.prompt_tokens is not None
+        ):
+            self._compute_block_hashes()
+
+    def _compute_block_hashes(self) -> None:
+        """Compute hashes for all complete blocks in the prompt.
+
+        After this call:
+        - precomputed_block_hashes is [] if prompt < block_size (no complete blocks)
+        - precomputed_block_hashes is [hash1, ...] for N complete blocks
+        """
+        self.precomputed_block_hashes = compute_block_hashes_batched(
+            self.prompt_tokens, self.block_size_tokens
+        )
+
     @property
     def remaining_prompt_length(self):
         """
@@ -410,13 +507,33 @@ def add_event_add_context(self):
         """Add 'add_context' event - called when request is added to context for prefill."""
         return self.add_event(DynamicInferenceEventType.ADD_CONTEXT)
 
-    def add_event_generated_token(self, token: int):
+    def add_event_generated_token(
+        self,
+        token: int,
+        blocks_total: Optional[int] = None,
+        blocks_hashed_total: Optional[int] = None,
+        blocks_hashed_active: Optional[int] = None,
+        blocks_ref_count: Optional[int] = None,
+    ):
         """Add 'generated_token' event - records each generated token.
 
         Args:
             token (int): The token ID that was generated.
+            blocks_total (int): Total block capacity from allocator.
+            blocks_hashed_total (int): All allocated (hashed) blocks.
+            blocks_hashed_active (int): Blocks with ref_count > 0.
+            blocks_ref_count (int): Sum of block ref counts from allocator.
         """
-        return self.add_event(DynamicInferenceEventType.GENERATED_TOKEN, {"token_id": token})
+        payload = {"token_id": token}
+        if blocks_total is not None:
+            payload["blocks_total"] = blocks_total
+        if blocks_hashed_total is not None:
+            payload["blocks_hashed_total"] = blocks_hashed_total
+        if blocks_hashed_active is not None:
+            payload["blocks_hashed_active"] = blocks_hashed_active
+        if blocks_ref_count is not None:
+            payload["blocks_ref_count"] = blocks_ref_count
+        return self.add_event(DynamicInferenceEventType.GENERATED_TOKEN, payload)
 
     def add_event_pause(self):
         """Add 'pause' event."""
diff --git a/megatron/core/inference/model_inference_wrappers/abstract_model_inference_wrapper.py b/megatron/core/inference/model_inference_wrappers/abstract_model_inference_wrapper.py
index bc08c418360..c1c3ac5ba23 100644
--- a/megatron/core/inference/model_inference_wrappers/abstract_model_inference_wrapper.py
+++ b/megatron/core/inference/model_inference_wrappers/abstract_model_inference_wrapper.py
@@ -141,7 +141,11 @@ def dummy_forward(self):
             (1, num_dummy_tokens), dtype=torch.long, device=torch.cuda.current_device()
         )
         attention_mask = None
-        return self.model(tokens, position_ids, attention_mask)
+        is_spec_decode = (
+            self.inference_context.is_dynamic_batching()
+            and self.inference_context.num_speculative_tokens > 0
+        )
+        return self.model(tokens, position_ids, attention_mask, is_spec_decode=is_spec_decode)
 
     def _get_batch_size_and_seq_len(
         self, tokens: torch.Tensor, recv_buffer_seq_len: Optional[int] = None
diff --git a/megatron/core/inference/quantization/mxfp8_tensor.py b/megatron/core/inference/quantization/mxfp8_tensor.py
index cadbd86b0ef..7cf7225c2bb 100644
--- a/megatron/core/inference/quantization/mxfp8_tensor.py
+++ b/megatron/core/inference/quantization/mxfp8_tensor.py
@@ -10,7 +10,7 @@
 
     HAVE_FLASHINFER = True
 except ImportError:
-    HAVE_FLASHINFER = True
+    HAVE_FLASHINFER = False
 
 
 @dataclass
diff --git a/megatron/core/inference/quantization/utils.py b/megatron/core/inference/quantization/utils.py
index c02f40cc185..daf46e11ccd 100644
--- a/megatron/core/inference/quantization/utils.py
+++ b/megatron/core/inference/quantization/utils.py
@@ -1,5 +1,7 @@
 # Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 
+from typing import Dict, Optional, Tuple
+
 import torch
 
 from megatron.core.inference.quantization.mxfp8_tensor import MXFP8Tensor
@@ -13,6 +15,7 @@
 
 try:
     from flashinfer import mm_mxfp8 as flashinfer_mm_mxfp8
+    from flashinfer import mxfp8_quantize
 
     HAVE_FLASHINFER = True
 except ImportError:
@@ -78,6 +81,115 @@ def replace_in_dict(attr_dict):
     return model
 
 
+def _should_quantize_param(val: torch.Tensor) -> bool:
+    """Return True if a parameter should be quantized to FlashInfer MXFP8."""
+    if not val.is_cuda:
+        return False
+    if HAVE_TE and isinstance(val, TEMXFP8Tensor):
+        return True
+    if (
+        isinstance(val, torch.nn.Parameter)
+        and val.dim() == 2
+        and val.dtype in (torch.bfloat16, torch.float16)
+    ):
+        return True
+    return False
+
+
+def _to_bf16(val: torch.Tensor) -> torch.Tensor:
+    """Convert a parameter value to BF16 for quantization."""
+    if HAVE_TE and isinstance(val, TEMXFP8Tensor):
+        return val.dequantize()
+    return val.data.to(torch.bfloat16)
+
+
+def collect_mxfp8_param_metadata(
+    model: torch.nn.Module,
+) -> Dict[str, Tuple[torch.Size, torch.dtype, torch.device]]:
+    """Record shape/dtype/device for each parameter that will be quantized.
+
+    Called once before the first quantization to record the original parameter
+    metadata (shape, dtype, device) before any format conversion.
+    """
+    metadata: Dict[str, Tuple[torch.Size, torch.dtype, torch.device]] = {}
+    for name, param in model.named_parameters():
+        if _should_quantize_param(param):
+            if HAVE_TE and isinstance(param, TEMXFP8Tensor):
+                bf16 = param.dequantize()
+                metadata[name] = (bf16.shape, bf16.dtype, bf16.device)
+            else:
+                metadata[name] = (param.shape, param.dtype, param.device)
+    return metadata
+
+
+def quantize_params_to_mxfp8(
+    model: torch.nn.Module,
+    persistent_buffers: Optional[Dict[str, MXFP8Tensor]] = None,
+    _prefix: str = "",
+) -> Dict[str, MXFP8Tensor]:
+    """Quantize model parameters to FlashInfer MXFP8Tensor format.
+
+    Handles both TEMXFP8Tensor (fp8_param=True) and BF16/FP16 nn.Parameter
+    inputs.  When *persistent_buffers* is provided, new quantized values are
+    ``copy_()``'d into the existing MXFP8Tensor objects so that CUDA-graph
+    device-pointer captures remain valid.
+
+    Args:
+        model: The model whose parameters should be quantized.
+        persistent_buffers: If not ``None``, a dict mapping fully-qualified
+            parameter names to previously-created ``MXFP8Tensor`` objects.
+            Updated in-place and returned.
+        _prefix: Internal recursion prefix – callers should not set this.
+
+    Returns:
+        The ``persistent_buffers`` dict (created on first call if ``None``).
+    """
+    assert HAVE_FLASHINFER
+
+    if persistent_buffers is None:
+        persistent_buffers = {}
+
+    # Recurse through child modules
+    for child_name, child_module in model.named_children():
+        child_prefix = f"{_prefix}{child_name}." if _prefix else f"{child_name}."
+        quantize_params_to_mxfp8(child_module, persistent_buffers, _prefix=child_prefix)
+
+    # Process parameters owned directly by this module
+    if hasattr(model, '_parameters') and model._parameters:
+        keys = list(model._parameters.keys())
+        for key in keys:
+            val = model._parameters[key]
+            if val is None:
+                continue
+            if not _should_quantize_param(val):
+                continue
+
+            fqn = f"{_prefix}{key}"
+            bf16_data = _to_bf16(val)
+
+            if fqn in persistent_buffers:
+                # Subsequent call: copy into existing tensors to preserve addresses
+                new_data, new_scale = mxfp8_quantize(bf16_data)
+                persistent_buffers[fqn].data.copy_(new_data)
+                persistent_buffers[fqn].scale.copy_(new_scale)
+                fi_tensor = persistent_buffers[fqn]
+            else:
+                # First call: create new MXFP8Tensor
+                fi_tensor = MXFP8Tensor.from_bf16(bf16_data)
+
+                # Verify correctness for TEMXFP8Tensor inputs
+                if HAVE_TE and isinstance(val, TEMXFP8Tensor):
+                    _verify_te_to_flashinfer_mxfp8_conversion(bf16_data, fi_tensor)
+
+                persistent_buffers[fqn] = fi_tensor
+
+            # Replace nn.Parameter with MXFP8Tensor attribute
+            del model._parameters[key]
+            setattr(model, key, fi_tensor)
+
+    return persistent_buffers
+
+
 def mm_mxfp8(x: torch.Tensor, weight: MXFP8Tensor, out: torch.Tensor = None):
     """
     Computes a matmul in MXFP8 using FlashInfer.
diff --git a/megatron/core/inference/text_generation_controllers/text_generation_controller.py b/megatron/core/inference/text_generation_controllers/text_generation_controller.py
index f56e5b1c761..3ec31797b5b 100644
--- a/megatron/core/inference/text_generation_controllers/text_generation_controller.py
+++ b/megatron/core/inference/text_generation_controllers/text_generation_controller.py
@@ -6,7 +6,7 @@
 import functools
 import inspect
 from collections import defaultdict
-from typing import Any, Dict, Iterator, List, Optional, OrderedDict, Tuple, Union
+from typing import Any, Dict, List, Optional, OrderedDict, Tuple, Union
 
 import torch
 import torch.nn.functional as F
@@ -62,6 +62,7 @@ def __init__(self, inference_wrapped_model: AbstractModelInferenceWrapper, token
         self.model_config = self.inference_wrapped_model.model.config
         inference_config = self.inference_wrapped_model.inference_context.config
         self.tokenizer = tokenizer
+        self.num_speculative_tokens = inference_config.num_speculative_tokens
 
         pg_collection = inference_config.pg_collection
         if pg_collection is not None:
@@ -80,11 +81,19 @@ def __init__(self, inference_wrapped_model: AbstractModelInferenceWrapper, token
             self.vocab_size = unwrapped_model.vocab_size
 
         self.sampling_rng = torch.Generator(device=torch.cuda.current_device())
+        self.num_mtp_heads = self._get_mtp_num_heads()
         self.sampling_rng.manual_seed(self.model_config.inference_sampling_seed)
 
         if self.inference_wrapped_model.inference_context.is_dynamic_batching():
             self._init_dynamic_sampling_tensors()
 
+    def _get_mtp_num_heads(self) -> int:
+        """Get the number of MTP layers from the model config."""
+        model = self.inference_wrapped_model.model
+        if hasattr(model, 'config') and hasattr(model.config, 'mtp_num_layers'):
+            return model.config.mtp_num_layers or 0
+        return 0
+
     def set_stop_word_finished_ids_callback(self, callback):
         """Set a callback to get request IDs that should be marked as finished due to stop words.
 
@@ -109,6 +118,12 @@ def _init_dynamic_sampling_tensors(self):
 
         self._sampling_backend = "torch"
         self._sampled_tokens_cuda = torch.empty(max_requests, dtype=torch.int64, device=device)
+        # Speculative tokens tensor will be allocated later when num_speculative_tokens is set by the engine
+        self._accepted_tokens_per_request = None
+        # MTP tensor will be allocated later when num_speculative_tokens is set by the engine
+        self._sampled_mtp_tokens_cuda = None
+        # Last accepted sequence indices for serial MTP computation
+        self._last_accepted_seq_indices = None
 
         # Keep track of request metadata.
         self._request_metadata: Dict[str, Tensor] = {}
@@ -122,7 +137,25 @@ def _init_dynamic_sampling_tensors(self):
 
         # Used for inefficient torch sampling.
         if self._sampling_backend == "torch":
-            self._torch_sampling_buckets: Iterator[Tuple] = []
+            self._torch_sampling_buckets: List[Tuple] = []
+
+        self._init_mtp_sampling_tensor()
+
+    def _init_mtp_sampling_tensor(self):
+        """Initialize the MTP sampling tensor after num_speculative_tokens is set."""
+        if self.num_speculative_tokens is not None and self.num_speculative_tokens > 0:
+            context = self.inference_wrapped_model.inference_context
+            max_requests = context.max_requests
+            device = torch.cuda.current_device()
+            self._sampled_mtp_tokens_cuda = torch.empty(
+                [self.num_speculative_tokens, max_requests], dtype=torch.int64, device=device
+            )
+            self._accepted_tokens_per_request = (
+                torch.ones(
+                    [max_requests, self.num_speculative_tokens], dtype=torch.int64, device=device
+                )
+                * -1
+            )
 
     def tokenize_prompt(self, prompt: str, add_BOS: bool = False) -> List[int]:
         """Utility to tokenize the input prompts.
@@ -275,6 +308,8 @@ def modify_logits_for_top_p_filtering(logits, top_p):
             # in the original implementation:
             #   https://github.com/ari-holtzman/degen/blob/master/gen.py
             # and I guess it is needed so keeping it for now.
+            # Clone needed: filter_[:, 1:] and filter_[:, :-1] are overlapping views;
+            # without clone, each write would corrupt the next read during the shift.
             filter_[:, 1:] = filter_[:, :-1].clone()
             # Make sure we at least have one token to select from.
             filter_[..., 0] = 0
@@ -287,6 +322,8 @@ def modify_logits_for_top_p_filtering(logits, top_p):
         if top_k == 1:
             sampled_logits = torch.argmax(last_token_logits, dim=-1)
         else:
+            # Clone needed: .div_() and masked_fill_() below modify in-place,
+            # which would mutate the caller's tensor without this clone.
             last_token_logits = last_token_logits.clone()
             if temperature != 1.0:
                 last_token_logits.div_(temperature)
@@ -517,7 +554,10 @@ def _dynamic_step_context_init(
         model_config = get_model_config(unwrapped_model)
 
         # Initialize attention state.
-        context.initialize_attention_state(construct_graph_dimensions=construct_graph_dimensions)
+        context.initialize_attention_state(
+            construct_graph_dimensions=construct_graph_dimensions,
+            is_expert_parallel_dummy_cuda_graph_step=is_dummy_forward,
+        )
 
         # If using symmetric kernels and we are using using nccl
         # for prefill turn off symmetric kernels
@@ -527,6 +567,12 @@ def _dynamic_step_context_init(
         moe_pad_experts_for_cuda_graph_inference = (
             self.model_config.moe_pad_experts_for_cuda_graph_inference
         )
+        is_inference_optimized = self.model_config.transformer_impl == "inference_optimized"
+        if is_inference_optimized:
+            assert not moe_pad_experts_for_cuda_graph_inference, (
+                "moe_pad_experts_for_cuda_graph_inference cannot be True when "
+                "transformer_impl is 'inference_optimized'"
+            )
         if moe_pad_experts_for_cuda_graph_inference:
             if context.using_cuda_graph_this_step():
                 capacity_factor = model_config.num_moe_experts / model_config.moe_router_topk
@@ -580,13 +626,18 @@ def _dynamic_step_forward_logits(self, input_ids: Tensor, position_ids: Tensor)
             logits = self.inference_wrapped_model.run_one_forward_step(
                 {"tokens": input_ids, "position_ids": position_ids, "attention_mask": None}
             )
+            # logits shape: [1, seq_len, vocab_size]
+
+        # Note: When speculative decoding is active (num_speculative_tokens > 0),
+        # the model skips MTP computation during the forward pass. MTP logits
+        # will be computed serially after verification to ensure they are
+        # conditioned on verified tokens only.
 
         if self.model_is_pipeline_parallel:
-            logits_seq_len = (
-                active_request_count
-                if context.config.materialize_only_last_token_logits
-                else input_ids.shape[1]
-            )
+            if context.config.materialize_only_last_token_logits:
+                logits_seq_len = active_request_count
+            else:
+                logits_seq_len = input_ids.shape[1]
             logits_shape = [1, logits_seq_len, self.vocab_size]
 
             if is_pipeline_last_stage(self.pp_group):
@@ -609,25 +660,528 @@ def _dynamic_step_sample_bookkeeping(self):
         if self._sampling_backend == "torch":
             # Bucketize the core sampling parameters.
             # Doing so via list comprehension is orders of magnitude faster than via torch.
-            bucket_map = {}
+            bucket_map = defaultdict(list)
 
             # Shorthands for the dictionary comprehension.
             temp = self._request_metadata["temperature"][active_request_slice].tolist()
             top_k = self._request_metadata["top_k"][active_request_slice].tolist()
             top_p = self._request_metadata["top_p"][active_request_slice].tolist()
 
-            for i, (t, k, p) in enumerate(zip(temp, top_k, top_p)):
-                h = (t, k, p)
-                bucket = bucket_map.get(h, None)
-                if bucket is None:
-                    bucket_map[h] = ([i], i)
-                else:
-                    bucket[0].append(i)
+            for request_index, (t, k, p) in enumerate(zip(temp, top_k, top_p)):
+                sampling_params = (t, k, p)
+                bucket_map[sampling_params].append(request_index)
+
+            # Just unpack the key directly!
+            self._torch_sampling_buckets = [
+                (indices, *sampling_params) for sampling_params, indices in bucket_map.items()
+            ]
+
+    def _rewind_kv_cache(self):
+        """Update the KV cache bookkeeping for speculative decoding.
+
+        After forward pass with speculative tokens, some tokens may be rejected.
+        This function "rewinds" the KV cache bookkeeping to reflect only the accepted tokens.
+
+        When speculative tokens are rejected, we need to:
+        1. Update request_kv_length_offsets (total sequence length)
+        2. Update request_last_kv_block_offset (position within last block)
+        3. If rewinding crosses a block boundary:
+           - Reduce request_kv_block_counts
+           - Update request_last_kv_block_id to point to the previous block
+           - Clear the entry in request_to_kv_block_ids for the released block
+           - Release the block back to the allocator
+        """
+        context = self.inference_wrapped_model.inference_context
+        active_request_count = context.total_request_count - context.paused_request_count
+        active_request_slice = slice(context.paused_request_count, context.total_request_count)
+
+        # Get the accepted token counts for each request
+        # Note: _accepted_token_counts is indexed from 0 to active_request_count-1
+        accepted_tokens_per_request = self._accepted_token_counts_per_request[:active_request_count]
+
+        # Number of tokens to rewind (rejected speculative tokens)
+        num_tokens_to_rewind = self.num_speculative_tokens - accepted_tokens_per_request
+
+        # For prefill requests, no speculative tokens were forwarded through the model,
+        # so there is nothing to rewind.
+        request_in_prefill_status = context.request_in_prefill_status_tensor[active_request_slice]
+        num_tokens_to_rewind[request_in_prefill_status == 1] = 0
+
+        # Save the original offset BEFORE modifying to correctly detect block boundary crossing
+        original_offset = context.request_last_kv_block_offset[active_request_slice].clone()
+
+        # Check which requests need to rewind to a previous block BEFORE modifying
+        # A request crosses back to a previous block if: original_offset - num_tokens_to_rewind < 0
+        remove_allocated_blocks_mask = (original_offset - num_tokens_to_rewind) < 0
+
+        # Update the offsets
+        context.request_last_kv_block_offset[active_request_slice] = (
+            original_offset - num_tokens_to_rewind
+        ) % context.block_size_tokens
+
+        context.request_kv_length_offsets[active_request_slice] = (
+            context.request_kv_length_offsets[active_request_slice] - num_tokens_to_rewind
+        )
+
+        # No need to update request_query_lengths (It will be set correctly in the next iteration)
+
+        # For requests that crossed back to a previous block, we need to:
+        # 1. Reduce the block count by 1
+        # 2. Get the block ID to release (current request_last_kv_block_id)
+        # 3. Update request_last_kv_block_id to point to the previous block
+        # 4. Clear the entry in request_to_kv_block_ids for the released block
+        # 5. Release the block back to the allocator
+        if remove_allocated_blocks_mask.any():
+            # Get indices of requests that need to release a block (relative to active requests)
+            requests_needing_release = torch.nonzero(remove_allocated_blocks_mask, as_tuple=True)[0]
+            # Convert to absolute indices in the context tensors
+            absolute_indices = requests_needing_release + context.paused_request_count
+
+            # No clone needed: advanced (fancy) indexing with a tensor already returns
+            # a copy, not a view.
+            blocks_to_release = context.request_last_kv_block_id[absolute_indices]
+
+            # Reduce block counts for requests that crossed back
+            context.request_kv_block_counts[absolute_indices] -= 1
+
+            # Get the new block counts after decrement
+            new_block_counts = context.request_kv_block_counts[absolute_indices]
+
+            # Update request_last_kv_block_id to point to the previous block
+            # and clear the released block entry in request_to_kv_block_ids
+            # Vectorized implementation using advanced indexing:
+            # Note: new_block_counts is guaranteed to be > 0 for all requests here, since
+            # crossing back to a previous block implies the request had at least 2 blocks.
+
+            # Update request_last_kv_block_id to point to the previous block (at index new_count - 1)
+            context.request_last_kv_block_id[absolute_indices] = context.request_to_kv_block_ids[
+                absolute_indices, new_block_counts - 1
+            ]
+
+            # Clear the released block entry (at index new_count, which was the old last block)
+            context.request_to_kv_block_ids[absolute_indices, new_block_counts] = -1
+
+            # Release the blocks back to the allocator
+            context.block_allocator.release_memory_blocks(blocks_to_release)
+
+        # Mamba speculative rewind state update
+        if context.is_hybrid_model:
+            active_mamba_indices = context.mamba_metadata.request_to_mamba_state_idx[
+                active_request_slice
+            ]
+            is_decode_mask = context.request_in_prefill_status_tensor[active_request_slice] == 0
+            decode_mamba_indices = active_mamba_indices[is_decode_mask]
+            accepted_tokens_per_decode_request = accepted_tokens_per_request[is_decode_mask]
+
+            if decode_mamba_indices.numel() > 0:
+                context.mamba_conv_states[:, decode_mamba_indices] = (
+                    context.mamba_intermediate_conv_states[
+                        :, decode_mamba_indices, accepted_tokens_per_decode_request
+                    ]
+                )
+                context.mamba_ssm_states[:, decode_mamba_indices] = (
+                    context.mamba_intermediate_ssm_states[
+                        :, decode_mamba_indices, accepted_tokens_per_decode_request
+                    ]
+                )
+
+    def _sample_from_logits_2d(self, logits_2d: Tensor) -> Tensor:
+        """Sample tokens from 2D logits using existing sampling parameters.
+
+        Args:
+            logits_2d (Tensor): Logits of shape [num_requests, vocab_size].
+
+        Returns:
+            Tensor: Sampled tokens of shape [num_requests].
+        """
+        spec_token_list = []
+        indices_list = []
+        for request_indices, temp, top_k, top_p in self._torch_sampling_buckets:
+            request_indices_tensor = torch.tensor(
+                request_indices, device=logits_2d.device, dtype=torch.long
+            )
+            spec_token_list.append(
+                self._torch_sampling_func(logits_2d[request_indices_tensor, :], temp, top_k, top_p)
+            )
+            indices_list.append(request_indices_tensor)
+
+        spec_tokens = torch.empty(logits_2d.shape[0], device=logits_2d.device, dtype=torch.int64)
+        for tokens, indices in zip(spec_token_list, indices_list):
+            spec_tokens[indices] = tokens
+        return spec_tokens
+
+    def _compute_serial_mtp_and_sample(self):
+        """Compute MTP logits serially after verification and sample speculative tokens.
+
+        This ensures that MTP predictions are always conditioned on verified tokens.
+        Each MTP depth receives the correctly sampled token from the previous depth
+        (or the base token for depth 0) rather than stale speculative tokens from
+        the previous step.
+        """
+        context = self.inference_wrapped_model.inference_context
+        active_request_count = context.total_request_count - context.paused_request_count
+        active_slice = slice(context.paused_request_count, context.total_request_count)
+
+        unwrapped_model = unwrap_model(self.inference_wrapped_model.model)
+
+        # On non-last pipeline stages, the model won't have decoder hidden states.
+        has_mtp = is_pipeline_last_stage(self.pp_group) and hasattr(
+            unwrapped_model, '_decoder_hidden_states_cache'
+        )
+
+        if has_mtp:
+            # Get decoder hidden states at last accepted positions.
+            hidden_states = unwrapped_model._decoder_hidden_states_cache
+            last_accepted_hidden = hidden_states[self._last_accepted_seq_indices, :, :]
+            # Shape: [active_request_count, 1, hidden_size]
+        else:
+            last_accepted_hidden = None
+
+        # Compute position IDs for the next tokens.
+        # After rewind, request_kv_length_offsets has been adjusted. The actual
+        # KV cache length is: adjusted_offset + (1 + num_speculative_tokens).
+        # The next position to predict starts at that cache length.
+        adjusted_offsets = context.request_kv_length_offsets[active_slice]
+        base_position = adjusted_offsets + (1 + self.num_speculative_tokens)
+
+        # Start with the freshly sampled base token.
+        next_token_ids = self._sampled_tokens_cuda[:active_request_count].clone()
+        current_hidden = last_accepted_hidden if has_mtp else None
+
+        num_depths = min(self.num_speculative_tokens, self.num_mtp_heads)
+        for depth in range(num_depths):
+            position_ids = (base_position + depth).unsqueeze(0)  # [1, active_request_count]
+            token_ids = next_token_ids.unsqueeze(0)  # [1, active_request_count]
+
+            mtp_logits_2d = None
+            if has_mtp:
+                current_hidden, mtp_logits = unwrapped_model.compute_mtp_single_step(
+                    hidden_states=current_hidden,
+                    next_token_ids=token_ids,
+                    position_ids=position_ids,
+                    depth=depth,
+                )
+                # mtp_logits: [active_request_count, 1, vocab_size]
+                mtp_logits_2d = mtp_logits.squeeze(1)  # [active_request_count, vocab_size]
+
+            # Broadcast MTP logits across pipeline stages.
+            if self.model_is_pipeline_parallel:
+                mtp_logits_2d = broadcast_from_last_pipeline_stage(
+                    [active_request_count, self.vocab_size],
+                    dtype=self.model_config.params_dtype,
+                    tensor=mtp_logits_2d,
+                    pp_group=self.pp_group,
+                )
+
+            # Sample speculative token using the same sampling parameters.
+            spec_tokens = self._sample_from_logits_2d(mtp_logits_2d)
+            self._sampled_mtp_tokens_cuda[depth, :active_request_count] = spec_tokens
+
+            # Use sampled token as input for the next depth.
+            next_token_ids = spec_tokens
+
+        # Clean up cached hidden states.
+        if has_mtp:
+            del unwrapped_model._decoder_hidden_states_cache
+
+    def _get_required_logit_indices(
+        self,
+        request_in_prefill_status_tensor: Tensor,
+        request_query_lengths: Tensor,
+        num_decode_requests: int,
+        num_prefill_requests: int,
+        device: torch.device,
+    ) -> Tensor:
+        """Get indices into the logits tensor for tokens that need sampling.
+
+        For decode requests, all tokens (base + speculative) are needed.
+        For prefill requests, only the last token logits are needed.
+        Decode requests will always be on the left, followed by prefill requests.
+
+        Example with 5 requests (2 spec tokens):
+            Assume input ids :                  [ a5  a6s  a7s |  b3    b4s  b5s   |  c6   c7s   c8s   |  d1    d2   | e1    e2    e3   e4]
+            Request to prefill                  [    0         |         0         |          0        |    1        |         1          ]
+            Request query lengths               [    3         |         3         |          3        |    2        |         4          ]
+            OUTPUT : required_logit_indices     [ 0    1    2  |  3     4     5    |  6     7     8    |      10     |         14         ]
+
+        Returns:
+            Tensor: Indices into the sequence dimension of the logits tensor.
+        """
+        decode_request_indices = torch.arange(
+            num_decode_requests * (self.num_speculative_tokens + 1), device=device
+        )
+        prefill_request_indices = (
+            request_query_lengths.cumsum(dim=0)[request_in_prefill_status_tensor == 1] - 1
+        )  # Last token indices for prefill requests
+        required_logit_indices = torch.cat([decode_request_indices, prefill_request_indices])
+        assert (
+            len(required_logit_indices)
+            == num_decode_requests * (self.num_speculative_tokens + 1) + num_prefill_requests
+        ), (
+            f"Expected length of required_logit_indices to be "
+            f"num_decode_requests * (self.num_speculative_tokens + 1) + num_prefill_requests, "
+            f"but got {len(required_logit_indices)} for num_decode_requests {num_decode_requests} "
+            f"and num_prefill_requests {num_prefill_requests}"
+        )
+        return required_logit_indices
+
+    def _sample_speculative_logits(
+        self,
+        required_logits: Tensor,
+        required_mtp_logits: Tensor,
+        request_in_prefill_status_tensor: Tensor,
+    ) -> tuple:
+        """Sample tokens from logits and MTP logits using sampling buckets.
+
+        For torch sampling buckets: [request_indices, temp, top_k, top_p]
+
+        Example with 5 requests:
+            token_to_request_idx :              [ 0    0     0  |  1     1     1     |  2     2     2     |   3    |   4  ]
+            required_logits :                   [ a5l  a6l  a7l |  b3l    b4l  b5l   |  c6l   c7l   c8l   |  d2l   | e4l  ]  # Shape [11, vocab_size]
+
+            Sampling buckets: [[[0,2], temp1, top_k1, top_p1], [[1], temp3, top_k3, top_p3], [[3, 4], temp2, top_k2, top_p2]]
+
+            Final output tokens : [a5s  a6s  a7s  c6s  c7s  c8s  b3s  b4s  b5s  d2s  e4s]  # Shape [11]
+            (Rearranged from sampling bucket order back to input order using token_order)
+
+        Returns:
+            tuple: (output_tokens, mtp_output_tokens, repeats) where output_tokens has shape
+                [total_required_tokens] and mtp_output_tokens has shape
+                [num_speculative_tokens, total_required_tokens].
+        """
+        repeats = torch.where(
+            request_in_prefill_status_tensor == 0, 1 + self.num_speculative_tokens, 1
+        )
+        token_to_request_index = torch.repeat_interleave(
+            torch.arange(
+                len(request_in_prefill_status_tensor),
+                device=request_in_prefill_status_tensor.device,
+            ),
+            repeats,
+        )
+
+        output_tokens_jumbled_list = []
+        mtp_output_tokens_jumbled_list = []
+        token_order_list = []
+
+        has_mtp_logits = required_mtp_logits is not None
+
+        for request_indices, temp, top_k, top_p in self._torch_sampling_buckets:
+            request_indices_tensor = torch.tensor(
+                request_indices, device=token_to_request_index.device
+            )
+            required_indices = torch.where(
+                torch.isin(token_to_request_index, request_indices_tensor)
+            )[0]
+            output_tokens_jumbled_list.append(
+                self._torch_sampling_func(required_logits[required_indices, :], temp, top_k, top_p)
+            )
+            if has_mtp_logits:
+                mtp_logits_slice = required_mtp_logits[:, required_indices, :]
+                num_spec, num_reqs, vocab = mtp_logits_slice.shape
+                sampled_mtp = self._torch_sampling_func(
+                    mtp_logits_slice.reshape(num_spec * num_reqs, vocab), temp, top_k, top_p
+                )
+                mtp_output_tokens_jumbled_list.append(sampled_mtp.reshape(num_spec, num_reqs))
+            token_order_list.append(required_indices)
+
+        output_tokens_jumbled = torch.cat(output_tokens_jumbled_list, dim=0)
+        output_tokens = torch.empty(
+            len(output_tokens_jumbled),
+            device=output_tokens_jumbled.device,
+            dtype=output_tokens_jumbled.dtype,
+        )
+        token_order = torch.cat(token_order_list, dim=0)
+        # Rearrange output tokens from sampling_bucket request order back to input ids order
+        output_tokens[token_order] = output_tokens_jumbled
+
+        mtp_output_tokens = None
+        if has_mtp_logits:
+            mtp_output_tokens_jumbled = torch.cat(
+                mtp_output_tokens_jumbled_list, dim=1
+            )  # Shape [num_speculative_tokens, total_tokens]
+            mtp_output_tokens = torch.empty_like(mtp_output_tokens_jumbled)
+            mtp_output_tokens[:, token_order] = mtp_output_tokens_jumbled
+
+        return output_tokens, mtp_output_tokens, repeats
+
+    def _verify_speculative_tokens(
+        self,
+        output_tokens: Tensor,
+        input_tokens_required: Tensor,
+        request_in_prefill_status_tensor: Tensor,
+        repeats: Tensor,
+        num_decode_requests: int,
+        num_prefill_requests: int,
+        active_request_count: int,
+    ) -> tuple:
+        """Verify speculative tokens against input tokens and compute acceptance.
+
+        Creates an accepted tokens mask where:
+        - For prefill requests, the token is always accepted.
+        - For decode requests, the first token (base token) is always accepted, then we compare
+          sampled tokens with input tokens and accept consecutive matches.
+        Then finds the index of the last accepted token per request.
+
+        Example (assume 1, 2, and 0 spec tokens are accepted in the first 3 decode requests):
+            input_tokens_required:              [ a5  a6s  a7s |  b3    b4s  b5s   |  c6   c7s   c8s   |     d2      |         e4         ]  # Size 11
+            Output tokens                       [ a6o a7o  a8o |  b40   b5o  b6o   |  c7o  c8o   c9o   |     d3o     |         e5o        ]
+            Output tokens right shift           [ d3o a6o  a7o |  a8o   b40  b5o   |  b6o  c7o   c8o   |     c9o     |         d3o        ]
+            Accepted tokens  mask               [  1   1    0  |  1      1    1    |   1    0     0    |      1      |         1          ]
+            Last one indices                    [      1       |         5         |        6          |      9      |         10         ]
+
+        Returns:
+            tuple: (last_one_indices, accepted_tokens_mask, input_tokens_required) where
+                last_one_indices contains the index of the last accepted token per request.
+        """
+        if input_tokens_required.ndim == 2:
+            assert (
+                input_tokens_required.shape[0] == 1
+            ), f"Expected input_tokens_required to have 1 row, but got {input_tokens_required.shape}"
+            input_tokens_required = input_tokens_required.squeeze(0)
+
+        # Initialize mask with False to prevent boundary bleed
+        accepted_tokens_mask = torch.zeros_like(input_tokens_required, dtype=torch.bool)
+
+        # Make all prefill tokens accepted
+        token_to_prefill_idx = torch.repeat_interleave(request_in_prefill_status_tensor, repeats)
+        accepted_tokens_mask[token_to_prefill_idx == 1] = True
+
+        # Safe decode token verification without cross-batch boundary contamination
+        decode_mask_2d = None
+        if num_decode_requests > 0:
+            decode_len = num_decode_requests * (self.num_speculative_tokens + 1)
+
+            decode_inputs = input_tokens_required[:decode_len].reshape(
+                num_decode_requests, self.num_speculative_tokens + 1
+            )
+            decode_outputs = output_tokens[:decode_len].reshape(
+                num_decode_requests, self.num_speculative_tokens + 1
+            )
+
+            # Shift outputs right by 1 *within* each request to align sampled tokens with input targets
+            decode_outputs_shifted = decode_outputs.roll(1, dims=1)
+            decode_mask_2d = decode_inputs == decode_outputs_shifted
+            # The first token (base token) is always accepted
+            decode_mask_2d[:, 0] = True
+            # Enforce consecutive acceptance: cummin propagates False to the right
+            decode_mask_2d = decode_mask_2d.cummin(dim=1).values
+            accepted_tokens_mask[:decode_len] = decode_mask_2d.flatten()
+
+        last_one_indices = torch.full(
+            (active_request_count,), -1, device=input_tokens_required.device
+        )
+
+        if num_decode_requests > 0:
+            # Summing the consecutive mask gives the count; subtract 1 for the local index
+            local_last_indices = decode_mask_2d.sum(dim=1) - 1
+            row_offsets = torch.arange(num_decode_requests, device=last_one_indices.device) * (
+                self.num_speculative_tokens + 1
+            )
+            last_one_indices[:num_decode_requests] = row_offsets + local_last_indices
+
+        if num_prefill_requests > 0:
+            decode_len = num_decode_requests * (self.num_speculative_tokens + 1)
+            prefill_valid = (
+                torch.nonzero(accepted_tokens_mask[decode_len:]).squeeze(-1) + decode_len
+            )
+            last_one_indices[num_decode_requests:] = prefill_valid
+
+        return last_one_indices, accepted_tokens_mask, input_tokens_required
+
+    def _dynamic_step_sample_logits_and_verify_tokens(
+        self, logits: Tensor, mtp_logits: Tensor, input_ids: Tensor
+    ):
+        """
+        Sample tokens from logits for dynamic batching with speculative tokens and verify the tokens.
+        """
+        context = self.inference_wrapped_model.inference_context
+        active_request_count = context.total_request_count - context.paused_request_count
+
+        request_in_prefill_status_tensor = context.request_in_prefill_status_tensor[
+            context.paused_request_count : context.total_request_count
+        ]
+        request_query_lengths = context.request_query_lengths[
+            context.paused_request_count : context.total_request_count
+        ]
+
+        num_prefill_requests = request_in_prefill_status_tensor.sum().item()
+        num_decode_requests = active_request_count - num_prefill_requests
 
-            # Store the buckets and their equivalence class representatives.
-            self._torch_sampling_buckets = (
-                (indices, temp[rep], top_k[rep], top_p[rep]) for indices, rep in bucket_map.values()
+        # Get the logit indices for tokens that need sampling.
+        required_logit_indices = self._get_required_logit_indices(
+            request_in_prefill_status_tensor,
+            request_query_lengths,
+            num_decode_requests,
+            num_prefill_requests,
+            logits.device,
+        )
+
+        required_logits = logits.squeeze(0)[
+            required_logit_indices, :
+        ]  # Shape [num_required, vocab_size]
+        required_mtp_logits = None
+        if mtp_logits is not None:
+            required_mtp_logits = mtp_logits[
+                :, required_logit_indices, :
+            ]  # Shape [num_speculative_tokens, num_required, vocab_size]
+
+        # Sample tokens from logits (and MTP logits if provided).
+        output_tokens, mtp_output_tokens, repeats = self._sample_speculative_logits(
+            required_logits, required_mtp_logits, request_in_prefill_status_tensor
+        )
+
+        # Verify speculative tokens against input tokens.
+        input_tokens_required = input_ids[0, required_logit_indices]
+        last_one_indices, accepted_tokens_mask, input_tokens_required = (
+            self._verify_speculative_tokens(
+                output_tokens,
+                input_tokens_required,
+                request_in_prefill_status_tensor,
+                repeats,
+                num_decode_requests,
+                num_prefill_requests,
+                active_request_count,
             )
+        )
+
+        # Store the final sampled tokens for the next forward pass.
+        final_sampled_tokens = output_tokens[last_one_indices]
+        self._sampled_tokens_cuda[: len(final_sampled_tokens)] = final_sampled_tokens
+
+        # Store MTP tokens if they were computed inline (non-serial path).
+        if mtp_output_tokens is not None:
+            self._sampled_mtp_tokens_cuda[:, : len(final_sampled_tokens)] = mtp_output_tokens[
+                :, last_one_indices
+            ]
+
+        # Store the last accepted positions in the packed sequence for serial
+        # MTP computation after verification.
+        self._last_accepted_seq_indices = required_logit_indices[last_one_indices]
+
+        # Extract accepted tokens and counts for decode requests.
+        # For prefill it is always set to 1. For decode, the first token is always accepted,
+        # then we compare with input tokens and accept the next tokens if its a match.
+        #
+        # Example (continuing from above):
+        #   input_tokens_required:              [ a5  a6s  a7s |  b3    b4s  b5s   |  c6   c7s   c8s   |     d2      |         e4         ]
+        #   Accepted tokens  mask               [  1   1    0  |  1      1    1    |   1    0     0    |      1      |         1          ]
+        #   Accepted tokens                     [   [a6s  -1]  |     [b4s  b5s]    |     [-1  -1]      ]  # Only decode requests (prefill defaults to -1)
+        #   Accepted token counts               [      1       |         2         |         0         ]  # Prefill defaults to 0
+        input_tokens_required[accepted_tokens_mask == 0] = -1  # Mask out non-accepted tokens
+        input_tokens_decode_mode = input_tokens_required[
+            : num_decode_requests * (self.num_speculative_tokens + 1)
+        ]
+        input_tokens_reshaped = input_tokens_decode_mode.reshape(
+            -1, self.num_speculative_tokens + 1
+        )  # shape: [num_decode_requests, num_speculative_tokens + 1]
+
+        # Skip the first token of every decode request (i.e a5, b3, c6)
+        accepted_tokens = input_tokens_reshaped[:, 1:]
+        self._accepted_tokens_per_request[: accepted_tokens.shape[0], :] = accepted_tokens
+        self._accepted_token_counts_per_request = (self._accepted_tokens_per_request != -1).sum(
+            dim=1
+        )
 
     def _dynamic_step_sample_logits(self, logits: Tensor):
         """Sample tokens from logits for dynamic batching.
@@ -643,24 +1197,31 @@ def _dynamic_step_sample_logits(self, logits: Tensor):
         if context.config.materialize_only_last_token_logits:
             # When materialize_only_last_token_logits is true, last_token_logits is
             # already called in the forward pass of GPT.
-            last_token_logits = logits.squeeze(0)
+            required_token_logits = logits.squeeze(0)
         else:
-            last_token_logits = context.last_token_logits(logits)
+            # todo : Should do verification here and get approrpiate las token logits
+            required_token_logits = context.last_token_logits(logits)
 
         if self._sampling_backend == "torch":
             # Concatenate the outputs once to prevent repeated small writes.
             token_list = []
             indices_list = []
 
+            # e.g torch sample buckets will be
+            # i.e (for all unique comibnation of t, topk, topk what are the associated
+            # requests indices (based on the active slices)
+            # [ [req at index 0, req at index 2], t1, topk1, topp1 ]]
+            # [ [req at index 1, req at index 3, req at index 4] , t2, topk2, topp2]
             for indices, temp, top_k, top_p in self._torch_sampling_buckets:
                 token_list.append(
-                    self._torch_sampling_func(last_token_logits[indices, :], temp, top_k, top_p)
+                    self._torch_sampling_func(required_token_logits[indices, :], temp, top_k, top_p)
                 )
                 indices_list.append(torch.tensor(indices))
 
             # Single write to the output tensor.
             sampled_tokens = torch.cat(token_list, dim=0)
             sampled_indices = torch.cat(indices_list, dim=0)
+
             self._sampled_tokens_cuda[sampled_indices] = sampled_tokens
 
     def _dynamic_step_log_probs_bookkeeping(self) -> Tuple[bool, bool]:
@@ -748,6 +1309,201 @@ def _dynamic_step_calculate_log_probs(self, logits: Tensor) -> Optional[Tensor]:
             only_last_token_logits=context.config.materialize_only_last_token_logits,
         )
 
+    def _dynamic_step_calculate_log_probs_speculative(
+        self, logits: Tensor
+    ) -> Tuple[List[List[float]], Tensor]:
+        """Calculate log probs from logits for speculative decoding.
+
+        For decode requests, computes log probs for each accepted speculative token
+        and the newly sampled token using the main model logits. For prefill requests,
+        handles prompt log probs the same way as non-speculative decoding.
+
+        The main model logits at position j predict the token at position j+1. So:
+        - log_prob(accepted_token[j]) comes from logits at position j
+        - log_prob(newly_sampled_token) comes from logits at position accepted_count
+
+        Args:
+            logits (Tensor): The main model logits [1, seq_len, vocab_size].
+
+        Returns:
+            Tuple of (log_probs_list, log_probs_tensor):
+                log_probs_list: List of lists, one per active request, containing
+                    log probs for the tokens emitted in this step.
+                log_probs_tensor: Full log_softmax tensor for top-n computation.
+        """
+        context = self.inference_wrapped_model.inference_context
+        active_request_count = context.total_request_count - context.paused_request_count
+
+        request_in_prefill_status_tensor = context.request_in_prefill_status_tensor[
+            context.paused_request_count : context.total_request_count
+        ]
+        request_query_lengths = context.request_query_lengths[
+            context.paused_request_count : context.total_request_count
+        ]
+
+        num_prefill_requests = request_in_prefill_status_tensor.sum().item()
+        num_decode_requests = active_request_count - num_prefill_requests
+
+        logits_squeezed = logits.squeeze(0).float()
+        log_probs_tensor = F.log_softmax(logits_squeezed[: context.active_token_count], dim=-1)
+
+        log_probs_list_decode = []
+
+        if num_decode_requests > 0:
+            decode_len = num_decode_requests * (self.num_speculative_tokens + 1)
+            decode_log_probs = log_probs_tensor[:decode_len].reshape(
+                num_decode_requests, self.num_speculative_tokens + 1, -1
+            )
+            accepted_counts = self._accepted_token_counts_per_request[:num_decode_requests]
+
+            # Build a [num_decode, num_spec+1] token ID matrix for gathering.
+            # Columns 0..num_spec-1 hold accepted speculative tokens (clamped to 0
+            # where rejected, since those positions will be masked out).
+            # At column accepted_count[i], place the newly sampled token.
+            gather_tokens = torch.zeros(
+                num_decode_requests,
+                self.num_speculative_tokens + 1,
+                device=logits.device,
+                dtype=torch.long,
+            )
+            gather_tokens[:, : self.num_speculative_tokens] = self._accepted_tokens_per_request[
+                :num_decode_requests
+            ].clamp(min=0)
+            gather_tokens[
+                torch.arange(num_decode_requests, device=logits.device), accepted_counts
+            ] = self._sampled_tokens_cuda[:num_decode_requests]
+
+            # Gather: [num_decode, num_spec+1]
+            gathered_log_probs = decode_log_probs.gather(2, gather_tokens.unsqueeze(-1)).squeeze(-1)
+
+            log_probs_list_decode = [
+                gathered_log_probs[i, : accepted_counts[i].item() + 1].tolist()
+                for i in range(num_decode_requests)
+            ]
+
+        log_probs_list_prefill = []
+        if num_prefill_requests > 0:
+            decode_len = num_decode_requests * (self.num_speculative_tokens + 1)
+            prefill_log_probs = log_probs_tensor[decode_len:]
+
+            prefill_token_ids = context.token_to_input_ids[
+                decode_len : context.active_token_count
+            ].roll(-1, 0)
+            prefill_query_lengths = request_query_lengths[request_in_prefill_status_tensor == 1]
+            new_token_idx = prefill_query_lengths.cumsum(0) - 1
+            prefill_new_tokens = self._sampled_tokens_cuda[num_decode_requests:active_request_count]
+            prefill_token_ids[new_token_idx] = prefill_new_tokens
+
+            prefill_token_count = context.active_token_count - decode_len
+            seq_idx = torch.arange(prefill_token_count, device=logits.device)
+            selected_log_probs = prefill_log_probs[seq_idx, prefill_token_ids]
+
+            prefill_log_probs_split = selected_log_probs.cpu().split(
+                prefill_query_lengths.tolist(), dim=0
+            )
+            log_probs_list_prefill = [lp.tolist() for lp in prefill_log_probs_split]
+
+        log_probs_list = log_probs_list_decode + log_probs_list_prefill
+
+        return log_probs_list, log_probs_tensor
+
+    def _dynamic_step_calculate_top_n_logprobs_speculative(
+        self, log_probs_tensor: Tensor
+    ) -> Optional[Dict[int, List[Tuple[Tensor, Tensor]]]]:
+        """Calculate top-n log probs for speculative decoding.
+
+        For decode requests, computes top-n at each position that produced an
+        emitted token (accepted speculative positions + the newly sampled position).
+        For prefill requests, behaves identically to the non-speculative path.
+
+        Args:
+            log_probs_tensor (Tensor): Pre-computed log_softmax tensor from
+                _dynamic_step_calculate_log_probs_speculative.
+
+        Returns:
+            A dictionary mapping request_idx to list of (top_n_values, top_n_indices)
+            tuples, one per emitted token position.
+        """
+        context = self.inference_wrapped_model.inference_context
+        active_request_count = context.total_request_count - context.paused_request_count
+        active_request_slice = slice(context.paused_request_count, context.total_request_count)
+
+        request_in_prefill_status_tensor = context.request_in_prefill_status_tensor[
+            context.paused_request_count : context.total_request_count
+        ]
+        request_query_lengths = context.request_query_lengths[
+            context.paused_request_count : context.total_request_count
+        ]
+
+        num_prefill_requests = request_in_prefill_status_tensor.sum().item()
+        num_decode_requests = active_request_count - num_prefill_requests
+
+        top_n_results = {}
+
+        if num_decode_requests > 0:
+            decode_len = num_decode_requests * (self.num_speculative_tokens + 1)
+            decode_log_probs = log_probs_tensor[:decode_len].reshape(
+                num_decode_requests, self.num_speculative_tokens + 1, -1
+            )
+            accepted_counts = self._accepted_token_counts_per_request[:num_decode_requests]
+            top_n_per_request = self._request_metadata["top_n_logprobs"][active_request_slice][
+                :num_decode_requests
+            ]
+            max_top_n = int(top_n_per_request.max().item())
+
+            if max_top_n > 0:
+
+                # Single batched topk on GPU: [num_decode, num_spec+1, max_top_n]
+                topk_results = torch.topk(decode_log_probs, k=max_top_n, dim=-1)
+
+                # Single CPU transfer instead of O(num_decode * num_spec) transfers
+                topk_values_cpu = topk_results.values.cpu()
+                topk_indices_cpu = topk_results.indices.cpu()
+
+                for i in range(num_decode_requests):
+                    top_n = int(top_n_per_request[i].item())
+                    if top_n > 0:
+                        num_valid = accepted_counts[i].item() + 1
+                        top_n_results[i] = [
+                            (topk_values_cpu[i, j, :top_n], topk_indices_cpu[i, j, :top_n])
+                            for j in range(num_valid)
+                        ]
+
+        if num_prefill_requests > 0:
+            decode_len = num_decode_requests * (self.num_speculative_tokens + 1)
+            prefill_log_probs = log_probs_tensor[decode_len:]
+            prefill_query_lengths = request_query_lengths[request_in_prefill_status_tensor == 1]
+            prefill_log_probs_per_request = prefill_log_probs.split(
+                prefill_query_lengths.tolist(), dim=0
+            )
+
+            for i in range(num_prefill_requests):
+                req_idx = num_decode_requests + i
+                top_n = int(
+                    self._request_metadata["top_n_logprobs"][active_request_slice][req_idx].item()
+                )
+                if top_n > 0:
+                    request_lp = prefill_log_probs_per_request[i]
+                    skip_prompt = bool(
+                        self._request_metadata["skip_prompt_log_probs"][req_idx].item()
+                    )
+
+                    if skip_prompt and request_lp.size(0) > 1:
+                        top_n_logits = torch.topk(request_lp[-1], k=top_n)
+                        top_n_results[req_idx] = [
+                            (top_n_logits.values.cpu(), top_n_logits.indices.cpu())
+                        ]
+                    else:
+                        top_n_logits = torch.topk(request_lp, k=top_n, dim=-1)
+                        top_n_values_cpu = top_n_logits.values.cpu()
+                        top_n_indices_cpu = top_n_logits.indices.cpu()
+                        top_n_results[req_idx] = [
+                            (top_n_values_cpu[t], top_n_indices_cpu[t])
+                            for t in range(request_lp.size(0))
+                        ]
+
+        return top_n_results if top_n_results else None
+
     def _dynamic_step_calculate_top_n_logprobs(
         self, logits: Tensor, log_probs_tensor: Optional[Tensor] = None
     ) -> Optional[Dict[int, List[Tuple[Tensor, Tensor]]]]:
@@ -839,16 +1595,24 @@ def dummy_forward(self):
         context = self.inference_wrapped_model.inference_context
         # if no cuda graphs, directly use dummy forward
         if not context.cuda_graph_batch_dimensions_list:
-            return self.inference_wrapped_model.dummy_forward()
+            # initialize symmetric memory if needed
+            unwrapped_model = unwrap_model(self.inference_wrapped_model.model)
+            model_config = get_model_config(unwrapped_model)
+            if model_config.transformer_impl == "inference_optimized":
+                context.maybe_initialize_symmetric_memory()
+            self.inference_wrapped_model.dummy_forward()
+
+            # Disable MoE padding for MTP computation
+            if self.model_config.moe_pad_experts_for_cuda_graph_inference:
+                unwrapped_model = unwrap_model(self.inference_wrapped_model.model)
+                set_decode_expert_padding(unwrapped_model, False)
+
+            self._dummy_serial_mtp_forward()
+
+            return
 
         # attempt to use cuda-graph if possible
-        # here we try to reuse the cuda-graph warmup code to run
-        # a dummy cuda graph.
-        input_ids, position_ids = self._dynamic_step_context_init(
-            # try to use the smallest cuda-graph config for dummy forward
-            construct_graph_dimensions=min(context.cuda_graph_batch_dimensions_list),
-            is_dummy_forward=True,
-        )
+        input_ids, position_ids = self._dynamic_step_context_init(is_dummy_forward=True)
 
         # _dynamic_step_context_init tries to find a cuda-graph that is compatible
         # with all EP ranks. It can also return no match, in which case
@@ -860,8 +1624,79 @@ def dummy_forward(self):
         else:
             # fallback to eager dummy forward
             self.inference_wrapped_model.dummy_forward()
+
+        # Disable MoE padding for MTP computation
+        if self.model_config.moe_pad_experts_for_cuda_graph_inference:
+            unwrapped_model = unwrap_model(self.inference_wrapped_model.model)
+            set_decode_expert_padding(unwrapped_model, False)
+
+        # When speculative decoding is active, the real EP ranks perform serial
+        # MTP forward passes after the main forward pass. MTP layers may contain
+        # MoE sublayers (inherited from the decoder spec), which require EP
+        # all-to-all collectives. The dummy rank must participate in these
+        # collectives to avoid a hang.
+        self._dummy_serial_mtp_forward()
+
+        # clear the context of any temporary state from the dummy forward
         context.reset()
 
+    def _dummy_serial_mtp_forward(self):
+        """Run dummy MTP forward passes to participate in EP collectives.
+
+        When speculative decoding is active and MTP layers contain MoE sublayers
+        (inherited from the decoder layer spec), each serial MTP step triggers
+        EP all-to-all collectives. The dummy EP rank must issue matching
+        collective calls so the real ranks do not hang.
+
+        This mirrors the structure of ``_compute_serial_mtp_and_sample``:
+        - On the last PP stage (where MTP resides): run ``compute_mtp_single_step``
+          with dummy tensors so the MoE all-to-all is executed.
+        - When PP > 1: participate in the ``broadcast_from_last_pipeline_stage``
+          that the real ranks also perform.
+        """
+        if self.num_speculative_tokens == 0 or self.num_mtp_heads == 0:
+            return
+        if self.model_config.expert_model_parallel_size <= 1:
+            return
+
+        unwrapped_model = unwrap_model(self.inference_wrapped_model.model)
+
+        is_last_stage = is_pipeline_last_stage(self.pp_group)
+        has_mtp = is_last_stage and hasattr(unwrapped_model, '_decoder_hidden_states_cache')
+        if not has_mtp and not self.model_is_pipeline_parallel:
+            # No MTP on this rank and no PP broadcast to participate in.
+            return
+
+        device = torch.cuda.current_device()
+        dtype = self.model_config.params_dtype
+        hidden_size = self.model_config.hidden_size
+        num_depths = min(self.num_speculative_tokens, self.num_mtp_heads)
+
+        dummy_hidden = None
+        if has_mtp:
+            # Minimal dummy tensors — just enough to drive the MTP layer forward
+            # so that the MoE all-to-all collectives are issued.
+            dummy_hidden = torch.zeros((1, 1, hidden_size), device=device, dtype=dtype)
+            dummy_token_ids = torch.zeros((1, 1), device=device, dtype=torch.long)
+            dummy_position_ids = torch.zeros((1, 1), device=device, dtype=torch.long)
+
+        for depth in range(num_depths):
+            mtp_logits_2d = None
+            if has_mtp:
+                dummy_hidden, mtp_logits = unwrapped_model.compute_mtp_single_step(
+                    hidden_states=dummy_hidden,
+                    next_token_ids=dummy_token_ids,
+                    position_ids=dummy_position_ids,
+                    depth=depth,
+                )
+                mtp_logits_2d = mtp_logits.squeeze(1)  # [1, vocab_size]
+
+            # Match the PP broadcast that real ranks do in _compute_serial_mtp_and_sample.
+            if self.model_is_pipeline_parallel:
+                broadcast_from_last_pipeline_stage(
+                    [1, self.vocab_size], dtype=dtype, tensor=mtp_logits_2d, pp_group=self.pp_group
+                )
+
     def _dynamic_step_context_bookkeeping(self) -> Dict[str, Tensor]:
         """Update the dynamic inference context after sampling.
 
@@ -884,7 +1719,13 @@ def _dynamic_step_context_bookkeeping(self) -> Dict[str, Tensor]:
         # Active sequence lengths.
         active_request_ids = context.request_ids[active_request_slice].long()
         active_sequence_lengths = context.get_active_sequence_lengths()
-        active_sequence_lengths += 1  # Account for the token we just generated
+
+        if self.num_speculative_tokens > 0:
+            active_sequence_lengths += (
+                self._accepted_token_counts_per_request[:active_request_count] + 1
+            )
+        else:
+            active_sequence_lengths += 1
         max_sequence_lengths = context.get_max_sequence_lengths()
 
         # Request finished if termination_id or length >= max_sequence_length.
@@ -908,11 +1749,19 @@ def _dynamic_step_context_bookkeeping(self) -> Dict[str, Tensor]:
         )
         finished_request_ids = context.request_ids[finished_idxs]
 
-        # New sample gets updated in update_requests, so we pass in a clone
+        # Clone needed: update_requests mutates next_tokens in-place via tensor_swap,
+        # which would corrupt the reused _sampled_tokens_cuda buffer.
         new_sample_copy = self._sampled_tokens_cuda[:active_request_count].clone()
 
         # Update requests.
-        update_result = context.update_requests(active_request_mask, new_sample_copy)
+        # _sampled_mtp_tokens_cuda has shape [num_speculative_tokens, max_requests]
+        if self.num_speculative_tokens > 0:
+            sampled_mtp_tokens_cuda = self._sampled_mtp_tokens_cuda[:, :active_request_count]
+        else:
+            sampled_mtp_tokens_cuda = None
+        update_result = context.update_requests(
+            active_request_mask, new_sample_copy, sampled_mtp_tokens_cuda
+        )
 
         return {
             "active_request_ids": active_request_ids,
@@ -956,6 +1805,8 @@ async def async_generate_output_tokens_dynamic_batch(
         if config.moe_enable_routing_replay:
             RouterReplay.set_global_router_replay_action(RouterReplayAction.RECORD)
 
+        # Forward pass produces only base logits. When speculative decoding is
+        # active, MTP logits are computed serially after verification.
         logits = self._dynamic_step_forward_logits(input_ids, position_ids)
 
         # Collect routing indices per request (must be done before context transitions)
@@ -969,19 +1820,44 @@ async def async_generate_output_tokens_dynamic_batch(
         # Todo [Siddharth]: Can we condition the sleep on a cuda event?
         # NOTE [TDE]: This will be moved once CPU and GPU methods are separated.
         await asyncio.sleep(0)
-
         return_log_probs, return_top_n_logprobs = self._dynamic_step_log_probs_bookkeeping()
+
         self._dynamic_step_sample_bookkeeping()
-        self._dynamic_step_sample_logits(logits)
+
+        if self.num_speculative_tokens > 0:
+            # Phase 1: Verify speculative tokens using base logits only.
+            # MTP logits are NOT passed here; they will be computed serially.
+            self._dynamic_step_sample_logits_and_verify_tokens(logits, None, input_ids)
+            # Phase 2: Rewind KV cache for rejected tokens.
+            self._rewind_kv_cache()
+
+            # Disable MoE padding for MTP computation
+            if self.model_config.moe_pad_experts_for_cuda_graph_inference:
+                unwrapped_model = unwrap_model(self.inference_wrapped_model.model)
+                set_decode_expert_padding(unwrapped_model, False)
+
+            # Phase 3: Compute MTP serially with correct (verified) inputs.
+            self._compute_serial_mtp_and_sample()
+        else:
+            self._dynamic_step_sample_logits(logits)
 
         log_probs = None
         top_n_logprobs = None
         if return_log_probs or return_top_n_logprobs:
-            log_probs, log_probs_tensor = self._dynamic_step_calculate_log_probs(logits)
-            if return_top_n_logprobs:
-                top_n_logprobs = self._dynamic_step_calculate_top_n_logprobs(
-                    logits, log_probs_tensor
+            if self.num_speculative_tokens > 0:
+                log_probs, log_probs_tensor = self._dynamic_step_calculate_log_probs_speculative(
+                    logits
                 )
+                if return_top_n_logprobs:
+                    top_n_logprobs = self._dynamic_step_calculate_top_n_logprobs_speculative(
+                        log_probs_tensor
+                    )
+            else:
+                log_probs, log_probs_tensor = self._dynamic_step_calculate_log_probs(logits)
+                if return_top_n_logprobs:
+                    top_n_logprobs = self._dynamic_step_calculate_top_n_logprobs(
+                        logits, log_probs_tensor
+                    )
 
         if skip_bookkeeping:
             request_bookkeeping = {}
@@ -989,12 +1865,22 @@ async def async_generate_output_tokens_dynamic_batch(
             request_bookkeeping = self._dynamic_step_context_bookkeeping()
 
         ret = {
-            "sample": self._sampled_tokens_cuda[:active_request_count],
+            # Clone needed: _sampled_tokens_cuda is a reused buffer overwritten each step.
+            "sample": self._sampled_tokens_cuda[:active_request_count].clone(),
+            "accepted_tokens": (
+                # Clone needed: .fill_(-1) on line 1480 would corrupt the returned value.
+                self._accepted_tokens_per_request.clone()
+                if self.num_speculative_tokens > 0
+                else None
+            ),
             "log_probs": log_probs,
             "top_n_logprobs": top_n_logprobs,
             "routing_indices_per_request": routing_indices_per_request,
             "cuda_graph_request_count": cuda_graph_request_count,
         }
+        if self.num_speculative_tokens > 0:
+            self._accepted_tokens_per_request.fill_(-1)
+            self._accepted_token_counts_per_request.fill_(0)
         ret.update(request_bookkeeping)
         return ret
 
diff --git a/megatron/core/inference/text_generation_server/dynamic_text_gen_server/__init__.py b/megatron/core/inference/text_generation_server/dynamic_text_gen_server/__init__.py
index 3ad54686261..c3441253c77 100644
--- a/megatron/core/inference/text_generation_server/dynamic_text_gen_server/__init__.py
+++ b/megatron/core/inference/text_generation_server/dynamic_text_gen_server/__init__.py
@@ -1,3 +1,3 @@
 # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
 
-from .flask_server import run_flask_server
+from .text_generation_server import start_text_gen_server, stop_text_gen_server
diff --git a/megatron/core/inference/text_generation_server/dynamic_text_gen_server/endpoints/chat_completions.py b/megatron/core/inference/text_generation_server/dynamic_text_gen_server/endpoints/chat_completions.py
index a4cb61fb962..98f5219a3bf 100644
--- a/megatron/core/inference/text_generation_server/dynamic_text_gen_server/endpoints/chat_completions.py
+++ b/megatron/core/inference/text_generation_server/dynamic_text_gen_server/endpoints/chat_completions.py
@@ -1,22 +1,209 @@
 # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
 
 import asyncio
+import json
 import logging
 import time
 import traceback
 import uuid
 import warnings
 
+from megatron.core.inference.inference_request import unwrap_serialized_tensors
 from megatron.core.inference.sampling_params import SamplingParams
 from megatron.core.tokenizers.text.parsers import PARSER_MAPPING
 
 logger = logging.getLogger(__name__)
 
+# pylint: disable=line-too-long
+
+
+def _get_field(obj, key, default=None):
+    """Read a field from dict-like or object-like values."""
+    if isinstance(obj, dict):
+        return obj.get(key, default)
+    return getattr(obj, key, default)
+
+
+def _normalize_tool_calls(tool_calls):
+    """Normalize tool calls to OpenAI-compatible JSON primitives."""
+    normalized = []
+    for call in tool_calls or []:
+        fn = _get_field(call, "function", {}) or {}
+        fn_name = _get_field(fn, "name")
+        fn_args = _get_field(fn, "arguments", "")
+        if fn_name is None:
+            continue
+        if not isinstance(fn_args, str):
+            try:
+                fn_args = json.dumps(fn_args, ensure_ascii=False)
+            except TypeError:
+                fn_args = str(fn_args)
+        normalized.append(
+            {
+                "id": str(_get_field(call, "id", f"call_{uuid.uuid4().hex[:24]}")),
+                "type": "function",
+                "function": {"name": str(fn_name), "arguments": fn_args},
+            }
+        )
+    return normalized
+
+
+def _coerce_arguments_mapping(arguments):
+    """Coerce function.arguments to a mapping for HF/Jinja chat templates.
+
+    Examples:
+    - {"x": 1} -> {"x": 1}
+    - '{"x": 1}' -> {"x": 1}
+    - "[1, 2]" -> {}  # JSON parses, but not a mapping
+    - "not-json" -> {}
+    - None -> {}
+    """
+    if isinstance(arguments, dict):
+        return arguments
+    if isinstance(arguments, str):
+        try:
+            parsed = json.loads(arguments)
+        except (TypeError, ValueError):
+            return {}
+        return parsed if isinstance(parsed, dict) else {}
+    return {}
+
+
+def _sanitize_messages_for_template(messages):
+    """Prepare messages so tokenizer chat templates can safely consume them.
+
+    This only normalizes tool-call argument payloads inside each message:
+    - messages[*].tool_calls[*].function.arguments is coerced to a dict.
+
+    Example transformation:
+    Input:
+      [{"role": "assistant", "tool_calls": [{"function": {"name": "f", "arguments": "{\"x\": 1}"}}]}]
+    Output:
+      [{"role": "assistant", "tool_calls": [{"function": {"name": "f", "arguments": {"x": 1}}}]}]
+
+    Another example:
+    - arguments: "[1,2,3]" -> arguments: {}
+    """
+    if not isinstance(messages, list):
+        return messages
+    sanitized = []
+    for message in messages:
+        if not isinstance(message, dict):
+            sanitized.append(message)
+            continue
+        msg_copy = dict(message)
+        tool_calls = msg_copy.get("tool_calls")
+        if isinstance(tool_calls, list):
+            sanitized_tool_calls = []
+            for call in tool_calls:
+                if not isinstance(call, dict):
+                    sanitized_tool_calls.append(call)
+                    continue
+                call_copy = dict(call)
+                function = call_copy.get("function")
+                if isinstance(function, dict):
+                    function_copy = dict(function)
+                    function_copy["arguments"] = _coerce_arguments_mapping(
+                        function_copy.get("arguments", {})
+                    )
+                    call_copy["function"] = function_copy
+                sanitized_tool_calls.append(call_copy)
+            msg_copy["tool_calls"] = sanitized_tool_calls
+        sanitized.append(msg_copy)
+    return sanitized
+
+
+def _sanitize_tools_for_template(tools):
+    """Ensure tools payload is template-safe and has mapping parameters.
+
+    Example transformations:
+    - {"function": {"name": "f", "parameters": "not-a-dict"}}
+      -> {"function": {"name": "f", "parameters": {"type": "object", "properties": {}}}}
+    - non-dict tool entries are dropped.
+    - non-list input returns None.
+    """
+    if not isinstance(tools, list):
+        return None
+
+    sanitized = []
+    for tool in tools:
+        if not isinstance(tool, dict):
+            continue
+        tool_copy = dict(tool)
+        function = tool_copy.get("function")
+        if isinstance(function, dict):
+            function_copy = dict(function)
+            if not isinstance(function_copy.get("parameters"), dict):
+                function_copy["parameters"] = {"type": "object", "properties": {}}
+            tool_copy["function"] = function_copy
+        sanitized.append(tool_copy)
+    return sanitized
+
+
+def _replace_prefix_tokens(
+    eos_token_id,
+    previous_turn_token_ids,
+    retokeenized_previous_turn_token_ids,
+    current_turn_token_ids,
+):
+    """Replace the token ids that are associated with the previous turn with the actual tokens
+    from the previous generation (rather than the ones from the chat template application)."""
+
+    # Strip the EOS from the previous turn token ids if it exists
+    if previous_turn_token_ids[-1] == eos_token_id:
+        previous_turn_token_ids = previous_turn_token_ids[:-1]
+
+    # Find the last EOS token id in the previous turn token ids
+    last_eos_token_id_index = len(retokeenized_previous_turn_token_ids) - 1
+    for i in reversed(range(len(retokeenized_previous_turn_token_ids))):
+        if current_turn_token_ids[i] == eos_token_id:
+            last_eos_token_id_index = i
+            break
+
+    # Replace the current turn token ids with the tokens from the previous generation
+    current_turn_additional_token_ids = current_turn_token_ids[last_eos_token_id_index:]
+
+    # Return the previous turn token ids + the current turn token ids
+    return previous_turn_token_ids + current_turn_additional_token_ids
+
+
+try:
+    import orjson
+
+    HAVE_ORJSON = True
+except ImportError:
+    HAVE_ORJSON = False
+
+
 try:
-    from flask import Blueprint, current_app, jsonify, request
+    from quart import Blueprint, Response, current_app, jsonify, request
 
     bp = Blueprint('chat_completions_api', __name__)
 
+    def apply_parsers(message_text, tools, parsers_list, tools_requested):
+        """Runs CPU-intensive text parsing."""
+        meta = {}
+        for parser in parsers_list:
+            if parser not in PARSER_MAPPING:
+                raise ValueError(f"Parser {parser} not found in PARSER_MAPPING")
+
+            prev_text = message_text
+            parsed_text, new_info = PARSER_MAPPING[parser].parse(message_text, tools=tools)
+            if "tool_calls" in new_info:
+                new_info["tool_calls"] = _normalize_tool_calls(new_info.get("tool_calls", []))
+                if not tools_requested:
+                    # Ignore incidental tool-call syntax in plain chat mode.
+                    parsed_text = prev_text
+                    new_info.pop("tool_calls", None)
+            message_text = parsed_text
+
+            assert not (
+                meta.keys() & new_info.keys()
+            ), "Multiple parsers found the same information."
+            meta.update(new_info)
+
+        return message_text, meta
+
     @bp.route('/chat/completions', methods=['POST'])
     @bp.route('/v1/chat/completions', methods=['POST'])
     async def chat_completions():
@@ -25,29 +212,99 @@ async def chat_completions():
         tokenizer = current_app.config['tokenizer']
         parsers = current_app.config['parsers']
 
-        req = request.get_json()
-
-        # --- 1. Parse Messages ---
+        req = await request.get_json()
+        tools = req.get("tools", None)
+        tools_requested = bool(tools)
         messages = req.get("messages")
+        chat_template_kwargs = req.get("chat_template_kwargs", {})
+        if not isinstance(chat_template_kwargs, dict):
+            logger.warning(
+                "Ignoring non-dict chat_template_kwargs: %s", type(chat_template_kwargs).__name__
+            )
+            chat_template_kwargs = {}
+        # --- 1. Parse Messages ---
         if not messages:
-            return "Missing 'messages' field", 400
+            return Response("Missing 'messages' field", status=400)
         if not isinstance(messages, list):
-            return "'messages' must be a list", 400
+            return Response("'messages' must be a list", status=400)
+        template_messages = _sanitize_messages_for_template(messages)
+        template_tools = _sanitize_tools_for_template(tools)
 
         try:
-            prompt_tokens = tokenizer.apply_chat_template(
-                messages, tokenize=True, add_generation_prompt=True, tools=req.get("tools", None)
-            )
-        except (AttributeError, AssertionError):
-            warnings.warn(
-                "Tokenizer does not support 'apply_chat_template'. Using tokenize instead."
-            )
-            prompt_tokens = tokenizer.tokenize(
-                "\n".join([message["content"] for message in messages])
-            )
+            if (
+                hasattr(tokenizer, 'apply_chat_template')
+                and getattr(tokenizer, "chat_template", None) is not None
+            ):
+                prompt_tokens = tokenizer.apply_chat_template(
+                    template_messages,
+                    tokenize=True,
+                    add_generation_prompt=True,
+                    tools=template_tools,
+                    **chat_template_kwargs,
+                )
+
+                if req.get("prevent_retokenization", True):
+                    # If we are avoiding retokenization, we need to replace some prompt tokens with the prompt/generation tokens from the previous generation
+                    # This improves prefix cache hits and reduces logprob variation between training and inference.
+
+                    eos_token_id = tokenizer.eos_id
+                    assert eos_token_id is not None, "Your tokenizer must have an EOS token ID!"
+
+                    warnings.warn(
+                        "Avoiding prefix retokenization."
+                        " This is a patch that ensures subsequent generations are not retokenized differently than the previous generation."
+                        " This may cause unexpected behavior if messages (including system messages) are altered between generations."
+                    )
+
+                    # Find the last assistant message
+                    last_assistant_message_idx = None
+                    for i in reversed(range(len(template_messages))):
+                        if template_messages[i]["role"] == "assistant":
+                            last_assistant_message_idx = i
+                            break
+
+                    # If there was a previous assistant message, we need to replace the prefix tokens with the tokens from the previous generation
+                    if last_assistant_message_idx is not None:
+                        messages_to_last_assistant_message = template_messages[
+                            : last_assistant_message_idx + 1
+                        ]
+
+                        # Get the templated tokenization of just the previous generation
+                        retokenized_previous_turn_token_ids = tokenizer.apply_chat_template(
+                            messages_to_last_assistant_message,
+                            tokenize=True,
+                            add_generation_prompt=False,
+                            tools=template_tools,
+                            **chat_template_kwargs,
+                        )
+
+                        # Replace the prefix tokens with the tokens from the previous generation
+                        last_assistant_message = template_messages[last_assistant_message_idx]
+                        assert (
+                            "prompt_token_ids" in last_assistant_message
+                            and "generation_token_ids" in last_assistant_message
+                        ), "Last assistant message must have prompt_token_ids and generation_token_ids from previous generation to avoid prefix retokenization"
+                        previous_turn_token_ids = (
+                            last_assistant_message["prompt_token_ids"]
+                            + last_assistant_message["generation_token_ids"]
+                        )
+                        prompt_tokens = _replace_prefix_tokens(
+                            eos_token_id,
+                            previous_turn_token_ids,
+                            retokenized_previous_turn_token_ids,
+                            prompt_tokens,
+                        )
+
+            else:
+                warnings.warn(
+                    "Tokenizer does not support 'apply_chat_template'. Using tokenize instead."
+                )
+                prompt_tokens = tokenizer.tokenize(
+                    "\n".join([message["content"] for message in messages])
+                )
         except Exception as e:
             logger.error(f"{traceback.format_exc()}")
-            return f"Error processing 'messages': {e}", 500
+            return Response(f"Error processing 'messages': {e}", status=500)
 
         # --- 2. Parse Sampling Params ---
         try:
@@ -70,33 +327,32 @@ async def chat_completions():
             # input. Since we pre-tokenize via apply_chat_template, we must handle
             # BOS ourselves, matching the logic in tokenize_prompt().
             if hasattr(tokenizer, 'bos') and tokenizer.bos is not None:
-                while prompt_tokens and prompt_tokens[0] == tokenizer.bos:
-                    prompt_tokens.pop(0)
+                start_idx = 0
+                while start_idx < len(prompt_tokens) and prompt_tokens[start_idx] == tokenizer.bos:
+                    start_idx += 1
+                if start_idx > 0:
+                    prompt_tokens = prompt_tokens[start_idx:]
+
                 if add_BOS:
                     prompt_tokens = [tokenizer.bos] + prompt_tokens
 
+            max_tokens = req.get("max_completion_tokens", None) or req.get("max_tokens", None)
+
             sampling_params = SamplingParams(
                 temperature=temperature,
                 top_k=top_k,
                 top_p=top_p,
                 return_log_probs=return_log_probs,
                 top_n_logprobs=top_n_logprobs,
-                num_tokens_to_generate=(
-                    int(max_tokens)
-                    if ((max_tokens := req.get("max_tokens", None)) is not None)
-                    else None
-                ),
+                num_tokens_to_generate=(int(max_tokens) if max_tokens is not None else None),
                 skip_prompt_log_probs=skip_prompt_log_probs,
                 add_BOS=add_BOS,
             )
         except ValueError as e:
-            return f"Invalid sampling parameter: {e}", 400
+            return Response(f"Invalid sampling parameter: {e}", status=400)
 
         # --- 3. Send Requests to Engine ---
-        # For chat, we run the *same* prompt 'n' times.
-        tasks = []
-        for _ in range(n):
-            tasks.append(client.add_request(prompt_tokens, sampling_params))
+        tasks = [client.add_request(prompt_tokens, sampling_params) for _ in range(n)]
 
         if current_app.config['verbose']:
             start_time = time.perf_counter()
@@ -105,7 +361,7 @@ async def chat_completions():
             batch_results = await asyncio.gather(*tasks)
         except Exception as e:
             logger.error(f"Error during inference: {e}")
-            return f"Error during inference: {e}", 500
+            return Response(f"Error during inference: {e}", status=500)
 
         if current_app.config['verbose']:
             logging.info(
@@ -119,22 +375,32 @@ async def chat_completions():
         prompt_tokens_counts = []
 
         request_idx = 0
-        for record in batch_results:
-            result = record.merge().serialize()
-            # Unwrap ("tensor", [...]) tuples from serialize() into plain lists.
-            result = {
-                k: v[1] if isinstance(v, (list, tuple)) and len(v) == 2 and v[0] == "tensor" else v
-                for k, v in result.items()
-            }
-            prompt_tokens = result["prompt_tokens"]  # The engine can modify prompt_tokens.
+        for result_item in batch_results:
+            result = result_item if isinstance(result_item, dict) else result_item.serialize()
+            result = unwrap_serialized_tensors(result)
+
+            if result["status"] == "FAILED":
+                if result["sampling_params"]["num_tokens_to_generate"] <= 0:
+                    return Response(
+                        f"Request {request_idx} failed due to context length overflow", status=400
+                    )
+                else:
+                    return Response(
+                        f"Request {request_idx} failed due to internal error {result['events']}",
+                        status=500,
+                    )
+
+            prompt_tokens_out = result["prompt_tokens"]  # The engine can modify prompt_tokens.
             text_output = result["generated_text"]
-            prompt_tokens_count = len(prompt_tokens) if prompt_tokens is not None else 0
+            prompt_tokens_count = len(prompt_tokens_out) if prompt_tokens_out is not None else 0
             prompt_tokens_counts.append(prompt_tokens_count)
 
             logprobs_content = None
             if sampling_params.return_log_probs:
                 token_logprobs = result.get('log_probs', [])
-                tokens = [tokenizer.detokenize([tok]) for tok in result["generated_tokens"]]
+
+                tokens_to_decode = [[tok] for tok in result["generated_tokens"]]
+                tokens = list(map(tokenizer.detokenize, tokens_to_decode))
 
                 # Get top_n_logprobs if available
                 generated_top_n_logprobs = result.get('generated_top_n_logprobs')
@@ -154,29 +420,25 @@ async def chat_completions():
                                 }
                             )
 
-                    entry = {
-                        "token": tok,
-                        "logprob": lp,
-                        "bytes": list(tok.encode("utf-8")),
-                        "top_logprobs": top_logprobs_list,
-                    }
-                    logprobs_content.append(entry)
+                    logprobs_content.append(
+                        {
+                            "token": tok,
+                            "logprob": lp,
+                            "bytes": list(tok.encode("utf-8")),
+                            "top_logprobs": top_logprobs_list,
+                        }
+                    )
 
             metadata = {}
             message_text = text_output
+
             if parsers:
-                for parser in parsers:
-                    if parser not in PARSER_MAPPING:
-                        raise ValueError(f"Parser {parser} not found in PARSER_MAPPING")
-                    message_text, new_info = PARSER_MAPPING[parser].parse(
-                        message_text, tools=req.get("tools", None)
-                    )
-                    assert not (
-                        metadata.keys() & new_info.keys()
-                    ), "Multiple parsers found the same information."
-                    metadata.update(new_info)
+                message_text, metadata = apply_parsers(
+                    message_text, req.get("tools", None), parsers, tools_requested
+                )
+
             message = {"role": "assistant", "content": message_text}
-            if "tool_calls" in metadata:
+            if metadata.get("tool_calls", []):
                 message["tool_calls"] = metadata["tool_calls"]
             if "reasoning" in metadata:
                 message["reasoning"] = metadata["reasoning"]
@@ -184,45 +446,52 @@ async def chat_completions():
             # Replicate data in the message field for compatibility.
             message["prompt_token_ids"] = result["prompt_tokens"]
             message["generation_token_ids"] = result["generated_tokens"]
-            message["generation_log_probs"] = result.get("generated_log_probs", None)
+            message["generation_log_probs"] = result.get("generated_log_probs", [])
             return_log_probs = sampling_params.return_log_probs
 
+            finish_reason = "tool_calls" if metadata.get("tool_calls", []) else "stop"
+            if (
+                len(result["generated_tokens"])
+                >= result["sampling_params"]["num_tokens_to_generate"]
+            ):
+                finish_reason = "length"
+
             choice_data = {
                 "index": request_idx,
                 "message": message,
                 "prompt_token_ids": result["prompt_tokens"],
                 "generation_token_ids": result["generated_tokens"],
-                "generation_log_probs": result["generated_log_probs"],
+                "generation_log_probs": result.get("generated_log_probs", []),
                 "raw_text": result["prompt"] + result["generated_text"],
                 # 'logprobs' in chat API is an object containing 'content'
                 # "logprobs": {"content": logprobs_content} if logprobs_content else None,
                 "logprobs": {"content": logprobs_content} if return_log_probs else None,
-                "finish_reason": (
-                    "tool_calls" if metadata.get("tool_calls", []) else "stop"
-                ),  # Original code hardcoded this.
+                "finish_reason": finish_reason,
             }
-            if result.get("policy_staleness") is not None:
-                choice_data["policy_staleness"] = result["policy_staleness"]
-            if result.get("kv_cache_staleness") is not None:
-                choice_data["kv_cache_staleness"] = result["kv_cache_staleness"]
-            events = result.get("events")
-            if events is not None:
-                num_evictions = sum(1 for e in events if e.get("type") == "EVICT")
-                if num_evictions > 0:
-                    choice_data["num_evictions"] = num_evictions
+            choice_data["policy_staleness"] = result["policy_staleness"]
+            choice_data["kv_cache_staleness"] = result["kv_cache_staleness"]
+            choice_data["num_evictions"] = sum(
+                1 for e in result["events"] if e.get("type") == "EVICT"
+            )
             if current_app.config['verbose']:
                 logging.info(result)
+
             if result["routing_indices"] is not None:
                 choice_data["moe_topk_indices"] = result["routing_indices"]
                 if prompt_tokens_count:
                     choice_data["prompt_moe_topk_indices"] = result["routing_indices"][
                         :prompt_tokens_count
                     ]
+
             choices.append(choice_data)
+            if choice_data["generation_log_probs"] is None:
+                logger.warning(
+                    "Generation log probs is None for request:\n%s", json.dumps(result, indent=4)
+                )
             total_completion_tokens += len(result["generated_tokens"])
             request_idx += 1
 
-        prompt_token_count = max(prompt_tokens_counts)
+        prompt_token_count = max(prompt_tokens_counts) if prompt_tokens_counts else 0
         response = {
             "id": str(uuid.uuid4()),
             "created": int(time.time()),
@@ -235,7 +504,12 @@ async def chat_completions():
                 "total_tokens": prompt_token_count + total_completion_tokens,
             },
         }
-        return jsonify(response)
+
+        if HAVE_ORJSON:
+            # Use orjson for faster serialization
+            return Response(orjson.dumps(response), mimetype="application/json")
+        else:
+            return jsonify(response)
 
 except ImportError as e:
-    logger.warning(f"Could not import flask: {e}")
+    logger.warning(f"Could not import quart: {e}")
diff --git a/megatron/core/inference/text_generation_server/dynamic_text_gen_server/endpoints/completions.py b/megatron/core/inference/text_generation_server/dynamic_text_gen_server/endpoints/completions.py
index fdab97eb7b5..af8ec41aac2 100644
--- a/megatron/core/inference/text_generation_server/dynamic_text_gen_server/endpoints/completions.py
+++ b/megatron/core/inference/text_generation_server/dynamic_text_gen_server/endpoints/completions.py
@@ -4,13 +4,14 @@
 import logging
 import time
 
+from megatron.core.inference.inference_request import unwrap_serialized_tensors
 from megatron.core.inference.sampling_params import SamplingParams
 
 logger = logging.getLogger(__name__)
 
 
 try:
-    from flask import Blueprint, current_app, jsonify, request
+    from quart import Blueprint, current_app, jsonify, request
 
     bp = Blueprint('completions_api', __name__)
 
@@ -21,7 +22,7 @@ async def completions():
         client = current_app.config['client']
         tokenizer = current_app.config['tokenizer']
 
-        req = request.get_json()
+        req = await request.get_json()
 
         # --- 1. Parse Prompt ---
         prompt_data = req.get("prompt")
@@ -128,30 +129,28 @@ async def completions():
         choices = []
 
         request_idx = 0
-        for record in batch_results:
-            result = record.merge()
-            full_text = result.generated_text or ""
+        for completed_request in batch_results:
+            request_dict = (
+                completed_request
+                if isinstance(completed_request, dict)
+                else completed_request.serialize()
+            )
+            result = unwrap_serialized_tensors(request_dict)
+            full_text = result["generated_text"] or ""
             text_output = (prompts_as_strings[request_idx] + full_text) if echo else full_text
 
             logprobs_data = None
             if sampling_params.return_log_probs:
                 # Get prompt tokens and logprobs
-                prompt_tokens_list = []
-                if result.prompt_tokens is not None:
-                    if hasattr(result.prompt_tokens, 'tolist'):
-                        prompt_tokens_list = result.prompt_tokens.tolist()
-                    else:
-                        prompt_tokens_list = list(result.prompt_tokens)
+                prompt_tokens_list = result["prompt_tokens"] or []
 
-                prompt_log_probs = getattr(result, 'prompt_log_probs', None) or []
-                prompt_top_n_logprobs = getattr(result, 'prompt_top_n_logprobs', None) or []
+                prompt_log_probs = result.get('prompt_log_probs') or []
+                prompt_top_n_logprobs = result.get('prompt_top_n_logprobs') or []
 
                 # Get generated tokens and logprobs
-                generated_tokens_list = (
-                    list(result.generated_tokens) if result.generated_tokens else []
-                )
-                generated_log_probs = getattr(result, 'generated_log_probs', None) or []
-                generated_top_n_logprobs = getattr(result, 'generated_top_n_logprobs', None) or []
+                generated_tokens_list = result["generated_tokens"] or []
+                generated_log_probs = result.get('generated_log_probs') or []
+                generated_top_n_logprobs = result.get('generated_top_n_logprobs') or []
 
                 if echo:
                     # When echo=True, include prompt tokens and their logprobs
@@ -204,17 +203,19 @@ async def completions():
                 }
 
             choices.append({"index": request_idx, "text": text_output, "logprobs": logprobs_data})
-            if result.routing_indices is not None:
-                choices[-1]["moe_topk_indices"] = result.routing_indices.tolist()
-                prompt_length = len(result.prompt_tokens) if result.prompt_tokens is not None else 0
+            if result["routing_indices"] is not None:
+                choices[-1]["moe_topk_indices"] = result["routing_indices"]
+                prompt_length = (
+                    len(result["prompt_tokens"]) if result["prompt_tokens"] is not None else 0
+                )
                 if prompt_length:
-                    choices[-1]["prompt_moe_topk_indices"] = result.routing_indices[
+                    choices[-1]["prompt_moe_topk_indices"] = result["routing_indices"][
                         :prompt_length
-                    ].tolist()
+                    ]
 
             request_idx += 1
 
         return jsonify({"choices": choices})
 
 except ImportError as e:
-    logger.warning(f"Could not import flask: {e}")
+    logger.warning(f"Could not import quart: {e}")
diff --git a/megatron/core/inference/text_generation_server/dynamic_text_gen_server/endpoints/health.py b/megatron/core/inference/text_generation_server/dynamic_text_gen_server/endpoints/health.py
index a9d0a678b44..5458266b276 100644
--- a/megatron/core/inference/text_generation_server/dynamic_text_gen_server/endpoints/health.py
+++ b/megatron/core/inference/text_generation_server/dynamic_text_gen_server/endpoints/health.py
@@ -5,7 +5,7 @@
 logger = logging.getLogger(__name__)
 
 try:
-    from flask import Blueprint, current_app, jsonify
+    from quart import Blueprint, current_app, jsonify
 
     bp = Blueprint('health_api', __name__)
 
@@ -35,4 +35,4 @@ async def health():
             return jsonify({"status": "error", "details": str(e)}), 500
 
 except ImportError as e:
-    logger.warning(f"Could not import flask: {e}")
+    logger.warning(f"Could not import quart: {e}")
diff --git a/megatron/core/inference/text_generation_server/dynamic_text_gen_server/flask_server.py b/megatron/core/inference/text_generation_server/dynamic_text_gen_server/flask_server.py
deleted file mode 100644
index 73b9684ad48..00000000000
--- a/megatron/core/inference/text_generation_server/dynamic_text_gen_server/flask_server.py
+++ /dev/null
@@ -1,109 +0,0 @@
-# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
-
-import asyncio
-import logging
-import socket
-from concurrent.futures import ThreadPoolExecutor
-from contextlib import contextmanager
-
-try:
-    from flask import Flask
-    from hypercorn.asyncio import serve
-    from hypercorn.config import Config
-    from hypercorn.middleware import AsyncioWSGIMiddleware
-
-    HAS_FLASK = True
-except ImportError as e:
-    HAS_FLASK = False
-
-import megatron.core.inference.text_generation_server.dynamic_text_gen_server.endpoints as endpoints
-from megatron.core.inference.inference_client import InferenceClient
-from megatron.core.utils import trace_async_exceptions
-
-logger = logging.getLogger(__name__)
-
-
-@contextmanager
-def temp_log_level(level, logger=None):
-    """Enables temporarily overriding the logging level."""
-    logger = logger or logging.getLogger()
-    old_level = logger.level
-    logger.setLevel(level)
-    try:
-        yield
-    finally:
-        logger.setLevel(old_level)
-
-
-@trace_async_exceptions
-async def run_flask_server_on_client(
-    client: InferenceClient,
-    tokenizer,
-    flask_port: int,
-    parsers: list[str] = None,
-    verbose: bool = False,
-):
-    """Initializes and runs the async Flask server using the provided InferenceClient."""
-    if not HAS_FLASK:
-        raise RuntimeError(f"Flask not available")
-
-    try:
-        hostname = socket.gethostname()
-    except Exception as e:
-        logger.warning(f"Could not get hostname: {e}")
-        hostname = "0.0.0.0"
-
-    app = Flask(__name__)
-
-    # Store client and tokenizer in app config for Blueprints to use
-    app.config['client'] = client
-    app.config['tokenizer'] = tokenizer
-    app.config['parsers'] = parsers
-    app.config['verbose'] = verbose
-
-    # Register all blueprints from the 'endpoints' package
-    for endpoint in endpoints.__all__:
-        app.register_blueprint(endpoint)
-
-    @app.route('/')
-    def health_check():
-        return "Megatron Dynamic Inference Server is running."
-
-    loop = asyncio.get_event_loop()
-
-    config = Config()
-    config.wsgi_max_body_size = 2**30  # 1 GB; needed for large prompts.
-    config.keep_alive_timeout = 30.0  # Keep connection alive between long-running requests.
-    config.backlog = 2**14  # Expect high load; ensure we do not drop connections.
-    config.h2_max_concurrent_streams = 2**14  # Allow many concurrent streams for HTTP/2 clients.
-    config.bind = [f"0.0.0.0:{flask_port}"]
-
-    # Force logging level to INFO to ensure that hostname is printed
-    with temp_log_level(logging.INFO, logger):
-        logger.info(f"Starting Flask server on http://{hostname}:{flask_port}")
-        logger.info(f"Using tokenizer: {type(tokenizer)}")
-        logger.info(f"Using parsers: {parsers}")
-
-    loop.set_default_executor(ThreadPoolExecutor(max_workers=8192))
-    await serve(AsyncioWSGIMiddleware(app, max_body_size=config.wsgi_max_body_size), config)
-
-
-@trace_async_exceptions
-async def run_flask_server(
-    coordinator_addr: str,
-    tokenizer,
-    rank: int,
-    flask_port: int,
-    parsers: list[str] = None,
-    verbose: bool = False,
-):
-    """Initializes and runs the async Flask server
-    starting an InferenceClient with the provided coordinator address."""
-    inference_client = InferenceClient(coordinator_addr)
-    await inference_client.start()
-    logger.info(f"Rank {rank}: InferenceClient connected.")
-    try:
-        await run_flask_server_on_client(inference_client, tokenizer, flask_port, parsers, verbose)
-    finally:
-        await inference_client.stop()
-        logger.info(f"Rank {rank}: Flask server and client shut down.")
diff --git a/megatron/core/inference/text_generation_server/dynamic_text_gen_server/text_generation_server.py b/megatron/core/inference/text_generation_server/dynamic_text_gen_server/text_generation_server.py
new file mode 100644
index 00000000000..adf5c39a9b4
--- /dev/null
+++ b/megatron/core/inference/text_generation_server/dynamic_text_gen_server/text_generation_server.py
@@ -0,0 +1,210 @@
+# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
+
+import asyncio
+import logging
+import multiprocessing as mp
+import socket
+from contextlib import contextmanager
+from typing import List, Optional
+
+try:
+    from hypercorn.asyncio import serve
+    from hypercorn.config import Config
+    from quart import Quart
+
+    HAS_BACKEND = True
+except ImportError as e:
+    HAS_BACKEND = False
+
+import megatron.core.inference.text_generation_server.dynamic_text_gen_server.endpoints as endpoints
+from megatron.core.inference.inference_client import InferenceClient
+from megatron.core.utils import trace_async_exceptions
+
+logger = logging.getLogger(__name__)
+
+# Global reference to manage the background server processes
+_SERVER_PROCESSES: List[mp.Process] = []
+_SHARED_SOCKET = None
+
+
+@contextmanager
+def temp_log_level(level, logger=None):
+    """Enables temporarily overriding the logging level."""
+    logger = logger or logging.getLogger()
+    old_level = logger.level
+    logger.setLevel(level)
+    try:
+        yield
+    finally:
+        logger.setLevel(old_level)
+
+
+@trace_async_exceptions
+async def _run_text_gen_server(
+    coordinator_addr: str,
+    tokenizer,
+    rank: int,
+    server_port: int,
+    parsers: Optional[List[str]] = None,
+    verbose: bool = False,
+    fd: Optional[int] = None,
+):
+    """
+    Initializes and runs the async web server. Automatically starts and
+    manages its own InferenceClient connected to the provided coordinator address.
+    """
+    if not HAS_BACKEND:
+        raise RuntimeError(f"Web backend framework (Quart) not available")
+
+    # Create and start the client locally inside this process
+    inference_client = InferenceClient(coordinator_addr)
+    inference_client.start()
+    logger.info(f"Rank {rank}: InferenceClient connected.")
+
+    try:
+        try:
+            hostname = socket.gethostname()
+        except Exception as e:
+            logger.warning(f"Could not get hostname: {e}")
+            hostname = "0.0.0.0"
+
+        app = Quart(__name__)
+
+        # Quart native way to handle max body size (1 GB; needed for large prompts)
+        app.config['MAX_CONTENT_LENGTH'] = 2**30
+
+        # Store client and tokenizer in app config for Blueprints to use
+        app.config['client'] = inference_client
+        app.config['tokenizer'] = tokenizer
+        app.config['parsers'] = parsers
+        app.config['verbose'] = verbose
+
+        # Register all blueprints from the 'endpoints' package
+        for endpoint in endpoints.__all__:
+            app.register_blueprint(endpoint)
+
+        config = Config()
+        config.keep_alive_timeout = 30.0  # Keep connection alive between long-running requests.
+        config.backlog = 2**14  # Expect high load; ensure we do not drop connections.
+        config.h2_max_concurrent_streams = (
+            2**14
+        )  # Allow many concurrent streams for HTTP/2 clients.
+
+        if fd is not None:
+            config.bind = [f"fd://{fd}"]
+        else:
+            config.bind = [f"0.0.0.0:{server_port}"]
+
+        with temp_log_level(logging.INFO, logger):
+            logger.info(f"Starting text generation server on http://{hostname}:{server_port}")
+            logger.info(f"Using tokenizer: {type(tokenizer)}")
+            logger.info(f"Using parsers: {parsers}")
+
+        # Quart is natively ASGI, so we can serve the app directly
+        await serve(app, config)
+
+    finally:
+        # Gracefully shut down the client when the server stops
+        inference_client.stop()
+        logger.info(f"Rank {rank}: Web server and client shut down.")
+
+
+def _server_process_worker(
+    coordinator_addr: str,
+    tokenizer,
+    rank: int,
+    server_port: int,
+    parsers: Optional[List[str]] = None,
+    verbose: bool = False,
+    fd: Optional[int] = None,
+):
+    """Synchronous worker function that sets up a new event loop for the separate process."""
+    loop = asyncio.new_event_loop()
+    asyncio.set_event_loop(loop)
+    try:
+        loop.run_until_complete(
+            _run_text_gen_server(
+                coordinator_addr, tokenizer, rank, server_port, parsers, verbose, fd
+            )
+        )
+    except KeyboardInterrupt:
+        logger.info(f"Rank {rank}: text gen server process interrupted.")
+    finally:
+        pending = asyncio.all_tasks(loop)
+        for task in pending:
+            task.cancel()
+        if pending:
+            loop.run_until_complete(asyncio.gather(*pending, return_exceptions=True))
+        loop.close()
+
+
+def start_text_gen_server(
+    coordinator_addr: str,
+    tokenizer,
+    rank: int,
+    server_port: int,
+    parsers: Optional[List[str]] = None,
+    verbose: bool = False,
+    num_replicas: int = 4,
+):
+    """Start the text generation server."""
+    global _SERVER_PROCESSES
+    global _SHARED_SOCKET
+
+    if _SERVER_PROCESSES:
+        logger.warning("Text gen server processes are already running.")
+        return
+
+    _SHARED_SOCKET = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+    _SHARED_SOCKET.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
+
+    if hasattr(socket, 'SO_REUSEPORT'):
+        try:
+            _SHARED_SOCKET.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEPORT, 1)
+        except OSError:
+            pass
+
+    _SHARED_SOCKET.bind(("0.0.0.0", server_port))
+    _SHARED_SOCKET.setblocking(False)
+
+    _SHARED_SOCKET.set_inheritable(True)
+    fd = _SHARED_SOCKET.fileno()
+
+    for i in range(num_replicas):
+        p = mp.Process(
+            target=_server_process_worker,
+            args=(coordinator_addr, tokenizer, rank, server_port, parsers, verbose, fd),
+            daemon=True,
+        )
+        p.start()
+        _SERVER_PROCESSES.append(p)
+        logger.info(f"Started text gen frontend replica {i+1}/{num_replicas} (PID: {p.pid})")
+
+
+def stop_text_gen_server():
+    """Stop the text generation server."""
+    global _SERVER_PROCESSES
+    global _SHARED_SOCKET
+
+    if not _SERVER_PROCESSES:
+        return
+
+    logger.info(f"Terminating {len(_SERVER_PROCESSES)} Text Gen frontend processes...")
+
+    for p in _SERVER_PROCESSES:
+        if p.is_alive():
+            p.terminate()
+
+    for p in _SERVER_PROCESSES:
+        p.join(timeout=3)
+        if p.is_alive():
+            p.kill()
+            p.join()
+
+    # Clean up the master socket
+    if _SHARED_SOCKET is not None:
+        _SHARED_SOCKET.close()
+        _SHARED_SOCKET = None
+
+    _SERVER_PROCESSES = []
+    logger.info("All text gen frontend processes terminated.")
diff --git a/megatron/core/inference/text_generation_server/text_generation_server.py b/megatron/core/inference/text_generation_server/text_generation_server.py
index 4d11a27c405..bac92e391b8 100644
--- a/megatron/core/inference/text_generation_server/text_generation_server.py
+++ b/megatron/core/inference/text_generation_server/text_generation_server.py
@@ -77,7 +77,7 @@ def put(self):
         if "temperature" in request.get_json():
             temperature = request.get_json()["temperature"]
             if not (isinstance(temperature, (int, float))):
-                return "temperature must be a positive number less than or equal to 1000.0"
+                return "temperature must be a positive number less than or equal to 100.0"
             if not (0.0 < temperature <= 100.0):
                 return "temperature must be a positive number less than or equal to 100.0"
 
diff --git a/megatron/core/inference/utils.py b/megatron/core/inference/utils.py
index 0bdaff64be1..2e6df77da75 100644
--- a/megatron/core/inference/utils.py
+++ b/megatron/core/inference/utils.py
@@ -10,6 +10,29 @@
 from megatron.core.utils import get_model_config
 
 
+def device_memory_summary() -> str:
+    """One-line GPU memory summary for torch_memory_saver logging."""
+    dev = torch.cuda.current_device()
+    stats = torch.cuda.memory_stats(dev)
+    try:
+        segs = torch.cuda.memory_snapshot(include_traces=False)
+    except TypeError:  # include_traces was added in PyTorch 2.11
+        segs = torch.cuda.memory_snapshot()
+    M = 1024**2
+    private = sum(
+        s.get("active_size", 0)
+        for s in segs
+        if s.get("device", dev) == dev and tuple(s.get("segment_pool_id", (0, 0))) != (0, 0)
+    )
+    alloc = stats.get("allocated_bytes.all.current", 0)
+    resv = stats.get("reserved_bytes.all.current", 0)
+    dev_mem = torch.cuda.device_memory_used()
+    return (
+        f"alloc={alloc/M:.0f}MiB private={private/M:.0f}MiB "
+        f"resv-alloc={(resv-alloc)/M:.0f}MiB resv={resv/M:.0f}MiB dev_mem={dev_mem/M:.0f}MiB"
+    )
+
+
 class Counter:
     """A simple counter class
 
@@ -132,6 +155,34 @@ def set_decode_expert_padding(model, set_to: bool = False, capacity_factor: int
             router.config.moe_pad_expert_input_to_capacity = bool(set_to)
 
 
+def set_inference_cuda_graphed_iteration_for_ep_inference(model):
+    """Enable CUDA graph compatibility for expert parallel inference.
+
+    Sets a flag in all MoELayers indicating the current iteration is being
+    captured/executed in a CUDA graph. This allows the dispatcher to adjust
+    its behavior for CUDA graph compatibility.
+    """
+    global moe_layer_cache
+    if moe_layer_cache is None:
+        _init_moe_expert_cache(model)
+
+    for moe_layer in moe_layer_cache:
+        moe_layer.set_inference_cuda_graphed_iteration()
+
+
+def unset_inference_cuda_graphed_iteration_for_ep_inference(model):
+    """Disable CUDA graph compatibility for expert parallel inference.
+
+    Clears the flag in all MoELayers, restoring standard dispatcher behavior.
+    """
+    global moe_layer_cache
+    if moe_layer_cache is None:
+        _init_moe_expert_cache(model)
+
+    for moe_layer in moe_layer_cache:
+        moe_layer.unset_inference_cuda_graphed_iteration()
+
+
 def tensor_swap(x, src_idxs, dst_idxs):
     """
     Swap x[src_idxs] and x[dst_idxs]
diff --git a/megatron/core/models/T5/t5_model.py b/megatron/core/models/T5/t5_model.py
index 6d134067ccc..b2feb974643 100644
--- a/megatron/core/models/T5/t5_model.py
+++ b/megatron/core/models/T5/t5_model.py
@@ -56,7 +56,11 @@ def __init__(
             config.hidden_size,
             vocab_size,
             config=config,
-            init_method=config.init_method,
+            init_method=(
+                config.embedding_init_method
+                if config.use_mup and not share_embeddings_and_output_weights
+                else config.init_method
+            ),
             bias=share_embeddings_and_output_weights,
             skip_bias_add=not share_embeddings_and_output_weights,
             gather_output=not self.parallel_output,
diff --git a/megatron/core/models/backends.py b/megatron/core/models/backends.py
index a4fc3165ba5..e867e91c003 100644
--- a/megatron/core/models/backends.py
+++ b/megatron/core/models/backends.py
@@ -3,14 +3,23 @@
 
 import warnings
 from abc import abstractmethod
-from typing import Optional, Protocol, cast
+from typing import Optional, Protocol, Tuple, cast
 
+from megatron.core.extensions.transformer_engine import (
+    TEColumnParallelGroupedLinear,
+    TERowParallelGroupedLinear,
+)
 from megatron.core.tensor_parallel.layers import ColumnParallelLinear, RowParallelLinear
 from megatron.core.transformer.dot_product_attention import DotProductAttention
 from megatron.core.transformer.mlp import MLPSubmodules, TEActivationFunctionBuilder
-from megatron.core.transformer.moe.experts import GroupedMLP, SequentialMLP, TEGroupedMLPSubmodules
+from megatron.core.transformer.moe.experts import (
+    InferenceGroupedMLP,
+    SequentialMLP,
+    TEGroupedMLPSubmodules,
+)
 from megatron.core.transformer.torch_norm import LayerNormBuilder, WrappedTorchNorm
 from megatron.core.typed_torch import not_none
+from megatron.core.utils import is_te_min_version
 
 try:
     import apex  # pylint: disable=unused-import
@@ -63,7 +72,9 @@ def column_parallel_layer_norm_linear(self) -> Optional[type]:
         ...
 
     @abstractmethod
-    def layer_norm(self, rms_norm: bool = False, for_qk: bool = False) -> LayerNormBuilder:
+    def layer_norm(
+        self, rms_norm: bool = False, for_qk: bool = False, has_residual: bool = False
+    ) -> LayerNormBuilder:
         """Which module for layernorm"""
         ...
 
@@ -74,7 +85,7 @@ def core_attention(self) -> type:
 
     @abstractmethod
     def grouped_mlp_modules(
-        self, moe_use_grouped_gemm: bool, moe_use_legacy_grouped_gemm: bool
+        self, moe_use_grouped_gemm: bool
     ) -> tuple[type, MLPSubmodules | TEGroupedMLPSubmodules | None]:
         """Which module and submodules to use for grouped mlp"""
         ...
@@ -104,7 +115,9 @@ def column_parallel_layer_norm_linear(self) -> Optional[type]:
         """Which module for sequential layernorm and linear"""
         return None
 
-    def layer_norm(self, rms_norm: bool = False, for_qk: bool = False) -> LayerNormBuilder:
+    def layer_norm(
+        self, rms_norm: bool = False, for_qk: bool = False, has_residual: bool = False
+    ) -> LayerNormBuilder:
         """Which module to use for layer norm"""
         if rms_norm:
             # Matching get_gpt_layer_local_spec.
@@ -118,19 +131,12 @@ def core_attention(self) -> type:
         return DotProductAttention
 
     def grouped_mlp_modules(
-        self, moe_use_grouped_gemm: bool, moe_use_legacy_grouped_gemm: bool
-    ) -> tuple[type[GroupedMLP], None] | tuple[type[SequentialMLP], MLPSubmodules]:
+        self, moe_use_grouped_gemm: bool
+    ) -> tuple[type[SequentialMLP], MLPSubmodules]:
         """Which module and submodules to use for grouped mlp"""
-        if moe_use_grouped_gemm:
-            warnings.warn(
-                "The legacy GroupedMLP will be deprecated in Megatron-Core v0.12.0. "
-                "Please update the TransformerEngine to version>=1.7.0 and use TEGroupedMLP."
-            )
-            return GroupedMLP, None
-        else:
-            return SequentialMLP, MLPSubmodules(
-                linear_fc1=ColumnParallelLinear, linear_fc2=RowParallelLinear
-            )
+        return SequentialMLP, MLPSubmodules(
+            linear_fc1=ColumnParallelLinear, linear_fc2=RowParallelLinear
+        )
 
     def activation_func(self) -> TEActivationFunctionBuilder | None:
         """Which module to use for activation function"""
@@ -160,7 +166,9 @@ def column_parallel_layer_norm_linear(self) -> type[InferenceLayerNormColumnPara
         """Which module for sequential layernorm and linear"""
         return InferenceLayerNormColumnParallelLinear
 
-    def layer_norm(self, rms_norm: bool = False, for_qk: bool = False) -> LayerNormBuilder:
+    def layer_norm(
+        self, rms_norm: bool = False, for_qk: bool = False, has_residual: bool = False
+    ) -> LayerNormBuilder:
         """Which module to use for layer norm"""
         if for_qk and not is_te_min_version("1.9.0"):
             # TENorm significantly harms convergence when used
@@ -180,8 +188,9 @@ def activation_func(self) -> TEActivationFunctionBuilder | None:
         return cast(TEActivationFunctionBuilder, TEActivationOp)
 
     def grouped_mlp_modules(
-        self, moe_use_grouped_gemm: bool, moe_use_legacy_grouped_gemm: bool
-    ) -> tuple[type, MLPSubmodules | TEGroupedMLPSubmodules | None]:
-        raise NotImplementedError(
-            "MOE is not supported with inference optimized transformer implementation."
+        self, moe_use_grouped_gemm: bool
+    ) -> Tuple[type, Optional[MLPSubmodules]]:
+        """Which module and submodules to use for grouped mlp"""
+        return InferenceGroupedMLP, MLPSubmodules(
+            linear_fc1=TEColumnParallelGroupedLinear, linear_fc2=TERowParallelGroupedLinear
         )
diff --git a/megatron/core/models/bert/bert_model.py b/megatron/core/models/bert/bert_model.py
index 70760e7f87f..3fd1e01f4a1 100644
--- a/megatron/core/models/bert/bert_model.py
+++ b/megatron/core/models/bert/bert_model.py
@@ -135,7 +135,11 @@ def __init__(
                 config.hidden_size,
                 self.vocab_size,
                 config=config,
-                init_method=config.init_method,
+                init_method=(
+                    config.embedding_init_method
+                    if config.use_mup and not self.share_embeddings_and_output_weights
+                    else config.init_method
+                ),
                 bias=True,
                 skip_bias_add=False,
                 gather_output=not self.parallel_output,
diff --git a/megatron/core/models/common/embeddings/language_model_embedding.py b/megatron/core/models/common/embeddings/language_model_embedding.py
index b410e0ad170..7e49ec6c02d 100644
--- a/megatron/core/models/common/embeddings/language_model_embedding.py
+++ b/megatron/core/models/common/embeddings/language_model_embedding.py
@@ -127,6 +127,10 @@ def forward(self, input_ids: Tensor, position_ids: Tensor, tokentype_ids: int =
         else:
             assert self.tokentype_embeddings is None
 
+        # MuP: scale embeddings by alpha_input.
+        if self.config.use_mup and self.config.mup_embedding_mult != 1.0:
+            embeddings = embeddings * self.config.mup_embedding_mult
+
         # If the input flag for fp32 residual connection is set, convert for float.
         if self.config.fp32_residual_connection:
             embeddings = embeddings.float()
diff --git a/megatron/core/models/common/embeddings/rotary_pos_embedding.py b/megatron/core/models/common/embeddings/rotary_pos_embedding.py
index 05a7e8f60bb..0e560f939f2 100644
--- a/megatron/core/models/common/embeddings/rotary_pos_embedding.py
+++ b/megatron/core/models/common/embeddings/rotary_pos_embedding.py
@@ -240,7 +240,15 @@ def get_rotary_seq_len(
             # by the tp and cp size.
             return max(packed_seq_params.max_seqlen_q, packed_seq_params.max_seqlen_kv)
         elif inference_context is not None:
-            rotary_seq_len = inference_context.max_sequence_length
+            # For dynamic batching, use the max of context's max_sequence_length and the actual
+            # input size to ensure rotary embeddings cover CUDA graph warmup token counts
+            context_max_seq_len = inference_context.max_sequence_length
+            input_seq_len = 0
+            if transformer_input is not None:
+                input_seq_len = transformer_input.size(0)
+            elif transformer is not None and transformer.input_tensor is not None:
+                input_seq_len = transformer.input_tensor.size(0)
+            rotary_seq_len = max(context_max_seq_len, input_seq_len)
         else:
             if transformer is not None and transformer.input_tensor is not None:
                 rotary_seq_len = transformer.input_tensor.size(0)
diff --git a/megatron/core/models/common/language_module/language_module.py b/megatron/core/models/common/language_module/language_module.py
index a290a61aa4c..6d6d4737c3d 100644
--- a/megatron/core/models/common/language_module/language_module.py
+++ b/megatron/core/models/common/language_module/language_module.py
@@ -177,14 +177,40 @@ def setup_embeddings_and_output_layer(self) -> None:
         This function initalizes word embeddings in the final stage when we are
         using pipeline parallelism and sharing word embeddings, and sets up param
         attributes on the embedding and output layers.
+
+        Parameter attributes set:
+        - `is_embedding_or_output_parameter`: True for embedding + output layer weights.
+          Used by decoupled_lr, Muon optimizer, and other Megatron features.
+        - `is_embedding_parameter`: True for MuP "embedding-class" parameters.
+          Used by MuP for table-8 style optimizer grouping (base LR/eps for vector-like params).
         """
 
-        # Set `is_embedding_or_output_parameter` attribute.
-        if self.pre_process:
+        # Mark embedding and output layer for decoupled_lr and other features.
+        # This is the original Megatron attribute used by decoupled_lr, Muon, FSDP, etc.
+        if self.pre_process and hasattr(self, 'embedding'):
             self.embedding.word_embeddings.weight.is_embedding_or_output_parameter = True
-        if self.post_process and self.output_layer.weight is not None:
+        if (
+            self.post_process
+            and hasattr(self, 'output_layer')
+            and self.output_layer.weight is not None
+        ):
             self.output_layer.weight.is_embedding_or_output_parameter = True
 
+        # Mark embedding-class parameters for MuP optimizer grouping.
+        # Under MuP table-8-style grouping, embeddings/output use base LR/eps while
+        # hidden matrix-like params use width-scaled LR/eps.
+        mtp_process = getattr(self, 'mtp_process', False)
+        if self.config.use_mup and (self.pre_process or mtp_process) and hasattr(self, 'embedding'):
+            for param in self.embedding.parameters():
+                param.is_embedding_parameter = True
+        if (
+            self.config.use_mup
+            and self.post_process
+            and hasattr(self, 'output_layer')
+            and self.output_layer.weight is not None
+        ):
+            self.output_layer.weight.is_embedding_parameter = True
+
         # If share_embeddings_and_output_weights is True, we need to maintain duplicated
         # embedding weights in post processing stage. If use Multi-Token Prediction (MTP),
         # we also need to maintain duplicated embedding weights in mtp process stage.
@@ -223,6 +249,9 @@ def setup_embeddings_and_output_layer(self) -> None:
             weight.data.fill_(0)
             weight.shared = True
             weight.shared_embedding = True
+            # Keep optimizer grouping consistent for tied embedding/output copies.
+            if self.config.use_mup:
+                weight.is_embedding_parameter = True
 
         # Parameters are shared between the word embeddings layers, and the
         # heads at the end of the model. In a pipelined setup with more than
@@ -255,6 +284,25 @@ def setup_embeddings_and_output_layer(self) -> None:
             )
             LanguageModule.embedding_warning_printed = True
 
+    def _scale_logits(self, logits: Tensor) -> Tensor:
+        """Apply MuP output scaling to logits.
+
+        When MuP is enabled, scales logits by mup_output_mult (auto-set to 1/width_mult
+        if left at default) to keep output variance stable across widths.
+
+        Args:
+            logits (Tensor): Raw logits from the output layer.
+
+        Returns:
+            Tensor: Scaled logits if MuP is enabled and mup_output_mult != 1.0,
+                    otherwise unchanged logits.
+        """
+        if not self.config.use_mup:
+            return logits
+        if self.config.mup_output_mult != 1.0:
+            return logits * self.config.mup_output_mult
+        return logits
+
     def shared_embedding_or_output_weight(self) -> Tensor:
         """Gets the embedding weight or output logit weights when share embedding and output weights set to True
           or when use Multi-Token Prediction (MTP).
diff --git a/megatron/core/models/gpt/experimental_attention_variant_module_specs.py b/megatron/core/models/gpt/experimental_attention_variant_module_specs.py
index 708722fa7b0..fa4e15db856 100644
--- a/megatron/core/models/gpt/experimental_attention_variant_module_specs.py
+++ b/megatron/core/models/gpt/experimental_attention_variant_module_specs.py
@@ -434,13 +434,13 @@ def _get_self_attention_module_spec(
         moe_grouped_gemm=config.moe_grouped_gemm,
         qk_layernorm=config.qk_layernorm,
         multi_latent_attention=config.multi_latent_attention,
-        moe_use_legacy_grouped_gemm=config.moe_use_legacy_grouped_gemm,
         qk_l2_norm=config.qk_l2_norm,
         use_kitchen=config.use_kitchen,
         use_te_activation_func=config.use_te_activation_func,
         fallback_to_eager_attn=config.fallback_to_eager_attn,
         use_kitchen_attention=config.use_kitchen_attention,
         kitchen_attention_backend=config.kitchen_attention_backend,
+        mla_down_proj_fusion=getattr(config, "mla_down_proj_fusion", False),
     )
     attn_spec = layer_spec.submodules.self_attention
     if config.multi_latent_attention:
@@ -487,7 +487,6 @@ def _get_moe_module_spec(
         backend=backend,
         num_experts=config.num_moe_experts,
         moe_grouped_gemm=config.moe_grouped_gemm,
-        moe_use_legacy_grouped_gemm=config.moe_use_legacy_grouped_gemm,
         use_te_activation_func=config.use_te_activation_func,
     )
     moe_spec.metainfo["fuse_pre_mlp_layernorm"] = False
diff --git a/megatron/core/models/gpt/gpt_layer_specs.py b/megatron/core/models/gpt/gpt_layer_specs.py
index 1238f0a7601..044ed0a00cd 100755
--- a/megatron/core/models/gpt/gpt_layer_specs.py
+++ b/megatron/core/models/gpt/gpt_layer_specs.py
@@ -16,6 +16,7 @@
 from megatron.core.transformer.identity_op import IdentityOp
 from megatron.core.transformer.mlp import MLP, MLPSubmodules
 from megatron.core.transformer.multi_latent_attention import (
+    FusedMLASelfAttention,
     MLASelfAttention,
     MLASelfAttentionSubmodules,
 )
@@ -79,6 +80,9 @@ def get_gpt_layer_with_inference_submodules(
     qk_layernorm: Optional[bool] = False,
     multi_latent_attention: Optional[bool] = False,
     qk_l2_norm: Optional[bool] = False,
+    num_experts: Optional[int] = None,
+    moe_grouped_gemm: Optional[bool] = False,
+    moe_use_legacy_grouped_gemm: Optional[bool] = False,
 ) -> TransformerLayerSubmodules:
     """Use these submodules for inference optimized linear layers.
     Args:
@@ -91,9 +95,8 @@ def get_gpt_layer_with_inference_submodules(
 
     mlp = get_mlp_module_spec_for_backend(
         backend=backend,
-        num_experts=None,
-        moe_grouped_gemm=False,
-        moe_use_legacy_grouped_gemm=False,
+        num_experts=num_experts,
+        moe_grouped_gemm=moe_grouped_gemm,
         use_te_op_fuser=False,
         use_te_activation_func=False,
     )
@@ -111,7 +114,7 @@ def get_gpt_layer_with_inference_submodules(
             else backend.column_parallel_linear()
         )
         return TransformerLayerSubmodules(
-            input_layernorm=backend.layer_norm(),
+            input_layernorm=backend.layer_norm(has_residual=True),
             self_attention=ModuleSpec(
                 module=MLASelfAttention,
                 params={"attn_mask_type": AttnMaskType.causal},
@@ -151,7 +154,7 @@ def get_gpt_layer_with_inference_submodules(
                 ),
             ),
             self_attn_bda=get_bias_dropout_add,
-            pre_mlp_layernorm=IdentityOp,
+            pre_mlp_layernorm=backend.layer_norm() if num_experts else IdentityOp,
             mlp=mlp,
             mlp_bda=get_bias_dropout_add,
             sharded_state_dict_keys_map={
@@ -179,7 +182,6 @@ def get_gpt_layer_with_transformer_engine_submodules(
     qk_layernorm: Optional[bool] = False,
     multi_latent_attention: Optional[bool] = False,
     fp8: Optional[str] = None,  # pylint: disable=unused-argument
-    moe_use_legacy_grouped_gemm: Optional[bool] = False,
     qk_l2_norm: Optional[bool] = False,
     use_te_op_fuser: Optional[bool] = False,
     use_kitchen: bool = False,
@@ -188,6 +190,7 @@ def get_gpt_layer_with_transformer_engine_submodules(
     use_kitchen_attention: bool = False,
     kitchen_attention_backend: str = "sdpa",
     enable_hyper_connection: bool = False,
+    mla_down_proj_fusion: bool = False,
 ) -> TransformerLayerSubmodules:
     """Use these submodules to use lower-level Transformer Engine modules (required for fp8
     training).
@@ -199,13 +202,14 @@ def get_gpt_layer_with_transformer_engine_submodules(
         qk_layernorm (bool, optional): To use layernorm for queries/keys. Defaults to False.
         multi_latent_attention (bool, optional): To use MLA. Defaults to False.
         fp8 (str, optional): Deprecated. For temporary Nemo compatibility.
-        moe_use_legacy_grouped_gemm (bool, optional): Force use the legacy GroupedMLP.
-                                                      Defaults to False.
         qk_l2_norm (bool, optional): To use l2 norm for queries/keys. Defaults to False.
         use_te_op_fuser (bool, optional): Use Transformer Engine's operation-based API, which may
                                           enable certain operation fusions. Defaults to False.
         enable_hyper_connection (bool): Use HyperConnectionTransformerLayer with
             HyperConnectionModule instead of plain TransformerLayer. Defaults to False.
+        mla_down_proj_fusion (bool, optional): Enable fused q/kv down-projection and fused input
+                                               layernorm when backend supports. Otherwise fall back
+                                               to the unfused MLA.
 
     Returns:
         TransformerLayerSubmodules: TE modules to construct a TransformerLayer
@@ -235,7 +239,6 @@ def get_gpt_layer_with_transformer_engine_submodules(
         backend=backend,
         num_experts=num_experts,
         moe_grouped_gemm=moe_grouped_gemm,
-        moe_use_legacy_grouped_gemm=moe_use_legacy_grouped_gemm,
         use_te_op_fuser=use_te_op_fuser,
         use_te_activation_func=use_te_activation_func,
     )
@@ -254,8 +257,47 @@ def get_gpt_layer_with_transformer_engine_submodules(
             if qk_layernorm
             else backend.column_parallel_linear()
         )
+
+        if mla_down_proj_fusion:
+            fuse_input_layernorm = backend.column_parallel_layer_norm_linear() is not None
+            input_layernorm = IdentityOp if fuse_input_layernorm else backend.layer_norm()
+            down_proj_linear = (
+                backend.column_parallel_layer_norm_linear()
+                if fuse_input_layernorm
+                else backend.linear()
+            )
+            return TransformerLayerSubmodules(
+                input_layernorm=input_layernorm,
+                self_attention=ModuleSpec(
+                    module=FusedMLASelfAttention,
+                    params={"attn_mask_type": AttnMaskType.causal},
+                    submodules=MLASelfAttentionSubmodules(
+                        linear_q_proj=backend.column_parallel_linear(),
+                        linear_qkv_down_proj=down_proj_linear,
+                        linear_q_up_proj=linear_q_up_proj,
+                        linear_kv_up_proj=linear_kv_up_proj,
+                        core_attention=backend.core_attention(),
+                        linear_proj=backend.row_parallel_linear(),
+                        q_layernorm=IdentityOp,
+                        kv_layernorm=IdentityOp,
+                    ),
+                ),
+                self_attn_bda=get_bias_dropout_add,
+                pre_mlp_layernorm=backend.layer_norm() if num_experts else IdentityOp,
+                mlp=mlp,
+                mlp_bda=get_bias_dropout_add,
+                sharded_state_dict_keys_map=(
+                    {
+                        "self_attention.linear_q_down_proj.layer_norm_": "input_layernorm.",
+                        "self_attention.linear_kv_down_proj.layer_norm_": "input_layernorm.",
+                        "self_attention.linear_qkv_down_proj.layer_norm_": "input_layernorm.",
+                    }
+                    if fuse_input_layernorm
+                    else {}
+                ),
+            )
         return TransformerLayerSubmodules(
-            input_layernorm=backend.layer_norm(),
+            input_layernorm=backend.layer_norm(has_residual=True),
             self_attention=ModuleSpec(
                 module=MLASelfAttention,
                 params={"attn_mask_type": AttnMaskType.causal},
@@ -273,7 +315,7 @@ def get_gpt_layer_with_transformer_engine_submodules(
             ),
             self_attn_bda=get_bias_dropout_add,
             self_attention_hyper_connection=hc_module,
-            pre_mlp_layernorm=backend.layer_norm() if num_experts else IdentityOp,
+            pre_mlp_layernorm=backend.layer_norm(has_residual=True) if num_experts else IdentityOp,
             mlp=mlp,
             mlp_bda=get_bias_dropout_add,
             mlp_hyper_connection=hc_module,
@@ -298,7 +340,7 @@ def get_gpt_layer_with_transformer_engine_submodules(
             ),
             self_attn_bda=get_bias_dropout_add,
             self_attention_hyper_connection=hc_module,
-            pre_mlp_layernorm=backend.layer_norm() if num_experts else IdentityOp,
+            pre_mlp_layernorm=backend.layer_norm(has_residual=True) if num_experts else IdentityOp,
             mlp=mlp,
             mlp_bda=get_bias_dropout_add,
             mlp_hyper_connection=hc_module,
@@ -330,7 +372,6 @@ def get_gpt_layer_local_submodules(
     qk_layernorm: Optional[bool] = False,
     multi_latent_attention: Optional[bool] = False,
     fp8: Optional[str] = None,  # pylint: disable=unused-argument
-    moe_use_legacy_grouped_gemm: Optional[bool] = False,
     normalization: Optional[str] = None,
     qk_l2_norm: Optional[bool] = False,
     use_kitchen: bool = False,
@@ -347,8 +388,6 @@ def get_gpt_layer_local_submodules(
         qk_layernorm (bool, optional): To use layernorm for queries/keys. Defaults to False.
         multi_latent_attention (bool, optional): To use MLA. Defaults to False.
         fp8 (str, optional): Deprecated. For temporary Nemo compatibility.
-        moe_use_legacy_grouped_gemm (bool, optional): Force use the legacy GroupedMLP.
-                                                      Defaults to False.
         qk_l2_norm (bool, optional): To use l2 norm for queries/keys. Defaults to False.
         enable_hyper_connection (bool): Use HyperConnectionTransformerLayer with
             HyperConnectionModule instead of plain TransformerLayer. Defaults to False.
@@ -368,10 +407,10 @@ def get_gpt_layer_local_submodules(
         backend = LocalSpecProvider()
     # Adjust for RMS norm.
     if normalization == "RMSNorm":
-        layer_norm = backend.layer_norm(rms_norm=True, for_qk=False)
+        layer_norm = backend.layer_norm(rms_norm=True, for_qk=False, has_residual=True)
         qk_norm = backend.layer_norm(rms_norm=True, for_qk=True)
     else:
-        layer_norm = backend.layer_norm(rms_norm=False, for_qk=False)
+        layer_norm = backend.layer_norm(rms_norm=False, for_qk=False, has_residual=True)
         qk_norm = backend.layer_norm(rms_norm=False, for_qk=True)
 
     if fp8 is not None:
@@ -381,10 +420,7 @@ def get_gpt_layer_local_submodules(
         )
 
     mlp = get_mlp_module_spec_for_backend(
-        backend=backend,
-        num_experts=num_experts,
-        moe_grouped_gemm=moe_grouped_gemm,
-        moe_use_legacy_grouped_gemm=moe_use_legacy_grouped_gemm,
+        backend=backend, num_experts=num_experts, moe_grouped_gemm=moe_grouped_gemm
     )
 
     hc_module = HyperConnectionModule if enable_hyper_connection else IdentityOp
@@ -461,7 +497,6 @@ def _get_mlp_module_spec(
     num_experts: Optional[int] = None,
     moe_grouped_gemm: Optional[bool] = False,
     fp8: Optional[str] = None,  # pylint: disable=unused-argument
-    moe_use_legacy_grouped_gemm: Optional[bool] = False,
 ):
     warnings.warn(
         """This private function is on a deprecation track. Please switch to `get_mlp_module_spec`
@@ -469,11 +504,7 @@ def _get_mlp_module_spec(
     )
 
     return get_mlp_module_spec(
-        use_te=use_te,
-        num_experts=num_experts,
-        moe_grouped_gemm=moe_grouped_gemm,
-        fp8=fp8,
-        moe_use_legacy_grouped_gemm=moe_use_legacy_grouped_gemm,
+        use_te=use_te, num_experts=num_experts, moe_grouped_gemm=moe_grouped_gemm, fp8=fp8
     )
 
 
@@ -482,7 +513,6 @@ def get_mlp_module_spec(
     num_experts: Optional[int] = None,
     moe_grouped_gemm: Optional[bool] = False,
     fp8: Optional[str] = None,  # pylint: disable=unused-argument
-    moe_use_legacy_grouped_gemm: Optional[bool] = False,
     use_te_op_fuser: Optional[bool] = False,
 ) -> ModuleSpec:
     """Helper function to get module spec for MLP/MoE"""
@@ -505,7 +535,6 @@ def get_mlp_module_spec(
         backend=TESpecProvider() if use_te else LocalSpecProvider(),
         num_experts=num_experts,
         moe_grouped_gemm=moe_grouped_gemm,
-        moe_use_legacy_grouped_gemm=moe_use_legacy_grouped_gemm,
         use_te_op_fuser=use_te_op_fuser,
     )
 
@@ -514,7 +543,6 @@ def get_mlp_module_spec_for_backend(
     backend: BackendSpecProvider,
     num_experts: Optional[int] = None,
     moe_grouped_gemm: Optional[bool] = False,
-    moe_use_legacy_grouped_gemm: Optional[bool] = False,
     use_te_op_fuser: Optional[bool] = False,
     use_te_activation_func: bool = False,
 ) -> ModuleSpec:
@@ -543,7 +571,6 @@ def get_mlp_module_spec_for_backend(
             backend=backend,
             num_experts=num_experts,
             moe_grouped_gemm=moe_grouped_gemm,
-            moe_use_legacy_grouped_gemm=moe_use_legacy_grouped_gemm,
             use_te_activation_func=use_te_activation_func,
         )
 
@@ -566,22 +593,41 @@ def get_gpt_decoder_layer_specs(
             moe_grouped_gemm=False,
             qk_layernorm=config.qk_layernorm,
             multi_latent_attention=config.multi_latent_attention,
-            moe_use_legacy_grouped_gemm=config.moe_use_legacy_grouped_gemm,
             qk_l2_norm=qk_l2_norm,
             use_kitchen=config.use_kitchen,
             use_te_activation_func=config.use_te_activation_func,
             enable_hyper_connection=config.enable_hyper_connections,
+            use_kitchen_attention=config.use_kitchen_attention,
+            kitchen_attention_backend=config.kitchen_attention_backend,
+            mla_down_proj_fusion=getattr(config, "mla_down_proj_fusion", False),
         )
         moe_layer_spec = get_gpt_layer_with_transformer_engine_spec(
             num_experts=config.num_moe_experts,
             moe_grouped_gemm=config.moe_grouped_gemm,
             qk_layernorm=config.qk_layernorm,
             multi_latent_attention=config.multi_latent_attention,
-            moe_use_legacy_grouped_gemm=config.moe_use_legacy_grouped_gemm,
             qk_l2_norm=qk_l2_norm,
             use_kitchen=config.use_kitchen,
             use_te_activation_func=config.use_te_activation_func,
             enable_hyper_connection=config.enable_hyper_connections,
+            use_kitchen_attention=config.use_kitchen_attention,
+            kitchen_attention_backend=config.kitchen_attention_backend,
+            mla_down_proj_fusion=getattr(config, "mla_down_proj_fusion", False),
+        )
+    elif config.transformer_impl == "inference_optimized":
+        layer_norm_impl = TENorm
+        dense_layer_spec = get_gpt_layer_with_inference_spec(
+            qk_layernorm=config.qk_layernorm,
+            multi_latent_attention=config.multi_latent_attention,
+            qk_l2_norm=qk_l2_norm,
+        )
+        moe_layer_spec = get_gpt_layer_with_inference_spec(
+            qk_layernorm=config.qk_layernorm,
+            multi_latent_attention=config.multi_latent_attention,
+            qk_l2_norm=qk_l2_norm,
+            num_experts=config.num_moe_experts,
+            moe_grouped_gemm=config.moe_grouped_gemm,
+            moe_use_legacy_grouped_gemm=config.moe_use_legacy_grouped_gemm,
         )
     else:
         dense_layer_spec = get_gpt_layer_local_spec(
@@ -589,7 +635,6 @@ def get_gpt_decoder_layer_specs(
             moe_grouped_gemm=False,
             qk_layernorm=config.qk_layernorm,
             multi_latent_attention=config.multi_latent_attention,
-            moe_use_legacy_grouped_gemm=config.moe_use_legacy_grouped_gemm,
             normalization=normalization,
             qk_l2_norm=qk_l2_norm,
             use_kitchen=config.use_kitchen,
@@ -600,7 +645,6 @@ def get_gpt_decoder_layer_specs(
             moe_grouped_gemm=config.moe_grouped_gemm,
             qk_layernorm=config.qk_layernorm,
             multi_latent_attention=config.multi_latent_attention,
-            moe_use_legacy_grouped_gemm=config.moe_use_legacy_grouped_gemm,
             normalization=normalization,
             qk_l2_norm=qk_l2_norm,
             use_kitchen=config.use_kitchen,
@@ -675,6 +719,8 @@ def get_gpt_decoder_block_spec(
     # Block spec.
     if use_transformer_engine:
         layer_norm_impl = TENorm
+    elif config.transformer_impl == "inference_optimized":
+        layer_norm_impl = TENorm
     else:
         layer_norm_impl = LNImpl
     block_spec = TransformerBlockSubmodules(
diff --git a/megatron/core/models/gpt/gpt_model.py b/megatron/core/models/gpt/gpt_model.py
index 27b62f91c34..efdd2572aa6 100644
--- a/megatron/core/models/gpt/gpt_model.py
+++ b/megatron/core/models/gpt/gpt_model.py
@@ -28,6 +28,7 @@
 from megatron.core.transformer.linear_cross_entropy import LinearCrossEntropyModule
 from megatron.core.transformer.multi_token_prediction import (
     MultiTokenPredictionBlock,
+    compute_mtp_inference_logits,
     mtp_on_this_rank,
     process_mtp_loss,
 )
@@ -248,7 +249,11 @@ def __init__(
                 config.hidden_size,
                 self.vocab_size,
                 config=config,
-                init_method=config.init_method,
+                init_method=(
+                    config.embedding_init_method
+                    if config.use_mup and not self.share_embeddings_and_output_weights
+                    else config.init_method
+                ),
                 bias=False,
                 skip_bias_add=False,
                 gather_output=not self.parallel_output,
@@ -488,6 +493,7 @@ def forward(
         inference_params: Optional[BaseInferenceContext] = None,
         loss_mask: Optional[Tensor] = None,
         padding_mask: Optional[Tensor] = None,
+        is_spec_decode: Optional[bool] = None,
     ) -> Tensor:
         """Forward function of the GPT Model This function passes the input tensors
         through the embedding layer, and then the decoder and finally into the post
@@ -501,6 +507,9 @@ def forward(
             padding_mask (Tensor, optional): Padding mask for MoE routing.
                 Shape [bsz, seq_length]. True = padding (exclude), False = valid (include).
                 Only used for MoE layers to exclude padding tokens from routing computations.
+            is_spec_decode (bool, optional): Explicitly override whether speculative
+                decoding is active.  When ``None`` (default) the flag is inferred from
+                ``inference_context.num_speculative_tokens``.
         """
         if self.config.fine_grained_activation_offloading:
             self.preprocess_for_fine_grained_offloading()
@@ -560,6 +569,7 @@ def forward(
             runtime_gather_output=runtime_gather_output,
             extra_block_kwargs=extra_block_kwargs,
             inference_context=inference_context,
+            is_spec_decode=is_spec_decode,
         )
 
     def _postprocess(
@@ -581,6 +591,7 @@ def _postprocess(
         runtime_gather_output=None,
         extra_block_kwargs=None,
         inference_context=None,
+        is_spec_decode=None,
     ):
         """Postprocesses decoder hidden states to generate logits or compute loss.
 
@@ -591,17 +602,27 @@ def _postprocess(
         if in_inference_mode:
             assert runtime_gather_output, "Inference must always gather TP logits"
 
+        # Check if speculative decoding is active. When it is, MTP must be
+        # computed *after* verification so that it is conditioned on verified
+        # tokens rather than stale speculative tokens from the previous step.
+        if is_spec_decode is None:
+            is_spec_decode = (
+                in_inference_mode
+                and inference_context.is_dynamic_batching()
+                and inference_context.num_speculative_tokens > 0
+            )
+
         # logits and loss
         output_weight = None
         if self.share_embeddings_and_output_weights:
             output_weight = self.shared_embedding_or_output_weight()
-        if mtp_in_postprocess:
+        if mtp_in_postprocess and not is_spec_decode:
             hidden_states = self.mtp(
                 input_ids=input_ids,
                 position_ids=position_ids,
                 hidden_states=hidden_states,
                 attention_mask=attention_mask,
-                inference_params=inference_params,
+                inference_params=None,  # MTP layers don't use KV cache
                 rotary_pos_emb=rotary_pos_emb,
                 rotary_pos_cos=rotary_pos_cos,
                 rotary_pos_sin=rotary_pos_sin,
@@ -615,19 +636,38 @@ def _postprocess(
             return hidden_states
 
         if self.config.mtp_num_layers:
-            hidden_states = process_mtp_loss(
-                hidden_states=hidden_states,
-                labels=labels,
-                loss_mask=loss_mask,
-                output_layer=self.output_layer,
-                output_weight=output_weight,
-                runtime_gather_output=runtime_gather_output,
-                is_training=self.training,
-                compute_language_model_loss=self.compute_language_model_loss,
-                config=self.config,
-                cp_group=self.pg_collection.cp,
-                packed_seq_params=packed_seq_params,
-            )
+            assert self.config.mtp_num_layers > 0
+            # The new process_mtp_loss function doesn't handle mtp_logits_cache,
+            # so we manually generate and cache MTP logits when in inference mode.
+            if in_inference_mode:
+                if is_spec_decode:
+                    # Cache decoder hidden states for serial MTP computation
+                    # after speculative token verification.
+                    self._decoder_hidden_states_cache = hidden_states
+                else:
+                    hidden_states, self._mtp_logits_cache = compute_mtp_inference_logits(
+                        hidden_states=hidden_states,
+                        mtp_num_layers=self.config.mtp_num_layers,
+                        output_layer=self.output_layer,
+                        output_weight=output_weight,
+                        runtime_gather_output=runtime_gather_output,
+                    )
+            else:
+                # In training/eval, use the utility function for processing MTP loss/scaling.
+                hidden_states = process_mtp_loss(
+                    hidden_states=hidden_states,
+                    labels=labels,
+                    loss_mask=loss_mask,
+                    output_layer=self.output_layer,
+                    output_weight=output_weight,
+                    runtime_gather_output=runtime_gather_output,
+                    is_training=self.training,
+                    compute_language_model_loss=self.compute_language_model_loss,
+                    config=self.config,
+                    cp_group=self.pg_collection.cp,
+                    packed_seq_params=packed_seq_params,
+                    scale_logits_fn=self._scale_logits if self.config.use_mup else None,
+                )
         sequence_parallel_override = False
 
         if in_inference_mode and inference_context.config.materialize_only_last_token_logits:
@@ -644,12 +684,10 @@ def _postprocess(
                     self.output_layer.sequence_parallel = False
                     sequence_parallel_override = True
 
-                # Reshape [B, 1, H] to [1, B, H] → extract each sample’s true last‐token hidden
-                # state ([B, H]) → unsqueeze back to [B, 1, H]
-                # (so that the output layer, which expects S×B×H, receives only the final token)
-                hidden_states = inference_context.last_token_logits(
-                    hidden_states.squeeze(1).unsqueeze(0)
-                ).unsqueeze(1)
+                # Reshape [S, B, H] (with B=1) to [1, S, H] for logit extraction,
+                # then back to [S’, B, H] for the output layer.
+                reshaped = hidden_states.squeeze(1).unsqueeze(0)
+                hidden_states = inference_context.last_token_logits(reshaped).unsqueeze(1)
 
         if has_config_logger_enabled(self.config) or labels is None:
             logits, _ = self.output_layer(
@@ -658,6 +696,9 @@ def _postprocess(
         else:
             logits = None
 
+        # Apply MuP output scaling to logits
+        logits = self._scale_logits(logits)
+
         # Restore sequence parallel execution to the output layer if necessary.
         if sequence_parallel_override:
             assert (
@@ -698,6 +739,49 @@ def _postprocess(
 
         return loss
 
+    @torch.inference_mode()
+    def compute_mtp_single_step(
+        self,
+        hidden_states: Tensor,
+        next_token_ids: Tensor,
+        position_ids: Tensor,
+        depth: int,
+        runtime_gather_output: bool = True,
+    ) -> tuple:
+        """Compute a single MTP depth for speculative decoding.
+
+        This is called after speculative token verification to compute MTP
+        predictions conditioned on verified tokens only.
+
+        Args:
+            hidden_states (Tensor): Hidden states at last accepted positions [N, 1, H].
+            next_token_ids (Tensor): Correct next token IDs [1, N].
+            position_ids (Tensor): Position IDs for the next tokens [1, N].
+            depth (int): MTP depth index (0-indexed).
+            runtime_gather_output (bool): Whether to gather output across TP.
+
+        Returns:
+            tuple: (new_hidden_states [N, 1, H], logits [N, 1, vocab_size]).
+        """
+        layer_idx = 0 if self.mtp.mtp_use_repeated_layer else depth
+        mtp_hidden = self.mtp.layers[layer_idx].forward_single_position(
+            hidden_states=hidden_states,
+            next_token_ids=next_token_ids,
+            position_ids=position_ids,
+            embedding=self.embedding,
+        )
+
+        output_weight = None
+        if self.share_embeddings_and_output_weights:
+            output_weight = self.shared_embedding_or_output_weight()
+
+        logits, _ = self.output_layer(
+            mtp_hidden, weight=output_weight, runtime_gather_output=runtime_gather_output
+        )
+        logits = self._scale_logits(logits)
+
+        return mtp_hidden, logits
+
     def build_schedule_plan(
         self,
         input_ids: Tensor,
diff --git a/megatron/core/models/gpt/moe_module_specs.py b/megatron/core/models/gpt/moe_module_specs.py
index 62ee4537cfc..f67f08331e6 100755
--- a/megatron/core/models/gpt/moe_module_specs.py
+++ b/megatron/core/models/gpt/moe_module_specs.py
@@ -3,9 +3,14 @@
 from typing import Optional
 
 from megatron.core.extensions.transformer_engine_spec_provider import TESpecProvider
-from megatron.core.models.backends import BackendSpecProvider, LocalSpecProvider
+from megatron.core.models.backends import (
+    BackendSpecProvider,
+    InferenceSpecProvider,
+    LocalSpecProvider,
+)
 from megatron.core.transformer.mlp import MLPSubmodules
 from megatron.core.transformer.moe.moe_layer import MoELayer, MoESubmodules
+from megatron.core.transformer.moe.router import InferenceTopKRouter
 from megatron.core.transformer.moe.shared_experts import SharedExpertMLP
 from megatron.core.transformer.spec_utils import ModuleSpec
 
@@ -14,18 +19,24 @@ def get_moe_module_spec(
     use_te: Optional[bool] = True,
     num_experts: Optional[int] = None,
     moe_grouped_gemm: Optional[bool] = False,
-    moe_use_legacy_grouped_gemm: Optional[bool] = False,
 ) -> ModuleSpec:
-    """Helper function to get module spec for MoE"""
+    """Helper function to get module spec for MoE.
+
+    Called by mamba_layer_specs.py for standard (non-inference) MoE specs.
+    The GPT layer specs call get_moe_module_spec_for_backend directly.
+
+    Args:
+        use_te: Whether to use Transformer Engine.
+        num_experts: Number of experts.
+        moe_grouped_gemm: Whether to use grouped GEMM.
+        moe_use_legacy_grouped_gemm: Whether to use legacy grouped GEMM.
+    """
     if use_te is not None and use_te:
         backend: BackendSpecProvider = TESpecProvider()
     else:
         backend = LocalSpecProvider()
     return get_moe_module_spec_for_backend(
-        backend=backend,
-        num_experts=num_experts,
-        moe_grouped_gemm=moe_grouped_gemm,
-        moe_use_legacy_grouped_gemm=moe_use_legacy_grouped_gemm,
+        backend=backend, num_experts=num_experts, moe_grouped_gemm=moe_grouped_gemm
     )
 
 
@@ -33,7 +44,6 @@ def get_moe_module_spec_for_backend(
     backend: BackendSpecProvider,
     num_experts: Optional[int] = None,
     moe_grouped_gemm: Optional[bool] = False,
-    moe_use_legacy_grouped_gemm: Optional[bool] = False,
     use_te_activation_func: bool = False,
 ) -> ModuleSpec:
     """Helper function to get module spec for MoE"""
@@ -48,8 +58,7 @@ def get_moe_module_spec_for_backend(
     )
 
     expert_module, expert_submodule = backend.grouped_mlp_modules(
-        moe_grouped_gemm is not None and moe_grouped_gemm,
-        moe_use_legacy_grouped_gemm is not None and moe_use_legacy_grouped_gemm,
+        moe_grouped_gemm is not None and moe_grouped_gemm
     )
     if expert_submodule is not None:
         expert_submodule.activation_func = activation_func
@@ -66,3 +75,38 @@ def get_moe_module_spec_for_backend(
         metainfo={"fuse_pre_mlp_layernorm": False},
     )
     return moe_module_spec
+
+
+def get_inference_optimized_moe_spec() -> ModuleSpec:
+    """MoE module spec for inference-optimized transformer impl.
+
+    Uses InferenceSpecProvider to select inference-optimized modules:
+    InferenceTopKRouter, InferenceGroupedMLP. MoELayer detects inference mode
+    via config.transformer_impl and sets up the inference dispatcher internally.
+
+    Called by mamba_layer_specs.py and gpt_layer_specs.py.
+    """
+    backend = InferenceSpecProvider()
+    activation_func = backend.activation_func()
+
+    expert_module, expert_submodule = backend.grouped_mlp_modules(True)
+    if expert_submodule is not None:
+        expert_submodule.activation_func = activation_func
+
+    experts = ModuleSpec(module=expert_module, submodules=expert_submodule)
+    shared_experts = ModuleSpec(
+        module=SharedExpertMLP,
+        submodules=MLPSubmodules(
+            linear_fc1=backend.column_parallel_linear(),
+            linear_fc2=backend.row_parallel_linear(),
+            activation_func=activation_func,
+        ),
+    )
+
+    return ModuleSpec(
+        module=MoELayer,
+        submodules=MoESubmodules(
+            router=InferenceTopKRouter, experts=experts, shared_experts=shared_experts
+        ),
+        metainfo={"fuse_pre_mlp_layernorm": False},
+    )
diff --git a/megatron/core/models/mamba/mamba_layer_specs.py b/megatron/core/models/mamba/mamba_layer_specs.py
index 6ca628475be..39f7fab0266 100755
--- a/megatron/core/models/mamba/mamba_layer_specs.py
+++ b/megatron/core/models/mamba/mamba_layer_specs.py
@@ -8,7 +8,10 @@
     TERowParallelLinear,
 )
 from megatron.core.fusions.fused_bias_dropout import get_bias_dropout_add
-from megatron.core.models.gpt.moe_module_specs import get_moe_module_spec
+from megatron.core.models.gpt.moe_module_specs import (
+    get_inference_optimized_moe_spec,
+    get_moe_module_spec,
+)
 from megatron.core.ssm.mamba_block import MambaStack, MambaStackSubmodules
 from megatron.core.ssm.mamba_layer import MambaLayer, MambaLayerSubmodules
 from megatron.core.ssm.mamba_mixer import MambaMixer, MambaMixerSubmodules
@@ -38,9 +41,11 @@
     use_te=True,
     num_experts=8,  # Can be any positive integer (must not be None).
     moe_grouped_gemm=True,
-    moe_use_legacy_grouped_gemm=False,
 )
 
+# Inference-optimized MoE spec
+moe_inference = get_inference_optimized_moe_spec()
+
 
 # MTP block spec for Mamba - provides norms and projection only.
 # Inner layers are built by MultiTokenPredictionLayer using nested MambaStack
@@ -173,10 +178,10 @@
             ),
         ),
         moe_layer=ModuleSpec(
-            # TODO (rwaleffe): change this to be an "MoELayer" to work with CudaGraphs?
+            # Use inference-optimized MoE layer for end-to-end CUDA graph support
             module=TransformerLayer,
             submodules=TransformerLayerSubmodules(
-                pre_mlp_layernorm=TENorm, mlp=moe, mlp_bda=get_bias_dropout_add
+                pre_mlp_layernorm=TENorm, mlp=moe_inference, mlp_bda=get_bias_dropout_add
             ),
         ),
         mtp_block_spec=_mamba_mtp_block_spec,
diff --git a/megatron/core/models/mamba/mamba_model.py b/megatron/core/models/mamba/mamba_model.py
index 0aada61d5d4..9f6b4c84a5c 100644
--- a/megatron/core/models/mamba/mamba_model.py
+++ b/megatron/core/models/mamba/mamba_model.py
@@ -1,7 +1,9 @@
 # Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
 
+import logging
 from typing import Literal, Optional
 
+import torch
 from torch import Tensor
 
 from megatron.core import tensor_parallel
@@ -18,6 +20,7 @@
 from megatron.core.transformer.enums import ModelType
 from megatron.core.transformer.multi_token_prediction import (
     MultiTokenPredictionBlock,
+    compute_mtp_inference_logits,
     mtp_on_this_rank,
     process_mtp_loss,
 )
@@ -26,8 +29,11 @@
     WrappedTensor,
     deprecate_inference_params,
     is_using_quantization_scales,
+    log_single_rank,
 )
 
+logger = logging.getLogger(__name__)
+
 
 class MambaModel(LanguageModule):
     """Mamba language model.
@@ -38,16 +44,25 @@ class MambaModel(LanguageModule):
         vocab_size (int): Vocabulary size
         max_sequence_length (int): maximum size of sequence.
             This is used for positional embedding
-        pre_process (bool, optional): Include embedding layer
-            (used with pipeline parallelism). Defaults to True.
-        hybrid_attention_ratio (float, optional): The target ratio of attention
-            layers to total layers
-        hybrid_mlp_ratio (float, optional): The target ratio of mlp layers to total layers
-        hybrid_override_pattern (str, optional): Unified hybrid layer pattern with optional MTP.
+        hybrid_layer_pattern (str): Unified hybrid layer pattern with optional MTP and
+            pipeline stage boundaries.
             Format: "<main_pattern>/<mtp_pattern>/<mtp_pattern>/..."
+            The main pattern may contain "|" to define pipeline stage boundaries.
             Examples:
                 - "M*M*" -> main decoder only, no MTP
                 - "M*M*/MM/MM" -> main="M*M*", mtp="MM", 2 depths
+                - "M-M-|M-M*-|M-M-|M-M*-" -> 4 pipeline segments
+        hybrid_attention_ratio (float, optional): Deprecated. Use hybrid_layer_pattern instead.
+            If set to a value > 0.0 and hybrid_layer_pattern is None, a pattern will be
+            generated from the ratio with a deprecation warning.
+        hybrid_mlp_ratio (float, optional): Deprecated. Use hybrid_layer_pattern instead.
+            If set to a value > 0.0 and hybrid_layer_pattern is None, a pattern will be
+            generated from the ratio with a deprecation warning.
+        hybrid_override_pattern (str, optional): Deprecated. Use hybrid_layer_pattern instead.
+            If set and hybrid_layer_pattern is None, the value is copied to hybrid_layer_pattern
+            with a deprecation warning.
+        pre_process (bool, optional): Include embedding layer
+            (used with pipeline parallelism). Defaults to True.
         post_process (bool, optional): Include an output layer (used with pipeline parallelism).
             Defaults to True.
         fp16_lm_cross_entropy (bool, optional): Defaults to False.
@@ -65,6 +80,7 @@ class MambaModel(LanguageModule):
             interpolating RoPE for longer sequences. The value must be a float larger than 1.0.
              Defaults to None.
         pg_collection (ProcessGroupCollection, optional): Model communication process groups.
+        vp_stage (Optional[int], optional): Virtual pipeline stage index. Defaults to None.
     """
 
     def __init__(
@@ -73,10 +89,11 @@ def __init__(
         mamba_stack_spec: ModuleSpec,
         vocab_size: int,
         max_sequence_length: int,
+        hybrid_layer_pattern: Optional[str] = None,
+        hybrid_attention_ratio: Optional[float] = None,
+        hybrid_mlp_ratio: Optional[float] = None,
+        hybrid_override_pattern: Optional[str] = None,
         pre_process: bool = True,
-        hybrid_attention_ratio: float = 0.0,
-        hybrid_mlp_ratio: float = 0.0,
-        hybrid_override_pattern: str = None,
         post_process: bool = True,
         fp16_lm_cross_entropy: bool = False,
         parallel_output: bool = True,
@@ -95,13 +112,19 @@ def __init__(
         if has_config_logger_enabled(config):
             log_config_to_disk(config, locals(), prefix=type(self).__name__)
 
+        if self.config.use_mup and not getattr(MambaModel, "mup_warning_printed", False):
+            log_single_rank(
+                logger,
+                logging.WARNING,
+                "MuP for MambaModel is experimental and not fully validated yet.",
+            )
+            MambaModel.mup_warning_printed = True
+
         self.mamba_stack_spec: ModuleSpec = mamba_stack_spec
         self.vocab_size = vocab_size
         self.max_sequence_length = max_sequence_length
+        self.hybrid_layer_pattern = hybrid_layer_pattern
         self.pre_process = pre_process
-        self.hybrid_attention_ratio = hybrid_attention_ratio
-        self.hybrid_mlp_ratio = hybrid_mlp_ratio
-        self.hybrid_override_pattern = hybrid_override_pattern
         self.post_process = post_process
         self.fp16_lm_cross_entropy = fp16_lm_cross_entropy
         self.parallel_output = parallel_output
@@ -109,18 +132,73 @@ def __init__(
         self.position_embedding_type = position_embedding_type
         self.vp_stage = vp_stage
 
-        # Parse unified pattern to extract main and MTP components
-        from megatron.core.ssm.mamba_hybrid_layer_allocation import parse_hybrid_pattern
+        # Backward compatibility for deprecated hybrid parameters
+        if hybrid_override_pattern is not None:
+            if self.hybrid_layer_pattern is None:
+                log_single_rank(
+                    logger,
+                    logging.WARNING,
+                    "hybrid_override_pattern has been deprecated. "
+                    "Use hybrid_layer_pattern instead.",
+                )
+                self.hybrid_layer_pattern = hybrid_override_pattern
+            else:
+                raise ValueError(
+                    "hybrid_override_pattern and hybrid_layer_pattern cannot both be set. "
+                    "hybrid_override_pattern has been deprecated; use hybrid_layer_pattern instead."
+                )
+        if (hybrid_attention_ratio is not None and hybrid_attention_ratio > 0.0) or (
+            hybrid_mlp_ratio is not None and hybrid_mlp_ratio > 0.0
+        ):
+            if hybrid_layer_pattern is not None:
+                raise ValueError(
+                    "hybrid_layer_pattern cannot be used together with "
+                    "hybrid_attention_ratio or hybrid_mlp_ratio. "
+                    "These ratios have been deprecated; use hybrid_layer_pattern alone."
+                )
+            log_single_rank(
+                logger,
+                logging.WARNING,
+                "hybrid_attention_ratio and hybrid_mlp_ratio have been deprecated. "
+                "Use hybrid_layer_pattern instead.",
+            )
+            if self.hybrid_layer_pattern is None:
+                from megatron.core.ssm.mamba_hybrid_layer_allocation import pattern_from_ratios
+
+                attn_ratio = hybrid_attention_ratio if hybrid_attention_ratio else 0.0
+                mlp_ratio = hybrid_mlp_ratio if hybrid_mlp_ratio else 0.0
+                self.hybrid_layer_pattern = pattern_from_ratios(
+                    config.num_layers, attn_ratio, mlp_ratio
+                )
+
+        # Parse unified pattern to extract main and MTP components, and
+        # determine the pipeline segment for this model instance.
+        from megatron.core.ssm.mamba_hybrid_layer_allocation import (
+            parse_hybrid_pattern,
+            select_pipeline_segment,
+        )
 
-        parsed = parse_hybrid_pattern(hybrid_override_pattern)
+        parsed = parse_hybrid_pattern(self.hybrid_layer_pattern)
         self.mtp_pattern = parsed.mtp_pattern
         self.mtp_num_depths = parsed.mtp_num_depths
 
+        layer_type_list, layer_offset = select_pipeline_segment(
+            parsed.main_pattern or '',
+            self.pg_collection.pp,
+            vp_stage,
+            first_stage_layers=self.config.num_layers_in_first_pipeline_stage,
+            last_stage_layers=self.config.num_layers_in_last_pipeline_stage,
+        )
+
         # Determine if MTP is needed (based on pattern parsing)
         self.mtp_process = (
             self.mtp_pattern is not None
             and self.mtp_num_depths > 0
-            and mtp_on_this_rank(self.config, vp_stage=self.vp_stage)
+            # The following forces MTP to be on the final pipeline stage. It might be more optimal
+            # to split the hybrid layer pattern into pipeline stages before parsing the pattern for
+            # the current pipeline stage. This could also enable MTP standalone (MTP in a pipeline
+            # stage separate from loss) to be supported in the hybrid model.
+            and mtp_on_this_rank(self.config, ignore_virtual=False, vp_stage=self.vp_stage)
         )
 
         # megatron core pipelining currently depends on model type
@@ -156,9 +234,8 @@ def __init__(
             mamba_stack_spec,
             self.config,
             pre_process=self.pre_process,
-            hybrid_attention_ratio=self.hybrid_attention_ratio,
-            hybrid_mlp_ratio=self.hybrid_mlp_ratio,
-            hybrid_override_pattern=parsed.main_pattern,
+            layer_type_list=layer_type_list,
+            pp_layer_offset=layer_offset,
             post_process=self.post_process,
             dtype=config.params_dtype,
             pg_collection=self.pg_collection,
@@ -189,7 +266,11 @@ def __init__(
                 config.hidden_size,
                 self.vocab_size,
                 config=config,
-                init_method=config.init_method,
+                init_method=(
+                    config.embedding_init_method
+                    if config.use_mup and not self.share_embeddings_and_output_weights
+                    else config.init_method
+                ),
                 bias=False,
                 skip_bias_add=False,
                 gather_output=not self.parallel_output,
@@ -236,6 +317,7 @@ def forward(
         loss_mask: Optional[Tensor] = None,
         packed_seq_params: Optional[PackedSeqParams] = None,
         padding_mask: Optional[Tensor] = None,
+        is_spec_decode: Optional[bool] = None,
     ) -> Tensor:
         """Forward function of the Mamba model. This function passes the input tensors
         through the embedding layer, and then the decoder and finally into the post
@@ -312,7 +394,18 @@ def forward(
         if self.share_embeddings_and_output_weights:
             output_weight = self.shared_embedding_or_output_weight()
 
-        if self.mtp_process:
+        # Check if speculative decoding is active. When it is, MTP must be
+        # computed *after* verification so that it is conditioned on verified
+        # tokens rather than stale speculative tokens from the previous step.
+        if is_spec_decode is None:
+            is_spec_decode = (
+                in_inference_mode
+                and inference_context.is_dynamic_batching()
+                and inference_context.num_speculative_tokens > 0
+            )
+
+        mtp_forward_ran = self.mtp_process and not is_spec_decode
+        if mtp_forward_ran:
             hidden_states = self.mtp(
                 input_ids=input_ids,
                 position_ids=position_ids,
@@ -327,21 +420,38 @@ def forward(
         if not self.post_process:
             return hidden_states
 
-        if self.config.mtp_num_layers is not None:
-            hidden_states = process_mtp_loss(
-                hidden_states=hidden_states,
-                labels=labels,
-                loss_mask=loss_mask,
-                output_layer=self.output_layer,
-                output_weight=output_weight,
-                runtime_gather_output=runtime_gather_output,
-                is_training=self.training,
-                compute_language_model_loss=self.compute_language_model_loss,
-                config=self.config,
-                cp_group=self.pg_collection.cp,
-                packed_seq_params=packed_seq_params,
-            )
-
+        if self.config.mtp_num_layers is not None and (mtp_forward_ran or is_spec_decode):
+            assert self.config.mtp_num_layers > 0
+            # The new process_mtp_loss function doesn't handle mtp_logits_cache,
+            # so we manually generate and cache MTP logits when in inference mode.
+            if in_inference_mode:
+                if is_spec_decode:
+                    # Cache decoder hidden states for serial MTP computation
+                    # after speculative token verification.
+                    self._decoder_hidden_states_cache = hidden_states
+                else:
+                    hidden_states, self._mtp_logits_cache = compute_mtp_inference_logits(
+                        hidden_states=hidden_states,
+                        mtp_num_layers=self.config.mtp_num_layers,
+                        output_layer=self.output_layer,
+                        output_weight=output_weight,
+                        runtime_gather_output=runtime_gather_output,
+                    )
+            else:
+                hidden_states = process_mtp_loss(
+                    hidden_states=hidden_states,
+                    labels=labels,
+                    loss_mask=loss_mask,
+                    output_layer=self.output_layer,
+                    output_weight=output_weight,
+                    runtime_gather_output=runtime_gather_output,
+                    is_training=self.training,
+                    compute_language_model_loss=self.compute_language_model_loss,
+                    config=self.config,
+                    cp_group=self.pg_collection.cp,
+                    packed_seq_params=packed_seq_params,
+                    scale_logits_fn=self._scale_logits if self.config.use_mup else None,
+                )
         sequence_parallel_override = False
         if in_inference_mode and inference_context.config.materialize_only_last_token_logits:
             if inference_context.is_static_batching():
@@ -357,17 +467,16 @@ def forward(
                     self.output_layer.sequence_parallel = False
                     sequence_parallel_override = True
 
-                # Reshape [B, 1, H] to [1, B, H] → extract each sample's true last‐token hidden
-                # state ([B, H]) → unsqueeze back to [B, 1, H]
-                # (so that the output layer, which expects S×B×H, receives only the final token)
-                hidden_states = inference_context.last_token_logits(
-                    hidden_states.squeeze(1).unsqueeze(0)
-                ).unsqueeze(1)
+                # Reshape [S, B, H] (with B=1) to [1, S, H] for logit extraction,
+                # then back to [S', B, H] for the output layer.
+                reshaped = hidden_states.squeeze(1).unsqueeze(0)
+                hidden_states = inference_context.last_token_logits(reshaped).unsqueeze(1)
 
         if labels is None:
             logits, _ = self.output_layer(
                 hidden_states, weight=output_weight, runtime_gather_output=runtime_gather_output
             )
+            logits = self._scale_logits(logits)
 
         # Restore sequence parallel execution to the output layer if necessary.
         if sequence_parallel_override:
@@ -396,3 +505,46 @@ def forward(
             loss = self.compute_language_model_loss(labels, logits)
 
         return loss
+
+    @torch.inference_mode()
+    def compute_mtp_single_step(
+        self,
+        hidden_states: Tensor,
+        next_token_ids: Tensor,
+        position_ids: Tensor,
+        depth: int,
+        runtime_gather_output: bool = True,
+    ) -> tuple:
+        """Compute a single MTP depth for speculative decoding.
+
+        This is called after speculative token verification to compute MTP
+        predictions conditioned on verified tokens only.
+
+        Args:
+            hidden_states (Tensor): Hidden states at last accepted positions [N, 1, H].
+            next_token_ids (Tensor): Correct next token IDs [1, N].
+            position_ids (Tensor): Position IDs for the next tokens [1, N].
+            depth (int): MTP depth index (0-indexed).
+            runtime_gather_output (bool): Whether to gather output across TP.
+
+        Returns:
+            tuple: (new_hidden_states [N, 1, H], logits [N, 1, vocab_size]).
+        """
+        layer_idx = 0 if self.mtp.mtp_use_repeated_layer else depth
+        mtp_hidden = self.mtp.layers[layer_idx].forward_single_position(
+            hidden_states=hidden_states,
+            next_token_ids=next_token_ids,
+            position_ids=position_ids,
+            embedding=self.embedding,
+        )
+
+        output_weight = None
+        if self.share_embeddings_and_output_weights:
+            output_weight = self.shared_embedding_or_output_weight()
+
+        logits, _ = self.output_layer(
+            mtp_hidden, weight=output_weight, runtime_gather_output=runtime_gather_output
+        )
+        logits = self._scale_logits(logits)
+
+        return mtp_hidden, logits
diff --git a/megatron/core/models/mimo/config/base_configs.py b/megatron/core/models/mimo/config/base_configs.py
index 8b170abe152..961f8930e23 100644
--- a/megatron/core/models/mimo/config/base_configs.py
+++ b/megatron/core/models/mimo/config/base_configs.py
@@ -20,6 +20,9 @@ class MimoModelConfig:
             Dictionary mapping modality names to their special token IDs.
             For example, {"vision": -200, "audio":32000}, these represent placeholders
             in the input_ids to insert the modality embeddings at the correct positions.
+        kv_format (str):
+            Key-value format for attention: "sbhd" (seq-batch-head-dim) or "thd" (total-head-dim).
+            Default is "sbhd".
     """
 
     warnings.warn(
@@ -32,3 +35,4 @@ class MimoModelConfig:
     language_model_spec: ModuleSpec = field(default_factory=ModuleSpec)
     modality_submodules_spec: Dict[str, ModuleSpec] = field(default_factory=dict)
     special_token_ids: Dict[str, int] = field(default_factory=dict)
+    kv_format: str = "sbhd"
diff --git a/megatron/core/models/mimo/model/base.py b/megatron/core/models/mimo/model/base.py
index 2f136a98466..bb1c92c9f80 100644
--- a/megatron/core/models/mimo/model/base.py
+++ b/megatron/core/models/mimo/model/base.py
@@ -4,9 +4,11 @@
 import warnings
 from typing import Any, Dict, Optional
 
-import torch
+import torch  # type: ignore[import-not-found]
 
 from megatron.core.models.mimo.config import MimoModelConfig
+from megatron.core.models.mimo.partition.utils import PartitionAdapter, PartitionConfig
+from megatron.core.packed_seq_params import PackedSeqParams
 from megatron.core.transformer import MegatronModule
 from megatron.core.transformer.spec_utils import build_module
 
@@ -32,7 +34,7 @@ class MimoModel(MegatronModule):
             Configuration for the model, including language model and modality submodules
     """
 
-    def __init__(self, mimo_config: MimoModelConfig) -> None:
+    def __init__(self, mimo_config: MimoModelConfig, cp_group=None, tp_group=None) -> None:
         """Initialize the multimodal model.
 
         Example:
@@ -58,6 +60,25 @@ def __init__(self, mimo_config: MimoModelConfig) -> None:
             mimo_config.special_token_ids.copy() if mimo_config.special_token_ids else {}
         )
 
+        # Extract language model config for partition adapter
+        language_config = mimo_config.language_model_spec.params['config']
+        assert (
+            language_config.pipeline_model_parallel_size == 1
+        ), "Pipeline parallelism is not supported in MimoModel"
+        max_seq_len = mimo_config.language_model_spec.params.get('max_sequence_length', 4096)
+
+        self.partition_adapter: Optional[PartitionAdapter] = None
+        # Create partition adapter only if parallelism is enabled
+        if language_config.context_parallel_size > 1 or language_config.sequence_parallel:
+            partition_config = PartitionConfig.from_mp_config(
+                mp=language_config,
+                max_seq_len=max_seq_len,
+                kv_format=mimo_config.kv_format,
+                cp_group=cp_group,
+                tp_group=tp_group,
+            )
+            self.partition_adapter = PartitionAdapter(partition_config)
+
         # Initialize modality submodules from specifications
         self.modality_submodules = torch.nn.ModuleDict()
         self._initialize_submodules()
@@ -73,14 +94,16 @@ def align_embeddings_by_token_positions(
 
         Args:
             modality_embeddings: Dictionary mapping modality names to their embeddings.
-                For all modalities: tensor of shape [num_tokens_for_modality, hidden_dim]
-            input_ids: Input token IDs of shape [batch_size, seq_len] containing special tokens
-                that mark where each modality's embeddings should go. The number of special tokens
-                for each modality should exactly match the number of embeddings for that modality.
+                For all modalities: tensor of shape (N, H).
+                Shape: (num_tokens_for_modality, hidden_dim)
+            input_ids: Input token IDs. Shape: (B, S) or (S,)
+                Contains special tokens that mark where each modality's embeddings should go.
+                The number of special tokens for each modality should exactly match the number
+                of embeddings for that modality.
             special_token_ids: Dictionary mapping modality names to their special token IDs
 
         Returns:
-            Combined embeddings tensor of shape [seq_len, batch_size, hidden_dim]
+            Combined embeddings tensor. Shape: (S, B, H)
         """
         # Ensure we have at least one modality
         if not modality_embeddings:
@@ -96,8 +119,7 @@ def align_embeddings_by_token_positions(
         device = reference_embeddings.device
         dtype = reference_embeddings.dtype
 
-        batch_size, seq_length = input_ids.size()  # input_ids is [b, s]
-
+        batch_size, seq_length = input_ids.size()  # input_ids is [B, S]
         logger.debug(
             f"Combined output tensor will have shape: [{seq_length}, {batch_size}, {hidden_dim}]"
         )
@@ -109,8 +131,7 @@ def align_embeddings_by_token_positions(
         # Process each modality in modality_embeddings
         for modality_name, modality_emb in modality_embeddings.items():
             if modality_name == "text":
-                # Text tokens: positions that are not any special token.
-                mask = torch.ones_like(input_ids, dtype=torch.bool)
+                mask = torch.ones_like(input_ids, dtype=torch.bool, device=input_ids.device)
                 for token_id in special_token_ids.values():
                     mask &= input_ids != token_id
             elif modality_name in special_token_ids:
@@ -126,13 +147,10 @@ def align_embeddings_by_token_positions(
                     f"number of {modality_name} embeddings ({modality_emb.size(0)})"
                 )
 
-            expanded_mask = (
-                mask.unsqueeze(-1).expand_as(combined_embeddings).to(combined_embeddings.device)
-            )
+            expanded_mask = mask.unsqueeze(-1).expand_as(combined_embeddings)
             combined_embeddings.masked_scatter_(expanded_mask, modality_emb.flatten())
-        return combined_embeddings.transpose(
-            0, 1
-        ).contiguous()  # Shape: [seq_length, batch_size, hidden_dim]
+
+        return combined_embeddings.transpose(0, 1).contiguous()  # [S, B, H]
 
     def _initialize_submodules(self) -> None:
         """Initialize modality submodules from the ModuleSpec configurations.
@@ -183,15 +201,16 @@ def get_text_embeddings(
     ) -> torch.Tensor:
         """Get embeddings for text tokens in the input.
         Args:
-            input_ids: Input token IDs of shape [batch_size, seq_len] containing text tokens
-                and potentially special tokens for other modalities.
+            input_ids: Input token IDs. Shape: (B, S)
+                Contains text tokens and potentially special tokens for other modalities.
             position_ids: Position IDs corresponding to input tokens, used for positional encoding.
-                Shape [batch_size, seq_len].
+                Shape: (B, S)
             special_token_ids: Dictionary mapping modality names to their special token IDs.
                 Used to identify non-text tokens in the input_ids.
 
         Returns:
-            torch.Tensor: Embeddings for text tokens, shape [num_text_tokens, hidden_dim].
+            torch.Tensor: Embeddings for text tokens.
+            Shape: (N, H), where N is the number of text tokens.
         """
         text_mask = torch.ones_like(input_ids, dtype=torch.bool)  # [b, s]
         for special_token_id in special_token_ids.values():
@@ -219,15 +238,16 @@ def forward(
         loss_mask: Optional[torch.Tensor] = None,
         labels: Optional[torch.Tensor] = None,
         modality_inputs: Optional[Dict[str, Dict[str, Any]]] = None,
+        packing_kwargs: Optional[dict] = None,
     ):
         """Forward pass through the multimodal model.
 
         Args:
-            input_ids: Input token IDs [batch_size, seq_length]
-            position_ids: Position IDs [batch_size, seq_length]
-            attention_mask: Attention mask [batch_size, seq_length]
-            loss_mask: Loss mask [batch_size, seq_length]
-            labels: Labels for training
+            input_ids: Input token IDs. Shape: (B, S)
+            position_ids: Position IDs. Shape: (B, S)
+            attention_mask: Attention mask. Shape: (B, S)
+            loss_mask: Loss mask. Shape: (B, S)
+            labels: Labels for training. Shape: (B, S)
             modality_inputs: Dictionary mapping modality names to encoder inputs. For example:
                 {
                     "images": {
@@ -238,10 +258,37 @@ def forward(
                         "whisper_encoder": {"input_features": whisper_features}
                     }
                 }
+            packing_kwargs: Optional dictionary of kwargs to construct PackedSeqParams
+                            if packed_seq_params is not provided. For example:
+                                {
+                                    "cu_seqlens_q": cu_seqlens,
+                                    "cu_seqlens_kv": cu_seqlens,
+                                    "cu_seqlens_q_padded": cu_seqlens_padded,
+                                    "cu_seqlens_kv_padded": cu_seqlens_padded,
+                                    "max_seqlen_q": torch.tensor(
+                                        max(seqlens_padded), dtype=torch.int32
+                                    ),
+                                    "max_seqlen_kv": torch.tensor(
+                                        max(seqlens_padded), dtype=torch.int32
+                                    ),
+                                }
 
         Returns:
             tuple: Tuple containing model outputs and loss mask
+                - lm_output: Model output. Shape: (B, S, ...) or (B, S, V)
+                - loss_mask: Loss mask. Shape: (B, S)
         """
+        # If packing_kwargs is provided, construct PackedSeqParams
+        packed_seq_params = None
+        if packing_kwargs is not None:
+            # Ensure correct dtype for seqlens tensors
+            for key in packing_kwargs:
+                if 'cu_seqlens' in key and packing_kwargs[key] is not None:
+                    packing_kwargs[key] = packing_kwargs[key].to(dtype=torch.int32)
+            packed_seq_params = PackedSeqParams(**packing_kwargs)
+            packed_seq_params.qkv_format = 'thd'
+            logger.debug(f"Packed sequence parameters: {packed_seq_params}")
+
         # 1. Process each modality to get embeddings
         modality_embeddings = {}
 
@@ -271,20 +318,42 @@ def forward(
         # 2. Merge embeddings from different modalities
         logger.debug(f"Merging embeddings from {len(modality_embeddings)} modalities")
         combined_embeddings = self.align_embeddings_by_token_positions(
-            modality_embeddings=modality_embeddings,  # [num_tokens, hidden_dim] for each modality
-            input_ids=input_ids,  # Pass in batch-first format [b, s]
+            modality_embeddings=modality_embeddings,
+            input_ids=input_ids,
             special_token_ids=self.special_token_ids,
-        )  # [s, b, h]
+        )
         logger.debug(f"Combined embeddings shape: {combined_embeddings.shape}")
 
-        # 3. Forward pass through language model
+        # 3. If sharding is needed, apply PartitionAdapter.
+        # combined_embeddings is [S, B, H]; transpose to [B, S, H] for shard() which expects
+        # batch-first layout (required by get_batch_on_this_cp_rank). After CP sharding each
+        # rank holds [B, S/cp, H]; transpose back to [S/cp, B, H] for the language model.
+        if self.partition_adapter is not None:
+            combined_embeddings = combined_embeddings.transpose(0, 1).contiguous()  # [B, S, H]
+            combined_embeddings, labels, loss_mask, _, packed_seq_params = (
+                self.partition_adapter.shard(
+                    embeddings=combined_embeddings,
+                    labels=labels,
+                    loss_mask=loss_mask,
+                    attention_mask=attention_mask,
+                    packed_seq_params=packed_seq_params,
+                )
+            )
+            # shard() returns embeddings in [B, S/cp, H]; transpose to [S/cp, B, H]
+            # which is what the language model expects.
+            if combined_embeddings is not None:
+                combined_embeddings = combined_embeddings.transpose(0, 1).contiguous()
+
+        # 5. Forward pass through language model
         lm_output = self.language_model(
             input_ids=None,
             position_ids=None,
             decoder_input=combined_embeddings,
             labels=labels,
-            attention_mask=attention_mask,
+            attention_mask=None,
+            packed_seq_params=packed_seq_params,
         )
+
         logger.debug(f"Language model output shape: {lm_output.shape}")
 
         return lm_output, loss_mask
diff --git a/megatron/core/models/mimo/partition/utils.py b/megatron/core/models/mimo/partition/utils.py
new file mode 100644
index 00000000000..0b43e5548ff
--- /dev/null
+++ b/megatron/core/models/mimo/partition/utils.py
@@ -0,0 +1,260 @@
+# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
+"""Token and weight partitioning helper (CP, TP, SP).
+
+The adapter slices sequences across *context-parallel* ranks and can further
+scatter them across *sequence-parallel* ranks when sequence-parallelism is
+enabled.
+"""
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Optional, Tuple
+
+import torch  # type: ignore[import-not-found]
+from torch.distributed import ProcessGroup  # type: ignore[import-not-found]
+
+from megatron.core import tensor_parallel
+from megatron.core.model_parallel_config import ModelParallelConfig
+from megatron.core.packed_seq_params import PackedSeqParams
+from megatron.core.parallel_state import get_context_parallel_group, get_tensor_model_parallel_group
+from megatron.core.utils import (
+    get_batch_on_this_cp_rank,
+    get_pg_rank,
+    get_pg_size,
+    is_te_min_version,
+)
+
+try:
+    import transformer_engine_torch as tex  # type: ignore
+
+    _HAVE_TEX = True
+except ModuleNotFoundError:  # pragma: no cover
+    tex = None  # type: ignore
+    _HAVE_TEX = False
+
+
+@dataclass(frozen=True)
+class PartitionConfig:
+    """Minimal runtime information needed to shard inputs.
+
+    NOTE: Always construct PartitionConfig using the provided classmethod
+    (from_mp_config) to ensure all fields, including cp_group and tp_group,
+    are set correctly.
+    """
+
+    seq_parallel: bool
+    use_cp: bool
+    tp_comm_overlap: bool
+    max_seq_len: int
+    kv_format: str = "sbhd"  # "sbhd" | "thd"
+    cp_group: Optional[ProcessGroup] = None
+    tp_group: Optional[ProcessGroup] = None
+
+    @property
+    def is_partitioning_enabled(self) -> bool:
+        """Returns True if context parallelism or sequence parallelism is active."""
+        return self.use_cp or self.seq_parallel
+
+    @classmethod
+    def from_mp_config(
+        cls,
+        mp: ModelParallelConfig,
+        *,
+        max_seq_len: int,
+        kv_format: str = "sbhd",
+        cp_group: Optional[ProcessGroup] = None,
+        tp_group: Optional[ProcessGroup] = None,
+    ) -> "PartitionConfig":
+        """
+        Creates a PartitionConfig from a ModelParallelConfig.
+        """
+        if not isinstance(mp, ModelParallelConfig):
+            raise TypeError("mp must be a ModelParallelConfig instance")
+
+        if mp.context_parallel_size > 1 and cp_group is None:
+            cp_group = get_context_parallel_group()
+
+        if mp.sequence_parallel and tp_group is None:
+            tp_group = get_tensor_model_parallel_group()
+
+        return cls(
+            seq_parallel=mp.sequence_parallel,
+            use_cp=get_pg_size(cp_group) > 1,
+            tp_comm_overlap=mp.tp_comm_overlap,
+            max_seq_len=max_seq_len,
+            kv_format=kv_format,
+            cp_group=cp_group,
+            tp_group=tp_group,
+        )
+
+
+class PartitionAdapter:
+    """Shard batch-first embeddings & label tensors for Context and Sequence Parallelism."""
+
+    def __init__(self, cfg: PartitionConfig):
+        """Initialize the partition adapter.
+        Args:
+            cfg: PartitionConfig, the configuration for the partition adapter.
+        """
+        self.cfg = cfg
+
+    def shard(
+        self,
+        embeddings: torch.Tensor,
+        labels: torch.Tensor,
+        loss_mask: torch.Tensor,
+        attention_mask: torch.Tensor,
+        packed_seq_params: Optional[PackedSeqParams] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, Optional[PackedSeqParams]]:
+        """
+        Apply context parallel (CP) and sequence parallel (SP) sharding to input tensors.
+
+        All input tensors must be in batch-first layout:
+            - embeddings: (B, S, H)
+            - labels / loss_mask / attention_mask: (B, S)
+
+        After this call embeddings are still in (B, S/cp, H) batch-first layout.
+        The caller is responsible for transposing to (S/cp, B, H) if the language model
+        requires sequence-first tensors.
+
+        Args:
+            embeddings (torch.Tensor):
+                Input embeddings tensor. Shape: (B, S, H)
+            labels (torch.Tensor):
+                Labels tensor. Shape: (B, S)
+            loss_mask (torch.Tensor):
+                Loss mask tensor. Shape: (B, S)
+            attention_mask (torch.Tensor):
+                Attention mask tensor. Shape: (B, S)
+            packed_seq_params (PackedSeqParams, optional):
+                Packed sequence parameters. Defaults to None.
+
+        Returns:
+            Tuple containing:
+                - embeddings (torch.Tensor): Sharded embeddings. Shape: (B, S/cp, H)
+                - labels (torch.Tensor): Possibly sharded labels. Shape: (B, S/cp)
+                - loss_mask (torch.Tensor): Possibly sharded loss mask. Shape: (B, S/cp)
+                - attention_mask (torch.Tensor): Possibly sharded attention mask. Shape: (B, S/cp)
+                - packed_seq_params (PackedSeqParams, optional): Updated packed sequence parameters.
+        """
+        if not (self.cfg.use_cp or self.cfg.seq_parallel):
+            return embeddings, labels, loss_mask, attention_mask, packed_seq_params
+
+        # Sanity-check the sequence length before any sharding happens.
+        if embeddings is not None:
+            shard_factor = None
+            seq_dim = None  # which dimension holds the token sequence
+
+            if self.cfg.use_cp and self.cfg.seq_parallel:
+                shard_factor = get_pg_size(self.cfg.tp_group) * get_pg_size(self.cfg.cp_group) * 2
+                seq_dim = 1  # embeddings shape: [B, S, H]
+            elif self.cfg.use_cp:
+                shard_factor = get_pg_size(self.cfg.cp_group) * 2
+                seq_dim = 1
+            elif self.cfg.seq_parallel:
+                shard_factor = get_pg_size(self.cfg.tp_group)
+                seq_dim = 0  # embeddings shape: [S, B, H]
+
+            if shard_factor is not None and (
+                packed_seq_params is None
+                or getattr(packed_seq_params, 'qkv_format', 'sbhd') == 'sbhd'
+            ):
+                assert embeddings.shape[seq_dim] % shard_factor == 0, (
+                    f"Sequence length should be divisible by {shard_factor} "
+                    "for Sequence/Context parallelism"
+                )
+
+                if self.cfg.seq_parallel and self.cfg.tp_comm_overlap:
+                    assert embeddings.shape[seq_dim] == self.cfg.max_seq_len, (
+                        "TP Comm overlap requires Vision+Text token length "
+                        "== language_max_sequence_length"
+                    )
+
+        if self.cfg.use_cp:
+            embeddings, labels, loss_mask, attention_mask, packed_seq_params = (
+                self._apply_context_parallel(
+                    embeddings, labels, loss_mask, attention_mask, packed_seq_params
+                )
+            )
+
+        if self.cfg.seq_parallel and embeddings is not None:
+            embeddings = tensor_parallel.scatter_to_sequence_parallel_region(embeddings)
+
+        return embeddings, labels, loss_mask, attention_mask, packed_seq_params
+
+    def _apply_context_parallel(
+        self,
+        embeddings: Optional[torch.Tensor],
+        labels: Optional[torch.Tensor],
+        loss_mask: Optional[torch.Tensor],
+        attention_mask: Optional[torch.Tensor],
+        packed_seq_params: Optional[PackedSeqParams],
+    ) -> Tuple[
+        Optional[torch.Tensor],
+        Optional[torch.Tensor],
+        Optional[torch.Tensor],
+        Optional[torch.Tensor],
+        Optional[PackedSeqParams],
+    ]:
+        """
+        Apply context parallel (CP) sharding to input tensors.
+
+        Args:
+            embeddings (Optional[torch.Tensor]):
+                Input embeddings tensor. Shape: (B, S, H)
+            labels (Optional[torch.Tensor]):
+                Labels tensor. Shape: (B, S)
+            loss_mask (Optional[torch.Tensor]):
+                Loss mask tensor. Shape: (B, S)
+            attention_mask (Optional[torch.Tensor]):
+                Attention mask tensor. Shape: (B, S)
+            packed_seq_params (PackedSeqParams, optional):
+                Packed sequence parameters. Defaults to None.
+
+        Returns:
+            Tuple containing:
+                - embeddings (Optional[torch.Tensor]): Sharded embeddings. Shape: (B, S/cp, H)
+                - labels (Optional[torch.Tensor]): Possibly sharded labels. Shape: (B, S/cp)
+                - loss_mask (Optional[torch.Tensor]): Possibly sharded loss mask. Shape: (B, S/cp)
+                - attention_mask (Optional[torch.Tensor]): Possibly sharded attention mask.
+                                                           Shape: (B, S/cp)
+                - packed_seq_params (PackedSeqParams, optional): Updated packed sequence parameters.
+        """
+        if not self.cfg.use_cp:
+            return embeddings, labels, loss_mask, attention_mask, packed_seq_params
+
+        # Distribute sequence across CP ranks
+        batch = dict()
+        if embeddings is not None:
+            batch["embeddings"] = embeddings
+        if labels is not None:
+            batch["labels"] = labels
+        if loss_mask is not None:
+            batch["loss_mask"] = loss_mask
+        if attention_mask is not None:
+            batch["attention_mask"] = attention_mask
+
+        if packed_seq_params is None or getattr(packed_seq_params, 'qkv_format', 'sbhd') == 'sbhd':
+            batch = get_batch_on_this_cp_rank(batch)
+        else:
+            assert _HAVE_TEX and is_te_min_version("1.10.0"), (
+                "Please update Transformer Engine to >= 1.10 "
+                "to use Context Parallel with THD format data"
+            )
+            assert self.cfg.cp_group is not None
+            cp_size = get_pg_size(self.cfg.cp_group)
+            cp_rank = get_pg_rank(self.cfg.cp_group)
+            for key, data in batch.items():
+                index = tex.thd_get_partitioned_indices(
+                    packed_seq_params.cu_seqlens_q_padded, data.size(1), cp_size, cp_rank
+                )
+                batch[key] = data.index_select(1, index)
+
+        # Extract sharded tensors; embeddings remain in [B, S/cp, H] — the caller
+        # is responsible for transposing to [S/cp, B, H] for the language model.
+        embeddings = batch.get("embeddings", None)
+        labels = batch.get("labels", None)
+        loss_mask = batch.get("loss_mask", None)
+        attention_mask = batch.get("attention_mask", None)
+
+        return embeddings, labels, loss_mask, attention_mask, packed_seq_params
diff --git a/megatron/core/models/multimodal/llava_model.py b/megatron/core/models/multimodal/llava_model.py
index 63f62e9eeea..383abcd94dc 100644
--- a/megatron/core/models/multimodal/llava_model.py
+++ b/megatron/core/models/multimodal/llava_model.py
@@ -115,9 +115,7 @@ def __init__(
         language_rotary_base: int = 10000,
         language_rope_scaling: bool = False,
         language_rope_scaling_factor: float = 8.0,
-        hybrid_attention_ratio: float = 1.0,
-        hybrid_mlp_ratio: float = 1.0,
-        hybrid_override_pattern: str = None,
+        hybrid_layer_pattern: str = None,
         fp16_lm_cross_entropy: bool = False,
         image_token_index: int = DEFAULT_IMAGE_TOKEN_INDEX,
         pixel_shuffle: bool = False,
@@ -206,9 +204,7 @@ def __init__(
                     parallel_output=parallel_output,
                     position_embedding_type=language_position_embedding_type,
                     pre_process=self.pre_process,
-                    hybrid_attention_ratio=hybrid_attention_ratio,
-                    hybrid_mlp_ratio=hybrid_mlp_ratio,
-                    hybrid_override_pattern=hybrid_override_pattern,
+                    hybrid_layer_pattern=hybrid_layer_pattern,
                     post_process=self.post_process,
                     rotary_percent=language_rotary_percent,
                     rotary_base=language_rotary_base,
diff --git a/megatron/core/optimizer/__init__.py b/megatron/core/optimizer/__init__.py
index 8babff5d4f5..497e9b7c2a3 100644
--- a/megatron/core/optimizer/__init__.py
+++ b/megatron/core/optimizer/__init__.py
@@ -114,6 +114,151 @@ def get_standard_config_overrides(config: OptimizerConfig) -> Dict[ParamKey, Par
     return config_overrides
 
 
+def get_mup_config_overrides(
+    config: OptimizerConfig, mup_width_mult: float, optimizer_type: str = 'adam'
+) -> Dict[ParamKey, ParamGroupOverride]:
+    """Get MuP config overrides for per-layer LR and Adam epsilon scaling.
+
+    In MuP, optimizer learning rates are adjusted by parameter class to ensure
+    stable update scales across model widths and enable hyperparameter transfer.
+
+    MuP optimizer scaling rules (as implemented here):
+    - Adam/AdamW:
+      - hidden (matrix-like) lr = base_lr / width_mult
+      - hidden (matrix-like) eps = base_eps / width_mult
+      - vector-like params keep base lr and eps
+    - SGD:
+      - vector-like lr = base_lr * width_mult
+      - hidden (matrix-like) lr keeps base_lr in the current uniform-width setup
+      - no eps override is applied
+    - Non-Adam optimizers:
+      - hidden (matrix-like) lr = base_lr / width_mult
+      - no eps override is applied.
+
+    With decoupled_lr enabled, embedding/output params continue using decoupled LR
+    and MuP will not override those explicit decoupled values.
+
+    Args:
+        config (OptimizerConfig): optimizer configuration object.
+        mup_width_mult (float): Width multiplier (hidden_size / base_hidden_size).
+        optimizer_type (str): Optimizer type string from config.optimizer.
+
+    Returns:
+        Dict[ParamKey, ParamGroupOverride]: MuP optimizer overrides.
+    """
+    optimizer_type_lower = optimizer_type.lower()
+    is_sgd_optimizer = optimizer_type_lower == 'sgd'
+    is_adam_optimizer = 'adam' in optimizer_type_lower
+
+    decoupled_lr_enabled = config.decoupled_lr is not None
+    if decoupled_lr_enabled:
+        message = (
+            "Both decoupled_lr and MuP LR scaling are enabled. decoupled_lr sets an "
+            "absolute LR for embedding+output params, and MuP LR scaling will not "
+            "override those parameters."
+        )
+        if is_adam_optimizer:
+            message += " MuP Adam epsilon scaling remains applied to hidden matrix-like parameters."
+        log_single_rank(logger, logging.WARNING, message)
+
+    if mup_width_mult == 1.0:
+        # No scaling needed when width_mult is 1
+        return {}
+
+    hidden_lr_mult = 1.0 / mup_width_mult
+    base_lr = config.lr
+    base_min_lr = config.min_lr
+
+    # Hidden matrix-like layers get scaled LR/eps; vector-like params keep base values.
+    # Prefer the explicit parameter attribute set by LanguageModule. Fall back to
+    # a conservative name check for older or non-language modules.
+    def is_embedding_parameter(param: torch.nn.Parameter, param_name: str) -> bool:
+        if getattr(param, 'shared_embedding', False):
+            return True
+        if hasattr(param, 'is_embedding_parameter'):
+            return bool(param.is_embedding_parameter)
+        return 'embedding' in param_name.lower()
+
+    def is_vector_like_parameter(param: torch.nn.Parameter, param_name: str) -> bool:
+        if is_embedding_parameter(param, param_name):
+            return True
+        if param.dim() <= 1:
+            return True
+        return False
+
+    def should_scale_lr_with_mup(param: torch.nn.Parameter, param_name: str) -> bool:
+        if decoupled_lr_enabled and getattr(param, 'is_embedding_or_output_parameter', False):
+            return False
+        return not is_vector_like_parameter(param, param_name)
+
+    def should_scale_vector_like_lr_with_mup(param: torch.nn.Parameter, param_name: str) -> bool:
+        if decoupled_lr_enabled and getattr(param, 'is_embedding_or_output_parameter', False):
+            return False
+        return is_vector_like_parameter(param, param_name)
+
+    def should_scale_eps_with_mup(param: torch.nn.Parameter, param_name: str) -> bool:
+        if is_vector_like_parameter(param, param_name):
+            return False
+        # MuP Appendix B.3: eps scales with fan_in when non-negligible.
+        # This implementation follows the common denominator form: sqrt(v) + eps.
+        return True
+
+    mup_overrides: Dict[ParamKey, ParamGroupOverride] = {}
+
+    if is_sgd_optimizer:
+        vector_like_lr_mult = mup_width_mult
+        vector_like_lr_override: ParamGroupOverride = {}
+        if base_lr is not None:
+            vector_like_lr_override["max_lr"] = base_lr * vector_like_lr_mult
+        if base_min_lr is not None:
+            vector_like_lr_override["min_lr"] = base_min_lr * vector_like_lr_mult
+
+        if vector_like_lr_override:
+            vector_like_predicate = ParamWithNamePredicate(
+                name="mup_sgd_vector_like_excluding_embedding_output",
+                fn=should_scale_vector_like_lr_with_mup,
+            )
+            mup_overrides[ParamKey(with_name_predicate=vector_like_predicate)] = (
+                vector_like_lr_override
+            )
+
+        return mup_overrides
+
+    lr_override: ParamGroupOverride = {}
+    if base_lr is not None:
+        lr_override["max_lr"] = base_lr * hidden_lr_mult
+    if base_min_lr is not None:
+        lr_override["min_lr"] = base_min_lr * hidden_lr_mult
+
+    eps_override: ParamGroupOverride = {}
+    if is_adam_optimizer and config.adam_eps is not None:
+        eps_override["eps"] = config.adam_eps * hidden_lr_mult
+
+    if decoupled_lr_enabled:
+        if lr_override:
+            hidden_predicate = ParamWithNamePredicate(
+                name="mup_hidden_only_excluding_embedding_output", fn=should_scale_lr_with_mup
+            )
+            mup_overrides[ParamKey(with_name_predicate=hidden_predicate)] = lr_override
+
+        if eps_override:
+            hidden_output_predicate = ParamWithNamePredicate(
+                name="mup_hidden_only_for_adam_eps", fn=should_scale_eps_with_mup
+            )
+            mup_overrides[ParamKey(with_name_predicate=hidden_output_predicate)] = eps_override
+    else:
+        combined_override: ParamGroupOverride = {}
+        combined_override.update(lr_override)
+        combined_override.update(eps_override)
+        if combined_override:
+            hidden_output_predicate = ParamWithNamePredicate(
+                name="mup_hidden_and_output", fn=should_scale_eps_with_mup
+            )
+            mup_overrides[ParamKey(with_name_predicate=hidden_output_predicate)] = combined_override
+
+    return mup_overrides
+
+
 def _get_param_groups(
     model_chunks: List[MegatronModule],
     config: OptimizerConfig,
diff --git a/megatron/core/optimizer/layer_wise_optimizer.py b/megatron/core/optimizer/layer_wise_optimizer.py
index d5dcef209a9..6e59e03ae42 100644
--- a/megatron/core/optimizer/layer_wise_optimizer.py
+++ b/megatron/core/optimizer/layer_wise_optimizer.py
@@ -45,6 +45,8 @@ def __init__(
         config: OptimizerConfig,
         pg_collection: Optional[ProcessGroupCollection] = None,
         init_state_fn_list: Optional[List[Callable]] = None,
+        model_chunks: Optional[List] = None,
+        async_allgather: bool = False,
     ) -> None:
         """
         Initialize LayerWiseDistributedOptimizer.
@@ -54,10 +56,21 @@ def __init__(
             config: OptimizerConfig.
             pg_collection: ProcessGroupCollection.
             init_state_fn_list: List of init state functions.
+            model_chunks: DDP-wrapped model chunks (needed for async_allgather).
+            async_allgather: If True, defer param all-gather to forward pre-hooks.
         """
 
         self.pg_collection = pg_collection
         self.shard_params(optimizers)
+
+        # Set up async all-gather using DDP bucket infrastructure.
+        self.async_allgather = async_allgather
+        if self.async_allgather:
+            assert (
+                model_chunks is not None
+            ), "model_chunks must be provided if async_allgather is True"
+            self.set_bucket_layerwise_params_list(model_chunks)
+
         if init_state_fn_list:
             assert len(init_state_fn_list) == len(
                 optimizers
@@ -141,34 +154,79 @@ def shard_params(self, optimizers):
         if expt_dp_size == 1 or len(self.expt_dp_params_list[0]) == 0:
             self.expt_dp_params_list = None
 
+    def set_bucket_layerwise_params_list(self, model_chunks):
+        """Map sharded params to DDP buckets for async all-gather.
+
+        For each bucket in each model chunk's bucket groups, build per-rank param lists
+        by cross-referencing the layer-wise sharded param lists with the bucket's params.
+
+        Args:
+            model_chunks: DDP-wrapped model chunks with bucket_groups.
+        """
+        for model_chunk in model_chunks:
+            for group in model_chunk.bucket_groups:
+                for bucket in group.buckets:
+                    bucket_params_list = [[] for _ in range(get_pg_size(self.pg_collection.dp_cp))]
+                    for bucket_list, full_params_list in zip(
+                        bucket_params_list, self.dp_cp_params_list
+                    ):
+                        for param in full_params_list:
+                            if param in bucket.params:
+                                bucket_list.append(param)
+                    bucket.set_layerwise_params_list(bucket_params_list)
+            # Do the same for expert parallel bucket groups.
+            if self.expt_dp_params_list is not None:
+                for group in model_chunk.expert_parallel_bucket_groups:
+                    for bucket in group.buckets:
+                        bucket_params_list = [
+                            [] for _ in range(get_pg_size(self.pg_collection.expt_dp))
+                        ]
+                        for bucket_list, full_params_list in zip(
+                            bucket_params_list, self.expt_dp_params_list
+                        ):
+                            for param in full_params_list:
+                                if param in bucket.params:
+                                    bucket_list.append(param)
+                        bucket.set_layerwise_params_list(bucket_params_list)
+
     @torch.no_grad()
     def allgather_params(self) -> None:
         """All-gather updated params from all ranks."""
 
-        # helper function to flatten local params, allgather, unflatten and copy to model params
+        # helper function to flatten local params, all-gather,
+        # unflatten and copy to model params
         def _allgather_helper(params_list, group):
-            # flatten this rank's params and create empty tensor output list
             device = params_list[0][0].device
             dtype = params_list[0][0].dtype
             rank = get_pg_rank(group)
-            # for rank without params create empty tensor and participate in allgather
+            dp_size = get_pg_size(group)
+            # Flatten this rank's params.
             src = (
                 _flatten_dense_tensors(params_list[rank])
                 if len(params_list[rank]) > 0
                 else torch.empty(0, device=device, dtype=dtype)
             )
-            output_list = [
-                torch.empty(sum([p.numel() for p in params]), device=device, dtype=dtype)
-                for params in params_list
-            ]
-            # single all_gather_v to collect all updated params
-            torch.distributed.all_gather(output_list, src, group=group)
-            # unflatten and copy gathered params for each rank i
-            for idx, (flat_params, params) in enumerate(zip(output_list, params_list)):
-                # skip local params and empty tensors
+            flat_sizes = [sum(p.numel() for p in params) for params in params_list]
+            if max(flat_sizes) == 0:
+                return
+
+            # Allocate per-rank receive buffers with actual sizes (no padding).
+            # PyTorch's NCCL backend handles uneven sizes in all_gather via
+            # grouped send/recv internally. Reuse src for local rank's slot.
+            gather_list = []
+            for i in range(dp_size):
+                if i == rank:
+                    gather_list.append(src)
+                else:
+                    gather_list.append(torch.empty(flat_sizes[i], device=device, dtype=dtype))
+
+            torch.distributed.all_gather(gather_list, src, group=group)
+
+            # Unflatten and copy gathered params for each rank.
+            for idx, params in enumerate(params_list):
                 if len(params) == 0 or idx == rank:
                     continue
-                updated_params = _unflatten_dense_tensors(flat_params, params)
+                updated_params = _unflatten_dense_tensors(gather_list[idx], params)
                 for updated_p, model_p in zip(updated_params, params):
                     model_p.data.copy_(updated_p)
 
@@ -221,8 +279,10 @@ def step(self):  # type: ignore[no-untyped-def]
         """step function for layer-wise optimizer."""
         update_successful, grad_norm, num_zeros_in_grad = super().step()
 
-        # All gather updated params.
-        self.allgather_params()
+        # All gather updated params. If async_allgather is True, the allgather
+        # is deferred to the forward pre-hooks via DDP bucket infrastructure.
+        if not self.async_allgather:
+            self.allgather_params()
 
         return update_successful, grad_norm, num_zeros_in_grad
 
diff --git a/megatron/core/packed_seq_params.py b/megatron/core/packed_seq_params.py
index 08ebdac67d8..ac677550cde 100644
--- a/megatron/core/packed_seq_params.py
+++ b/megatron/core/packed_seq_params.py
@@ -1,6 +1,7 @@
 # Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
 from dataclasses import dataclass
 
+import torch
 import torch.distributed as dist
 from torch import Tensor
 
@@ -21,3 +22,38 @@ class PackedSeqParams:
     max_seqlen_kv: int = None
     local_cp_size: int = None
     cp_group: dist.ProcessGroup = None
+    total_tokens: int = None
+    seq_idx: Tensor = None
+
+    def __post_init__(self):
+        """Pre-compute seq_idx for Mamba mixer CUDA graph compatibility.
+
+        If total_tokens is 16 (for example), this method takes packed_seq_params.cu_seqlens_q_padded
+        (or cu_seqlens_q) which is of the form [0, 5, 7, 11] and returns a tensor of the form
+        [0, 0, 0, 0, 0, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 3],
+        which is [0]*(5-0) + [1]*(7-5) + [2]*(11-7) + [3]*(16-11)
+        In the above example, there are three sequences in the pack.
+        In general, the output has an additional sequence index (e.g. 0, 1, 2, 3) so that any tokens
+        beyond the last padded input sequence are accounted for as an extra sequence. However, If
+        cu_seqlens_q_padded[-1] == max_seqlen then this additional sequence index will not be
+        included.
+        """
+        cu_seqlens = (
+            self.cu_seqlens_q_padded if self.cu_seqlens_q_padded is not None else self.cu_seqlens_q
+        )
+        if isinstance(cu_seqlens, Tensor) and self.total_tokens is not None:
+            total_tokens_tensor = torch.tensor(
+                [self.total_tokens], dtype=cu_seqlens.dtype, device=cu_seqlens.device
+            )
+            # Example: [0, 5, 7, 11] -> [0, 5, 7, 11, 16]
+            cu_seqlens_with_max = torch.cat([cu_seqlens, total_tokens_tensor])
+            # Example: [0, 5, 7, 11, 16] -> [5, 2, 4, 5]
+            seq_lengths = cu_seqlens_with_max[1:] - cu_seqlens_with_max[:-1]
+            # Example: [5, 2, 4, 5] -> [0, 0, 0, 0, 0, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 3]
+            self.seq_idx = (
+                torch.repeat_interleave(
+                    torch.arange(seq_lengths.numel(), device=cu_seqlens.device), seq_lengths
+                )
+                .to(torch.int32)
+                .unsqueeze(0)  # Add a batch dimension
+            )
diff --git a/megatron/core/parallel_state.py b/megatron/core/parallel_state.py
index a0e1b392b43..927d306ce7b 100644
--- a/megatron/core/parallel_state.py
+++ b/megatron/core/parallel_state.py
@@ -23,8 +23,6 @@
 except ImportError:
     HAVE_EINOPS = False
 
-logger = logging.getLogger(__name__)
-
 # Intra-layer model parallel group that the current rank belongs to.
 _TENSOR_MODEL_PARALLEL_GROUP = None
 # Inter-layer model parallel group that the current rank belongs to.
@@ -140,8 +138,9 @@
 # Memory buffers to avoid dynamic memory allocation
 _GLOBAL_MEMORY_BUFFER = None
 
-# Global symmetric memory buffer for inference
-_GLOBAL_SYMMETRIC_MEMORY_BUFFER = None
+# Global symmetric memory buffers for inference
+_GLOBAL_SYMMETRIC_MEMORY_BUFFER_TP = None
+_GLOBAL_SYMMETRIC_MEMORY_BUFFER_EP = None
 
 # List of all process groups
 # Used for updating the timeout for all process groups
@@ -568,6 +567,8 @@ def initialize_model_parallel(
     high_priority_stream_groups: Optional[List[str]] = None,
     sharp_enabled_group: Optional[str] = None,
     create_all_gather_group: Optional[bool] = False,
+    rank_offset: int = 0,
+    local_world_size: Optional[int] = None,
 ) -> None:
     """Initialize model data parallel groups.
 
@@ -735,7 +736,9 @@ def initialize_model_parallel(
 
     # Get world size and rank. Ensure some consistencies.
     assert torch.distributed.is_initialized()
-    world_size: int = torch.distributed.get_world_size()
+    world_size: int = (
+        local_world_size if local_world_size is not None else torch.distributed.get_world_size()
+    )
 
     model_size = tensor_model_parallel_size * pipeline_model_parallel_size * context_parallel_size
 
@@ -781,7 +784,7 @@ def initialize_model_parallel(
         pp=pipeline_model_parallel_size,
         cp=context_parallel_size,
         order=order,
-        rank_offset=0,
+        rank_offset=rank_offset,
     )
 
     # Build expert rank generator
@@ -804,7 +807,7 @@ def initialize_model_parallel(
         pp=pipeline_model_parallel_size,
         cp=1,
         order=order,
-        rank_offset=0,
+        rank_offset=rank_offset,
     )
 
     assert (
@@ -2027,14 +2030,24 @@ def _set_global_memory_buffer():
 
 def _set_global_symmetric_memory_buffer():
     """Initialize global buffer."""
-    global _GLOBAL_SYMMETRIC_MEMORY_BUFFER
-    assert _GLOBAL_SYMMETRIC_MEMORY_BUFFER is None, "global memory buffer is already initialized"
+    global _GLOBAL_SYMMETRIC_MEMORY_BUFFER_TP, _GLOBAL_SYMMETRIC_MEMORY_BUFFER_EP
+    assert (
+        _GLOBAL_SYMMETRIC_MEMORY_BUFFER_TP is None
+    ), "global symmetric memory buffer for TP is already initialized"
+    assert (
+        _GLOBAL_SYMMETRIC_MEMORY_BUFFER_EP is None
+    ), "global symmetric memory buffer for EP is already initialized"
 
-    _GLOBAL_SYMMETRIC_MEMORY_BUFFER = GlobalSymmetricMemoryBuffer(
+    _GLOBAL_SYMMETRIC_MEMORY_BUFFER_TP = GlobalSymmetricMemoryBuffer(
         size_in_mb=256,  # todo: set from an argument?
         process_group=get_tensor_model_parallel_group(),
     )
 
+    _GLOBAL_SYMMETRIC_MEMORY_BUFFER_EP = GlobalSymmetricMemoryBuffer(
+        size_in_mb=256,  # todo: set from an argument?
+        process_group=get_expert_model_parallel_group(),
+    )
+
 
 def get_global_memory_buffer():
     """Return the global GlobalMemoryBuffer object"""
@@ -2042,12 +2055,20 @@ def get_global_memory_buffer():
     return _GLOBAL_MEMORY_BUFFER
 
 
-def get_global_symmetric_memory_buffer():
+def get_global_symmetric_memory_buffer_tp():
     """Return the global GlobalSymmetricMemoryBuffer object"""
     assert (
-        _GLOBAL_SYMMETRIC_MEMORY_BUFFER is not None
+        _GLOBAL_SYMMETRIC_MEMORY_BUFFER_TP is not None
     ), "global symmetric memory buffer is not initialized"
-    return _GLOBAL_SYMMETRIC_MEMORY_BUFFER
+    return _GLOBAL_SYMMETRIC_MEMORY_BUFFER_TP
+
+
+def get_global_symmetric_memory_buffer_ep():
+    """Return the global GlobalSymmetricMemoryBuffer object"""
+    assert (
+        _GLOBAL_SYMMETRIC_MEMORY_BUFFER_EP is not None
+    ), "global symmetric memory buffer is not initialized"
+    return _GLOBAL_SYMMETRIC_MEMORY_BUFFER_EP
 
 
 def destroy_global_memory_buffer():
@@ -2058,8 +2079,9 @@ def destroy_global_memory_buffer():
 
 def destroy_global_symmetric_memory_buffer():
     """Sets the global symmetric memory buffer to None"""
-    global _GLOBAL_SYMMETRIC_MEMORY_BUFFER
-    _GLOBAL_SYMMETRIC_MEMORY_BUFFER = None
+    global _GLOBAL_SYMMETRIC_MEMORY_BUFFER_TP, _GLOBAL_SYMMETRIC_MEMORY_BUFFER_EP
+    _GLOBAL_SYMMETRIC_MEMORY_BUFFER_TP = None
+    _GLOBAL_SYMMETRIC_MEMORY_BUFFER_EP = None
 
 
 def get_all_ranks():
@@ -2140,8 +2162,11 @@ def destroy_model_parallel():
     global _GLOBAL_MEMORY_BUFFER
     _GLOBAL_MEMORY_BUFFER = None
 
-    global _GLOBAL_SYMMETRIC_MEMORY_BUFFER
-    _GLOBAL_SYMMETRIC_MEMORY_BUFFER = None
+    global _GLOBAL_SYMMETRIC_MEMORY_BUFFER_TP
+    _GLOBAL_SYMMETRIC_MEMORY_BUFFER_TP = None
+
+    global _GLOBAL_SYMMETRIC_MEMORY_BUFFER_EP
+    _GLOBAL_SYMMETRIC_MEMORY_BUFFER_EP = None
 
     global _DATA_PARALLEL_GROUP_GLOO
     if (
diff --git a/megatron/core/pipeline_parallel/schedules.py b/megatron/core/pipeline_parallel/schedules.py
index ed3794208f0..a266b2b34bc 100644
--- a/megatron/core/pipeline_parallel/schedules.py
+++ b/megatron/core/pipeline_parallel/schedules.py
@@ -20,7 +20,7 @@
     is_vp_last_stage,
 )
 from megatron.core.process_groups_config import ProcessGroupCollection
-from megatron.core.transformer.cuda_graphs import create_cudagraphs
+from megatron.core.transformer.cuda_graphs import create_cudagraphs, set_current_microbatch
 from megatron.core.transformer.enums import CudaGraphScope
 from megatron.core.transformer.moe.router import MoEAuxLossAutoScaler
 from megatron.core.utils import (
@@ -197,27 +197,6 @@ def custom_backward(output, grad_output):
     )
 
 
-def set_current_microbatch(model, microbatch_id):
-    """Set the current microbatch."""
-    decoder_exists = True
-    model_with_decoder = None
-    try:
-        model_with_decoder = get_attr_wrapped_model(
-            model, "decoder", allow_none=False, return_model_obj=True
-        )
-    except RuntimeError:
-        decoder_exists = False
-    if decoder_exists and model_with_decoder is not None:
-        for layer in model_with_decoder.decoder.layers:
-            layer.current_microbatch = microbatch_id
-        if hasattr(model_with_decoder, 'mtp'):
-            for layer in model_with_decoder.mtp.layers:
-                assert hasattr(
-                    layer, 'mtp_model_layer'
-                ), f"MTP layer {layer} must have 'mtp_model_layer' attribute"
-                layer.mtp_model_layer.current_microbatch = microbatch_id
-
-
 def forward_step_calc_loss(
     model,
     output_tensor,
@@ -689,7 +668,7 @@ def forward_backward_no_pipelining(
             force_all_reduce=force_all_reduce,
         )
 
-    if not forward_only and config.fine_grained_activation_offloading:
+    if getattr(config, 'fine_grained_activation_offloading', False):
         off_interface.reset()
 
     if config.timers is not None:
@@ -2062,7 +2041,7 @@ def pp_post_backward(input_tensor_grad, vp_stage=None):
             force_all_reduce=force_all_reduce,
         )
 
-    if not forward_only and config.fine_grained_activation_offloading:
+    if getattr(config, 'fine_grained_activation_offloading', False):
         off_interface.reset()
     # Restore config.grad_sync_func and config.param_sync_func.
     if forward_only:
@@ -2484,7 +2463,7 @@ def enable_grad_sync():
             force_all_reduce=force_all_reduce,
         )
 
-    if not forward_only and config.fine_grained_activation_offloading:
+    if getattr(config, 'fine_grained_activation_offloading', False):
         off_interface.reset()
 
     if config.timers is not None:
diff --git a/megatron/core/post_training/modelopt/layers.py b/megatron/core/post_training/modelopt/layers.py
index 4a82048d255..91071a0d50e 100644
--- a/megatron/core/post_training/modelopt/layers.py
+++ b/megatron/core/post_training/modelopt/layers.py
@@ -29,8 +29,6 @@
     else:
         HAVE_TE = False
 
-logger = logging.getLogger(__name__)
-
 
 FP8_PER_TENSOR_REAL_QUANT_CFG = {
     "quant_cfg": {
diff --git a/megatron/core/resharding/__init__.py b/megatron/core/resharding/__init__.py
index 083c4518c0e..8c59b6ef809 100644
--- a/megatron/core/resharding/__init__.py
+++ b/megatron/core/resharding/__init__.py
@@ -7,11 +7,14 @@
     reshard_model_weights,
     swap_model_weights,
 )
+from .transforms import MXFP8ReshardTransform, ReshardTransform
 from .utils import ParameterMetadata, ReshardPlan, ShardingDescriptor, TransferOp
 
 __all__ = [
     "build_centralized_reshard_plan",
     "execute_reshard_plan",
+    "MXFP8ReshardTransform",
+    "ReshardTransform",
     "swap_model_weights",
     "reshard_model_weights",
     "get_or_create_service",
diff --git a/megatron/core/resharding/copy_services/gloo_copy_service.py b/megatron/core/resharding/copy_services/gloo_copy_service.py
index c9c83ca74a5..5db53c4af3b 100644
--- a/megatron/core/resharding/copy_services/gloo_copy_service.py
+++ b/megatron/core/resharding/copy_services/gloo_copy_service.py
@@ -37,10 +37,15 @@ class GlooCopyService(CopyService):
     process group instead of NCCL.
     """
 
-    def __init__(self):
-        self.rank = dist.get_rank()
-        self.world_size = dist.get_world_size()
-        self.gloo_pg = dist.new_group(backend="gloo")
+    def __init__(self, group=None):
+        if group is not None:
+            self.gloo_pg = group
+            self.rank = group.rank()
+            self.world_size = group.size()
+        else:
+            self.rank = dist.get_rank()
+            self.world_size = dist.get_world_size()
+            self.gloo_pg = dist.new_group(backend="gloo")
         self.send_ops: List[SendOp] = []
         self.recv_ops: List[Tuple[RecvOp, torch.Tensor]] = []
         self._copy_stream = torch.cuda.Stream()
@@ -124,11 +129,18 @@ def run(self):
 
         # Build Gloo P2P ops over CPU tensors. For sends we clone to CPU;
         # for recvs we use the preallocated CPU buffers.
+        # Use group_peer (not peer) to pass ranks directly in group space,
+        # avoiding the global-to-group rank conversion in P2POp which doesn't
+        # work for cross-world ProcessGroups.
         for op in remote_sends:
             cpu_tensor = op.tensor.detach().to("cpu").contiguous()
-            p2p_ops.append(dist.P2POp(dist.isend, cpu_tensor, op.dest_rank, group=self.gloo_pg))
+            p2p_ops.append(
+                dist.P2POp(dist.isend, cpu_tensor, group=self.gloo_pg, group_peer=op.dest_rank)
+            )
         for recv, _dst_tensor in remote_recvs:
-            p2p_ops.append(dist.P2POp(dist.irecv, recv.tensor, recv.src_rank, group=self.gloo_pg))
+            p2p_ops.append(
+                dist.P2POp(dist.irecv, recv.tensor, group=self.gloo_pg, group_peer=recv.src_rank)
+            )
 
         if p2p_ops:
             reqs = dist.batch_isend_irecv(p2p_ops)
diff --git a/megatron/core/resharding/copy_services/nccl_copy_service.py b/megatron/core/resharding/copy_services/nccl_copy_service.py
index 8724279b991..dbf7e122ee3 100644
--- a/megatron/core/resharding/copy_services/nccl_copy_service.py
+++ b/megatron/core/resharding/copy_services/nccl_copy_service.py
@@ -37,9 +37,11 @@ class NCCLCopyService(CopyService):
     a batch of point-to-point sends and recvs.
     """
 
-    def __init__(self):
-        self.rank = dist.get_rank()
-        self.world_size = dist.get_world_size()
+    def __init__(self, group=None):
+        self.group = group
+        # Use group.rank()/size() to support cross-cluster ProcessGroups
+        self.rank = group.rank() if group is not None else dist.get_rank()
+        self.world_size = group.size() if group is not None else dist.get_world_size()
         self.send_ops: List[SendOp] = []
         self.recv_ops: List[RecvOp] = []
         # Dedicated stream for local (same-rank) copies to avoid unnecessary
@@ -111,9 +113,9 @@ def run(self):
 
         p2p_ops = []
         for op in remote_sends:
-            p2p_ops.append(dist.P2POp(dist.isend, op.tensor, op.dest_rank))
+            p2p_ops.append(dist.P2POp(dist.isend, op.tensor, op.dest_rank, group=self.group))
         for op in remote_recvs:
-            p2p_ops.append(dist.P2POp(dist.irecv, op.tensor, op.src_rank))
+            p2p_ops.append(dist.P2POp(dist.irecv, op.tensor, op.src_rank, group=self.group))
 
         if p2p_ops:
             reqs = dist.batch_isend_irecv(p2p_ops)
diff --git a/megatron/core/resharding/copy_services/nvshmem_copy_service.py b/megatron/core/resharding/copy_services/nvshmem_copy_service.py
index 14d83a1864e..a0aea54676f 100644
--- a/megatron/core/resharding/copy_services/nvshmem_copy_service.py
+++ b/megatron/core/resharding/copy_services/nvshmem_copy_service.py
@@ -17,12 +17,13 @@
 class NVSHMEMCopyService(CopyService):
     """CopyService implementation backed by NVSHMEM RemoteCopyService."""
 
-    def __init__(self):
+    def __init__(self, group=None):
         if not dist.is_initialized():
             raise RuntimeError("torch.distributed must be initialized before NVSHMEMCopyService()")
 
-        self.rank = dist.get_rank()
-        self._remote = RemoteCopyService()
+        self._group = group
+        self.rank = group.rank() if group is not None else dist.get_rank()
+        self._remote = RemoteCopyService(group=group)
         # Lazily initialized on first use to avoid side effects at import time
         self._initialized = False
 
diff --git a/megatron/core/resharding/execution.py b/megatron/core/resharding/execution.py
index 7c7c4700754..e0ed0e09fb9 100644
--- a/megatron/core/resharding/execution.py
+++ b/megatron/core/resharding/execution.py
@@ -2,22 +2,34 @@
 from __future__ import annotations
 
 import logging
-from typing import List, Tuple
+from typing import Optional
 
 import torch
 import torch.distributed as dist
 
 from .copy_services.base import CopyService
+from .transforms import ReshardTransform, _ensure_sendable
 from .utils import ReshardPlan
 
 logger = logging.getLogger(__name__)
 
 
+def _is_mxfp8_tensor(param):
+    """Check if param is a TE MXFP8Tensor (fp8_param=true)."""
+    return (
+        hasattr(param, 'quantize_')
+        and hasattr(param, 'dequantize')
+        and hasattr(param, '_rowwise_data')
+    )
+
+
 def execute_reshard_plan(
     plan: ReshardPlan,
     src_module: torch.nn.Module,
     dst_module: torch.nn.Module,
     service: CopyService,
+    group=None,
+    transform: Optional[ReshardTransform] = None,
 ) -> None:
     """
     Execute a reshard plan (from centralized controller).
@@ -28,6 +40,11 @@ def execute_reshard_plan(
     - src_module=None: Rank only receives data (destination-only)
     - dst_module=None: Rank only sends data (source-only)
     - Both provided: Rank participates in both send and recv (collocated mode)
+
+    When *transform* is provided, parameters for which
+    ``transform.should_transform(param_name)`` returns True use the
+    transform's prepare_send / prepare_recv / finalize_recv methods instead
+    of the default slice-and-copy logic.
     """
 
     # Extract parameters from models if present
@@ -43,35 +60,92 @@ def execute_reshard_plan(
 
     # Submit sends (only if we have source model)
     for op in plan.send_ops:
-        src_param = src_params.get(op.param_name)
-        if src_param is not None:
-            src_view = src_param.data[op.my_slice].contiguous()
-            if submit_send_with_id is not None and op.task_id is not None:
-                submit_send_with_id(op.task_id, src_view, op.peer_rank)
-            else:
-                service.submit_send(src_view, op.peer_rank)
+        if transform is not None and transform.should_transform(op.param_name):
+            src_param = src_params.get(op.param_name)
+            if src_param is not None:
+                tensors = transform.prepare_send(op.param_name, op.my_slice, src_param)
+                for t in tensors:
+                    buf = t.contiguous()
+                    if submit_send_with_id is not None and op.task_id is not None:
+                        submit_send_with_id(op.task_id, buf, op.peer_rank)
+                    else:
+                        service.submit_send(buf, op.peer_rank)
+        else:
+            src_param = src_params.get(op.param_name)
+            if src_param is not None:
+                src_view = _ensure_sendable(src_param)[op.my_slice].contiguous()
+                if submit_send_with_id is not None and op.task_id is not None:
+                    submit_send_with_id(op.task_id, src_view, op.peer_rank)
+                else:
+                    service.submit_send(src_view, op.peer_rank)
 
     # Submit recvs (only if we have destination model)
-    recv_writebacks: List[Tuple[torch.Tensor, torch.nn.Parameter, tuple[slice, ...]]] = []
+    # Writebacks: each entry is either
+    #   ('default', recv_buffer, dst_param, dst_slice)  or
+    #   ('transform', param_name, dst_slice, [recv_buffers])
+    recv_writebacks: list = []
+
     for op in plan.recv_ops:
-        dst_param = dst_params.get(op.param_name)
-        if dst_param is not None:
-            dst_slice_view = dst_param.data[op.my_slice]
-            recv_buffer = torch.empty_like(dst_slice_view.contiguous())
-            if submit_recv_with_id is not None and op.task_id is not None:
-                submit_recv_with_id(op.task_id, recv_buffer, op.peer_rank)
-            else:
-                service.submit_recv(recv_buffer, op.peer_rank)
-            recv_writebacks.append((recv_buffer, dst_param, op.my_slice))
+        if transform is not None and transform.should_transform(op.param_name):
+            recv_bufs = transform.prepare_recv(op.param_name, op.my_slice)
+            for buf in recv_bufs:
+                if submit_recv_with_id is not None and op.task_id is not None:
+                    submit_recv_with_id(op.task_id, buf, op.peer_rank)
+                else:
+                    service.submit_recv(buf, op.peer_rank)
+            recv_writebacks.append(('transform', op.param_name, op.my_slice, recv_bufs))
+        else:
+            dst_param = dst_params.get(op.param_name)
+            if dst_param is not None:
+                dst_slice_view = dst_param.data[op.my_slice]
+                recv_buffer = torch.empty_like(dst_slice_view.contiguous())
+                if submit_recv_with_id is not None and op.task_id is not None:
+                    submit_recv_with_id(op.task_id, recv_buffer, op.peer_rank)
+                else:
+                    service.submit_recv(recv_buffer, op.peer_rank)
+                recv_writebacks.append(('default', recv_buffer, dst_param, op.my_slice))
 
     # Execute
     logger.info(f"Executing {len(plan.send_ops)} sends + {len(plan.recv_ops)} recvs")
     service.run()
-    dist.barrier()
+    torch.cuda.synchronize()
+    dist.barrier(group=group)
 
-    # Write back received buffers into their destination parameter slices
-    for recv_buffer, dst_param, dst_slice in recv_writebacks:
+    # Write back received buffers into their destination parameter slices.
+    #
+    # For quantized destination params (fp8_param=true on receiver),
+    # accumulate ALL BF16 slices per-param before calling quantize_() once.
+    # This avoids corrupting MXFP8 per-block scales from partial-slice updates.
+    pending_quantized: dict[int, tuple[torch.nn.Parameter, torch.Tensor, list]] = {}
+
+    for wb in recv_writebacks:
         with torch.no_grad():
-            dst_param.data[dst_slice].copy_(recv_buffer)
+            if wb[0] == 'transform':
+                _, param_name, dst_slice, recv_bufs = wb
+                transform.finalize_recv(param_name, dst_slice, recv_bufs)
+            else:
+                _, recv_buffer, dst_param, dst_slice = wb
+                if _is_mxfp8_tensor(dst_param):
+                    # Accumulate BF16 slices for deferred quantization
+                    param_id = id(dst_param)
+                    if param_id not in pending_quantized:
+                        full_bf16 = dst_param.dequantize().clone()
+                        pending_quantized[param_id] = (dst_param, full_bf16, [])
+                    pending_quantized[param_id][2].append((dst_slice, recv_buffer))
+                    pending_quantized[param_id][1][dst_slice].copy_(recv_buffer)
+                else:
+                    dst_param.data[dst_slice].copy_(recv_buffer)
+
+    # Finalize deferred quantized param updates
+    for param_id, (dst_param, full_bf16, slices) in pending_quantized.items():
+        with torch.no_grad():
+            dst_param.quantize_(full_bf16)
+
+    # Ensure all writeback copies are visible to subsequent CUDA ops (e.g. CUDA
+    # graph warmup).  The synchronize() above fires *before* the writeback loop,
+    # so without this second sync the .copy_() kernels are still async when
+    # execute_reshard_plan returns — creating a race with callers that immediately
+    # inspect or capture (via CUDA graphs) the destination parameters.
+    torch.cuda.synchronize()
 
     logger.info("Reshard complete")
diff --git a/megatron/core/resharding/nvshmem_copy_service/__init__.py b/megatron/core/resharding/nvshmem_copy_service/__init__.py
index 2ab8cde81fe..00c1d808a68 100644
--- a/megatron/core/resharding/nvshmem_copy_service/__init__.py
+++ b/megatron/core/resharding/nvshmem_copy_service/__init__.py
@@ -8,6 +8,10 @@
 can use it without relying on an external library.
 """
 
+from .compat import ensure_nvshmem_compat
+
+ensure_nvshmem_compat()
+
 from . import nvshmem_types
 from .core import GPUResourceManager, KernelLauncher, PipelineExecutor
 from .memory import DoubleBufferManager, TensorPointerExtractor
diff --git a/megatron/core/resharding/nvshmem_copy_service/compat.py b/megatron/core/resharding/nvshmem_copy_service/compat.py
new file mode 100644
index 00000000000..be624c3ba26
--- /dev/null
+++ b/megatron/core/resharding/nvshmem_copy_service/compat.py
@@ -0,0 +1,49 @@
+# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
+
+"""
+Compatibility layer for cuda-core version differences.
+
+cuda-core >=0.5 removed the ``cuda.core.experimental._memory`` and
+``cuda.core.experimental._stream`` private submodules, but nvshmem4py
+still imports from them.  We register ``sys.modules`` shims so those
+imports resolve to the new ``cuda.core._memory`` / ``cuda.core._stream``
+paths.
+
+This module should be imported before any nvshmem.core usage.
+"""
+
+import importlib
+import sys
+
+
+def _patch_cuda_core_experimental():
+    """Register cuda.core._memory / _stream as cuda.core.experimental._memory / _stream."""
+    for submod in ("_memory", "_stream"):
+        exp_key = f"cuda.core.experimental.{submod}"
+        new_key = f"cuda.core.{submod}"
+        if exp_key not in sys.modules:
+            try:
+                sys.modules[exp_key] = importlib.import_module(new_key)
+            except ImportError:
+                pass  # old cuda-core that still has experimental._memory
+
+
+def get_cuda_core_device_class():
+    """Return the ``Device`` class from whichever cuda-core location is available.
+
+    cuda-core <0.5: ``cuda.core.experimental.Device``
+    cuda-core >=0.5: ``cuda.core.Device``
+    """
+    try:
+        from cuda.core import Device
+
+        return Device
+    except ImportError:
+        from cuda.core.experimental import Device
+
+        return Device
+
+
+def ensure_nvshmem_compat():
+    """Apply all compatibility patches.  Safe to call multiple times."""
+    _patch_cuda_core_experimental()
diff --git a/megatron/core/resharding/nvshmem_copy_service/core/gpu_resource_manager.py b/megatron/core/resharding/nvshmem_copy_service/core/gpu_resource_manager.py
index 6e03b914b26..db6a7743e7c 100644
--- a/megatron/core/resharding/nvshmem_copy_service/core/gpu_resource_manager.py
+++ b/megatron/core/resharding/nvshmem_copy_service/core/gpu_resource_manager.py
@@ -10,9 +10,14 @@
 import logging
 from typing import Dict, Optional
 
+from ..compat import ensure_nvshmem_compat, get_cuda_core_device_class
+
+ensure_nvshmem_compat()
+
 try:
     import nvshmem.core
-    from cuda.core.experimental import Device
+
+    Device = get_cuda_core_device_class()
 
     HAVE_NVSHMEM = True
 except ImportError:
@@ -40,19 +45,23 @@ def __init__(self):
         self.copy_stream = None
 
         # PyTorch stream wrappers
-        self.torch_pack_stream = None
-        self.torch_unpack_stream = None
-        self.torch_send_stream = None
-        self.torch_copy_stream = None
+        self.torch_pack_stream_wrapper = None
+        self.torch_unpack_stream_wrapper = None
+        self.torch_send_stream_wrapper = None
+        self.torch_copy_stream_wrapper = None
 
         # Stream name to PyTorch stream mapping
         self._torch_streams: Dict[str, torch.cuda.ExternalStream] = {}
 
-    def init(self) -> None:
+    def init(self, group=None) -> None:
         """
         Initialize NVSHMEM, CUDA device, and streams.
 
         Expects torch.distributed to be already initialized.
+
+        Args:
+            group: Optional ProcessGroup for distributed operations.
+                   If None, uses the default process group.
         """
         if self.initialized:
             return
@@ -75,9 +84,12 @@ def init(self) -> None:
         self.device = Device(local_rank)
         self.device.set_current()
 
-        # Extract rank, nranks from the default process group
-        num_ranks = dist.get_world_size()
-        rank_id = dist.get_rank()
+        # Extract rank, nranks from the process group.
+        # Use group.rank()/size() instead of dist.get_rank(group=) because
+        # dist.get_rank(group=) maps via the default PG rank, which is wrong
+        # for cross-cluster ProcessGroups where workers share default PG rank 0.
+        num_ranks = group.size() if group is not None else dist.get_world_size()
+        rank_id = group.rank() if group is not None else dist.get_rank()
 
         # Create/Broadcast UniqueID using broadcast_object_list
         uniqueid = nvshmem.core.get_unique_id(empty=True)
@@ -87,11 +99,11 @@ def init(self) -> None:
         else:
             broadcast_objects = [None]
 
-        # Broadcast ID to all ranks using the default group
-        dist.broadcast_object_list(broadcast_objects, src=0)
+        # Broadcast ID to all ranks
+        dist.broadcast_object_list(broadcast_objects, src=0, group=group)
 
         # Barrier to ensure everyone has the ID before NVSHMEM init
-        dist.barrier()
+        dist.barrier(group=group)
 
         # Initialize NVSHMEM with the broadcasted UID
         nvshmem.core.init(
@@ -119,17 +131,17 @@ def init(self) -> None:
         _, send_stream_ptr = self.send_stream.__cuda_stream__()
         _, copy_stream_ptr = self.copy_stream.__cuda_stream__()
 
-        self.torch_pack_stream = torch.cuda.ExternalStream(pack_stream_ptr)
-        self.torch_unpack_stream = torch.cuda.ExternalStream(unpack_stream_ptr)
-        self.torch_send_stream = torch.cuda.ExternalStream(send_stream_ptr)
-        self.torch_copy_stream = torch.cuda.ExternalStream(copy_stream_ptr)
+        self.torch_pack_stream_wrapper = torch.cuda.ExternalStream(pack_stream_ptr)
+        self.torch_unpack_stream_wrapper = torch.cuda.ExternalStream(unpack_stream_ptr)
+        self.torch_send_stream_wrapper = torch.cuda.ExternalStream(send_stream_ptr)
+        self.torch_copy_stream_wrapper = torch.cuda.ExternalStream(copy_stream_ptr)
 
         # Build stream mapping
         self._torch_streams = {
-            "pack": self.torch_pack_stream,
-            "unpack": self.torch_unpack_stream,
-            "send": self.torch_send_stream,
-            "copy": self.torch_copy_stream,
+            "pack": self.torch_pack_stream_wrapper,
+            "unpack": self.torch_unpack_stream_wrapper,
+            "send": self.torch_send_stream_wrapper,
+            "copy": self.torch_copy_stream_wrapper,
         }
 
         logger.info("Stream mapping built")
@@ -171,18 +183,19 @@ def get_torch_stream(self, name: str) -> Optional[torch.cuda.ExternalStream]:
 
     def create_events(self, num_events: int = 2):
         """
-        Create double-buffered CUDA events for pack and unpack operations.
+        Create double-buffered CUDA events for pack, unpack, and barrier operations.
 
         Args:
             num_events: Number of events to create for each type
                 (default: 2 for double buffering)
 
         Returns:
-            tuple: (pack_events, unpack_events) lists of torch.cuda.Event
+            tuple: (pack_events, unpack_events, barrier_events) lists of torch.cuda.Event
         """
         pack_events = [torch.cuda.Event(enable_timing=False) for _ in range(num_events)]
         unpack_events = [torch.cuda.Event(enable_timing=False) for _ in range(num_events)]
-        return pack_events, unpack_events
+        barrier_events = [torch.cuda.Event(enable_timing=False) for _ in range(num_events)]
+        return pack_events, unpack_events, barrier_events
 
     def finalize(self) -> None:
         """Cleanup resources (streams are automatically managed by CUDA)."""
diff --git a/megatron/core/resharding/nvshmem_copy_service/core/kernel_launcher.py b/megatron/core/resharding/nvshmem_copy_service/core/kernel_launcher.py
index 4e86d6a9505..1e49451741c 100644
--- a/megatron/core/resharding/nvshmem_copy_service/core/kernel_launcher.py
+++ b/megatron/core/resharding/nvshmem_copy_service/core/kernel_launcher.py
@@ -64,7 +64,7 @@ def launch_pack(
         self,
         gpu_plan: Tuple[Any, Any, Any, int],
         pack_stream,
-        torch_pack_stream: torch.cuda.ExternalStream,
+        torch_pack_stream_wrapper: torch.cuda.ExternalStream,
         pack_event: torch.cuda.Event,
     ) -> None:
         """
@@ -75,7 +75,7 @@ def launch_pack(
                 as CuPy arrays
             pack_stream: CUDA stream (cuda.core.experimental.Stream) - unused,
                 kept for compatibility
-            torch_pack_stream: PyTorch external stream wrapper
+            torch_pack_stream_wrapper: PyTorch external stream wrapper
             pack_event: CUDA event to record after kernel launch
         """
         nvtx.range_push("Launch Pack Kernel")
@@ -101,13 +101,13 @@ def launch_pack(
         )
         nvtx.range_pop()
         # Record event on PyTorch stream
-        pack_event.record(stream=torch_pack_stream)
+        pack_event.record(stream=torch_pack_stream_wrapper)
 
     def launch_unpack(
         self,
         gpu_plan: Tuple[Any, Any, Any, int],
         unpack_stream,
-        torch_unpack_stream: torch.cuda.ExternalStream,
+        torch_unpack_stream_wrapper: torch.cuda.ExternalStream,
         unpack_event: torch.cuda.Event,
     ) -> None:
         """
@@ -118,7 +118,7 @@ def launch_unpack(
                 as CuPy arrays
             unpack_stream: CUDA stream (cuda.core.experimental.Stream) - unused,
             kept for compatibility
-            torch_unpack_stream: PyTorch external stream wrapper
+            torch_unpack_stream_wrapper: PyTorch external stream wrapper
             unpack_event: CUDA event to record after kernel launch
         """
         nvtx.range_push("Launch Unpack Kernel")
@@ -144,4 +144,4 @@ def launch_unpack(
         )
         nvtx.range_pop()
         # Record event on PyTorch stream
-        unpack_event.record(stream=torch_unpack_stream)
+        unpack_event.record(stream=torch_unpack_stream_wrapper)
diff --git a/megatron/core/resharding/nvshmem_copy_service/core/pipeline_executor.py b/megatron/core/resharding/nvshmem_copy_service/core/pipeline_executor.py
index 5ba07f9956a..6e78c55d98b 100644
--- a/megatron/core/resharding/nvshmem_copy_service/core/pipeline_executor.py
+++ b/megatron/core/resharding/nvshmem_copy_service/core/pipeline_executor.py
@@ -9,6 +9,10 @@
 
 from typing import Dict, List, Optional
 
+from ..compat import ensure_nvshmem_compat
+
+ensure_nvshmem_compat()
+
 try:
     import nvshmem.core
 
@@ -48,13 +52,15 @@ def __init__(
         self.send_stream = None
         self.copy_stream = None
 
-        self.torch_pack_stream = None
-        self.torch_unpack_stream = None
-        self.torch_copy_stream = None
+        self.torch_pack_stream_wrapper = None
+        self.torch_unpack_stream_wrapper = None
+        self.torch_send_stream_wrapper = None
+        self.torch_copy_stream_wrapper = None
 
         # Events for double-buffered synchronization
         self.pack_events = []
         self.unpack_events = []
+        self.barrier_events = []
 
     def set_streams(
         self,
@@ -62,9 +68,10 @@ def set_streams(
         unpack_stream,
         send_stream,
         copy_stream,
-        torch_pack_stream,
-        torch_unpack_stream,
-        torch_copy_stream,
+        torch_pack_stream_wrapper,
+        torch_unpack_stream_wrapper,
+        torch_send_stream_wrapper,
+        torch_copy_stream_wrapper,
     ):
         """Set CUDA streams for execution."""
         self.pack_stream = pack_stream
@@ -72,14 +79,16 @@ def set_streams(
         self.send_stream = send_stream
         self.copy_stream = copy_stream
 
-        self.torch_pack_stream = torch_pack_stream
-        self.torch_unpack_stream = torch_unpack_stream
-        self.torch_copy_stream = torch_copy_stream
+        self.torch_pack_stream_wrapper = torch_pack_stream_wrapper
+        self.torch_unpack_stream_wrapper = torch_unpack_stream_wrapper
+        self.torch_send_stream_wrapper = torch_send_stream_wrapper
+        self.torch_copy_stream_wrapper = torch_copy_stream_wrapper
 
-    def set_events(self, pack_events: List, unpack_events: List):
+    def set_events(self, pack_events: List, unpack_events: List, barrier_events: List):
         """Set double-buffered CUDA events."""
         self.pack_events = pack_events
         self.unpack_events = unpack_events
+        self.barrier_events = barrier_events
 
     def execute_pipeline(
         self, iter_schedules: List[Dict[str, Optional[ScheduledBatch]]], num_iterations: int
@@ -90,22 +99,28 @@ def execute_pipeline(
         Pipeline stages:
         1. Pack NEXT iteration (async)
         2. Unpack PRIOR iteration (async)
-        3. Send CURRENT iteration (sync)
-        4. Barrier
+        3. Send CURRENT iteration
+        4. Barrier + record barrier event
         5. Wait for async pack/unpack to complete
 
+        Cross-stream synchronization uses lightweight CUDA events instead of
+        cudaDeviceSynchronize (torch.cuda.synchronize). The pack kernel includes
+        __threadfence_system() to ensure writes are visible to the NIC's DMA
+        engine, and barrier_events propagate NVSHMEM RDMA completion from
+        send_stream to unpack_stream.
+
         Args:
             iter_schedules: List of iteration schedules
             num_iterations: Total number of iterations
         """
         PELogger.info(f"Executing pipeline: {num_iterations} iterations")
 
-        # Priming: Pack iteration 0 and WAIT for completion
+        # Priming: Pack iteration 0 (async, no CPU sync needed —
+        # step 3 uses GPU-level event wait for pack→put ordering)
         if num_iterations > 0 and iter_schedules[0]["send"]:
             torch.cuda.nvtx.range_push("Priming")
             PELogger.debug("Priming: Packing iteration 0")
             self._launch_pack(0, iter_schedules[0]["send"])
-            self.pack_events[0].synchronize()
             torch.cuda.nvtx.range_pop()
 
         for i in range(num_iterations):
@@ -153,6 +168,9 @@ def execute_pipeline(
                     f"  Unpack prior (iter {i-1}): {prior_batch.total_size} bytes "
                     f"← PE {prior_batch.src_pe}"
                 )
+                # GPU-level event wait: ensures send_stream's barrier_all from
+                # the prior iteration has completed before unpack_stream proceeds.
+                self.torch_unpack_stream_wrapper.wait_event(self.barrier_events[(i - 1) % 2])
                 self._launch_unpack(i - 1, prior_batch)
                 torch.cuda.nvtx.range_pop()
 
@@ -164,6 +182,12 @@ def execute_pipeline(
                 transfer_size = batch.total_size
                 PELogger.debug(f"  Send current: {transfer_size} bytes → PE {batch.dest_pe}")
 
+                # GPU-level event wait: ensures pack data in send_slot is visible
+                # to send_stream before NVSHMEM put reads it. The pack kernel's
+                # __threadfence_system() guarantees the writes are also visible to
+                # the NIC's DMA engine.
+                self.torch_send_stream_wrapper.wait_event(self.pack_events[slot])
+
                 nvshmem.core.put(
                     self.buffer_manager.recv_slots[slot][0:transfer_size],
                     self.buffer_manager.send_slots[slot][0:transfer_size],
@@ -172,20 +196,29 @@ def execute_pipeline(
                 )
                 torch.cuda.nvtx.range_pop()
 
-            # Ensure send completes
-            self.send_stream.sync()
+            # Step 4a: Wait for prior unpack to complete BEFORE the barrier.
+            torch.cuda.nvtx.range_push("Step 4a: Wait Unpack")
+            if has_prior_recv:
+                self.unpack_events[(i - 1) % 2].synchronize()
+            torch.cuda.nvtx.range_pop()
+
+            # Ensure all NVSHMEM operations on send_stream complete (stream-ordered)
             nvshmem.core.quiet(stream=self.send_stream)
 
-            # Step 4: Global barrier
-            torch.cuda.nvtx.range_push("Step 4: Barrier")
+            # Step 4b: Global barrier + CPU sync + record event
+            torch.cuda.nvtx.range_push("Step 4b: Barrier")
             nvshmem.core.barrier_all(stream=self.send_stream)
-            self.send_stream.sync()
+            # CPU-sync the send_stream to ensure barrier_all has actually
+            # completed (not just submitted). Without this, the barrier_event
+            # can fire before RDMA data from the remote PE is visible, because
+            # stream-ordered operations are only guaranteed to be submitted,
+            # not completed, when the event is recorded.
+            self.torch_send_stream_wrapper.synchronize()
+            self.barrier_events[slot].record(stream=self.torch_send_stream_wrapper)
             torch.cuda.nvtx.range_pop()
 
-            # Step 5: Wait for async pack/unpack to complete
-            torch.cuda.nvtx.range_push("Step 5: Wait Async")
-            if has_prior_recv:
-                self.unpack_events[(i - 1) % 2].synchronize()
+            # Step 5: Wait for async pack to complete (double-buffer safety)
+            torch.cuda.nvtx.range_push("Step 5: Wait Pack")
             if has_next_send:
                 self.pack_events[(i + 1) % 2].synchronize()
             torch.cuda.nvtx.range_pop()
@@ -198,6 +231,10 @@ def execute_pipeline(
             PELogger.debug(f"Final unpack: iteration {num_iterations-1}")
             last_recv = iter_schedules[num_iterations - 1]["recv"]
             assert last_recv is not None
+            # GPU-level event wait for NVSHMEM RDMA data visibility
+            self.torch_unpack_stream_wrapper.wait_event(
+                self.barrier_events[(num_iterations - 1) % 2]
+            )
             self._launch_unpack(num_iterations - 1, last_recv)
             self.unpack_events[(num_iterations - 1) % 2].synchronize()
             torch.cuda.nvtx.range_pop()
@@ -212,7 +249,7 @@ def _launch_pack(self, iteration: int, batch: ScheduledBatch) -> None:
         self.kernel_launcher.launch_pack(
             batch.gpu_plan,
             self.pack_stream,
-            self.torch_pack_stream,
+            self.torch_pack_stream_wrapper,
             self.pack_events[iteration % 2],
         )
 
@@ -224,7 +261,7 @@ def _launch_unpack(self, iteration: int, batch: ScheduledBatch) -> None:
         self.kernel_launcher.launch_unpack(
             batch.gpu_plan,
             self.unpack_stream,
-            self.torch_unpack_stream,
+            self.torch_unpack_stream_wrapper,
             self.unpack_events[iteration % 2],
         )
 
@@ -248,7 +285,7 @@ def process_self_moves(
             PELogger.debug(f"Processing {len(local_recvs)} self-moves")
 
         num_processed = 0
-        with torch.cuda.stream(self.torch_copy_stream):
+        with torch.cuda.stream(self.torch_copy_stream_wrapper):
             for recv_req in local_recvs:
                 if recv_req.task_id in local_sends:
                     send_req = local_sends[recv_req.task_id]
@@ -269,7 +306,7 @@ def process_self_moves(
                     num_processed += 1
 
         # Synchronize the PyTorch stream
-        self.torch_copy_stream.synchronize()
+        self.torch_copy_stream_wrapper.synchronize()
 
         if num_processed > 0:
             PELogger.info("Self-moves complete: %d transfers", num_processed)
diff --git a/megatron/core/resharding/nvshmem_copy_service/kernels/chunked_kernel.cu b/megatron/core/resharding/nvshmem_copy_service/kernels/chunked_kernel.cu
index e5b8fcc9a85..8b46c4f4ac7 100644
--- a/megatron/core/resharding/nvshmem_copy_service/kernels/chunked_kernel.cu
+++ b/megatron/core/resharding/nvshmem_copy_service/kernels/chunked_kernel.cu
@@ -96,6 +96,13 @@ __global__ void chunked_batched_copy_kernel(
             }
         }
     }
+
+    // System-wide memory fence: ensures all writes from this thread are visible
+    // to system agents (NIC DMA engines, other GPUs via PCIe/NVLink).
+    // Required so that NVSHMEM put() on another CUDA stream can read correct
+    // pack data from the send buffer. Without this, writes may remain in L2
+    // cache, invisible to the NIC's DMA engine.
+    __threadfence_system();
 }
 
 }
diff --git a/megatron/core/resharding/nvshmem_copy_service/memory/double_buffer_manager.py b/megatron/core/resharding/nvshmem_copy_service/memory/double_buffer_manager.py
index 079b2c17610..bfdaac7eb4b 100644
--- a/megatron/core/resharding/nvshmem_copy_service/memory/double_buffer_manager.py
+++ b/megatron/core/resharding/nvshmem_copy_service/memory/double_buffer_manager.py
@@ -6,6 +6,10 @@
 Manages send and receive buffers with double-buffering for pipelined communication.
 """
 
+from ..compat import ensure_nvshmem_compat
+
+ensure_nvshmem_compat()
+
 try:
     import nvshmem.core.interop.torch
 
diff --git a/megatron/core/resharding/nvshmem_copy_service/planning/communication_scheduler.py b/megatron/core/resharding/nvshmem_copy_service/planning/communication_scheduler.py
index 842f2b9d300..629c9958db9 100644
--- a/megatron/core/resharding/nvshmem_copy_service/planning/communication_scheduler.py
+++ b/megatron/core/resharding/nvshmem_copy_service/planning/communication_scheduler.py
@@ -17,7 +17,7 @@ def __init__(self):
         self.num_iterations = 0
 
     def build_schedule(
-        self, workloads: Dict[int, List[WorkloadGroup]], my_pe: int, n_pes: int
+        self, workloads: Dict[int, List[WorkloadGroup]], my_pe: int, n_pes: int, group=None
     ) -> Tuple[Dict[int, List[ScheduledBatch]], Dict[Tuple[int, int, int], WorkloadSummary]]:
         """
         Main scheduling method.
@@ -26,13 +26,19 @@ def build_schedule(
         3. Returns:
            - local schedule (iteration -> list of batches)
            - global workload summaries (key: (src, dest, batch_idx) -> summary)
+
+        Args:
+            workloads: Dict mapping destination PE to list of workload groups.
+            my_pe: This PE's rank.
+            n_pes: Total number of PEs.
+            group: Optional ProcessGroup for distributed operations.
         """
         total_local_batches = sum(len(groups) for groups in workloads.values())
         PELogger.info(f"Building schedule: {total_local_batches} local batches, {n_pes} PEs")
 
         # Step 1: Collect all batches across all PE pairs
         PELogger.debug("Collecting batches from all PEs...")
-        all_batches = self._collect_all_batches(workloads, my_pe, n_pes)
+        all_batches = self._collect_all_batches(workloads, my_pe, n_pes, group=group)
         PELogger.debug(f"Collected {len(all_batches)} total batches globally")
 
         # Step 2: Assign batches to iterations using greedy conflict-free algorithm
@@ -43,7 +49,7 @@ def build_schedule(
         # Step 3: Exchange detailed workload summaries (Task IDs/Sizes)
         # This is needed for receivers to know what tasks are in each batch
         PELogger.debug("Exchanging workload summaries...")
-        global_summaries = self._exchange_workload_summaries(workloads, my_pe, n_pes)
+        global_summaries = self._exchange_workload_summaries(workloads, my_pe, n_pes, group=group)
         PELogger.debug(f"Exchanged {len(global_summaries)} workload summaries")
 
         # Step 4: Build schedule map for this PE
@@ -57,7 +63,7 @@ def build_schedule(
         return final_schedule, global_summaries
 
     def _collect_all_batches(
-        self, workloads: Dict[int, List[WorkloadGroup]], my_pe: int, n_pes: int
+        self, workloads: Dict[int, List[WorkloadGroup]], my_pe: int, n_pes: int, group=None
     ) -> List[ScheduledBatch]:
         """
         Exchanges batch counts and details with all PEs to build a global view.
@@ -78,7 +84,7 @@ def _collect_all_batches(
 
         # Gather all batches from all PEs using torch.distributed
         all_batches_list: List[List[Tuple[int, int, int]] | None] = [None] * n_pes
-        dist.all_gather_object(all_batches_list, local_batches)
+        dist.all_gather_object(all_batches_list, local_batches, group=group)
 
         # Flatten into global batch list
         global_batches: List[ScheduledBatch] = []
@@ -187,7 +193,7 @@ def has_conflict(batch: ScheduledBatch, iteration_state: Dict) -> bool:
         )
 
     def _exchange_workload_summaries(
-        self, workloads: Dict[int, List[WorkloadGroup]], my_pe: int, n_pes: int
+        self, workloads: Dict[int, List[WorkloadGroup]], my_pe: int, n_pes: int, group=None
     ) -> Dict[Tuple[int, int, int], WorkloadSummary]:
         """
         Exchange detailed workload content using torch.distributed.
@@ -204,15 +210,15 @@ def _exchange_workload_summaries(
         for dest_pe, groups in workloads.items():
             if dest_pe == my_pe:
                 continue
-            for batch_idx, group in enumerate(groups):
+            for batch_idx, wl_group in enumerate(groups):
                 key = (my_pe, dest_pe, batch_idx)
                 local_summaries[key] = {
-                    "total_size": group.total_size,
-                    "task_ids": [t.task_id for t in group.tasks],
-                    "task_sizes": [t.size for t in group.tasks],
+                    "total_size": wl_group.total_size,
+                    "task_ids": [t.task_id for t in wl_group.tasks],
+                    "task_sizes": [t.size for t in wl_group.tasks],
                 }
                 batch_count += 1
-                total_tasks += len(group.tasks)
+                total_tasks += len(wl_group.tasks)
 
         PELogger.debug(f"  Local summaries: {batch_count} batches, {total_tasks} tasks")
 
@@ -220,7 +226,7 @@ def _exchange_workload_summaries(
         all_summaries_list: List[Dict[Tuple[int, int, int], Dict[str, object]] | None] = [
             None
         ] * n_pes
-        dist.all_gather_object(all_summaries_list, local_summaries)
+        dist.all_gather_object(all_summaries_list, local_summaries, group=group)
 
         # Merge into global map
         global_map: Dict[Tuple[int, int, int], WorkloadSummary] = {}
diff --git a/megatron/core/resharding/nvshmem_copy_service/service.py b/megatron/core/resharding/nvshmem_copy_service/service.py
index c538fd64f5b..f5665088bfd 100644
--- a/megatron/core/resharding/nvshmem_copy_service/service.py
+++ b/megatron/core/resharding/nvshmem_copy_service/service.py
@@ -10,6 +10,10 @@
 
 from typing import Dict, List, Optional, Tuple
 
+from .compat import ensure_nvshmem_compat
+
+ensure_nvshmem_compat()
+
 try:
     import nvshmem.core
 
@@ -17,6 +21,7 @@
 except ImportError:
     HAVE_NVSHMEM = False
 
+import torch
 import torch.cuda.nvtx as nvtx
 
 from .core import GPUResourceManager, KernelLauncher, PipelineExecutor
@@ -34,7 +39,7 @@ class RemoteCopyService:
     and executing pipelined communication with NVSHMEM.
     """
 
-    def __init__(self):
+    def __init__(self, group=None):
         # Core components
         self.gpu_resources = GPUResourceManager()
         self.buffer_manager = DoubleBufferManager()
@@ -47,6 +52,9 @@ def __init__(self):
         self.comm_scheduler = CommunicationScheduler()
         self.gpu_planner = GPUExecutionPlanner()
 
+        # Optional process group for distributed operations
+        self._group = group
+
         # State
         self.send_requests: List[SendRequest] = []
         self.receive_requests: List[ReceiveRequest] = []
@@ -56,6 +64,7 @@ def __init__(self):
         # Events for double-buffering
         self.pack_events = []
         self.unpack_events = []
+        self.barrier_events = []
 
     @property
     def my_pe(self) -> int:
@@ -93,7 +102,7 @@ def init(self, log_level: str = "INFO") -> None:
             )
 
         # Initialize GPU resources (NVSHMEM, device, streams)
-        self.gpu_resources.init()
+        self.gpu_resources.init(group=self._group)
 
         # Initialize logger after PE ID is known
         PELogger.init(self.my_pe, level=log_level)
@@ -108,6 +117,12 @@ def init(self, log_level: str = "INFO") -> None:
 
         # Allocate double-buffered send/recv slots
         self.buffer_manager.allocate()
+        # The .zero_() calls inside allocate() go to the default CUDA stream.
+        # Sync it now so the zeros are fully committed before any NVShmem
+        # operations (which bypass CUDA streams via RDMA) touch the buffers.
+        # Without this, a still-running zero() can race with the first
+        # nvshmem.core.put() and overwrite received data.
+        torch.cuda.synchronize()
 
         # Barrier to ensure all PEs complete buffer allocation before proceeding
         nvshmem.core.barrier_all(stream=self.gpu_resources.send_stream)
@@ -135,9 +150,10 @@ def init(self, log_level: str = "INFO") -> None:
             self.gpu_resources.unpack_stream,
             self.gpu_resources.send_stream,
             self.gpu_resources.copy_stream,
-            self.gpu_resources.torch_pack_stream,
-            self.gpu_resources.torch_unpack_stream,
-            self.gpu_resources.torch_copy_stream,
+            self.gpu_resources.torch_pack_stream_wrapper,
+            self.gpu_resources.torch_unpack_stream_wrapper,
+            self.gpu_resources.torch_send_stream_wrapper,
+            self.gpu_resources.torch_copy_stream_wrapper,
         )
 
         # Synchronize all NVSHMEM streams before returning
@@ -232,7 +248,7 @@ def schedule(self) -> None:
         # Step 3: Schedule workloads to iterations
         PELogger.debug("Step 3: Building communication schedule...")
         schedule, global_summaries = self.comm_scheduler.build_schedule(
-            workloads, self.my_pe, self.n_pes
+            workloads, self.my_pe, self.n_pes, group=self._group
         )
 
         self.num_iterations = self.comm_scheduler.num_iterations
@@ -255,8 +271,10 @@ def schedule(self) -> None:
 
         # Step 6: Create double-buffered events
         PELogger.debug("Step 6: Creating synchronization events...")
-        self.pack_events, self.unpack_events = self.gpu_resources.create_events(num_events=2)
-        self.pipeline_executor.set_events(self.pack_events, self.unpack_events)
+        self.pack_events, self.unpack_events, self.barrier_events = (
+            self.gpu_resources.create_events(num_events=2)
+        )
+        self.pipeline_executor.set_events(self.pack_events, self.unpack_events, self.barrier_events)
 
         PELogger.info(f"Schedule complete: {self.num_iterations} iterations ready")
 
@@ -313,6 +331,7 @@ def clear_requests(self) -> None:
         self.num_iterations = 0
         self.pack_events = []
         self.unpack_events = []
+        self.barrier_events = []
 
     def finalize(self) -> None:
         """Cleanup resources."""
diff --git a/megatron/core/resharding/planner.py b/megatron/core/resharding/planner.py
index f9d8752a26b..444ea5a673a 100644
--- a/megatron/core/resharding/planner.py
+++ b/megatron/core/resharding/planner.py
@@ -222,7 +222,12 @@ def _determine_source_ranks_for_dst_param(
 
 
 def build_centralized_reshard_plan(
-    src_module: torch.nn.Module, dst_module: torch.nn.Module, num_experts: int = None
+    src_module: torch.nn.Module,
+    dst_module: torch.nn.Module,
+    num_experts: int = None,
+    group=None,
+    src_rank_offset: int = 0,
+    dst_rank_offset: int = 0,
 ) -> ReshardPlan:
     """
     Centralized planning: Rank 0 builds complete plan for all ranks, then scatters.
@@ -237,8 +242,10 @@ def build_centralized_reshard_plan(
     This metadata is sufficient for rank 0 to build correct transfer plans without
     requiring dummy models.
     """
-    my_global_rank = dist.get_rank()
-    world_size = dist.get_world_size()
+    # Use group.rank() instead of dist.get_rank(group) to support cross-cluster
+    # ProcessGroups where members have independent default PGs (same default rank).
+    my_global_rank = group.rank() if group is not None else dist.get_rank()
+    world_size = group.size() if group is not None else dist.get_world_size()
 
     # Extract metadata from source model if present
     if src_module is not None:
@@ -255,6 +262,7 @@ def build_centralized_reshard_plan(
                 src_pg,
                 num_experts=num_experts,
                 layer_module_prefix_map=src_layer_prefix_map,
+                rank_offset=src_rank_offset,
             )
             for name, p in my_src_params.items()
         ]
@@ -277,6 +285,7 @@ def build_centralized_reshard_plan(
                 dst_pg,
                 num_experts=num_experts,
                 layer_module_prefix_map=dst_layer_prefix_map,
+                rank_offset=dst_rank_offset,
             )
             for name, p in my_dst_params.items()
         ]
@@ -286,8 +295,8 @@ def build_centralized_reshard_plan(
 
     all_src_metadata_by_rank = [None] * world_size
     all_dst_metadata_by_rank = [None] * world_size
-    dist.all_gather_object(all_src_metadata_by_rank, my_src_metadata)
-    dist.all_gather_object(all_dst_metadata_by_rank, my_dst_metadata)
+    dist.all_gather_object(all_src_metadata_by_rank, my_src_metadata, group=group)
+    dist.all_gather_object(all_dst_metadata_by_rank, my_dst_metadata, group=group)
 
     # Parameter to metadata maps keyed by resolved_name
     src_param_metadata_by_rank = {}
@@ -362,7 +371,9 @@ def build_centralized_reshard_plan(
         plans_list = [plans_for_all_ranks[r] for r in range(world_size)]
     else:
         plans_list = [None] * world_size
-    torch.distributed.broadcast_object_list(plans_list, src=0)
+    # Use group_src= (group rank) instead of src= (default PG global rank) to support
+    # cross-cluster ProcessGroups where members share the same default PG rank.
+    torch.distributed.broadcast_object_list(plans_list, group_src=0, group=group)
     my_plan = plans_list[my_global_rank]
 
     logger.info(
diff --git a/megatron/core/resharding/refit.py b/megatron/core/resharding/refit.py
index 2a9e58ecc22..b4d86b6a311 100644
--- a/megatron/core/resharding/refit.py
+++ b/megatron/core/resharding/refit.py
@@ -3,6 +3,7 @@
 
 """
 High-level refit/reshard orchestration:
+- prepare_swap_model_weights: build and cache the reshard plan without any transfer.
 - swap_model_weights: public API; accepts a backend name or CopyService and delegates.
 - reshard_model_weights: transport-agnostic core; builds/caches plan and executes.
 """
@@ -13,6 +14,10 @@
 import torch
 
 from megatron.core import parallel_state
+from megatron.core.inference.quantization.utils import (
+    _should_quantize_param,
+    quantize_params_to_mxfp8,
+)
 from megatron.core.models.common.language_module.language_module import LanguageModule
 from megatron.core.utils import unwrap_model
 
@@ -21,6 +26,7 @@
 from .copy_services.gloo_copy_service import GlooCopyService
 from .copy_services.nccl_copy_service import NCCLCopyService
 from .copy_services.nvshmem_copy_service import NVSHMEMCopyService
+from .transforms import MXFP8ReshardTransform, ReshardTransform
 
 # Supported refit backend names
 RefitBackendName = Literal["nccl", "gloo", "nvshmem"]
@@ -66,18 +72,22 @@ def _get_config_tuple(core) -> Optional[Tuple[int, int, int, int, int]]:
     )
 
 
-def _build_plan_cache_key(src_core, tgt_core, num_experts: Optional[int]) -> _PlanCacheKey:
+def _build_plan_cache_key(
+    src_core, tgt_core, num_experts: Optional[int], group=None
+) -> _PlanCacheKey:
     """Build cache key for reshard plan.
 
     Args:
         src_core: Source model core (or None for non-collocated destination/idle ranks)
         tgt_core: Target model core (or None for non-collocated source/idle ranks)
         num_experts: Number of MoE experts (or None for non-MoE models)
+        group: Optional process group for rank query
 
     Returns:
         Cache key that uniquely identifies this reshard configuration for this rank
     """
-    rank = torch.distributed.get_rank()
+    # Use group.rank() to support cross-cluster ProcessGroups
+    rank = group.rank() if group is not None else torch.distributed.get_rank()
     src_config = _get_config_tuple(src_core)
     dst_config = _get_config_tuple(tgt_core)
     return _PlanCacheKey(
@@ -90,21 +100,25 @@ def _build_plan_cache_key(src_core, tgt_core, num_experts: Optional[int]) -> _Pl
 _plan_cache: dict[_PlanCacheKey, Any] = {}
 
 
-def get_or_create_service(backend: RefitBackendName) -> CopyService:
+def get_or_create_service(backend: RefitBackendName, group=None) -> CopyService:
     """Get or create a cached CopyService instance for the given backend.
 
     This avoids expensive repeated allocations (especially for NVSHMEM buffers)
     when swap_model_weights is called multiple times with the same backend.
+
+    Args:
+        backend: Backend name ("nccl", "gloo", or "nvshmem").
+        group: Optional process group for NCCL backend.
     """
     if backend in _service_cache:
         return _service_cache[backend]
 
     if backend == "nccl":
-        service = NCCLCopyService()
+        service = NCCLCopyService(group=group)
     elif backend == "gloo":
-        service = GlooCopyService()
+        service = GlooCopyService(group=group)
     elif backend == "nvshmem":
-        service = NVSHMEMCopyService()
+        service = NVSHMEMCopyService(group=group)
     else:
         raise ValueError(f"Unknown backend '{backend}'")
 
@@ -148,29 +162,228 @@ def clear_all_caches():
     clear_plan_cache()
 
 
+def _unwrap_model_cores(src_model, target_model):
+    """Extract (src_core, tgt_core, num_experts) from model arguments.
+
+    Handles list-wrapped modules and None (non-collocated) models.
+    Fills in missing DP groups from Megatron's parallel state on the source.
+
+    Returns:
+        (src_core, tgt_core, num_experts)
+    """
+    src_core = None
+    tgt_core = None
+    num_experts = None
+
+    if src_model is not None:
+        src_lm = src_model[0] if isinstance(src_model, (list, tuple)) else src_model
+        num_experts = src_lm.config.num_moe_experts
+        src_core = unwrap_model(src_lm)
+        if not hasattr(src_core, "pg_collection") or src_core.pg_collection is None:
+            raise RuntimeError("Source model missing pg_collection required for reshard")
+        # Fill missing DP group on the source using Megatron's parallel state if not provided
+        if getattr(src_core.pg_collection, "dp", None) is None:
+            src_core.pg_collection.dp = parallel_state.get_data_parallel_group()
+
+    if target_model is not None:
+        tgt_lm = target_model[0] if isinstance(target_model, (list, tuple)) else target_model
+        if num_experts is None:
+            num_experts = tgt_lm.config.num_moe_experts
+        tgt_core = unwrap_model(tgt_lm)
+        if not hasattr(tgt_core, "pg_collection") or tgt_core.pg_collection is None:
+            raise RuntimeError("Target model missing pg_collection required for reshard")
+
+    return src_core, tgt_core, num_experts
+
+
+def _build_or_get_plan(src_core, tgt_core, num_experts, group, src_rank_offset, dst_rank_offset):
+    """Return the cached reshard plan, building it (collectively) if not yet cached.
+
+    All participating ranks must call this simultaneously when the plan is not
+    yet cached, because build_centralized_reshard_plan uses collective communication.
+    """
+    global _plan_cache
+    cache_key = _build_plan_cache_key(src_core, tgt_core, num_experts, group=group)
+    if cache_key not in _plan_cache:
+        _plan_cache[cache_key] = build_centralized_reshard_plan(
+            src_core,
+            tgt_core,
+            num_experts=num_experts,
+            group=group,
+            src_rank_offset=src_rank_offset,
+            dst_rank_offset=dst_rank_offset,
+        )
+    return _plan_cache[cache_key]
+
+
+def _needs_mxfp8_conversion(model) -> bool:
+    """Check if a model uses FlashInfer MXFP8 inference and needs weight conversion."""
+    if model is None:
+        return False
+    lm = model[0] if isinstance(model, (list, tuple)) else model
+    config = lm.config
+    return (
+        getattr(config, 'transformer_impl', None) == 'inference_optimized'
+        and getattr(config, 'fp8_recipe', None) == 'mxfp8'
+    )
+
+
+def _setup_mxfp8_transform_on_plan(plan, target_model) -> None:
+    """Detect MXFP8 needs and attach a transform to the plan if required.
+
+    If the *target_model* uses an inference-optimized layer spec with MXFP8,
+    this function:
+      1. Computes which params are eligible for MXFP8 conversion.
+      2. Quantizes the target model's decoder weights to FlashInfer MXFP8Tensor
+         (creating persistent buffers whose addresses are later captured by
+         CUDA graphs).
+      3. Builds an ``MXFP8ReshardTransform`` and attaches it to the plan as
+         ``plan.transform``.
+
+    If the model doesn't need MXFP8, ``plan.transform`` is set to None.
+    Subsequent calls are no-ops if the plan already has a transform attribute.
+    """
+    if hasattr(plan, 'transform'):
+        return  # Already set up
+
+    if not _needs_mxfp8_conversion(target_model):
+        plan.transform = None
+        return
+
+    lm = target_model[0] if isinstance(target_model, (list, tuple)) else target_model
+    core = unwrap_model(lm)
+    decoder = core.decoder if hasattr(core, 'decoder') else core
+
+    # 1. Compute which parameters are eligible for MXFP8 conversion.
+    #    Must be done while params are still visible as nn.Parameter (BF16).
+    convertible: set[str] = set()
+    for name, param in decoder.named_parameters():
+        if _should_quantize_param(param):
+            convertible.add(f"decoder.{name}")
+
+    # 2. Quantize decoder weights → persistent MXFP8Tensor buffers.
+    persistent_buffers = quantize_params_to_mxfp8(decoder)
+
+    # 3. Build the transform and attach it to the plan.
+    plan.transform = MXFP8ReshardTransform(
+        convertible_params=convertible,
+        persistent_buffers=persistent_buffers,
+        buffer_key_prefix="decoder.",
+    )
+
+
+def prepare_swap_model_weights(
+    src_model: LanguageModule,
+    target_model: LanguageModule,
+    group=None,
+    src_rank_offset: int = 0,
+    dst_rank_offset: int = 0,
+):
+    """Pre-build and cache the reshard plan and any format-conversion transforms.
+
+    Call this during initialization while models are in their native (BF16) format,
+    before any weight format conversion (e.g., MXFP8).  The plan is stored in the
+    same module-level cache as swap_model_weights, so subsequent calls reuse it
+    without needing to inspect named_parameters() again.
+
+    If the *target_model* uses an inference-optimized layer spec with MXFP8
+    (``config.transformer_impl == 'inference_optimized'`` and
+    ``config.fp8_recipe == 'mxfp8'``), this function also:
+      - computes which parameters are eligible for MXFP8 conversion,
+      - quantizes the target decoder weights to persistent FlashInfer
+        MXFP8Tensor buffers (whose addresses are later baked into CUDA graphs),
+      - creates an ``MXFP8ReshardTransform`` that subsequent
+        ``swap_model_weights`` calls use automatically.
+
+    Callers do **not** need to know about MXFP8 — the transform is created and
+    cached transparently.
+
+    All participating ranks must call this simultaneously — the plan builder uses
+    collective communication internally.
+
+    Args:
+        src_model: Source model, or None if this rank only receives weights.
+        target_model: Target model, or None if this rank only sends weights.
+        group: Optional process group for collective communication.
+        src_rank_offset: Rank offset for source (training) workers.
+        dst_rank_offset: Rank offset for destination (inference) workers.
+    """
+    src_core, tgt_core, num_experts = _unwrap_model_cores(src_model, target_model)
+    plan = _build_or_get_plan(
+        src_core, tgt_core, num_experts, group, src_rank_offset, dst_rank_offset
+    )
+
+    # Auto-detect and set up MXFP8 transform on the plan for the target model.
+    # This must happen after the plan is built (while BF16 params are still visible)
+    # and before any swap_model_weights call.
+    _setup_mxfp8_transform_on_plan(plan, target_model)
+
+
 def swap_model_weights(
     src_model: LanguageModule,
     target_model: LanguageModule,
     refit_method: Union[RefitBackendName, CopyService],
+    group=None,
+    src_rank_offset: int = 0,
+    dst_rank_offset: int = 0,
+    transform: Optional[ReshardTransform] = None,
 ):
     """
     Orchestrate weight swap/refit.
-    - refit_method can be:
-        * a string backend name (one of the supported refit backends), or
-        * a CopyService instance.
+
+    If *transform* is not explicitly provided, the function automatically uses
+    any ``MXFP8ReshardTransform`` that was created and cached by a prior
+    ``prepare_swap_model_weights`` call for the same model pair.  This makes
+    MXFP8 handling transparent to callers.
+
+    Args:
+        refit_method: a string backend name (one of the supported refit
+            backends) or a CopyService instance.
+        group: Optional process group for communication.
+        src_rank_offset / dst_rank_offset: Offsets applied to local process
+            group ranks so that metadata contains globally unique rank IDs
+            across independent torch.distributed worlds.
+        transform: Optional ReshardTransform for custom format conversion.
+            If None, the cached transform (from prepare_swap_model_weights)
+            is used automatically when the receiver needs MXFP8 conversion.
     """
-    if isinstance(refit_method, CopyService):
+    if isinstance(refit_method, str):
+        service = get_or_create_service(refit_method, group=group)
+    elif hasattr(refit_method, 'submit_send') and hasattr(refit_method, 'run'):
         service = refit_method
-        reshard_model_weights(src_model, target_model, service=service)
-    elif isinstance(refit_method, str):
-        service = get_or_create_service(refit_method)
-        reshard_model_weights(src_model, target_model, service=service)
     else:
-        raise TypeError("refit_method must be a str backend name or a CopyService instance")
+        raise TypeError(
+            "refit_method must be a str backend name or a CopyService-compatible instance"
+        )
+
+    # Auto-resolve MXFP8 transform from the cached plan when no
+    # explicit transform was provided.
+    if transform is None:
+        src_core, tgt_core, num_experts = _unwrap_model_cores(src_model, target_model)
+        plan = _build_or_get_plan(
+            src_core, tgt_core, num_experts, group, src_rank_offset, dst_rank_offset
+        )
+        transform = getattr(plan, 'transform', None)
+
+    reshard_model_weights(
+        src_model,
+        target_model,
+        service=service,
+        group=group,
+        src_rank_offset=src_rank_offset,
+        dst_rank_offset=dst_rank_offset,
+        transform=transform,
+    )
 
 
 def reshard_model_weights(
-    src_model: LanguageModule, target_model: LanguageModule, service: CopyService
+    src_model: LanguageModule,
+    target_model: LanguageModule,
+    service: CopyService,
+    group=None,
+    src_rank_offset: int = 0,
+    dst_rank_offset: int = 0,
+    transform: Optional[ReshardTransform] = None,
 ):
     """Reshard and copy model weights from ``src_model`` to ``target_model`` using ``service``.
 
@@ -180,23 +393,34 @@ def reshard_model_weights(
     - (None, target_model): Destination rank - only receives data (non-collocated)
     - (None, None): Idle rank - participates in collectives but has no transfers (non-collocated)
 
-    In non-collocated mode, metadata includes local rank positions within parallel groups,
-    allowing the planner to correctly map between different process group configurations
-    without requiring dummy models on every rank.
+    Args:
+        group: Optional process group for collective communication.
+        src_rank_offset / dst_rank_offset: Offsets for mapping local ranks to global ranks
+            in independent torch.distributed worlds.
+        transform: Optional ReshardTransform for custom format conversion.
     """
     global _plan_cache
 
     # Handle idle ranks (both models None) - they participate in collectives but have no work
     if src_model is None and target_model is None:
-        cache_key = _build_plan_cache_key(src_core=None, tgt_core=None, num_experts=None)
+        cache_key = _build_plan_cache_key(
+            src_core=None, tgt_core=None, num_experts=None, group=group
+        )
 
         # Use cached plan if available, otherwise build (with collective participation)
         if cache_key not in _plan_cache:
-            plan = build_centralized_reshard_plan(None, None, num_experts=None)
+            plan = build_centralized_reshard_plan(
+                None,
+                None,
+                num_experts=None,
+                group=group,
+                src_rank_offset=src_rank_offset,
+                dst_rank_offset=dst_rank_offset,
+            )
             _plan_cache[cache_key] = plan
         else:
             plan = _plan_cache[cache_key]
-        execute_reshard_plan(plan, None, None, service=service)
+        execute_reshard_plan(plan, None, None, service=service, group=group, transform=transform)
         return
 
     # Handle None models - extract core modules only from non-None models
@@ -229,13 +453,22 @@ def reshard_model_weights(
             raise RuntimeError("Target model missing pg_collection required for reshard")
 
     # Build or retrieve cached plan
-    cache_key = _build_plan_cache_key(src_core, tgt_core, num_experts)
+    cache_key = _build_plan_cache_key(src_core, tgt_core, num_experts, group=group)
 
     if cache_key not in _plan_cache:
         # All ranks must participate in planning (collective operations)
-        plan = build_centralized_reshard_plan(src_core, tgt_core, num_experts=num_experts)
+        plan = build_centralized_reshard_plan(
+            src_core,
+            tgt_core,
+            num_experts=num_experts,
+            group=group,
+            src_rank_offset=src_rank_offset,
+            dst_rank_offset=dst_rank_offset,
+        )
         _plan_cache[cache_key] = plan
     else:
         plan = _plan_cache[cache_key]
 
-    execute_reshard_plan(plan, src_core, tgt_core, service=service)
+    execute_reshard_plan(
+        plan, src_core, tgt_core, service=service, group=group, transform=transform
+    )
diff --git a/megatron/core/resharding/transforms.py b/megatron/core/resharding/transforms.py
new file mode 100644
index 00000000000..45a250d3541
--- /dev/null
+++ b/megatron/core/resharding/transforms.py
@@ -0,0 +1,261 @@
+# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
+from __future__ import annotations
+
+"""
+Reshard transforms for custom send/recv/writeback during weight transfer.
+
+- ReshardTransform: base class for pluggable format conversion hooks.
+- MXFP8ReshardTransform: writes received BF16 data into persistent FlashInfer
+  MXFP8Tensor buffers so CUDA-graph device-pointer captures remain valid.
+"""
+
+import torch
+
+from megatron.core.inference.quantization.mxfp8_tensor import MXFP8Tensor
+
+
+class ReshardTransform:
+    """Hook for custom send/recv/writeback during reshard execution.
+
+    Implementations override the four methods below.  When an instance is
+    passed to ``execute_reshard_plan``, each ``TransferOp`` is checked via
+    ``should_transform(param_name)``; if True the transform methods are used
+    instead of the default send/recv/writeback logic.
+
+    The transform may change the wire format (e.g. send MXFP8 data+scale
+    instead of BF16) **or** keep the same wire format and only post-process
+    on the receive side (e.g. receive BF16, convert to MXFP8 in
+    ``finalize_recv``).  The only constraint is that ``prepare_send`` and
+    ``prepare_recv`` must return the same number of tensors for a given
+    parameter so that send/recv pairs match.
+    """
+
+    def should_transform(self, param_name: str) -> bool:
+        """Return True if *param_name* should use the transform path."""
+        return False
+
+    def prepare_send(
+        self, param_name: str, src_slice: tuple[slice, ...], src_param: torch.nn.Parameter
+    ) -> list[torch.Tensor]:
+        """Produce tensor(s) to send for *param_name*.
+
+        May return multiple tensors (e.g. data + scale when converting to
+        MXFP8 on the sender side).  The default implementation sends the
+        BF16 slice unchanged (single tensor).
+        """
+        raise NotImplementedError
+
+    def prepare_recv(self, param_name: str, dst_slice: tuple[slice, ...]) -> list[torch.Tensor]:
+        """Allocate receive buffer(s).  Count must match ``prepare_send`` output."""
+        raise NotImplementedError
+
+    def finalize_recv(
+        self, param_name: str, dst_slice: tuple[slice, ...], recv_buffers: list[torch.Tensor]
+    ) -> None:
+        """Write received data into final destination (e.g. persistent buffers).
+
+        This is where receiver-side format conversion can happen (e.g.
+        converting a BF16 recv buffer to MXFP8 before writing into
+        persistent storage).
+        """
+        raise NotImplementedError
+
+
+# ---------------------------------------------------------------------------
+# MXFP8 transform helpers
+# ---------------------------------------------------------------------------
+
+
+def _scale_slice_from_data_slice(
+    data_slice: tuple[slice, ...], block_size: int = 32
+) -> tuple[slice, ...]:
+    """Convert an MXFP8 data slice to the corresponding scale slice.
+
+    In MXFP8, each group of ``block_size`` elements along the last (K)
+    dimension shares a single scale value.  All dimensions except the last
+    are passed through unchanged; the last ``slice`` has its start/stop
+    divided by ``block_size``.  Integer index on the last dim is converted
+    to scale index as idx // block_size.
+    """
+    adjusted = list(data_slice)
+    last = adjusted[-1]
+    if isinstance(last, slice):
+        if last.start is not None and last.start % block_size != 0:
+            raise AssertionError(
+                f"MXFP8 data slice last dim ({last}) must be aligned to block_size={block_size}"
+            )
+        if last.stop is not None and last.stop % block_size != 0:
+            raise AssertionError(
+                f"MXFP8 data slice last dim ({last}) must be aligned to block_size={block_size}"
+            )
+        scale_start = (last.start // block_size) if last.start is not None else None
+        scale_stop = (last.stop // block_size) if last.stop is not None else None
+        # Scale has one value per block; do not use last.step (would index scale wrong).
+        adjusted[-1] = slice(scale_start, scale_stop)
+    elif isinstance(last, int):
+        adjusted[-1] = last // block_size
+    return tuple(adjusted)
+
+
+def _ensure_sendable(param: torch.Tensor) -> torch.Tensor:
+    """Return a standard-dtype tensor suitable for wire transmission.
+
+    Quantized parameter types (e.g., Transformer Engine MXFP8Tensor) are
+    dequantized to their original precision (usually BF16).  Standard
+    parameters are returned via ``.data`` (unwrapped from autograd).
+    """
+    try:
+        from transformer_engine.pytorch.tensor.mxfp8_tensor import MXFP8Tensor as _TEMXFP8
+
+        if isinstance(param, _TEMXFP8):
+            return param.dequantize()
+    except ImportError:
+        pass
+    return param.data
+
+
+class MXFP8ReshardTransform(ReshardTransform):
+    """MXFP8 format-conversion transform for reshard.
+
+    Writes received weight data directly into persistent ``MXFP8Tensor``
+    buffers so that CUDA-graph device-pointer captures remain valid across
+    refits.
+
+    Two modes are supported, controlled by *convert_on_send*:
+
+    ``convert_on_send=False`` (default — **receiver-side conversion**):
+        The sender transmits plain BF16 (one tensor per op, identical to the
+        default reshard path).  The receiver allocates a BF16 receive buffer,
+        then ``finalize_recv`` converts BF16 → MXFP8 and writes into the
+        persistent buffers.  Because the wire format is unchanged the sender
+        does **not** need a transform — only the receiver creates one.  This
+        is the simplest mode and avoids any sender/receiver coordination.
+
+    ``convert_on_send=True`` (**sender-side conversion**):
+        The sender converts each BF16 slice to MXFP8 and sends **two**
+        tensors (data + scale) per op.  The receiver allocates matching
+        MXFP8 buffers and ``finalize_recv`` copies them directly.  Both
+        sender and receiver must use the transform so that tensor counts
+        match.  This mode halves wire bandwidth (~1 byte/elem vs 2).
+
+        **Caveat**: CopyService backends that match local (same-rank)
+        transfers by ``task_id`` (Gloo, NVSHMEM) will break if multiple
+        tensors share the same ``task_id``.  This mode is therefore only
+        safe for non-colocated setups where sender and receiver are on
+        different ranks.  A future fix could generate unique sub-IDs.
+
+    Args:
+        convertible_params: set of fully-qualified parameter names that
+            should use this transform.
+        persistent_buffers: dict mapping parameter names (without
+            *buffer_key_prefix*) to ``MXFP8Tensor`` objects that hold the
+            receiver's persistent data/scale storage.  Empty on the sender
+            when using ``convert_on_send=True``.
+        buffer_key_prefix: prefix to strip from ``param_name`` when looking
+            up entries in *persistent_buffers* (e.g. ``"decoder."``).
+        convert_on_send: if True, convert BF16 → MXFP8 on the sender and
+            transmit two tensors (data + scale).  If False (default),
+            transmit BF16 and convert on the receiver in ``finalize_recv``.
+    """
+
+    def __init__(
+        self,
+        convertible_params: set[str],
+        persistent_buffers: dict,
+        buffer_key_prefix: str = "",
+        convert_on_send: bool = False,
+    ):
+        self.convertible_params = convertible_params
+        self.persistent_buffers = persistent_buffers
+        self.buffer_key_prefix = buffer_key_prefix
+        self.convert_on_send = convert_on_send
+        # Accumulation buffers for 1D-scale params that arrive in partial slices.
+        # The 1D swizzled FlashInfer scale can't be updated partially; we collect
+        # all BF16 slices here and quantize the full weight once it's assembled.
+        # Maps buf_key -> (full-size BF16 accumulation tensor, elements written so far).
+        self._pending_1d: dict = {}
+
+    def should_transform(self, param_name: str) -> bool:
+        return param_name in self.convertible_params
+
+    # -- send ----------------------------------------------------------------
+
+    def prepare_send(self, param_name, src_slice, src_param):
+        src_data = _ensure_sendable(src_param)
+        if self.convert_on_send:
+
+            bf16_data = src_data[src_slice].contiguous().to(torch.bfloat16)
+            mxfp8 = MXFP8Tensor.from_bf16(bf16_data)
+            return [mxfp8.data.contiguous(), mxfp8.scale.contiguous()]
+        else:
+            # BF16 on the wire — same as the default reshard path.
+            return [src_data[src_slice].contiguous()]
+
+    # -- recv ----------------------------------------------------------------
+
+    def prepare_recv(self, param_name, dst_slice):
+        buf_key = param_name.removeprefix(self.buffer_key_prefix)
+        buf = self.persistent_buffers[buf_key]
+
+        if self.convert_on_send:
+            # Receive MXFP8 data + scale (2 buffers).
+            if buf.scale.ndim == 1:
+                # 1D swizzled scale can't be partially reconstructed from sender-quantized
+                # slices.  Use convert_on_send=False for models with 1D-scale params.
+                raise NotImplementedError(
+                    f"convert_on_send=True is not supported for parameters with 1D swizzled "
+                    f"scale (param={param_name!r}).  Use convert_on_send=False instead, which "
+                    f"receives BF16 and quantizes the full weight on the receiver."
+                )
+            scale_slice = _scale_slice_from_data_slice(dst_slice)
+            return [
+                torch.empty_like(buf.data[dst_slice].contiguous()),
+                torch.empty_like(buf.scale[scale_slice].contiguous()),
+            ]
+        else:
+            # Receive BF16 (1 buffer, same shape as the MXFP8 data slice).
+            shape = buf.data[dst_slice].shape
+            return [torch.empty(shape, dtype=torch.bfloat16, device=buf.data.device)]
+
+    def finalize_recv(self, param_name, dst_slice, recv_buffers):
+        buf_key = param_name.removeprefix(self.buffer_key_prefix)
+        buf = self.persistent_buffers[buf_key]
+
+        if self.convert_on_send:
+            # Already MXFP8 on the wire — copy data and 2D scale slices directly.
+            # (1D scale is rejected at prepare_recv time, so only 2D reaches here.)
+            buf.data[dst_slice].copy_(recv_buffers[0])
+            scale_slice = _scale_slice_from_data_slice(dst_slice)
+            buf.scale[scale_slice].copy_(recv_buffers[1])
+        elif buf.scale.ndim == 1:
+            # 1D swizzled scale (FlashInfer format) encodes scale values across the
+            # full weight tensor; partial updates would corrupt the swizzle layout.
+            # Accumulate BF16 slices and quantize once all slices are assembled.
+            if buf_key not in self._pending_1d:
+                # Use zeros so that any un-filled slice produces zeros rather than garbage.
+                self._pending_1d[buf_key] = [
+                    torch.zeros_like(buf.data, dtype=torch.bfloat16),
+                    0,  # elements written so far
+                ]
+            accum, written = self._pending_1d[buf_key]
+            accum[dst_slice].copy_(recv_buffers[0])
+            written += recv_buffers[0].numel()
+            if written >= buf.data.numel():
+                if written != buf.data.numel():
+                    raise AssertionError(
+                        f"1D-scale param {param_name!r}: received {written} elements, "
+                        f"expected {buf.data.numel()} (duplicate or missing slices?)"
+                    )
+                mxfp8 = MXFP8Tensor.from_bf16(accum)
+                buf.data.copy_(mxfp8.data)
+                buf.scale.copy_(mxfp8.scale)
+                del self._pending_1d[buf_key]
+            else:
+                self._pending_1d[buf_key][1] = written
+        else:
+            # 2D scale: each scale row covers exactly one data row, so partial
+            # row-wise updates are independent and can be applied immediately.
+            mxfp8 = MXFP8Tensor.from_bf16(recv_buffers[0])
+            buf.data[dst_slice].copy_(mxfp8.data)
+            scale_slice = _scale_slice_from_data_slice(dst_slice)
+            buf.scale[scale_slice].copy_(mxfp8.scale)
diff --git a/megatron/core/resharding/utils.py b/megatron/core/resharding/utils.py
index 1dc2f8f85a8..9edcad9d51b 100644
--- a/megatron/core/resharding/utils.py
+++ b/megatron/core/resharding/utils.py
@@ -251,6 +251,7 @@ def extract_param_metadata(
     pg_collection,
     num_experts: Optional[int] = None,
     layer_module_prefix_map: Mapping[str, str] | None = None,
+    rank_offset: int = 0,
 ) -> ParameterMetadata:
     """Extract metadata from a parameter for cross-rank communication."""
     # TP flags from attributes (set by Megatron linear layers)
@@ -275,26 +276,39 @@ def extract_param_metadata(
     data_parallel_group_ranks: list[int] | None = None
     pipeline_parallel_group_ranks: list[int] | None = None
 
+    def _offset_ranks(ranks: list[int]) -> list[int]:
+        return [r + rank_offset for r in ranks] if rank_offset else ranks
+
     if is_ep:
-        expert_parallel_group_ranks = dist.get_process_group_ranks(pg_collection.ep)
+        expert_parallel_group_ranks = _offset_ranks(dist.get_process_group_ranks(pg_collection.ep))
         # For MoE params, prefer expert TP group when available, else regular TP
         if is_tp and hasattr(pg_collection, 'expt_tp') and pg_collection.expt_tp is not None:
-            tensor_parallel_group_ranks = dist.get_process_group_ranks(pg_collection.expt_tp)
+            tensor_parallel_group_ranks = _offset_ranks(
+                dist.get_process_group_ranks(pg_collection.expt_tp)
+            )
         elif is_tp and hasattr(pg_collection, 'tp') and pg_collection.tp is not None:
-            tensor_parallel_group_ranks = dist.get_process_group_ranks(pg_collection.tp)
-        data_parallel_group_ranks = dist.get_process_group_ranks(pg_collection.dp)
+            tensor_parallel_group_ranks = _offset_ranks(
+                dist.get_process_group_ranks(pg_collection.tp)
+            )
+        data_parallel_group_ranks = _offset_ranks(dist.get_process_group_ranks(pg_collection.dp))
     elif is_tp:
         # Non-EP: use regular TP group
         if hasattr(pg_collection, 'tp') and pg_collection.tp is not None:
-            tensor_parallel_group_ranks = dist.get_process_group_ranks(pg_collection.tp)
-        data_parallel_group_ranks = dist.get_process_group_ranks(pg_collection.dp)
+            tensor_parallel_group_ranks = _offset_ranks(
+                dist.get_process_group_ranks(pg_collection.tp)
+            )
+        data_parallel_group_ranks = _offset_ranks(dist.get_process_group_ranks(pg_collection.dp))
     else:
-        data_parallel_group_ranks = dist.get_process_group_ranks(pg_collection.dp)
+        data_parallel_group_ranks = _offset_ranks(dist.get_process_group_ranks(pg_collection.dp))
 
     if hasattr(pg_collection, 'pp') and pg_collection.pp is not None:
-        pipeline_parallel_group_ranks = dist.get_process_group_ranks(pg_collection.pp)
+        pipeline_parallel_group_ranks = _offset_ranks(
+            dist.get_process_group_ranks(pg_collection.pp)
+        )
     else:
-        pipeline_parallel_group_ranks = list(range(dist.get_world_size()))
+        pipeline_parallel_group_ranks = list(
+            range(rank_offset, rank_offset + dist.get_world_size())
+        )
 
     meta = ParameterMetadata(
         name=param_name,
diff --git a/megatron/core/ssm/gated_delta_net.py b/megatron/core/ssm/gated_delta_net.py
index 9cb50ba6953..03897ed7465 100644
--- a/megatron/core/ssm/gated_delta_net.py
+++ b/megatron/core/ssm/gated_delta_net.py
@@ -178,8 +178,10 @@ def __init__(
             dtype=config.params_dtype,
         )
         setattr(self.conv1d.weight, "tensor_model_parallel", True)
+        setattr(self.conv1d.weight, "partition_dim", 0)
         if conv_bias:
             setattr(self.conv1d.bias, "tensor_model_parallel", True)
+            setattr(self.conv1d.bias, "partition_dim", 0)
 
         # Time step projection (discretization)
         self.num_v_heads_local_tp = self.num_value_heads // self.tp_size
@@ -192,6 +194,7 @@ def __init__(
             )
         )
         setattr(self.dt_bias, "tensor_model_parallel", True)
+        setattr(self.dt_bias, "partition_dim", 0)
         # A_log parameter
         self.A_log = nn.Parameter(
             torch.empty(
@@ -201,6 +204,7 @@ def __init__(
             )
         )
         setattr(self.A_log, "tensor_model_parallel", True)
+        setattr(self.A_log, "partition_dim", 0)
 
         if self.config.deterministic_mode:
             self.gated_delta_rule = torch_chunk_gated_delta_rule
diff --git a/megatron/core/ssm/mamba_block.py b/megatron/core/ssm/mamba_block.py
index 3eb37955901..65e95d038c5 100644
--- a/megatron/core/ssm/mamba_block.py
+++ b/megatron/core/ssm/mamba_block.py
@@ -22,7 +22,6 @@
 from megatron.core.packed_seq_params import PackedSeqParams
 from megatron.core.process_groups_config import ProcessGroupCollection
 from megatron.core.ssm.mamba_hybrid_layer_allocation import Symbols as LayerSymbols
-from megatron.core.ssm.mamba_hybrid_layer_allocation import allocate_layers
 from megatron.core.transformer import TransformerConfig
 from megatron.core.transformer.enums import CudaGraphScope
 from megatron.core.transformer.identity_op import IdentityOp
@@ -55,12 +54,11 @@ class MambaStack(GraphableMegatronModule, MegatronModule):
         submodules (MambaStackSubmodules): the submodules for the stack
         pre_process (bool, optional): whether to include an embedding layer.
             Defaults to True.
-        hybrid_attention_ratio (float, optional): the target ratio of attention layers to
-            total layers. Defaults to 0.0.
-        hybrid_mlp_ratio (float, optional): the target ratio of mlp layers to total
-            layers. Defaults to 0.0.
-        hybrid_override_pattern (str, optional): the hybrid layer pattern to override
-             with. Defaults to None.
+        layer_type_list (list, optional): pre-computed list of layer type symbols for
+            this pipeline segment. When provided (by MambaModel), pipeline stage
+            selection has already been done via '|' separators in the pattern.
+        pp_layer_offset (int, optional): the global layer offset for this pipeline
+            segment. Defaults to 0.
         post_layer_norm (bool, optional): whether to include a final layer norm.
             Defaults to True.
         post_process (bool, optional): whether to include an output layer.
@@ -69,6 +67,7 @@ class MambaStack(GraphableMegatronModule, MegatronModule):
         dtype (optional): the data type to use. Defaults to None.
         pg_collection (ProcessGroupCollection): the required model communication
             process groups to use.
+        is_mtp_layer (bool, optional): whether this is an MTP layer. Defaults to False.
     """
 
     def __init__(
@@ -76,9 +75,8 @@ def __init__(
         config: TransformerConfig,
         submodules: MambaStackSubmodules,
         pre_process: bool = True,
-        hybrid_attention_ratio: float = 0.0,
-        hybrid_mlp_ratio: float = 0.0,
-        hybrid_override_pattern: str = None,
+        layer_type_list: Optional[list[str]] = None,
+        pp_layer_offset: int = 0,
         post_layer_norm: bool = True,
         post_process: bool = True,
         device=None,
@@ -99,38 +97,18 @@ def __init__(
 
         # Required for pipeline parallel schedules
         self.input_tensor = None
-
-        self.hybrid_attention_ratio = hybrid_attention_ratio
-        self.hybrid_mlp_ratio = hybrid_mlp_ratio
-        self.hybrid_override_pattern = hybrid_override_pattern
         self.pg_collection = pg_collection
 
-        # For MTP layers, always use pattern length (config.num_layers is for main decoder)
-        if self.is_mtp_layer:
-            num_layers_for_allocation = len(self.hybrid_override_pattern)
-        else:
-            num_layers_for_allocation = (
-                self.config.num_layers
-                if self.config.num_layers is not None
-                else len(self.hybrid_override_pattern)
-            )
-
-        self.layer_type_list = allocate_layers(
-            num_layers_for_allocation,
-            self.hybrid_attention_ratio,
-            self.hybrid_mlp_ratio,
-            self.hybrid_override_pattern,
-            silent=self.is_mtp_layer,
+        assert layer_type_list is not None, (
+            "layer_type_list must be provided. It should be pre-computed from "
+            "--hybrid-layer-pattern by MambaModel."
         )
+        self.layer_type_list = layer_type_list
 
-        pp_layer_offset = 0
-        if self.pp_group.size() > 1 and not self.is_mtp_layer:
-            pp_layer_offset, self.layer_type_list = self._select_layers_for_pipeline_parallel(
-                self.layer_type_list
-            )
-        # Build main decoder layers using shared layer builder
+        # Build layers from the pre-selected segment
         self.layers = nn.ModuleList()
         for i, layer_type in enumerate(self.layer_type_list):
+            layer_number = i + 1 + pp_layer_offset
             if self.config.fp8:
                 quant_init_context = get_fp8_context(self.config, i + pp_layer_offset, is_init=True)
             elif self.config.fp4:
@@ -142,34 +120,35 @@ def __init__(
                     layer = build_module(
                         submodules.mamba_layer,
                         config=self.config,
-                        layer_number=i + 1 + pp_layer_offset,
+                        layer_number=layer_number,
                         pp_layer_offset=pp_layer_offset,
                         pg_collection=pg_collection,
                     )
                 elif layer_type == LayerSymbols.ATTENTION:
-                    # Transformer layers apply their own pp_layer_offset
                     layer = build_module(
                         submodules.attention_layer,
                         config=self.config,
-                        layer_number=i + 1,
+                        layer_number=layer_number,
                         pg_collection=pg_collection,
                         is_mtp_layer=is_mtp_layer,
+                        add_layer_offset=False,
+                        pp_layer_offset=pp_layer_offset,
                     )
                 elif layer_type == LayerSymbols.MLP:
-                    # MLP layers apply their own pp_layer_offset
                     layer = build_module(
                         submodules.mlp_layer,
                         config=self.config,
-                        layer_number=i + 1,
+                        layer_number=layer_number,
                         pg_collection=pg_collection,
+                        add_layer_offset=False,
                     )
                 elif layer_type == LayerSymbols.MOE:
-                    # MoE layers apply their own pp_layer_offset
                     layer = build_module(
                         submodules.moe_layer,
                         config=self.config,
-                        layer_number=i + 1,
+                        layer_number=layer_number,
                         pg_collection=pg_collection,
+                        add_layer_offset=False,
                     )
                 else:
                     assert False, "unexpected layer_type"
@@ -186,57 +165,6 @@ def __init__(
                 eps=self.config.layernorm_epsilon,
             )
 
-    def _select_layers_for_pipeline_parallel(self, layer_type_list):
-        assert self.config.virtual_pipeline_model_parallel_size is None, (
-            "The Mamba hybrid model does not currently support "
-            "virtual/interleaved pipeline parallelism"
-        )
-
-        pp_rank = self.pp_group.rank()
-        pp_size = self.pp_group.size()
-
-        num_layers_in_first = self.config.num_layers_in_first_pipeline_stage
-        num_layers_in_last = self.config.num_layers_in_last_pipeline_stage
-
-        if num_layers_in_first is not None or num_layers_in_last is not None:
-            # Uneven pipeline parallelism: mirror the logic in
-            # get_transformer_layer_offset so that MambaStack and
-            # TransformerLayer agree on layer placement.
-            first = 0 if num_layers_in_first is None else num_layers_in_first
-            last = 0 if num_layers_in_last is None else num_layers_in_last
-            middle_num_layers = self.config.num_layers - first - last
-
-            middle_pipeline_stages = pp_size - sum(
-                1 for x in (num_layers_in_first, num_layers_in_last) if x is not None
-            )
-
-            if middle_pipeline_stages > 0:
-                layers_per_middle = middle_num_layers // middle_pipeline_stages
-            else:
-                layers_per_middle = 0
-
-            is_first_stage = num_layers_in_first is not None and pp_rank == 0
-            is_last_stage = num_layers_in_last is not None and pp_rank == pp_size - 1
-
-            if is_first_stage:
-                offset = 0
-                num_layers_this_rank = first
-            elif is_last_stage:
-                offset = self.config.num_layers - last
-                num_layers_this_rank = last
-            else:
-                middle_rank = pp_rank if num_layers_in_first is None else pp_rank - 1
-                offset = middle_rank * layers_per_middle + first
-                num_layers_this_rank = layers_per_middle
-        else:
-            num_layers_per_pipeline_rank = self.config.num_layers // pp_size
-            offset = pp_rank * num_layers_per_pipeline_rank
-            num_layers_this_rank = num_layers_per_pipeline_rank
-
-        selected_list = layer_type_list[offset : offset + num_layers_this_rank]
-
-        return offset, selected_list
-
     def set_input_tensor(self, input_tensor: Tensor):
         """Set input tensor to be used instead of forward()'s input.
 
diff --git a/megatron/core/ssm/mamba_hybrid_layer_allocation.py b/megatron/core/ssm/mamba_hybrid_layer_allocation.py
index d7002b2915d..92dde8132ce 100644
--- a/megatron/core/ssm/mamba_hybrid_layer_allocation.py
+++ b/megatron/core/ssm/mamba_hybrid_layer_allocation.py
@@ -4,34 +4,23 @@
 from dataclasses import dataclass
 from typing import Dict, List, Optional, Tuple
 
-if __name__ != "__main__":
-    from megatron.core.utils import log_single_rank
-else:
-    from typing import Any
-
-    import torch
-
-    def log_single_rank(logger: logging.Logger, *args: Any, rank: int = 0, **kwargs: Any):
-        """Logs a message to the given rank."""
-        if torch.distributed.is_initialized():
-            if torch.distributed.get_rank() == rank:
-                logger.log(*args, **kwargs)
-        else:
-            logger.log(*args, **kwargs)
+import torch
 
+from megatron.core.utils import log_on_each_pipeline_stage, log_single_rank
 
 logger = logging.getLogger(__name__)
 
 
 class Symbols:
-    """Symbols for different layer types."""
+    """Symbols for different layer types and pattern separators."""
 
     MAMBA = "M"
     ATTENTION = "*"
     MLP = "-"
     MOE = 'E'
+    PIPE = '|'
     MTP_SEPARATOR = "/"
-    VALID = {MAMBA, ATTENTION, MLP, MOE}
+    VALID_LAYERS = {MAMBA, ATTENTION, MLP, MOE}
 
 
 @dataclass
@@ -39,7 +28,9 @@ class ParsedHybridPattern:
     """Result of parsing a unified hybrid pattern string.
 
     A unified pattern encodes both the main decoder pattern and the MTP pattern
-    in a single string using "/" as a separator.
+    in a single string using "/" as a separator. The main pattern may also
+    contain "|" pipe symbols to define pipeline stage boundaries for flexible
+    virtual pipeline parallelism (fVPP).
 
     Format: "<main_pattern>/<mtp_pattern>/<mtp_pattern>/..."
 
@@ -47,12 +38,15 @@ class ParsedHybridPattern:
         - "M*M*" -> main="M*M*", mtp=None, depths=0 (no MTP)
         - "M*M*/MM/MM" -> main="M*M*", mtp="MM", depths=2
         - "MMMM/*M/*M/*M" -> main="MMMM", mtp="*M", depths=3
+        - "M-M-|M-M*-/MM/MM" -> main="M-M-|M-M*-" (2 PP stages), mtp="MM", depths=2
 
     The "/" symbol introduces MTP patterns. Each repeated pattern after the main
     decoder represents one MTP prediction depth.
 
+    The "|" symbol in the main pattern defines pipeline stage boundaries.
+
     Attributes:
-        main_pattern: The main decoder layer pattern (e.g., "M*M*")
+        main_pattern: The main decoder layer pattern (e.g., "M*M*" or "M-M-|M-M*-")
         mtp_pattern: The MTP layer pattern per depth (e.g., "MM"), or None if no MTP
         mtp_num_depths: Number of MTP prediction depths (0 if no MTP)
     """
@@ -62,12 +56,139 @@ class ParsedHybridPattern:
     mtp_num_depths: int
 
 
+def pattern_from_ratios(
+    num_layers: int, attention_ratio: float = 0.0, mlp_ratio: float = 0.0
+) -> str:
+    """Convert deprecated ratio arguments to a layer pattern string.
+
+    Generates an evenly-spaced hybrid layer pattern from target attention and MLP
+    ratios. This exists for backward compatibility with code that uses the deprecated
+    hybrid_attention_ratio and hybrid_mlp_ratio parameters.
+
+    Args:
+        num_layers: Total number of layers.
+        attention_ratio: Target ratio of attention layers to total layers.
+        mlp_ratio: Target ratio of MLP layers to total layers.
+
+    Returns:
+        A layer pattern string (e.g., "MMM*MMM*MM").
+    """
+    assert num_layers > 0
+    assert 0.0 <= attention_ratio <= 1.0
+    assert 0.0 <= mlp_ratio <= 1.0
+    assert attention_ratio + mlp_ratio <= 1.0
+
+    # Allocate attention layers (evenly spaced, starting and ending with mamba)
+    attention_count = round(num_layers * attention_ratio)
+    mamba_count = num_layers - attention_count
+    sections = attention_count + 1
+    section_len = mamba_count / sections
+
+    layer_types = [Symbols.MAMBA] * num_layers
+    x = section_len
+    for i in range(num_layers):
+        if x < 0.5:
+            layer_types[i] = Symbols.ATTENTION
+            x += section_len
+        else:
+            x -= 1
+
+    # Allocate MLP layers (evenly distributed, not replacing attention)
+    mlp_count = round(num_layers * mlp_ratio)
+    if mlp_count > 0:
+        mamba_count -= mlp_count
+        ratio = mamba_count / mlp_count
+        x = ratio
+        for i in range(num_layers):
+            if layer_types[i] == Symbols.MAMBA:
+                if x < 0.5:
+                    layer_types[i] = Symbols.MLP
+                    x += ratio
+                else:
+                    x -= 1
+
+    return ''.join(layer_types)
+
+
+def get_hybrid_total_layer_count(pattern: str) -> int:
+    """Returns the total number of main decoder layers in a hybrid layer pattern.
+
+    Extracts the main pattern (before the first MTP separator '/'), strips
+    pipeline stage separators '|', and returns the character count.
+
+    Args:
+        pattern: Full hybrid layer pattern, possibly including MTP and pipe separators.
+
+    Returns:
+        Total number of layers in the main decoder pattern.
+    """
+    main_pattern = pattern.split(Symbols.MTP_SEPARATOR)[0]
+    _validate_pattern(main_pattern, "main", allow_pipe=True)
+    return len(main_pattern.replace(Symbols.PIPE, ''))
+
+
+def get_hybrid_total_pipeline_segment_count(pattern: str) -> int:
+    """Returns the number of pipeline segments in a hybrid layer pattern.
+
+    Extracts the main pattern (before the first MTP separator '/') and counts
+    the number of segments delimited by '|'.
+
+    Args:
+        pattern: Full hybrid layer pattern, possibly including MTP and pipe separators.
+
+    Returns:
+        Number of pipeline segments (pipe count + 1).
+    """
+    main_pattern = pattern.split(Symbols.MTP_SEPARATOR)[0]
+    return main_pattern.count(Symbols.PIPE) + 1
+
+
+def get_hybrid_layer_counts(pattern: str) -> Dict[str, int]:
+    """Count layers by type across the full hybrid pattern (main + MTP).
+
+    Parses the pattern to extract main and MTP components, then counts
+    each layer type. Main pattern '|' separators are skipped. MTP layers
+    are counted once per MTP depth.
+
+    Args:
+        pattern: Full hybrid layer pattern string.
+
+    Returns:
+        Dictionary mapping layer symbol to count. Keys are Symbols.ATTENTION,
+        Symbols.MAMBA, Symbols.MLP, and Symbols.MOE.
+
+    Examples:
+        >>> get_hybrid_layer_counts("M*M*")
+        {'*': 2, 'M': 2, '-': 0, 'E': 0}
+
+        >>> get_hybrid_layer_counts("M-M-|M-M*-/MM/MM")
+        {'*': 1, 'M': 8, '-': 4, 'E': 0}
+    """
+    parsed = parse_hybrid_pattern(pattern)
+    counts = {Symbols.ATTENTION: 0, Symbols.MAMBA: 0, Symbols.MLP: 0, Symbols.MOE: 0}
+
+    # Count main decoder layers (skip '|' pipe separators)
+    if parsed.main_pattern:
+        for char in parsed.main_pattern:
+            if char in counts:
+                counts[char] += 1
+
+    # Count MTP layers (pattern repeated mtp_num_depths times)
+    if parsed.mtp_pattern and parsed.mtp_num_depths > 0:
+        for char in parsed.mtp_pattern:
+            if char in counts:
+                counts[char] += parsed.mtp_num_depths
+
+    return counts
+
+
 def parse_hybrid_pattern(pattern: Optional[str]) -> ParsedHybridPattern:
     """Parse a unified hybrid pattern string into main and MTP components.
 
     The pattern uses "/" as a separator between the main decoder pattern and
     MTP patterns. Each MTP pattern after the separator represents one prediction
-    depth.
+    depth. The main pattern may contain "|" pipe symbols for pipeline stage
+    boundaries.
 
     Format: "<main_pattern>/<mtp_pattern>/<mtp_pattern>/..."
 
@@ -90,6 +211,9 @@ def parse_hybrid_pattern(pattern: Optional[str]) -> ParsedHybridPattern:
 
         >>> parse_hybrid_pattern("MMMM/*M/*M/*M")
         ParsedHybridPattern(main_pattern="MMMM", mtp_pattern="*M", mtp_num_depths=3)
+
+        >>> parse_hybrid_pattern("M-M-|M-M*-/MM/MM")
+        ParsedHybridPattern(main_pattern="M-M-|M-M*-", mtp_pattern="MM", mtp_num_depths=2)
     """
     if pattern is None:
         return ParsedHybridPattern(main_pattern=None, mtp_pattern=None, mtp_num_depths=0)
@@ -99,13 +223,13 @@ def parse_hybrid_pattern(pattern: Optional[str]) -> ParsedHybridPattern:
     if len(parts) == 1:
         # No MTP separator found - pattern is main decoder only
         main_pattern = parts[0]
-        _validate_pattern(main_pattern, "main")
+        _validate_pattern(main_pattern, "main", allow_pipe=True)
         return ParsedHybridPattern(main_pattern=main_pattern, mtp_pattern=None, mtp_num_depths=0)
 
     # First part is main decoder pattern
     main_pattern = parts[0]
     if main_pattern:
-        _validate_pattern(main_pattern, "main")
+        _validate_pattern(main_pattern, "main", allow_pipe=True)
 
     # Remaining parts are MTP patterns (one per depth)
     mtp_parts = parts[1:]
@@ -126,7 +250,7 @@ def parse_hybrid_pattern(pattern: Optional[str]) -> ParsedHybridPattern:
                 f"Full pattern: '{pattern}'"
             )
 
-    _validate_pattern(mtp_pattern, "MTP")
+    _validate_pattern(mtp_pattern, "MTP", allow_pipe=False)
 
     return ParsedHybridPattern(
         main_pattern=main_pattern if main_pattern else None,
@@ -135,162 +259,206 @@ def parse_hybrid_pattern(pattern: Optional[str]) -> ParsedHybridPattern:
     )
 
 
-def _validate_pattern(pattern: str, pattern_name: str) -> None:
+def _validate_pattern(pattern: str, pattern_name: str, allow_pipe: bool = False) -> None:
     """Validate that a pattern contains only valid layer symbols.
 
     Args:
         pattern: Layer pattern string to validate
         pattern_name: Name of pattern for error messages (e.g., "main" or "MTP")
+        allow_pipe: Whether to allow the pipe '|' separator (for main patterns)
 
     Raises:
         ValueError: If pattern contains invalid symbols
     """
+    valid_chars = Symbols.VALID_LAYERS | {Symbols.PIPE} if allow_pipe else Symbols.VALID_LAYERS
     for char in pattern:
-        if char not in Symbols.VALID:
+        if char not in valid_chars:
             raise ValueError(
                 f"In {pattern_name} pattern, '{char}' is not a valid layer symbol. "
-                f"Valid symbols are: {Symbols.VALID}"
+                f"Valid symbols are: {valid_chars}"
             )
 
 
-def _allocate_auto(
-    total_layers_count: int, target_attention_ratio: float, target_mlp_ratio: float
-) -> list:
-    # First, allocate attention (evenly spaced, starting and ending with mamba)
-    attention_layers_count: int = round(total_layers_count * target_attention_ratio)
-    mamba_layers_count: int = total_layers_count - attention_layers_count
-    mamba_sections_count: int = attention_layers_count + 1
-    mamba_section_length: float = mamba_layers_count / mamba_sections_count
+def validate_segment_layers(segment: str) -> List[str]:
+    """Validate and convert a single pipeline segment pattern to a layer type list.
 
-    layer_type_list = [Symbols.MAMBA] * total_layers_count
-    x: float = mamba_section_length
-    for l in range(total_layers_count):
-        if x < 0.5:
-            layer_type_list[l] = Symbols.ATTENTION
-            x += mamba_section_length
-        else:
-            x -= 1
+    This is used after the main pattern has been split by '|' into segments.
+    Each segment should contain only valid layer symbols (no '|').
 
-    # Next, allocate mlp
-    # (evenly distributed, but right-justified, not replacing attention)
-    mlp_layers_count: int = round(total_layers_count * target_mlp_ratio)
-    if mlp_layers_count > 0:
-        mamba_layers_count -= mlp_layers_count
-        mamba_to_mlp_ratio: float = mamba_layers_count / mlp_layers_count
+    Args:
+        segment: A single pipeline segment pattern string (e.g., "M-M*-")
 
-        x: float = mamba_to_mlp_ratio
-        for l in range(total_layers_count):
-            if layer_type_list[l] == Symbols.MAMBA:
-                if x < 0.5:
-                    layer_type_list[l] = Symbols.MLP
-                    x += mamba_to_mlp_ratio
-                else:
-                    x -= 1
+    Returns:
+        List of layer type characters.
 
+    Raises:
+        ValueError: If segment contains invalid layer symbols.
+    """
+    layer_type_list = list(segment)
+    for layer_char in layer_type_list:
+        if layer_char not in Symbols.VALID_LAYERS:
+            raise ValueError(
+                f"In hybrid layer pattern segment, '{layer_char}' is not "
+                f"one of {Symbols.VALID_LAYERS}"
+            )
     return layer_type_list
 
 
-def _allocate_override(total_layers_count: int, override_pattern: str) -> list:
-    layer_type_list = list(override_pattern)
-    override_pattern_length = len(layer_type_list)
-    if override_pattern_length != total_layers_count:
-        raise ValueError(
-            "The hybrid override pattern is the wrong "
-            f"length: got {override_pattern_length}, expected "
-            f"{total_layers_count}"
-        )
-    for l in layer_type_list:
-        if l not in Symbols.VALID:
-            raise ValueError(f"In hybrid override pattern, '{l}' is not one of {Symbols.VALID}")
+def select_pipeline_segment(
+    main_pattern: str,
+    pp_group: Optional[torch.distributed.ProcessGroup],
+    vp_stage: Optional[int],
+    first_stage_layers: Optional[int] = None,
+    last_stage_layers: Optional[int] = None,
+) -> Tuple[List[str], int]:
+    """Select and validate the pipeline segment for the given PP rank and VP stage.
 
-    return layer_type_list
+    When the main pattern contains '|' pipe separators, splits by '|' into
+    pipeline segments and selects the segment for the current PP rank / VP stage.
 
+    When the pattern has no pipes but pp_size > 1, falls back to runtime layer
+    slicing (for backwards compatibility), supporting both even and uneven PP splits
+    via first_stage_layers / last_stage_layers.
 
-def _layer_counts_match(a: list, b: list) -> bool:
-    for s in Symbols.VALID:
-        if a.count(s) != b.count(s):
-            return False
-    return True
-
-
-def allocate_layers(
-    total_layers_count: int,
-    target_attention_ratio: float,
-    target_mlp_ratio: float,
-    override_pattern: str = None,
-    silent: bool = False,
-) -> list:
-    """Allocates layers according to the requested distribution of layer types."""
-    assert total_layers_count > 0
-    assert target_attention_ratio >= 0.0 and target_attention_ratio <= 1.0
-    assert target_mlp_ratio >= 0.0 and target_mlp_ratio <= 1.0
-    assert target_attention_ratio + target_mlp_ratio <= 1.0
-    maybe_log_single_rank = (lambda *args, **kwargs: None) if silent else log_single_rank
-    # Note: target_mamba_ratio = 1.0 - target_attention_ratio - target_mlp_ratio
-
-    layer_type_list = _allocate_auto(total_layers_count, target_attention_ratio, target_mlp_ratio)
-
-    if override_pattern is not None:
-        layer_type_list_override = _allocate_override(total_layers_count, override_pattern)
-        maybe_log_single_rank(logger, logging.INFO, "Using hybrid override pattern")
-        if (target_attention_ratio > 0.0 or target_mlp_ratio > 0.0) and not _layer_counts_match(
-            layer_type_list_override, layer_type_list
-        ):
+    Args:
+        main_pattern: Main decoder pattern (may contain '|' separators).
+            Empty string is allowed (produces one empty segment).
+        pp_group: Pipeline parallel process group, or None if not using PP.
+        vp_stage: Virtual pipeline stage, or None if not using VPP.
+        first_stage_layers: Number of layers on the first pipeline stage for
+            uneven PP. Only valid when the pattern has no pipe separators.
+        last_stage_layers: Number of layers on the last pipeline stage for
+            uneven PP. Only valid when the pattern has no pipe separators.
+
+    Returns:
+        Tuple of (layer_type_list, layer_offset) where layer_type_list is
+        the list of layer type characters for this segment, and layer_offset
+        is the sum of layer counts from all preceding segments.
+
+    Raises:
+        ValueError: If the segment contains invalid layer symbols, if
+            first/last_stage_layers are used with pipe separators, if VPP is
+            requested without pipe separators, or if layer counts are not
+            evenly divisible across pipeline stages.
+    """
+    segments = main_pattern.split(Symbols.PIPE) if main_pattern else ['']
+
+    pp_rank = torch.distributed.get_rank(pp_group) if pp_group is not None else 0
+    pp_size = torch.distributed.get_world_size(pp_group) if pp_group is not None else 1
+
+    if len(segments) > 1 and (first_stage_layers is not None or last_stage_layers is not None):
+        raise ValueError(
+            "Cannot specify num_layers_in_first_pipeline_stage or "
+            "num_layers_in_last_pipeline_stage when hybrid_layer_pattern "
+            "contains pipe ('|') separators. The pipeline layout is already "
+            "explicitly defined by the pipe separators."
+        )
+
+    if len(segments) == 1 and pp_size > 1:
+        if vp_stage is not None:
             raise ValueError(
-                "The number of each type of layer in the override "
-                "pattern must match the number in the overridden "
-                "pattern."
-            )
-        if layer_type_list_override == layer_type_list:
-            maybe_log_single_rank(
-                logger, logging.INFO, "The override pattern matches the overridden pattern"
+                "Virtual pipeline parallelism (vp_stage != None) is not supported "
+                "when hybrid_layer_pattern has no pipe ('|') separators. "
+                "Add '|' separators to define explicit pipeline/virtual-pipeline "
+                "stage boundaries."
             )
-        else:
-            maybe_log_single_rank(
-                logger, logging.INFO, "Warning: overriding pattern A with pattern B"
-            )
-            maybe_log_single_rank(logger, logging.INFO, f"A: {''.join(layer_type_list)}")
-            maybe_log_single_rank(logger, logging.INFO, f"B: {''.join(layer_type_list_override)}")
-        layer_type_list = layer_type_list_override
-
-    if target_attention_ratio > 0.0 or target_mlp_ratio > 0.0 or override_pattern is not None:
-        actual_attention_layers_count = layer_type_list.count(Symbols.ATTENTION)
-        actual_attention_ratio = actual_attention_layers_count / total_layers_count
-        actual_mlp_layers_count = layer_type_list.count(Symbols.MLP)
-        actual_mlp_ratio = actual_mlp_layers_count / total_layers_count
-        allocation_string = "".join(layer_type_list)
-        maybe_log_single_rank(
+        log_single_rank(
             logger,
-            logging.INFO,
-            f"Hybrid allocation ({Symbols.MAMBA} is mamba, "
-            f"{Symbols.ATTENTION} is attention, "
-            f"{Symbols.MLP} is mlp):",
+            logging.WARNING,
+            "DEPRECATION: Using hybrid_layer_pattern without pipe ('|') separators "
+            "with pipeline_model_parallel_size > 1 is deprecated. Please add '|' "
+            "separators to explicitly define pipeline stage boundaries. "
+            "Example: 'M*M*M*M*' with pp_size=2 should become 'M*M*|M*M*'.",
         )
-        maybe_log_single_rank(logger, logging.INFO, allocation_string)
-        maybe_log_single_rank(
-            logger,
-            logging.INFO,
-            f"{actual_attention_layers_count} attention layers in "
-            f"{total_layers_count} total layers.",
-        )
-        maybe_log_single_rank(
+        full_pattern = segments[0]
+        layer_type_list = validate_segment_layers(full_pattern)
+        num_layers = len(layer_type_list)
+
+        if first_stage_layers is not None or last_stage_layers is not None:
+            first = first_stage_layers or 0
+            last = last_stage_layers or 0
+            middle_num_layers = num_layers - first - last
+            middle_stages = pp_size - sum(
+                1 for x in (first_stage_layers, last_stage_layers) if x is not None
+            )
+            if middle_stages > 0:
+                if middle_num_layers % middle_stages != 0:
+                    raise ValueError(
+                        f"Middle layers ({middle_num_layers}) must be evenly divisible "
+                        f"by middle pipeline stages ({middle_stages})."
+                    )
+                layers_per_middle = middle_num_layers // middle_stages
+            else:
+                layers_per_middle = 0
+
+            is_first = first_stage_layers is not None and pp_rank == 0
+            is_last = last_stage_layers is not None and pp_rank == pp_size - 1
+
+            if is_first:
+                offset = 0
+                count = first
+            elif is_last:
+                offset = num_layers - last
+                count = last
+            else:
+                middle_rank = pp_rank if first_stage_layers is None else pp_rank - 1
+                offset = middle_rank * layers_per_middle + first
+                count = layers_per_middle
+        else:
+            if num_layers % pp_size != 0:
+                raise ValueError(
+                    f"Number of layers ({num_layers}) must be evenly divisible "
+                    f"by pipeline-model-parallel-size ({pp_size}) when no pipe "
+                    f"separators are specified in the pattern."
+                )
+            layers_per_rank = num_layers // pp_size
+            offset = pp_rank * layers_per_rank
+            count = layers_per_rank
+
+        selected = layer_type_list[offset : offset + count]
+        log_on_each_pipeline_stage(
             logger,
             logging.INFO,
-            f"Target attention ratio: {target_attention_ratio:.2f}. "
-            f"Actual attention ratio: {actual_attention_ratio:.2f}.",
+            f"MambaModel: pp_rank={pp_rank}/{pp_size}, vp_stage={vp_stage}, "
+            f"layers='{''.join(selected)}' ({len(selected)} layers), "
+            f"layer_offset={offset} (auto-split)",
         )
-        maybe_log_single_rank(
-            logger,
-            logging.INFO,
-            f"{actual_mlp_layers_count} mlp layers in " f"{total_layers_count} total layers.",
+        return selected, offset
+
+    # Pipe-based segment selection
+    if len(segments) > 1 and len(segments) % pp_size != 0:
+        raise ValueError(
+            f"The number of pipe-delimited segments ({len(segments)}) in "
+            f"hybrid_layer_pattern must be evenly divisible by "
+            f"pipeline_model_parallel_size ({pp_size})."
         )
-        maybe_log_single_rank(
-            logger,
-            logging.INFO,
-            f"Target mlp ratio: {target_mlp_ratio:.2f}. "
-            f"Actual mlp ratio: {actual_mlp_ratio:.2f}.",
+
+    vp_rel = vp_stage if vp_stage is not None else 0
+    segment_index = vp_rel * pp_size + pp_rank
+
+    if segment_index >= len(segments):
+        raise ValueError(
+            f"Pipeline segment index {segment_index} (pp_rank={pp_rank}, "
+            f"vp_stage={vp_rel}) is out of range for {len(segments)} segments. "
+            f"The pattern does not define enough pipe-delimited segments for "
+            f"the current PP/VPP configuration."
         )
-    return layer_type_list
+
+    layer_offset = sum(len(segments[i]) for i in range(segment_index))
+    my_segment = segments[segment_index]
+
+    layer_type_list = validate_segment_layers(my_segment)
+
+    log_on_each_pipeline_stage(
+        logger,
+        logging.INFO,
+        f"MambaModel: pp_rank={pp_rank}/{pp_size}, vp_stage={vp_rel}, "
+        f"segment_index={segment_index}/{len(segments)}, "
+        f"layers='{my_segment}' ({len(layer_type_list)} layers), "
+        f"layer_offset={layer_offset}",
+    )
+
+    return layer_type_list, layer_offset
 
 
 def get_layer_maps_from_layer_type_list(
@@ -307,38 +475,3 @@ def get_layer_maps_from_layer_type_list(
         local_layer_idx = len(layer_map)
         layer_map[global_layer_idx] = local_layer_idx
     return [layer_maps[layer_type] for layer_type in layer_types]
-
-
-if __name__ == "__main__":
-    test_cases = [
-        # (10, 0.2, 0.0),
-        # (48, 0.0, 0.0), # will not print anything
-        # (48, 0.1, 0.0),
-        # 48, 0.3, 0.0),
-        # (48, 0.5, 0.0),
-        # (48, 0.6, 0.0),
-        # (48, 0.7, 0.0),
-        # (10, 0.0, 0.1),
-        # (10, 0.0, 0.3),
-        # (10, 0.0, 0.5),
-        # (10, 0.1, 0.1),
-        # (10, 0.2, 0.2),
-        # (10, 0.3, 0.3),
-        # (10, 0.5, 0.5),
-        # (48, 0.2, 0.3),
-        # (48, 0.5, 0.2),
-        # (48, 0.5, 0.2, "MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-"),
-        # (48, 0.25, 0.25, "MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-"),
-        # (48, 0.25, 0.25, "MM-*MM-*MM*-MM*-MM*-MM*-M*M-M*M-M*M-M*M-*MM-*MM-"),
-        # (48, 0.0, 0.2, "MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-"),
-        # (48, 0.2, 0.0, "MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-"),
-        # (48, 0.0, 0.0, "MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-"),
-        # (48, 0.5, 0.5),
-        # (10, 0.3, 0.2, "MMM*-*M*M-"),
-        # (10, 0.3, 0.2, "MM*M-*M*M-"),
-        (9, 0.0, 0.0, "M*-M*-M*-"),
-        (9, 0.0, 0.0, "MMMMMMMMM"),
-    ]
-    for t in test_cases:
-        logging.info("")
-        allocate_layers(*t)
diff --git a/megatron/core/ssm/mamba_mixer.py b/megatron/core/ssm/mamba_mixer.py
index 4df8fc3f4a0..c3d50de164f 100644
--- a/megatron/core/ssm/mamba_mixer.py
+++ b/megatron/core/ssm/mamba_mixer.py
@@ -25,6 +25,8 @@
 )
 from megatron.core.packed_seq_params import PackedSeqParams
 from megatron.core.process_groups_config import ProcessGroupCollection
+from megatron.core.ssm.ops.causal_conv1d_triton import causal_conv1d_update
+from megatron.core.ssm.ops.mamba_ssm import selective_state_update
 from megatron.core.tensor_parallel import get_cuda_rng_tracker
 from megatron.core.transformer import TransformerConfig
 from megatron.core.transformer.module import MegatronModule
@@ -45,16 +47,11 @@
 from .mamba_context_parallel import MambaContextParallel
 
 try:
-    from mamba_ssm.ops.triton.selective_state_update import selective_state_update
-except ImportError:
-    selective_state_update = None
-
-try:
-    from causal_conv1d import causal_conv1d_fn, causal_conv1d_update
+    from causal_conv1d import causal_conv1d_fn
     from causal_conv1d.causal_conv1d_varlen import causal_conv1d_varlen_states
+
 except ImportError:
     causal_conv1d_fn = None
-    causal_conv1d_update = None
 
 try:
     from mamba_ssm.ops.triton.layernorm_gated import RMSNorm as RMSNormGated
@@ -273,7 +270,9 @@ def __init__(
                 dtype=config.params_dtype,
             )
             setattr(self.conv1d.weight, "tensor_model_parallel", True)
+            setattr(self.conv1d.weight, "partition_dim", 0)
             setattr(self.conv1d.bias, "tensor_model_parallel", True)
+            setattr(self.conv1d.bias, "partition_dim", 0)
             if self.config.perform_initialization:
                 if self.conv_init is not None:
                     nn.init.uniform_(self.conv1d.weight, -self.conv_init, self.conv_init)
@@ -306,6 +305,7 @@ def __init__(
 
             self.dt_bias = nn.Parameter(inv_dt)
             setattr(self.dt_bias, "tensor_model_parallel", True)
+            setattr(self.dt_bias, "partition_dim", 0)
 
             # A parameter
             assert A_init_range[0] > 0 and A_init_range[1] >= A_init_range[0]
@@ -317,6 +317,7 @@ def __init__(
             A_log = torch.log(A)  # Keep A_log in fp32
             self.A_log = nn.Parameter(A_log)
             setattr(self.A_log, "tensor_model_parallel", True)
+            setattr(self.A_log, "partition_dim", 0)
 
         # D "skip" parameter
         self.D = nn.Parameter(
@@ -326,6 +327,7 @@ def __init__(
             )
         )  # Keep in fp32
         setattr(self.D, "tensor_model_parallel", True)
+        setattr(self.D, "partition_dim", 0)
 
         if self.rmsnorm:
             assert RMSNormGated is not None
@@ -338,6 +340,7 @@ def __init__(
                 dtype=config.params_dtype,
             )
             setattr(self.norm.weight, "tensor_model_parallel", True)
+            setattr(self.norm.weight, "partition_dim", 0)
 
         # Assume sequence parallelism: input is partitioned along d_inner and
         # output is partitioned along the sequence dimension
@@ -436,8 +439,18 @@ def _dynamic_inference(self, hidden_states: torch.Tensor, context: DynamicInfere
         )
         assert sequence_packing_available, reason_for_no_sequence_packing
 
+        # Grab standard states
         conv_state, ssm_state = context.mamba_states_cache(self.layer_number - self.pp_layer_offset)
 
+        # Fetch intermediate states for speculative decoding
+        # (just buffers, existing data is overwritten)
+        int_conv_state = None
+        int_ssm_state = None
+        if context.num_speculative_tokens > 0:
+            int_conv_state, int_ssm_state = context.mamba_states_cache(
+                self.layer_number - self.pp_layer_offset, intermediate=True
+            )
+
         padded_dims = context.padded_batch_dimensions
         token_count = padded_dims.token_count
         decode_req_count = padded_dims.decode_req_count
@@ -452,14 +465,25 @@ def _dynamic_inference(self, hidden_states: torch.Tensor, context: DynamicInfere
         # Decode
         if decode_req_count > 0:
             # For mixed batch, the decode tokens are at the start of zxBCdt
-            zxBCdt_decode = zxBCdt[:decode_req_count] if prefill_req_count > 0 else zxBCdt
+            seq_len = 1 + context.num_speculative_tokens
+            decode_token_count = decode_req_count * seq_len
+
+            zxBCdt_decode = zxBCdt[:decode_token_count] if prefill_req_count > 0 else zxBCdt
+
+            # Reshape from [N*S, 1, d] to [N, S, d] for the 3D Triton kernels
+            zxBCdt_decode = zxBCdt_decode.squeeze(1).view(decode_req_count, seq_len, -1)
 
             y_decode = self._ssm_decode(
-                zxBCdt_decode.transpose(0, 1),
+                zxBCdt_decode,
                 conv_state,
                 ssm_state,
-                context.mamba_metadata.batch_indices_decode,
-            ).transpose(0, 1)
+                batch_indices=context.mamba_metadata.batch_indices_decode,
+                intermediate_conv_state=int_conv_state,
+                intermediate_ssm_state=int_ssm_state,
+            )
+
+            # Flatten back to [N*S, 1, d] to match merge logic
+            y_decode = y_decode.view(decode_token_count, 1, -1)
 
         # Prefill
         if prefill_req_count > 0:
@@ -636,7 +660,7 @@ def _ssm_training(
                 _check_mamba_sequence_packing_support(for_inference_not_training=False)
             )
             assert sequence_packing_available, reason_for_no_sequence_packing
-            seq_idx = self._create_packed_seq_idx(packed_seq_params, zxBCdt.shape[1])
+            seq_idx = packed_seq_params.seq_idx
 
         y = mamba_split_conv1d_scan_combined(
             zxBCdt,
@@ -665,36 +689,6 @@ def _ssm_training(
 
         return y
 
-    def _create_packed_seq_idx(self, packed_seq_params: PackedSeqParams, total_tokens: int):
-        """
-        If total_tokens is 16 (for example), this method takes packed_seq_params.cu_seqlens_q_padded
-        (or cu_seqlens_q) which is of the form [0, 5, 7, 11] and returns a tensor of the form
-        [0, 0, 0, 0, 0, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 3],
-        which is [0]*(5-0) + [1]*(7-5) + [2]*(11-7) + [3]*(16-11)
-        In the above example, there are three sequences in the pack.
-        In general, the output has an additional sequence index (e.g. 0, 1, 2, 3) so that any tokens
-        beyond the last padded input sequence are accounted for as an extra sequence. However, If
-        cu_seqlens_q_padded[-1] == max_seqlen then this additional sequence index will not be
-        included.
-        """
-        # Example: [0, 5, 7, 11] -> [0, 5, 7, 11, 16]
-        if packed_seq_params.cu_seqlens_q_padded is not None:
-            cu_seqlens = packed_seq_params.cu_seqlens_q_padded
-        else:
-            cu_seqlens = packed_seq_params.cu_seqlens_q
-        total_tokens_tensor = torch.tensor(
-            [total_tokens], dtype=cu_seqlens.dtype, device=cu_seqlens.device
-        )
-        cu_seqlens_with_max = torch.cat([cu_seqlens, total_tokens_tensor])
-        # Example: [0, 5, 7, 11, 16] -> [5, 2, 4, 5]
-        seq_lengths = cu_seqlens_with_max[1:] - cu_seqlens_with_max[:-1]
-        # Example: [5, 2, 4, 5] -> [0, 0, 0, 0, 0, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 3]
-        seq_idx = torch.repeat_interleave(
-            torch.arange(seq_lengths.numel(), device=cu_seqlens.device), seq_lengths
-        )
-        seq_idx = seq_idx.to(torch.int32).unsqueeze(0)  # Add a batch dimension
-        return seq_idx
-
     def _ssm_prefill(
         self,
         zxBCdt: torch.Tensor,
@@ -784,14 +778,20 @@ def _ssm_prefill(
             xBC = self.act(self.cp.conv1d(xBC)[..., :seqlen])
         else:
             assert self.activation in ["silu", "swish"]
+            # Conv state dtype might differ from params dtype, so cast xBC and weight / bias
+            # tensors to the conv state dtype for causal_conv1d_fn and then cast xBC
+            # back to the original dtype
+            xBC_dtype = xBC.dtype
+            conv_state_dtype = xBC_dtype if conv_state is None else conv_state.dtype
+            weight = rearrange(self.cp.get_conv1d_weight(), "d 1 w -> d w")
             xBC = causal_conv1d_fn(
-                x=xBC,
-                weight=rearrange(self.cp.get_conv1d_weight(), "d 1 w -> d w"),
-                bias=self.cp.get_conv1d_bias(),
+                x=xBC.to(conv_state_dtype),
+                weight=weight.to(conv_state_dtype),
+                bias=self.cp.get_conv1d_bias().to(conv_state_dtype),
                 activation=self.activation,
                 seq_idx=seq_idx,
                 initial_states=initial_conv_state,
-            )
+            ).to(xBC_dtype)
 
         # transpose b pd l --> b l pd
         xBC = rearrange(xBC, "b d l -> b l d").contiguous()
@@ -885,27 +885,29 @@ def _ssm_decode(
         conv_state: torch.Tensor,
         ssm_state: torch.Tensor,
         batch_indices: Optional[torch.Tensor] = None,
+        intermediate_conv_state: Optional[torch.Tensor] = None,
+        intermediate_ssm_state: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
         """
         Performs SSM computation for inference decode step.
 
         Args:
-            zxBCdt: The input tensor of shape (l, b, d), which is a concatenation of
-                z, x, B, C, and dt projections. For decoding, l must be 1.
+            zxBCdt: The input tensor of shape (b, s, d), which is a concatenation of
+                z, x, B, C, and dt projections.
+                s is the sequence length (1 + num_speculative_tokens).
             conv_state: The convolution state tensor for inference.
             ssm_state: The selective scan state tensor for inference.
-            batch_indices: A map from batch id to position in the Mamba state tensors for
-                dynamic inference.
+            batch_indices: A map from batch id to position in the Mamba state tensors.
+            intermediate_conv_state: Optional buffer for storing conv state at each
+                sequence step (for speculative decoding rollback).
+            intermediate_ssm_state: Optional buffer for storing SSM state at each
+                sequence step (for speculative decoding rollback).
 
         Returns:
-            The output tensor of shape (l, b, d).
+            The output tensor of shape (b, s, d).
         """
-        seq_len, batch_size, _ = zxBCdt.shape
+        batch_size, seq_len, _ = zxBCdt.shape
         dtype = zxBCdt.dtype
-        assert seq_len == 1, "Only support decoding with 1 token at a time for now"
-
-        # Remove sequence dimension
-        zxBCdt = zxBCdt.squeeze(0)
 
         z, xBC, dt = torch.split(
             zxBCdt,
@@ -919,23 +921,32 @@ def _ssm_decode(
 
         # Conv step
         if causal_conv1d_update is None:
+            # TODO(ksanthanam): Consider deprecating this path
+            assert seq_len == 1, "Native PyTorch fallback only supports 1 token at a time"
+            xBC_squeeze = xBC.squeeze(1)
             conv_state.copy_(torch.roll(conv_state, shifts=-1, dims=-1))  # Update state (B D W)
-            conv_state[:, :, -1] = xBC
-            xBC = torch.sum(
+            conv_state[:, :, -1] = xBC_squeeze
+            xBC_squeeze = torch.sum(
                 conv_state * rearrange(self.conv1d.weight, "d 1 w -> d w"), dim=-1
             )  # (B D)
             if self.conv1d.bias is not None:
-                xBC = xBC + self.conv1d.bias
-            xBC = self.act(xBC).to(dtype=xBC.dtype)
+                xBC_squeeze = xBC_squeeze + self.conv1d.bias
+            xBC = self.act(xBC_squeeze).to(dtype=xBC.dtype).unsqueeze(1)
         else:
+            # Conv state dtype might differ from params dtype, so cast xBC and weight / bias
+            # tensors to the conv state dtype for causal_conv1d_update and then cast xBC
+            # back to the original dtype
+            xBC_dtype = xBC.dtype
+            weight = rearrange(self.conv1d.weight, "d 1 w -> d w")
             xBC = causal_conv1d_update(
-                xBC,
+                xBC.to(conv_state.dtype),
                 conv_state,
-                rearrange(self.conv1d.weight, "d 1 w -> d w"),
-                self.conv1d.bias,
+                weight.to(conv_state.dtype),
+                self.conv1d.bias.to(conv_state.dtype),
                 self.activation,
                 conv_state_indices=batch_indices,
-            )
+                intermediate_conv_states=intermediate_conv_state,
+            ).to(xBC_dtype)
 
         x, B, C = torch.split(
             xBC,
@@ -950,6 +961,16 @@ def _ssm_decode(
 
         # SSM step
         if selective_state_update is None:
+            # TODO(ksanthanam): Consider deprecating this path
+            assert seq_len == 1, "Native PyTorch fallback only supports 1 token at a time"
+
+            x = x.squeeze(1)
+            B = B.squeeze(1)
+            C = C.squeeze(1)
+            dt = dt.squeeze(1)
+            if z is not None:
+                z = z.squeeze(1)
+
             if self.ngroups_local_tp > 1:
                 B = rearrange(B, "b (g n) -> b g n", n=self.d_state)
                 C = rearrange(C, "b (g n) -> b g n", n=self.d_state)
@@ -994,16 +1015,20 @@ def _ssm_decode(
                 y = rearrange(y, "b h p -> b (h p)")
                 if not self.rmsnorm:
                     y = y * self.act(z)  # (B D)
+
+            y = y.unsqueeze(1)  # Restore seq dimension
         else:
             A = repeat(A, "h -> h p n", p=self.headdim, n=self.d_state).to(dtype=torch.float32)
-            dt = repeat(dt, "b h -> b h p", p=self.headdim)
+
+            # Incorporate sequence dimension in einops rearrengements
+            dt = repeat(dt, "b s h -> b s h p", p=self.headdim)
             dt_bias = repeat(self.dt_bias, "h -> h p", p=self.headdim)
             D = repeat(self.D, "h -> h p", p=self.headdim)
-            B = rearrange(B, "b (g n) -> b g n", g=self.ngroups_local_tp)
-            C = rearrange(C, "b (g n) -> b g n", g=self.ngroups_local_tp)
-            x_reshaped = rearrange(x, "b (h p) -> b h p", p=self.headdim)
+            B = rearrange(B, "b s (g n) -> b s g n", g=self.ngroups_local_tp)
+            C = rearrange(C, "b s (g n) -> b s g n", g=self.ngroups_local_tp)
+            x_reshaped = rearrange(x, "b s (h p) -> b s h p", p=self.headdim)
             if not self.rmsnorm:
-                z = rearrange(z, "b (h p) -> b h p", p=self.headdim)
+                z = rearrange(z, "b s (h p) -> b s h p", p=self.headdim)
 
             # Upcast the batch_indices to prevent integer overflow errors in the case of
             # large max request counts.
@@ -1022,14 +1047,14 @@ def _ssm_decode(
                 dt_bias=dt_bias,
                 dt_softplus=True,
                 state_batch_indices=batch_indices,
+                intermediate_ssm_states=intermediate_ssm_state,  # SSM only
             )
-            y = rearrange(y, "b h p -> b (h p)")
+            y = rearrange(y, "b s h p -> b s (h p)")
 
         if self.rmsnorm:
             y = self.norm(y, z)
 
-        # Restore sequence dimension
-        return y.unsqueeze(0)
+        return y
 
     def mamba_state_shapes_per_request(self) -> Tuple[Tuple[int], Tuple[int]]:
         """Returns the Mamba conv and ssm states shapes per request."""
diff --git a/megatron/core/ssm/mlp_layer.py b/megatron/core/ssm/mlp_layer.py
index 19aec5878b2..e1668a01381 100644
--- a/megatron/core/ssm/mlp_layer.py
+++ b/megatron/core/ssm/mlp_layer.py
@@ -20,6 +20,7 @@ def __init__(
         layer_number: int = 1,
         hidden_dropout: float = None,
         pg_collection: Optional[ProcessGroupCollection] = None,
+        add_layer_offset: bool = True,
     ):
         super().__init__(
             config=config,
@@ -27,4 +28,5 @@ def __init__(
             layer_number=layer_number,
             hidden_dropout=hidden_dropout,
             pg_collection=pg_collection,
+            add_layer_offset=add_layer_offset,
         )
diff --git a/megatron/core/ssm/ops/__init__.py b/megatron/core/ssm/ops/__init__.py
new file mode 100644
index 00000000000..3e4afde2e29
--- /dev/null
+++ b/megatron/core/ssm/ops/__init__.py
@@ -0,0 +1 @@
+# Copyright (c) 2026, NVIDIA CORPORATION.  All rights reserved.
diff --git a/megatron/core/ssm/ops/causal_conv1d_triton.py b/megatron/core/ssm/ops/causal_conv1d_triton.py
new file mode 100644
index 00000000000..36d14a1d91b
--- /dev/null
+++ b/megatron/core/ssm/ops/causal_conv1d_triton.py
@@ -0,0 +1,274 @@
+# Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved.
+
+# Some of this code was adopted from https://github.com/Dao-AILab/causal-conv1d/
+# This source code is licensed under the BSD license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+import triton
+import triton.language as tl
+
+
+@triton.jit
+def causal_conv1d_update_kernel(
+    x_ptr,
+    x_b_stride,
+    x_s_stride,
+    x_c_stride,
+    conv_state_ptr,
+    conv_state_b_stride,
+    conv_state_c_stride,
+    conv_state_l_stride,
+    int_state_ptr,
+    int_state_b_stride,
+    int_state_s_stride,
+    int_state_c_stride,
+    int_state_l_stride,
+    weight_ptr,
+    weight_c_stride,
+    weight_width_stride,
+    bias_ptr,
+    bias_stride,
+    out_ptr,
+    out_b_stride,
+    out_s_stride,
+    out_c_stride,
+    conv_state_indices_ptr,
+    batch,
+    seq_len,
+    dim,
+    state_len,
+    WIDTH: tl.constexpr,
+    BLOCK_DIM: tl.constexpr,
+    HAS_BIAS: tl.constexpr,
+    HAS_STATE_INDICES: tl.constexpr,
+    HAS_INT_STATE: tl.constexpr,
+    SILU_ACTIVATION: tl.constexpr,
+):
+    """Triton implementation of causal_conv1d_update (kernel)."""
+    batch_id = tl.program_id(0)
+    channel_block_id = tl.program_id(1)
+
+    channel_offsets = channel_block_id * BLOCK_DIM + tl.arange(0, BLOCK_DIM)
+    mask = channel_offsets < dim
+
+    # State batch coordinate mapping
+    if HAS_STATE_INDICES:
+        state_batch_coord = tl.load(conv_state_indices_ptr + batch_id)
+    else:
+        state_batch_coord = batch_id
+
+    # Base Pointers
+    conv_state_ptrs = (
+        conv_state_ptr
+        + state_batch_coord * conv_state_b_stride
+        + channel_offsets * conv_state_c_stride
+    )
+    weight_ptrs = weight_ptr + channel_offsets * weight_c_stride
+
+    # Skip padding tokens (block-level uniform condition)
+    if state_batch_coord < 0:
+        for s in range(seq_len):
+            out_ptrs = (
+                out_ptr
+                + batch_id * out_b_stride
+                + s * out_s_stride
+                + channel_offsets * out_c_stride
+            )
+            tl.store(out_ptrs, 0.0, mask=mask)
+        return
+
+    # Load Bias
+    if HAS_BIAS:
+        bias_val = tl.load(bias_ptr + channel_offsets * bias_stride, mask=mask).to(tl.float32)
+    else:
+        bias_val = tl.zeros([BLOCK_DIM], dtype=tl.float32)
+
+    # Load Weights
+    if WIDTH == 2:
+        w0 = tl.load(weight_ptrs + 0 * weight_width_stride, mask=mask).to(tl.float32)
+        w1 = tl.load(weight_ptrs + 1 * weight_width_stride, mask=mask).to(tl.float32)
+    elif WIDTH == 3:
+        w0 = tl.load(weight_ptrs + 0 * weight_width_stride, mask=mask).to(tl.float32)
+        w1 = tl.load(weight_ptrs + 1 * weight_width_stride, mask=mask).to(tl.float32)
+        w2 = tl.load(weight_ptrs + 2 * weight_width_stride, mask=mask).to(tl.float32)
+    elif WIDTH == 4:
+        w0 = tl.load(weight_ptrs + 0 * weight_width_stride, mask=mask).to(tl.float32)
+        w1 = tl.load(weight_ptrs + 1 * weight_width_stride, mask=mask).to(tl.float32)
+        w2 = tl.load(weight_ptrs + 2 * weight_width_stride, mask=mask).to(tl.float32)
+        w3 = tl.load(weight_ptrs + 3 * weight_width_stride, mask=mask).to(tl.float32)
+
+    # Initialize independent x_vals to match unrolled float array
+    x_val_0 = tl.zeros([BLOCK_DIM], dtype=tl.float32)
+    x_val_1 = tl.zeros([BLOCK_DIM], dtype=tl.float32)
+    x_val_2 = tl.zeros([BLOCK_DIM], dtype=tl.float32)
+    x_val_3 = tl.zeros([BLOCK_DIM], dtype=tl.float32)
+
+    # Loop over the sequence dimension (e.g., speculative tokens)
+    for s in range(seq_len):
+        x_ptrs = x_ptr + batch_id * x_b_stride + s * x_s_stride + channel_offsets * x_c_stride
+        out_ptrs = (
+            out_ptr + batch_id * out_b_stride + s * out_s_stride + channel_offsets * out_c_stride
+        )
+
+        # Load the last (WIDTH - 1) elements to use them BEFORE they are overwritten
+        # by the shift
+        if WIDTH >= 2:
+            x_val_0 = tl.load(
+                conv_state_ptrs + (state_len - WIDTH + 1) * conv_state_l_stride, mask=mask
+            ).to(tl.float32)
+        if WIDTH >= 3:
+            x_val_1 = tl.load(
+                conv_state_ptrs + (state_len - WIDTH + 2) * conv_state_l_stride, mask=mask
+            ).to(tl.float32)
+        if WIDTH >= 4:
+            x_val_2 = tl.load(
+                conv_state_ptrs + (state_len - WIDTH + 3) * conv_state_l_stride, mask=mask
+            ).to(tl.float32)
+
+        # Shift the linear state buffer left by 1
+        i = 0
+        while i < state_len - 1:
+            val = tl.load(conv_state_ptrs + (i + 1) * conv_state_l_stride, mask=mask)
+            tl.store(conv_state_ptrs + i * conv_state_l_stride, val, mask=mask)
+            i += 1
+
+        # Process the single token for the current sequence step
+        x_val = tl.load(x_ptrs, mask=mask)
+
+        # Store the new token at the end of the linear state buffer
+        tl.store(conv_state_ptrs + (state_len - 1) * conv_state_l_stride, x_val, mask=mask)
+
+        # Write out to the intermediate state buffer if requested
+        if HAS_INT_STATE:
+            i = 0
+            while i < state_len:
+                val = tl.load(conv_state_ptrs + i * conv_state_l_stride, mask=mask)
+                int_ptr = (
+                    int_state_ptr
+                    + state_batch_coord * int_state_b_stride
+                    + s * int_state_s_stride
+                    + channel_offsets * int_state_c_stride
+                    + i * int_state_l_stride
+                )
+                tl.store(int_ptr, val, mask=mask)
+                i += 1
+
+        # Advance registers for calculation
+        x_val_f32 = x_val.to(tl.float32)
+        if WIDTH == 2:
+            x_val_1 = x_val_f32
+        elif WIDTH == 3:
+            x_val_2 = x_val_f32
+        elif WIDTH == 4:
+            x_val_3 = x_val_f32
+
+        # Compute output
+        out_val = bias_val
+        if WIDTH == 2:
+            out_val += w0 * x_val_0 + w1 * x_val_1
+        elif WIDTH == 3:
+            out_val += w0 * x_val_0 + w1 * x_val_1 + w2 * x_val_2
+        elif WIDTH == 4:
+            out_val += w0 * x_val_0 + w1 * x_val_1 + w2 * x_val_2 + w3 * x_val_3
+
+        if SILU_ACTIVATION:
+            out_val = out_val * tl.sigmoid(out_val)
+
+        tl.store(out_ptrs, out_val.to(out_ptrs.dtype.element_ty), mask=mask)
+
+
+def causal_conv1d_update(
+    x: torch.Tensor,
+    conv_state: torch.Tensor,
+    weight: torch.Tensor,
+    bias: torch.Tensor | None,
+    silu_activation: bool,
+    conv_state_indices: torch.Tensor | None,
+    intermediate_conv_states: torch.Tensor | None = None,
+) -> torch.Tensor:
+    """Triton implementation of causal_conv1d_update (entrypoint)."""
+
+    # Check if input is 2D, temporarily treat as 3D for uniform processing
+    is_2d = x.dim() == 2
+    if is_2d:
+        x = x.unsqueeze(1)
+
+    batch, seq_len, dim = x.shape
+    out = torch.empty_like(x)
+    state_len = conv_state.shape[-1]
+    width = weight.shape[-1]
+
+    if bias is not None:
+        bias_stride = bias.stride(0)
+        has_bias = True
+    else:
+        bias = x  # Dummy pointer
+        bias_stride = 0
+        has_bias = False
+
+    if conv_state_indices is not None:
+        has_state_indices = True
+    else:
+        conv_state_indices = x  # Dummy pointer
+        has_state_indices = False
+
+    # Extract intermediate state strides if provided
+    if intermediate_conv_states is not None:
+        has_int_state = True
+        int_state_ptr = intermediate_conv_states
+        int_state_b_stride = intermediate_conv_states.stride(0)
+        int_state_s_stride = intermediate_conv_states.stride(1)
+        int_state_c_stride = intermediate_conv_states.stride(2)
+        int_state_l_stride = intermediate_conv_states.stride(3)
+    else:
+        has_int_state = False
+        int_state_ptr = x  # Dummy pointer
+        int_state_b_stride = 0
+        int_state_s_stride = 0
+        int_state_c_stride = 0
+        int_state_l_stride = 0
+
+    BLOCK_DIM = 64
+    grid = (batch, triton.cdiv(dim, BLOCK_DIM))
+
+    causal_conv1d_update_kernel[grid](
+        x,
+        x.stride(0),
+        x.stride(1),
+        x.stride(2),
+        conv_state,
+        conv_state.stride(0),
+        conv_state.stride(1),
+        conv_state.stride(2),
+        int_state_ptr,
+        int_state_b_stride,
+        int_state_s_stride,
+        int_state_c_stride,
+        int_state_l_stride,
+        weight,
+        weight.stride(0),
+        weight.stride(1),
+        bias,
+        bias_stride,
+        out,
+        out.stride(0),
+        out.stride(1),
+        out.stride(2),
+        conv_state_indices,
+        batch,
+        seq_len,
+        dim,
+        state_len,
+        WIDTH=width,
+        BLOCK_DIM=BLOCK_DIM,
+        HAS_BIAS=has_bias,
+        HAS_STATE_INDICES=has_state_indices,
+        HAS_INT_STATE=has_int_state,
+        SILU_ACTIVATION=silu_activation == "silu",
+    )
+
+    if is_2d:
+        out = out.squeeze(1)
+
+    return out
diff --git a/megatron/core/ssm/ops/mamba_ssm.py b/megatron/core/ssm/ops/mamba_ssm.py
new file mode 100644
index 00000000000..cd2041eb084
--- /dev/null
+++ b/megatron/core/ssm/ops/mamba_ssm.py
@@ -0,0 +1,441 @@
+# Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2024, Tri Dao, Albert Gu.
+
+# Some of this code was adopted from https://github.com/state-spaces/mamba/
+# This source code is licensed under the Apache license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+import triton
+import triton.language as tl
+from packaging import version
+
+TRITON3 = version.parse(triton.__version__) >= version.parse("3.0.0")
+
+
+if TRITON3:
+
+    @triton.jit
+    def softplus(dt):
+        """Optimized softplus."""
+        return tl.math.log(tl.math.exp(dt) + 1)
+
+else:
+
+    @triton.jit
+    def softplus(dt):
+        """Optimized softplus."""
+        return tl.math.log1p(tl.exp(dt))
+
+
+@triton.heuristics({"HAS_DT_BIAS": lambda args: args["dt_bias_ptr"] is not None})
+@triton.heuristics({"HAS_D": lambda args: args["D_ptr"] is not None})
+@triton.heuristics({"HAS_Z": lambda args: args["z_ptr"] is not None})
+@triton.heuristics(
+    {"HAS_STATE_BATCH_INDICES": lambda args: args["state_batch_indices_ptr"] is not None}
+)
+@triton.heuristics({"HAS_INT_STATE": lambda args: args["int_state_ptr"] is not None})
+@triton.heuristics({"BLOCK_SIZE_DSTATE": lambda args: triton.next_power_of_2(args["dstate"])})
+@triton.jit
+def _selective_scan_update_kernel(
+    # Pointers to matrices
+    state_ptr,
+    x_ptr,
+    dt_ptr,
+    dt_bias_ptr,
+    A_ptr,
+    B_ptr,
+    C_ptr,
+    D_ptr,
+    z_ptr,
+    out_ptr,
+    state_batch_indices_ptr,
+    int_state_ptr,
+    # Matrix dimensions
+    batch,
+    seq_len,
+    nheads,
+    dim,
+    dstate,
+    nheads_ngroups_ratio,
+    # Strides
+    stride_state_batch,
+    stride_state_head,
+    stride_state_dim,
+    stride_state_dstate,
+    stride_x_batch,
+    stride_x_seq,
+    stride_x_head,
+    stride_x_dim,
+    stride_dt_batch,
+    stride_dt_seq,
+    stride_dt_head,
+    stride_dt_dim,
+    stride_dt_bias_head,
+    stride_dt_bias_dim,
+    stride_A_head,
+    stride_A_dim,
+    stride_A_dstate,
+    stride_B_batch,
+    stride_B_seq,
+    stride_B_group,
+    stride_B_dstate,
+    stride_C_batch,
+    stride_C_seq,
+    stride_C_group,
+    stride_C_dstate,
+    stride_D_head,
+    stride_D_dim,
+    stride_z_batch,
+    stride_z_seq,
+    stride_z_head,
+    stride_z_dim,
+    stride_out_batch,
+    stride_out_seq,
+    stride_out_head,
+    stride_out_dim,
+    stride_int_batch,
+    stride_int_seq,
+    stride_int_head,
+    stride_int_dim,
+    stride_int_dstate,
+    # Meta-parameters
+    DT_SOFTPLUS: tl.constexpr,
+    TIE_HDIM: tl.constexpr,
+    BLOCK_SIZE_M: tl.constexpr,
+    HAS_DT_BIAS: tl.constexpr,
+    HAS_D: tl.constexpr,
+    HAS_Z: tl.constexpr,
+    HAS_STATE_BATCH_INDICES: tl.constexpr,
+    HAS_INT_STATE: tl.constexpr,
+    BLOCK_SIZE_DSTATE: tl.constexpr,
+):
+    pid_m = tl.program_id(axis=0)
+    pid_b = tl.program_id(axis=1)
+    pid_h = tl.program_id(axis=2)
+
+    offs_m = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
+    offs_n = tl.arange(0, BLOCK_SIZE_DSTATE)
+
+    out_ptr += pid_b * stride_out_batch + pid_h * stride_out_head
+    out_ptrs = out_ptr + offs_m * stride_out_dim
+
+    # 1. State Mapping (handles dynamic batching slot allocation)
+    if HAS_STATE_BATCH_INDICES:
+        state_batch_indices_ptr += pid_b
+        state_batch_idx = tl.load(state_batch_indices_ptr)
+        # Skip padding tokens (e.g. from graph capture or inactive slots)
+        if state_batch_idx < 0:
+            for s in range(seq_len):
+                out_s_ptrs = out_ptrs + s * stride_out_seq
+                tl.store(out_s_ptrs, 0.0, mask=offs_m < dim)
+            return
+        state_ptr += state_batch_idx * stride_state_batch + pid_h * stride_state_head
+        if HAS_INT_STATE:
+            int_state_ptr += state_batch_idx * stride_int_batch + pid_h * stride_int_head
+    else:
+        state_ptr += pid_b * stride_state_batch + pid_h * stride_state_head
+        if HAS_INT_STATE:
+            int_state_ptr += pid_b * stride_int_batch + pid_h * stride_int_head
+
+    # Base Pointers for Sequence iteration
+    x_ptr += pid_b * stride_x_batch + pid_h * stride_x_head
+    dt_ptr += pid_b * stride_dt_batch + pid_h * stride_dt_head
+    if HAS_DT_BIAS:
+        dt_bias_ptr += pid_h * stride_dt_bias_head
+
+    A_ptr += pid_h * stride_A_head
+    B_ptr += pid_b * stride_B_batch + (pid_h // nheads_ngroups_ratio) * stride_B_group
+    C_ptr += pid_b * stride_C_batch + (pid_h // nheads_ngroups_ratio) * stride_C_group
+    if HAS_Z:
+        z_ptr += pid_b * stride_z_batch + pid_h * stride_z_head
+
+    # Constant offsets (A, D, and bias do not have a sequence dimension)
+    state_ptrs = state_ptr + (
+        offs_m[:, None] * stride_state_dim + offs_n[None, :] * stride_state_dstate
+    )
+    if HAS_INT_STATE:
+        int_state_ptrs = int_state_ptr + (
+            offs_m[:, None] * stride_int_dim + offs_n[None, :] * stride_int_dstate
+        )
+
+    x_ptrs = x_ptr + offs_m * stride_x_dim
+    dt_ptrs = dt_ptr + offs_m * stride_dt_dim
+
+    if HAS_DT_BIAS:
+        dt_bias_ptrs = dt_bias_ptr + offs_m * stride_dt_bias_dim
+    if HAS_D:
+        D_ptr += pid_h * stride_D_head
+        D_ptrs = D_ptr + offs_m * stride_D_dim
+
+    A_ptrs = A_ptr + (offs_m[:, None] * stride_A_dim + offs_n[None, :] * stride_A_dstate)
+    B_ptrs = B_ptr + offs_n * stride_B_dstate
+    C_ptrs = C_ptr + offs_n * stride_C_dstate
+
+    if HAS_Z:
+        z_ptrs = z_ptr + offs_m * stride_z_dim
+
+    # Load initial historical state and constant parameters
+    state = tl.load(
+        state_ptrs, mask=(offs_m[:, None] < dim) & (offs_n[None, :] < dstate), other=0.0
+    ).to(tl.float32)
+
+    if not TIE_HDIM:
+        A = tl.load(
+            A_ptrs, mask=(offs_m[:, None] < dim) & (offs_n[None, :] < dstate), other=0.0
+        ).to(tl.float32)
+    else:
+        A = tl.load(A_ptr).to(tl.float32)
+
+    if HAS_D:
+        D = tl.load(D_ptrs, mask=offs_m < dim, other=0.0).to(tl.float32)
+
+    # ----------------------------------------------------
+    # Sequence Loop (Processes Main Token + Speculative Drafts)
+    # ----------------------------------------------------
+    for s in range(seq_len):
+        x_s_ptrs = x_ptrs + s * stride_x_seq
+        dt_s_ptrs = dt_ptrs + s * stride_dt_seq
+        B_s_ptrs = B_ptrs + s * stride_B_seq
+        C_s_ptrs = C_ptrs + s * stride_C_seq
+        if HAS_Z:
+            z_s_ptrs = z_ptrs + s * stride_z_seq
+
+        x = tl.load(x_s_ptrs, mask=offs_m < dim, other=0.0).to(tl.float32)
+
+        # Calculate dt and dA
+        if not TIE_HDIM:
+            dt = tl.load(dt_s_ptrs, mask=offs_m < dim, other=0.0).to(tl.float32)
+            if HAS_DT_BIAS:
+                dt += tl.load(dt_bias_ptrs, mask=offs_m < dim, other=0.0).to(tl.float32)
+            if DT_SOFTPLUS:
+                dt = tl.where(dt <= 20.0, softplus(dt), dt)
+            dA = tl.exp(A * dt[:, None])
+        else:
+            dt = tl.load(dt_ptr + s * stride_dt_seq).to(tl.float32)
+            if HAS_DT_BIAS:
+                dt += tl.load(dt_bias_ptr).to(tl.float32)
+            if DT_SOFTPLUS:
+                dt = tl.where(dt <= 20.0, softplus(dt), dt)
+            dA = tl.exp(A * dt)
+
+        # Load B and C
+        B = tl.load(B_s_ptrs, mask=offs_n < dstate, other=0.0).to(tl.float32)
+        C = tl.load(C_s_ptrs, mask=offs_n < dstate, other=0.0).to(tl.float32)
+        if HAS_Z:
+            z = tl.load(z_s_ptrs, mask=offs_m < dim, other=0.0).to(tl.float32)
+
+        if not TIE_HDIM:
+            dB = B[None, :] * dt[:, None]
+        else:
+            dB = B * dt
+
+        # ----------------------------------------------------
+        # The Core State Recurrence (h_t = dA * h_{t-1} + dB * x_t)
+        # ----------------------------------------------------
+        state = state * dA + dB * x[:, None]
+
+        # ----------------------------------------------------
+        # Dump Intermediate Speculative State Snapshot
+        # ----------------------------------------------------
+        if HAS_INT_STATE:
+            int_state_s_ptrs = int_state_ptrs + s * stride_int_seq
+            tl.store(
+                int_state_s_ptrs, state, mask=(offs_m[:, None] < dim) & (offs_n[None, :] < dstate)
+            )
+
+        # Calculate Output
+        out = tl.sum(state * C[None, :], axis=1)
+        if HAS_D:
+            out += x * D
+        if HAS_Z:
+            out *= z * tl.sigmoid(z)
+
+        out_s_ptrs = out_ptrs + s * stride_out_seq
+        tl.store(out_s_ptrs, out, mask=offs_m < dim)
+
+    # After processing all sequence steps, persist the final state back to HBM
+    tl.store(state_ptrs, state, mask=(offs_m[:, None] < dim) & (offs_n[None, :] < dstate))
+
+
+def selective_state_update(
+    state,
+    x,
+    dt,
+    A,
+    B,
+    C,
+    D=None,
+    z=None,
+    dt_bias=None,
+    dt_softplus=False,
+    state_batch_indices=None,
+    intermediate_ssm_states=None,
+):
+    """
+    Argument:
+        state: (batch, dim, dstate) or (batch, nheads, dim, dstate)
+        x: (batch, dim), (batch, seqlen, dim), (batch, nheads, dim) or (batch, seqlen, nheads, dim)
+        dt: Matches x
+        A: (dim, dstate) or (nheads, dim, dstate)
+        B: (batch, dstate), (batch, seqlen, dstate), (batch, ngroups, dstate) or
+            (batch, seqlen, ngroups, dstate)
+        C: Matches B
+        D: (dim,) or (nheads, dim)
+        z: Matches x
+        dt_bias: (dim,) or (nheads, dim)
+        intermediate_ssm_states: Optional buffer of shape (batch, seqlen, nheads, dim, dstate)
+                                 or (batch, seqlen, dim, dstate)
+    Return:
+        out: shape matches x
+    """
+    has_heads = state.dim() > 3
+    if not has_heads:
+        state = state.unsqueeze(1)
+
+    # Standardize inputs to explicit sequence and head dimensions: (batch, seq_len, nheads, dim)
+    is_seq_unsq = False
+    if has_heads:
+        if x.dim() == 3:  # (batch, nheads, dim) -> (batch, 1, nheads, dim)
+            x = x.unsqueeze(1)
+            dt = dt.unsqueeze(1)
+            B = B.unsqueeze(1)
+            C = C.unsqueeze(1)
+            if z is not None:
+                z = z.unsqueeze(1)
+            is_seq_unsq = True
+    else:
+        if x.dim() == 2:  # (batch, dim) -> (batch, 1, 1, dim)
+            x = x.unsqueeze(1).unsqueeze(2)
+            dt = dt.unsqueeze(1).unsqueeze(2)
+            B = B.unsqueeze(1).unsqueeze(2)
+            C = C.unsqueeze(1).unsqueeze(2)
+            if z is not None:
+                z = z.unsqueeze(1).unsqueeze(2)
+            is_seq_unsq = True
+        elif x.dim() == 3:  # (batch, seqlen, dim) -> (batch, seqlen, 1, dim)
+            x = x.unsqueeze(2)
+            dt = dt.unsqueeze(2)
+            B = B.unsqueeze(2)
+            C = C.unsqueeze(2)
+            if z is not None:
+                z = z.unsqueeze(2)
+
+    if A.dim() == 2:
+        A = A.unsqueeze(0)
+    if D is not None and D.dim() == 1:
+        D = D.unsqueeze(0)
+    if dt_bias is not None and dt_bias.dim() == 1:
+        dt_bias = dt_bias.unsqueeze(0)
+
+    # Set up Intermediate State standardization
+    if intermediate_ssm_states is not None:
+        if not has_heads and intermediate_ssm_states.dim() == 4:
+            intermediate_ssm_states = intermediate_ssm_states.unsqueeze(
+                2
+            )  # (batch, seqlen, 1, dim, dstate)
+        int_state_strides = (
+            intermediate_ssm_states.stride(0),
+            intermediate_ssm_states.stride(1),
+            intermediate_ssm_states.stride(2),
+            intermediate_ssm_states.stride(3),
+            intermediate_ssm_states.stride(4),
+        )
+    else:
+        intermediate_ssm_states = x  # Dummy pointer
+        int_state_strides = (0, 0, 0, 0, 0)
+
+    batch, seq_len, nheads, dim = x.shape
+    dstate = state.shape[-1]
+    ngroups = B.shape[-2]
+
+    out = torch.empty_like(x)
+    grid = lambda META: (triton.cdiv(dim, META['BLOCK_SIZE_M']), batch, nheads)
+    z_strides = (
+        (z.stride(0), z.stride(1), z.stride(2), z.stride(3)) if z is not None else (0, 0, 0, 0)
+    )
+
+    BLOCK_SIZE_M, num_warps = (
+        (32, 4)
+        if dstate <= 16
+        else (
+            (16, 4)
+            if dstate <= 32
+            else ((8, 4) if dstate <= 64 else ((4, 4) if dstate <= 128 else ((4, 8))))
+        )
+    )
+
+    tie_hdim = (
+        A.stride(-1) == 0
+        and A.stride(-2) == 0
+        and dt.stride(-1) == 0
+        and (dt_bias is None or dt_bias.stride(-1) == 0)
+    )
+
+    with torch.cuda.device(x.device.index):
+        _selective_scan_update_kernel[grid](
+            state,
+            x,
+            dt,
+            dt_bias,
+            A,
+            B,
+            C,
+            D,
+            z,
+            out,
+            state_batch_indices,
+            intermediate_ssm_states,
+            batch,
+            seq_len,
+            nheads,
+            dim,
+            dstate,
+            nheads // ngroups,
+            state.stride(0),
+            state.stride(1),
+            state.stride(2),
+            state.stride(3),
+            x.stride(0),
+            x.stride(1),
+            x.stride(2),
+            x.stride(3),
+            dt.stride(0),
+            dt.stride(1),
+            dt.stride(2),
+            dt.stride(3),
+            *(dt_bias.stride(0), dt_bias.stride(1)) if dt_bias is not None else (0, 0),
+            A.stride(0),
+            A.stride(1),
+            A.stride(2),
+            B.stride(0),
+            B.stride(1),
+            B.stride(2),
+            B.stride(3),
+            C.stride(0),
+            C.stride(1),
+            C.stride(2),
+            C.stride(3),
+            *(D.stride(0), D.stride(1)) if D is not None else (0, 0),
+            z_strides[0],
+            z_strides[1],
+            z_strides[2],
+            z_strides[3],
+            out.stride(0),
+            out.stride(1),
+            out.stride(2),
+            out.stride(3),
+            *int_state_strides,
+            dt_softplus,
+            tie_hdim,
+            BLOCK_SIZE_M,
+            num_warps=num_warps,
+        )
+
+    # Revert dimensions back to match original x format
+    if not has_heads:
+        out = out.squeeze(2)
+    if is_seq_unsq:
+        out = out.squeeze(1)
+
+    return out
diff --git a/megatron/core/tensor_parallel/inference_layers.py b/megatron/core/tensor_parallel/inference_layers.py
index 0addc64a65f..cb367e43bd6 100644
--- a/megatron/core/tensor_parallel/inference_layers.py
+++ b/megatron/core/tensor_parallel/inference_layers.py
@@ -9,6 +9,7 @@
     TERowParallelLinear,
 )
 from megatron.core.inference.communication.torch_symm_triton import (
+    are_tensors_nvls_eligible,
     fused_multimem_rs_add_norm_ag,
     multimem_all_gather,
     multimem_reduce_scatter,
@@ -16,7 +17,7 @@
 from megatron.core.inference.quantization.mxfp8_tensor import MXFP8Tensor
 from megatron.core.inference.quantization.utils import mm_mxfp8
 from megatron.core.model_parallel_config import ModelParallelConfig
-from megatron.core.parallel_state import get_global_symmetric_memory_buffer
+from megatron.core.parallel_state import get_global_symmetric_memory_buffer_tp
 from megatron.core.transformer.transformer_config import TransformerConfig
 from megatron.core.utils import get_tensor_model_parallel_group_if_none
 
@@ -108,6 +109,8 @@ def __init__(
                 config.sequence_parallel
             ), "--transformer-impl=inference_optimized requires --sequence-parallel"
 
+        self.triton_nvls_kernels_allowed = not config.inference_disable_triton_nvls_kernels
+
         # Boolean to be toggled externally for skipping norm and all-gather.
         # This is used when enabling fused reduce-scatter + add + rms-norm + all-gather
         # in tensor parallelism. In this case, the preceeding RowParallelLinear layer
@@ -120,7 +123,7 @@ def _maybe_allocate_symmetric_buffer(self, x: torch.Tensor):
         """
         symm_mem_buffer_dims = list(x.size())
         symm_mem_buffer_dims[0] *= self.tp_size
-        symm_mem_buffer = get_global_symmetric_memory_buffer().maybe_get_tensor(
+        symm_mem_buffer = get_global_symmetric_memory_buffer_tp().maybe_get_tensor(
             symm_mem_buffer_dims, dtype=x.dtype
         )
         return symm_mem_buffer
@@ -133,16 +136,14 @@ def _all_gather(self, x: torch.Tensor, symm_mem_buffer: dict) -> None:
         if self.tp_size == 1:
             return x
 
-        # 1. check if bf16
-        is_bf16 = x.dtype == torch.bfloat16
-        # 2. check if hopper or newer
-        is_hopper_or_newer = torch.cuda.get_device_properties(x.device).major >= 9
-        # 3. check if symmetric memory buffer is available
-        has_enough_symmetric_memory = symm_mem_buffer["handle"] is not None
-        can_use_custom_nvls_collectives = (
-            is_bf16 and is_hopper_or_newer and has_enough_symmetric_memory
+        # Check input only: if input is 16-byte divisible, the output
+        # (world_size * input) is too.
+        can_use_nvls = (
+            self.triton_nvls_kernels_allowed
+            and are_tensors_nvls_eligible(x)
+            and symm_mem_buffer["handle"] is not None
         )
-        if can_use_custom_nvls_collectives:
+        if can_use_nvls:
             # do multimem all gather
             multimem_all_gather(symm_mem_buffer["tensor"], x, symm_mem_buffer["handle"])
             return symm_mem_buffer["tensor"]
@@ -221,6 +222,10 @@ def __init__(
                 config.sequence_parallel
             ), "--transformer-impl=inference_optimized requires --sequence-parallel"
 
+        self.triton_nvls_kernels_allowed = not getattr(
+            config, 'inference_disable_triton_nvls_kernels', False
+        )
+
         # Placeholder for next layer norm weights for fused
         # reduce-scatter + add + rms-norm + all-gather
         self.next_layer_norm_weights = None
@@ -233,27 +238,27 @@ def _matmul_reduce_scatter(self, x, residual=None):
         and perform an NVLS multicast reduce-scatter. If that is not possible,
         it will revert to torch.dist (NCCL) reduce-scatter.
         """
-        # 1. check if bf16
-        is_bf16 = x.dtype == torch.bfloat16
-        # 2. check if mxfp8
         use_mxfp8 = self.config.fp8_recipe == "mxfp8"
-        # 3. check if hopper or newer
-        is_hopper_or_newer = torch.cuda.get_device_properties(x.device).major >= 9
-        # 4. attempt to ask for symmetric memory
         symm_mem_buffer_dims = list(x.size())
         if use_mxfp8:
             # Remove batch dimension for FlashInfer mxfp8
             del symm_mem_buffer_dims[1]
         symm_mem_buffer_dims[-1] = self.weight.size(0)
-        symm_mem_buffer = get_global_symmetric_memory_buffer().maybe_get_tensor(
+        symm_mem_buffer = get_global_symmetric_memory_buffer_tp().maybe_get_tensor(
             symm_mem_buffer_dims, dtype=x.dtype
         )
-        has_enough_symmetric_memory = symm_mem_buffer["handle"] is not None
-        can_use_custom_nvls_collectives = (
-            is_bf16 and is_hopper_or_newer and has_enough_symmetric_memory
+
+        # RS requires bf16 (hardware multimem reduce is bf16-only).
+        # Check the matmul output shape: if it is NVLS-eligible, the RS output
+        # (world_size times smaller on dim 0) is too.
+        can_use_nvls = (
+            self.triton_nvls_kernels_allowed
+            and x.dtype == torch.bfloat16
+            and are_tensors_nvls_eligible(x)
+            and symm_mem_buffer["handle"] is not None
         )
 
-        if can_use_custom_nvls_collectives:
+        if can_use_nvls:
             # Write output of matmul directly onto the symmetric memory buffer
 
             x = _apply_linear(x, self.weight, self.config, out=symm_mem_buffer["tensor"])
diff --git a/megatron/core/tokenizers/text/libraries/tiktoken_tokenizer.py b/megatron/core/tokenizers/text/libraries/tiktoken_tokenizer.py
index 48c46e9949f..39228ad4afd 100644
--- a/megatron/core/tokenizers/text/libraries/tiktoken_tokenizer.py
+++ b/megatron/core/tokenizers/text/libraries/tiktoken_tokenizer.py
@@ -17,16 +17,15 @@
 
 logger = logging.getLogger(__name__)
 
-PATTERN_TIKTOKEN_V1 = (
+_PATTERN_TIKTOKEN_V1 = (
     r"[^\r\n\p{L}\p{N}]?+\p{L}+|\p{N}| ?[^\s\p{L}\p{N}]++[\r\n]*|\s*[\r\n]|\s+(?!\S)|\s+"
 )
-PATTERN_TIKTOKEN_V2 = "[^\\r\\n\\p{L}\\p{N}]?[\\p{Lu}\\p{Lt}\\p{Lm}\\p{Lo}\\p{M}]*[\\p{Ll}\\p{Lm}\\p{Lo}\\p{M}]+|[^\\r\\n\\p{L}\\p{N}]?[\\p{Lu}\\p{Lt}\\p{Lm}\\p{Lo}\\p{M}]+[\\p{Ll}\\p{Lm}\\p{Lo}\\p{M}]*|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n/]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+"  # pylint: disable=line-too-long
+_PATTERN_TIKTOKEN_V2 = "[^\\r\\n\\p{L}\\p{N}]?[\\p{Lu}\\p{Lt}\\p{Lm}\\p{Lo}\\p{M}]*[\\p{Ll}\\p{Lm}\\p{Lo}\\p{M}]+|[^\\r\\n\\p{L}\\p{N}]?[\\p{Lu}\\p{Lt}\\p{Lm}\\p{Lo}\\p{M}]+[\\p{Ll}\\p{Lm}\\p{Lo}\\p{M}]*|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n/]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+"  # pylint: disable=line-too-long
 DEFAULT_TIKTOKEN_MAX_VOCAB = 2**17  # 131072
 SPECIAL_TOKENS = ["<unk>", "<s>", "</s>", "<mask>", "<pad>", "<cls>", "<sep>"]
 SPECIAL_TOKEN_TEMPLATE = "<SPECIAL_{id}>"
 
-
-logger = logging.getLogger(__name__)
+__all__ = ["TikTokenTokenizer", "reload_mergeable_ranks"]
 
 
 def reload_mergeable_ranks(
@@ -103,9 +102,9 @@ def __init__(
             special_tokens = SPECIAL_TOKENS.copy()
 
         if pattern == "v1":
-            pattern = PATTERN_TIKTOKEN_V1
+            pattern = _PATTERN_TIKTOKEN_V1
         elif pattern == "v2":
-            pattern = PATTERN_TIKTOKEN_V2
+            pattern = _PATTERN_TIKTOKEN_V2
         else:
             raise ValueError(f"Expected tiktoken pattern to be `v1` or `v2`, but got {pattern}.")
 
diff --git a/megatron/core/transformer/attention.py b/megatron/core/transformer/attention.py
index 3b054ccc4b1..a98539060d2 100644
--- a/megatron/core/transformer/attention.py
+++ b/megatron/core/transformer/attention.py
@@ -252,11 +252,13 @@ def __init__(
         attention_type: str,
         cp_comm_type: str | None = None,
         pg_collection: ProcessGroupCollection | None = None,
+        pp_layer_offset: Optional[int] = None,
     ):
         super().__init__(config=config)
 
         self.config = config
         self.layer_number = layer_number
+        self._pp_layer_offset = pp_layer_offset
 
         self.attn_mask_type = attn_mask_type
         self.attention_type = attention_type
@@ -433,7 +435,16 @@ def _allocate_memory(self, inference_max_sequence_length, batch_size, dim, dtype
         )
 
     def _get_pp_layer_offset_for_inference(self):
-        """Return the pipeline parallel layer offset for inference."""
+        """Return the pipeline parallel layer offset for inference.
+
+        When pp_layer_offset was explicitly provided (e.g. by MambaBlock for
+        hybrid models using --hybrid-layer-pattern with fVPP), use that value
+        directly.  Otherwise fall back to the standard computation which assumes
+        uniform layer distribution across pipeline stages.
+        """
+        if self._pp_layer_offset is not None:
+            return self._pp_layer_offset
+
         assert (
             self.config.virtual_pipeline_model_parallel_size is None
         ), "Virtual pipeline parallelism is not supported for inference"
@@ -789,7 +800,7 @@ def flash_decode_and_prefill(
         assert block_table is not None
 
         # Flash attn kernel.
-        if not is_decode_only:
+        if max_seqlen_q > 1:
             q = q.squeeze(1)
             if getattr(self, "softmax_scale", None) is not None:
                 softmax_scale = self.softmax_scale
@@ -1264,6 +1275,7 @@ def __init__(
         attn_mask_type: AttnMaskType = AttnMaskType.padding,
         cp_comm_type: str | None = None,
         pg_collection: ProcessGroupCollection | None = None,
+        pp_layer_offset: Optional[int] = None,
     ):
         super().__init__(
             config=config,
@@ -1273,6 +1285,7 @@ def __init__(
             attention_type="self",
             cp_comm_type=cp_comm_type,
             pg_collection=pg_collection,
+            pp_layer_offset=pp_layer_offset,
         )
 
         self.linear_qkv_out_dim = self.query_projection_size + 2 * self.kv_projection_size
diff --git a/megatron/core/transformer/cuda_graphs.py b/megatron/core/transformer/cuda_graphs.py
index 0f7341f253e..6cdcf7037e8 100644
--- a/megatron/core/transformer/cuda_graphs.py
+++ b/megatron/core/transformer/cuda_graphs.py
@@ -928,6 +928,7 @@ def clone_ten(ten):
                         only_inputs=True,
                         allow_unused=True,
                     )
+
             _set_warmup_end()
 
             with self.get_quantization_context():
@@ -1131,7 +1132,18 @@ def replace_with_weak_ref(arg):
                 if not torch.is_tensor(arg):
                     return arg
 
-                ref = make_weak_ref(arg)
+                try:
+                    ref = make_weak_ref(arg)
+                except RuntimeError:
+                    # Fallback to keeping a strong reference. There is a known bug where some
+                    # dtypes (e.g. torch.float64) are not mapped to a representation in
+                    # transformer_engine/pytorch/utils.py.
+                    if torch.distributed.get_rank() == 0:
+                        logger.warning(
+                            f"Could not create weak ref for tensor with dtype {arg.dtype}; "
+                            f"keeping strong ref with a potential memory overhead."
+                        )
+                    return arg
                 ref.requires_grad = arg.requires_grad
                 if hasattr(arg, "can_skip_replay_copy"):
                     ref.can_skip_replay_copy = arg.can_skip_replay_copy
@@ -1270,17 +1282,19 @@ def check(val, ref, context):
             _check_supported_type(ref)
 
             if val.type != ref.type and not (is_dataclass(val.value) and is_dataclass(ref.value)):
-                add_error(f"Type mismatch at {context}: {val.type} vs {ref.type}")
+                add_error(
+                    f"Type mismatch at {context}: Received {val.type} but expected {ref.type}"
+                )
                 return False
 
             if ref.type == torch.Tensor or issubclass(ref.type, torch.Tensor):
                 mismatches = []
                 if val.shape != ref.shape:
-                    mismatches.append(f"expected shape {val.shape} vs. {ref.shape}")
+                    mismatches.append(f"Received shape {val.shape} but expected {ref.shape}")
                 if val.dtype != ref.dtype:
-                    mismatches.append(f"expected dtype {val.dtype} vs. {ref.dtype}")
+                    mismatches.append(f"Received dtype {val.dtype} but expected {ref.dtype}")
                 if val.device != ref.device:
-                    mismatches.append(f"expected device {val.device} vs. {ref.device}")
+                    mismatches.append(f"Received device {val.device} but expected {ref.device}")
                 if mismatches:
                     add_error(f"Tensor mismatch at {context}: {', '.join(mismatches)}")
 
@@ -1704,14 +1718,21 @@ def __init__(self, model, config, seq_length, micro_batch_size, optimizers=[]):
         # Number of microbatches to capture. The value will be set in _get_cuda_graph_input_data().
         self.num_microbatches = None
 
-        # Get callables with captureable layers.
+        self._discover_layers()
+
+        # One helper object can only capture CUDA Graphs once. Use this flag to check if the graphs
+        # have been created.
+        self._graphs_created = False
+
+    def _discover_layers(self):
+        """Discover captureable layers from the model and populate internal data structures."""
         self.chunks_with_decoder = []
         self.num_layers_per_chunk = []
         self.callables_per_chunk = []
         self.callables_per_chunk_is_mtp = []
         self.flattened_callables = []
         self.flattened_callables_is_mtp = []
-        for chunk_number, model_chunk in enumerate(model):
+        for chunk_number, model_chunk in enumerate(self.model):
             try:
                 chunk_with_decoder = get_attr_wrapped_model(
                     model_chunk, 'decoder', allow_none=False, return_model_obj=True
@@ -1736,13 +1757,13 @@ def __init__(self, model, config, seq_length, micro_batch_size, optimizers=[]):
                 callables, callables_is_mtp = [], []
                 for layer_number in range(num_decoder_layers):
                     layer = chunk_with_decoder.decoder.layers[layer_number]
-                    if _layer_is_graphable(layer, config):
+                    if _layer_is_graphable(layer, self.config):
                         num_graphable_layers += 1
                         callables.append(layer)
                         callables_is_mtp.append(False)
                 for layer_number in range(num_mtp_layers):
                     layer = chunk_with_decoder.mtp.layers[layer_number].mtp_model_layer
-                    if _layer_is_graphable(layer, config):
+                    if _layer_is_graphable(layer, self.config):
                         num_graphable_layers += 1
                         callables.append(layer)
                         callables_is_mtp.append(True)
@@ -1778,10 +1799,6 @@ def __init__(self, model, config, seq_length, micro_batch_size, optimizers=[]):
             f'{len(self.flattened_callables)} graphable layers.',
         )
 
-        # One helper object can only capture CUDA Graphs once. Use this flag to check if the graphs
-        # have been created.
-        self._graphs_created = False
-
     def graphs_created(self):
         """
         Returns whether the CUDA Graphs have been created.
@@ -2434,3 +2451,316 @@ def get_layer_range(c_id):
                 add_order(c_id, l_b, is_wgrad=True)
 
     return new_order, chunk_id_list
+
+
+# ---------------------------------------------------------------------------
+# set_current_microbatch: sets per-layer microbatch index for TE graph replay
+# ---------------------------------------------------------------------------
+
+
+def set_current_microbatch(model, microbatch_id):
+    """Set the current microbatch on all layers that use TE CUDA graph replay.
+
+    current_microbatch is read by _te_cuda_graph_replay to select the
+    correct graph index.  This helper is called from the pipeline-parallel
+    schedule before each forward step.
+    """
+    decoder_exists = True
+    model_with_decoder = None
+    try:
+        model_with_decoder = get_attr_wrapped_model(
+            model, "decoder", allow_none=False, return_model_obj=True
+        )
+    except RuntimeError:
+        decoder_exists = False
+    if decoder_exists and model_with_decoder is not None:
+        for layer in model_with_decoder.decoder.layers:
+            layer.current_microbatch = microbatch_id
+        if hasattr(model_with_decoder, 'mtp'):
+            for layer in model_with_decoder.mtp.layers:
+                assert hasattr(
+                    layer, 'mtp_model_layer'
+                ), f"MTP layer {layer} must have 'mtp_model_layer' attribute"
+                layer.mtp_model_layer.current_microbatch = microbatch_id
+
+    # Also set current_microbatch on vision encoder layers so that
+    # _te_cuda_graph_replay selects the correct graph index. Without this,
+    # vision layers always use graph 0 (since current_microbatch defaults to 0),
+    # causing all microbatch forwards to overwrite the same static buffers.
+    # When backward runs for earlier microbatches, the buffers contain stale
+    # data from later forwards, producing NaN gradients.
+    try:
+        model_with_vision = get_attr_wrapped_model(
+            model, "vision_model", allow_none=True, return_model_obj=True
+        )
+    except RuntimeError:
+        model_with_vision = None
+    if model_with_vision is not None and hasattr(model_with_vision, 'vision_model'):
+        vision_model = model_with_vision.vision_model
+        if hasattr(vision_model, 'decoder') and hasattr(vision_model.decoder, 'layers'):
+            for layer in vision_model.decoder.layers:
+                layer.current_microbatch = microbatch_id
+
+
+# ---------------------------------------------------------------------------
+# Vision encoder CUDA graph helpers
+# ---------------------------------------------------------------------------
+
+
+def _wrap_graph_for_vision(graph_fn):
+    """Wrap a graphed callable to filter out None outputs.
+
+    During make_graphed_callables warmup, vision encoder layers go through their
+    normal forward() path which returns (output, context=None). _te_cuda_graph_replay
+    asserts len(output) == 1 but gets 2 elements. This wrapper filters out None
+    values so replay sees (output,) instead of (output, None).
+    """
+
+    def wrapped(*args, **kwargs):
+        result = graph_fn(*args, **kwargs)
+        if isinstance(result, tuple):
+            filtered = tuple(r for r in result if r is not None)
+            return filtered if filtered else result
+        return result
+
+    for attr in ('backward_dw', 'reset'):
+        if hasattr(graph_fn, attr):
+            setattr(wrapped, attr, getattr(graph_fn, attr))
+    return wrapped
+
+
+def get_vision_cuda_graph_seq_length(vision_config, default_seq_length: int = 4096) -> int:
+    """Calculate the sequence length for vision encoder CUDA graphs.
+
+    For vision encoders, the sequence length depends on:
+    - max_vision_cuda_graph_seq_length: explicit maximum (if set)
+    - num_position_embeddings: maximum number of patches
+    - spatial_merge_size: pooling factor that reduces sequence length
+
+    Args:
+        vision_config: The TransformerConfig for vision encoder
+        default_seq_length: Default sequence length if cannot be calculated
+
+    Returns:
+        The sequence length to use for CUDA graph capture
+    """
+    if (
+        hasattr(vision_config, 'max_vision_cuda_graph_seq_length')
+        and vision_config.max_vision_cuda_graph_seq_length
+    ):
+        return vision_config.max_vision_cuda_graph_seq_length
+
+    if hasattr(vision_config, 'num_position_embeddings'):
+        seq_length = vision_config.num_position_embeddings
+        if hasattr(vision_config, 'spatial_merge_size'):
+            merge_factor = vision_config.spatial_merge_size**2
+            seq_length = seq_length // merge_factor
+        return seq_length
+
+    return default_seq_length
+
+
+class VisionTECudaGraphHelper(TECudaGraphHelper):
+    """Helper to capture CUDA Graphs for vision encoder layers using TE.
+
+    Inherits from TECudaGraphHelper and overrides only the
+    vision-specific behaviour:
+
+    * Layer discovery finds vision_model.decoder.layers instead of the
+      language decoder layers.
+    * num_model_chunks is always 1 (vision has no virtual pipeline stages).
+    * Batch dimension is always 1 (images are concatenated along the sequence
+      dimension).
+    * Sample argument generation uses a simple loop (no rotary embeddings or
+      buffer-reuse optimization).
+    * Captured graph outputs are wrapped to filter None values that arise
+      from vision encoder layers returning (output, None).
+
+    Args:
+        model: The full model (list of model chunks) containing vision_model.
+        vision_config: TransformerConfig for the vision encoder.
+        vision_seq_length: Sequence length for vision (max vision tokens).
+        micro_batch_size: Micro-batch size (unused for sample-arg generation
+            since the vision encoder always uses batch-dim = 1).
+        num_microbatches: Number of microbatches per step.
+    """
+
+    def __init__(
+        self,
+        model,
+        vision_config,
+        vision_seq_length: int,
+        micro_batch_size: int,
+        num_microbatches: int = 1,
+    ):
+        super().__init__(model, vision_config, vision_seq_length, micro_batch_size)
+        # Vision encoder concatenates all images along the sequence dimension
+        # with a fixed batch dimension of 1, regardless of the training MBS.
+        self.micro_batch_size = 1
+        self.num_model_chunks = 1
+        self.num_microbatches = num_microbatches
+
+    def _discover_layers(self):
+        """Discover captureable layers from the vision encoder."""
+        self.vision_model = None
+        vision_layers = []
+
+        for model_chunk in self.model:
+            try:
+                unwrapped = get_attr_wrapped_model(
+                    model_chunk, 'vision_model', allow_none=True, return_model_obj=True
+                )
+                if unwrapped is not None and hasattr(unwrapped, 'vision_model'):
+                    self.vision_model = unwrapped.vision_model
+                    break
+            except (RuntimeError, AttributeError):
+                continue
+
+        if self.vision_model is not None:
+            if hasattr(self.vision_model, 'decoder') and hasattr(
+                self.vision_model.decoder, 'layers'
+            ):
+                for layer in self.vision_model.decoder.layers:
+                    if _layer_is_graphable(layer, self.config):
+                        vision_layers.append(layer)
+
+        if vision_layers:
+            self.chunks_with_decoder = [self.vision_model]
+            self.num_layers_per_chunk = [len(vision_layers)]
+            self.callables_per_chunk = [vision_layers]
+            self.callables_per_chunk_is_mtp = [[False] * len(vision_layers)]
+            self.flattened_callables = list(vision_layers)
+            self.flattened_callables_is_mtp = [False] * len(vision_layers)
+        else:
+            if self.vision_model is None:
+                logger.warning(
+                    'VisionTECudaGraphHelper: No vision_model found in model. '
+                    'CUDA graphs will not be captured for vision encoder.'
+                )
+            self.chunks_with_decoder = [None]
+            self.num_layers_per_chunk = [0]
+            self.callables_per_chunk = [[]]
+            self.callables_per_chunk_is_mtp = [[]]
+            self.flattened_callables = []
+            self.flattened_callables_is_mtp = []
+
+        # backward-compat aliases used by callers / tests
+        self.callables = vision_layers
+        self.num_layers = len(vision_layers)
+
+        if vision_layers:
+            logger.info(
+                f'VisionTECudaGraphHelper: Found {self.num_layers} graphable vision encoder '
+                f'layers. seq_length={self.seq_length} (all images concatenated, batch_dim=1)'
+            )
+
+    def _start_capturing(self):
+        """Start capturing for vision encoder.
+
+        Unlike the parent, this skips torch.distributed.barrier() because
+        with PP > 1 only the first pipeline stage has vision layers — other
+        ranks return early from create_cudagraphs and never reach this
+        point, so a barrier would deadlock.
+        """
+        assert not self._graphs_created, 'CUDA Graphs have already been created.'
+        gc.collect()
+        torch.cuda.empty_cache()
+        if FREEZE_GC:
+            gc.freeze()
+        _set_capture_start()
+        log_single_rank(logger, logging.INFO, 'Start vision encoder CUDA Graphs capture...')
+        return time.time()
+
+    def _finish_capturing(self, start_time):
+        """Finish capturing for vision encoder.
+
+        Unlike the parent, this skips:
+        - torch.distributed.barrier() (asymmetric: only first PP stage captures).
+        - model_chunk.zero_grad_buffer() / optimizer.zero_grad() (handled
+          by the LM decoder helper's _finish_capturing which runs on all ranks).
+        - clear_aux_losses_tracker / reset_model_temporary_tensors
+          (LM-specific cleanup already handled by the LM helper).
+        """
+        log_single_rank(
+            logger,
+            logging.INFO,
+            f'Time spent in vision encoder CUDA Graphs capture on rank '
+            f'{torch.distributed.get_rank()}: {time.time() - start_time}s',
+        )
+        _set_capture_end()
+        if FREEZE_GC:
+            gc.unfreeze()
+        gc.collect()
+        torch.cuda.empty_cache()
+        self._graphs_created = True
+
+    def _get_sample_arguments(self, order, chunk_id_list=None):
+        """Generate sample arguments for vision encoder CUDA Graph capturing.
+
+        Vision uses a simple per-layer-per-microbatch loop with batch_dim=1
+        and no rotary embeddings (unlike the parent's buffer-reuse
+        optimization). The order and chunk_id_list arguments are
+        unused because vision has num_model_chunks=1 and does not need
+        the pipeline-schedule-aware buffer lifecycle tracking.
+
+        Returns:
+            Tuple of (sample_args, sample_kwargs) lists for each
+            (layer, microbatch) pair.
+        """
+        if not self.flattened_callables:
+            return [], []
+
+        sample_args = []
+        sample_kwargs_list = []
+        hidden_size = self.config.hidden_size
+
+        for _microbatch_idx in range(self.num_microbatches):
+            for layer in self.flattened_callables:
+                hidden_states = torch.zeros(
+                    self.seq_length,
+                    1,
+                    hidden_size,
+                    dtype=torch.bfloat16,
+                    device='cuda',
+                    requires_grad=True,
+                )
+
+                if hasattr(layer, 'get_layer_static_inputs'):
+                    static_inputs = layer.get_layer_static_inputs(self.seq_length, 1)
+                    hidden_states = static_inputs.pop('hidden_states', hidden_states)
+                    sample_args.append((hidden_states,))
+                    sample_kwargs_list.append(static_inputs)
+                else:
+                    sample_args.append((hidden_states,))
+                    sample_kwargs_list.append({})
+
+        return sample_args, sample_kwargs_list
+
+    def create_cudagraphs(self):
+        """Capture CUDA Graphs for vision encoder layers per microbatch.
+
+        Delegates to the parent's capture workflow, then wraps the captured
+        graphs with _wrap_graph_for_vision to filter None from
+        (output, None) tuples so that _te_cuda_graph_replay's
+        len == 1 assertion passes.
+        """
+        if not self.flattened_callables:
+            logger.warning(
+                'VisionTECudaGraphHelper: No graphable layers found. '
+                'Skipping CUDA graph capture.'
+            )
+            return
+
+        super().create_cudagraphs()
+
+        for layer in self.flattened_callables:
+            if hasattr(layer, 'cuda_graphs'):
+                layer.cuda_graphs = [_wrap_graph_for_vision(g) for g in layer.cuda_graphs]
+
+    def cuda_graph_set_manual_hooks(self):
+        """No-op: vision encoder layers do not use DDP parameter-gather hooks.
+
+        The parent derives hooks from model_chunk._make_forward_pre_hook which
+        requires overlap_param_gather=True.  Vision encoder parameters are not
+        distributed with the same overlap strategy, so we skip hook setup.
+        """
diff --git a/megatron/core/transformer/moe/README.md b/megatron/core/transformer/moe/README.md
index e5eff445cac..558e2ccd39a 100644
--- a/megatron/core/transformer/moe/README.md
+++ b/megatron/core/transformer/moe/README.md
@@ -687,7 +687,7 @@ torchrun ${DISTRIBUTED_ARGS[@]} pretrain_gpt.py \
 
 ## Contributing
 
-We welcome contributions! Please see [CONTRIBUTING.md](../../../../CONTRIBUTING.md) for guidelines.
+We welcome contributions! Please see [CONTRIBUTING.md](https://github.com/NVIDIA/Megatron-LM/blob/main/CONTRIBUTING.md) for guidelines.
 
 ## Support
 
diff --git a/megatron/core/transformer/moe/grouped_gemm_util.py b/megatron/core/transformer/moe/grouped_gemm_util.py
index 5dd344816bd..b40dcce8c25 100644
--- a/megatron/core/transformer/moe/grouped_gemm_util.py
+++ b/megatron/core/transformer/moe/grouped_gemm_util.py
@@ -1,7 +1,7 @@
 # Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
 
 try:
-    import grouped_gemm
+    import grouped_gemm  # pyright: ignore[reportMissingImports]
 except ImportError:
     grouped_gemm = None
 
diff --git a/megatron/core/transformer/moe/moe_layer.py b/megatron/core/transformer/moe/moe_layer.py
index 3d9d0b092aa..8277486b03b 100644
--- a/megatron/core/transformer/moe/moe_layer.py
+++ b/megatron/core/transformer/moe/moe_layer.py
@@ -2,6 +2,7 @@
 
 from __future__ import annotations
 
+import warnings
 from abc import ABC, abstractmethod
 from dataclasses import dataclass
 from typing import Optional, Protocol, Union
@@ -24,11 +25,30 @@
     MoEFlexTokenDispatcher,
     MoETokenDispatcher,
 )
+from megatron.core.transformer.moe.token_dispatcher_inference import (
+    InferenceCUDAGraphTokenDispatcher,
+)
 from megatron.core.transformer.spec_utils import ModuleSpec, build_module
 from megatron.core.transformer.transformer_config import TransformerConfig
 from megatron.core.typed_torch import apply_module
 from megatron.core.utils import internal_api
 
+try:
+    import flashinfer  # pylint: disable=unused-import
+
+    HAVE_FLASHINFER = True
+except ImportError:
+    HAVE_FLASHINFER = False
+
+if HAVE_FLASHINFER:
+    try:
+        import flashinfer_cubin  # pylint: disable=unused-import
+        import flashinfer_jit_cache  # pylint: disable=unused-import
+
+        HAVE_FLASHINFER_CUBIN_AND_JIT_CACHE = True
+    except ImportError:
+        HAVE_FLASHINFER_CUBIN_AND_JIT_CACHE = False
+
 try:
     import transformer_engine as te  # pylint: disable=unused-import
 
@@ -246,10 +266,76 @@ def __init__(
             if self.shared_expert_overlap:
                 self.token_dispatcher.set_shared_experts(self.shared_experts)
 
+        # Inference-optimized mode setup
+        if config.transformer_impl == "inference_optimized":
+            assert (
+                HAVE_FLASHINFER
+            ), "flashinfer-python is required for inference-optimized MoE implementation."
+            if not HAVE_FLASHINFER_CUBIN_AND_JIT_CACHE:
+                warnings.warn(
+                    "flashinfer-cubin and/or flashinfer-jit-cache not found. "
+                    "The FlashInfer cutlass kernel will be JIT compiled,"
+                    "which may take a long time."
+                )
+            self._setup_inference_mode(pg_collection)
+
         # Cudagraph tensor store for resuming the forward pass from the end of the cudagraph.
         self.cudagraph_tensor_store = MoECudaGraphTensorStore()
         self.fwd_execution_map = ["route", "expert_compute", "postprocess"]
 
+    def _setup_inference_mode(self, pg_collection):
+        """Set up inference-optimized token dispatcher and state.
+
+        Called from __init__ when config.transformer_impl == "inference_optimized".
+        Creates an InferenceCUDAGraphTokenDispatcher alongside the standard dispatcher,
+        which is swapped in during CUDA-graphed forward passes.
+        """
+
+        assert self.config.moe_token_dispatcher_type == "alltoall", (
+            f"Inference-optimized MoE requires 'alltoall' dispatcher, "
+            f"got '{self.config.moe_token_dispatcher_type}'"
+        )
+        self.is_inference_cuda_graphed_iteration = False
+        self._inference_token_dispatcher = InferenceCUDAGraphTokenDispatcher(
+            self.num_local_experts,
+            self.local_expert_indices,
+            config=self.config,
+            pg_collection=pg_collection,
+        )
+
+    def set_inference_cuda_graphed_iteration(self):
+        """Enable CUDA-graphed iteration mode on this layer, its router, and its experts.
+
+        Swaps in the inference-optimized token dispatcher and disables
+        shared expert overlap.
+        """
+        self.is_inference_cuda_graphed_iteration = True
+        if hasattr(self.router, "set_inference_cuda_graphed_iteration"):
+            self.router.set_inference_cuda_graphed_iteration()
+        if hasattr(self.experts, "set_inference_cuda_graphed_iteration"):
+            self.experts.set_inference_cuda_graphed_iteration()
+
+        if self._inference_token_dispatcher is not None:
+            self._saved_token_dispatcher = self.token_dispatcher
+            self.token_dispatcher = self._inference_token_dispatcher
+            self._saved_shared_expert_overlap = self.shared_expert_overlap
+            self.shared_expert_overlap = False
+
+    def unset_inference_cuda_graphed_iteration(self):
+        """Disable CUDA-graphed iteration mode on this layer, its router, and its experts.
+
+        Restores the standard token dispatcher and shared expert overlap setting.
+        """
+        self.is_inference_cuda_graphed_iteration = False
+        if hasattr(self.router, "unset_inference_cuda_graphed_iteration"):
+            self.router.unset_inference_cuda_graphed_iteration()
+        if hasattr(self.experts, "unset_inference_cuda_graphed_iteration"):
+            self.experts.unset_inference_cuda_graphed_iteration()
+
+        if hasattr(self, "_saved_token_dispatcher"):
+            self.token_dispatcher = self._saved_token_dispatcher
+            self.shared_expert_overlap = self._saved_shared_expert_overlap
+
     @maybe_skip_or_early_return_by_cudagraph("route")
     def route(self, hidden_states: torch.Tensor, padding_mask: Optional[torch.Tensor] = None):
         """Compute token routing for preprocessing.
@@ -328,7 +414,18 @@ def routed_experts_compute(self, hidden_states: torch.Tensor, probs: torch.Tenso
         dispatched_input, tokens_per_expert, permuted_probs = (
             self.token_dispatcher.dispatch_postprocess(hidden_states, probs)
         )
-        expert_output, mlp_bias = self.experts(dispatched_input, tokens_per_expert, permuted_probs)
+        if (
+            hasattr(self, "_inference_token_dispatcher")
+            and self.is_inference_cuda_graphed_iteration
+        ):
+            routing_map = self.token_dispatcher.routing_map
+            expert_output, mlp_bias = self.experts(
+                dispatched_input, tokens_per_expert, permuted_probs, routing_map=routing_map
+            )
+        else:
+            expert_output, mlp_bias = self.experts(
+                dispatched_input, tokens_per_expert, permuted_probs
+            )
         assert mlp_bias is None, f"mlp_bias is not supported for {type(self.token_dispatcher)}"
         output = self.token_dispatcher.combine_preprocess(expert_output)
 
@@ -437,7 +534,7 @@ def custom_forward(hidden_states, intermediate_tensors=None, padding_mask=None):
 
             return output, mlp_bias
 
-        if self.moe_layer_recompute:
+        if self.moe_layer_recompute and self.training:
             if self.config.fp8 or self.config.fp4:
                 outputs = te_checkpoint(
                     custom_forward,
diff --git a/megatron/core/transformer/moe/router.py b/megatron/core/transformer/moe/router.py
index c9a2a469531..b675d33cd21 100644
--- a/megatron/core/transformer/moe/router.py
+++ b/megatron/core/transformer/moe/router.py
@@ -716,3 +716,115 @@ def _save_to_state_dict(self, *args, **kwargs):
         """Save the state dict of the router."""
         self._maintain_float32_expert_bias()  # switch to float32 before saving
         return super()._save_to_state_dict(*args, **kwargs)
+
+
+class InferenceTopKRouter(TopKRouter):
+    """Inference-only top-k router that strips out training-specific overhead.
+
+    A stripped-down version of TopKRouter that skips z-loss, auxiliary load
+    balancing losses, token dropping, and expert bias updates. The _forward()
+    method is @torch.compile()'d and returns dense [num_tokens, topk] tensors
+    instead of sparse [num_tokens, num_experts] for compatibility with FlashInfer.
+
+    Falls back to the parent TopKRouter.forward() for training or
+    non-CUDA-graphed inference iterations.
+    """
+
+    def __init__(
+        self,
+        config: TransformerConfig,
+        pg_collection: Optional[ProcessGroupCollection] = None,
+        is_mtp_layer: bool = False,
+    ) -> None:
+        """Initialize the specialized inference top-k router.
+
+        Args:
+            config (TransformerConfig): The configuration for the transformer model.
+            pg_collection (ProcessGroupCollection, optional): Process groups for MoE operations.
+        """
+        # Enforce constraints before calling super().__init__
+        assert config.moe_router_num_groups is None, (
+            f"InferenceTopKRouter requires moe_router_num_groups=None, "
+            f"got {config.moe_router_num_groups}"
+        )
+        assert config.moe_router_score_function in ["sigmoid", "softmax"], (
+            f"InferenceTopKRouter requires moe_router_score_function in "
+            f"['sigmoid', 'softmax'], got '{config.moe_router_score_function}'"
+        )
+
+        super().__init__(config=config, pg_collection=pg_collection)
+
+        self.is_inference_cuda_graphed_iteration = False
+
+    def set_inference_cuda_graphed_iteration(self):
+        """Enable CUDA graph-compatible operations for the router."""
+        self.is_inference_cuda_graphed_iteration = True
+
+    def unset_inference_cuda_graphed_iteration(self):
+        """Disable CUDA graph-compatible operations for the router."""
+        self.is_inference_cuda_graphed_iteration = False
+
+    @staticmethod
+    @torch.compile
+    def _compiled_topk_routing(
+        logits,
+        topk,
+        use_pre_softmax,
+        num_groups,
+        group_topk,
+        scaling_factor,
+        score_function,
+        expert_bias,
+        fused,
+        router_replay,
+        dense_output,
+    ):
+        return topk_routing_with_score_function(
+            logits,
+            topk,
+            use_pre_softmax=use_pre_softmax,
+            num_groups=num_groups,
+            group_topk=group_topk,
+            scaling_factor=scaling_factor,
+            score_function=score_function,
+            expert_bias=expert_bias,
+            fused=fused,
+            router_replay=router_replay,
+            dense_output=dense_output,
+        )
+
+    def _forward(self, input: torch.Tensor, padding_mask: Optional[torch.Tensor] = None):
+        logits = self.gating(input).squeeze(1)  # [num_tokens, num_experts]
+
+        probs, top_indices = self._compiled_topk_routing(
+            logits,
+            self.topk,
+            use_pre_softmax=self.config.moe_router_pre_softmax,
+            num_groups=self.config.moe_router_num_groups,
+            group_topk=self.config.moe_router_group_topk,
+            scaling_factor=self.config.moe_router_topk_scaling_factor,
+            score_function=self.score_function,
+            expert_bias=self.expert_bias,
+            fused=self.config.moe_router_fusion,
+            router_replay=self.router_replay,
+            dense_output=True,
+        )
+        return probs.squeeze(1), top_indices.squeeze(1)
+
+    def forward(self, input: torch.Tensor, padding_mask: Optional[torch.Tensor] = None):
+        """Simplified forward pass for inference - returns dense tensors only.
+
+        Args:
+            input (torch.Tensor): Input tensor of shape [seq_length, bsz, hidden_size].
+            padding_mask (torch.Tensor, optional): Not used in inference.
+
+        Returns:
+            Tuple[torch.Tensor, torch.Tensor]:
+                - probs: Normalized routing probabilities [num_tokens, topk]
+                - top_indices: Selected expert indices [num_tokens, topk]
+        """
+
+        if self.training or not self.is_inference_cuda_graphed_iteration:
+            return super().forward(input, padding_mask)
+
+        return self._forward(input, padding_mask)
diff --git a/megatron/core/transformer/moe/token_dispatcher.py b/megatron/core/transformer/moe/token_dispatcher.py
index 62e7ff41b87..993e91aee80 100644
--- a/megatron/core/transformer/moe/token_dispatcher.py
+++ b/megatron/core/transformer/moe/token_dispatcher.py
@@ -682,6 +682,7 @@ def token_dispatch(self, permutated_local_input_tokens, permuted_probs):
         Returns:
             A tuple of tokens and probabilities after All-to-All.
         """
+
         # Perform expert parallel AlltoAll communication
         self.tokens_per_expert = self._maybe_dtoh_and_synchronize(
             "before_ep_alltoall", self.tokens_per_expert
diff --git a/megatron/core/transformer/moe/token_dispatcher_inference.py b/megatron/core/transformer/moe/token_dispatcher_inference.py
new file mode 100644
index 00000000000..6b851c252c5
--- /dev/null
+++ b/megatron/core/transformer/moe/token_dispatcher_inference.py
@@ -0,0 +1,326 @@
+# Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+
+"""
+CUDA-graph-compatible token dispatcher for inference.
+
+This dispatcher is only used during CUDA-graphed inference iterations. It replaces
+AlltoAll with AllGather/ReduceScatter for token exchange, keeping all metadata
+GPU-resident to avoid host synchronizations that would break CUDA graph capture.
+
+Supports latency-optimized NVLS collectives (multimem all-gather/reduce-scatter)
+on Hopper+ GPUs with BF16, with automatic fallback to NCCL.
+"""
+
+from typing import List, Optional
+
+import torch
+
+from megatron.core.inference.communication.torch_symm_triton import (
+    are_tensors_nvls_eligible,
+    multimem_all_gather_fused,
+    multimem_reduce_scatter,
+)
+from megatron.core.parallel_state import get_global_symmetric_memory_buffer_ep
+from megatron.core.process_groups_config import ProcessGroupCollection
+from megatron.core.tensor_parallel import (
+    gather_from_sequence_parallel_region,
+    reduce_scatter_to_sequence_parallel_region,
+)
+from megatron.core.transformer.moe.token_dispatcher import MoEAllGatherTokenDispatcher
+from megatron.core.transformer.transformer_config import TransformerConfig
+
+
+class InferenceCUDAGraphTokenDispatcher(MoEAllGatherTokenDispatcher):
+    """
+    CUDA-graph-compatible AllGather token dispatcher for inference.
+
+    Only used during CUDA-graphed inference iterations. Swapped in by
+    MoELayer.set_inference_cuda_graphed_iteration() before graph capture
+    and swapped out by MoELayer.unset_inference_cuda_graphed_iteration() after.
+
+    Key features:
+    - AllGather/ReduceScatter instead of AlltoAll for CUDA graph compatibility
+    - GPU-resident metadata (no host synchronization)
+    - NVLS collectives on Hopper+ with automatic NCCL fallback
+    """
+
+    def __init__(
+        self,
+        num_local_experts: int,
+        local_expert_indices: List[int],
+        config: TransformerConfig,
+        pg_collection: Optional[ProcessGroupCollection] = None,
+    ) -> None:
+        """
+        Initialize the InferenceCUDAGraphTokenDispatcher.
+
+        Args:
+            num_local_experts: Number of experts on this rank.
+            local_expert_indices: Global indices of experts on this rank.
+            config: Transformer configuration.
+            pg_collection: Process group collection for distributed ops.
+        """
+        super().__init__(
+            num_local_experts=num_local_experts,
+            local_expert_indices=local_expert_indices,
+            config=config,
+            pg_collection=pg_collection,
+        )
+        self.topk = config.moe_router_topk
+
+        self.triton_nvls_kernels_allowed = not self.config.inference_disable_triton_nvls_kernels
+
+    def _maybe_allocate_ag_buffers(
+        self, routing_map: torch.Tensor, probs: torch.Tensor, hidden_states: torch.Tensor
+    ) -> dict:
+        """Allocate a single symmetric memory output buffer for fused all-gather.
+
+        Creates one contiguous symmetric memory buffer sized for the gathered
+        (global) routing_map, probs, and hidden_states, then returns sliced views
+        into it. This allows a single fused NVLS all-gather kernel to write all
+        three outputs in one launch.
+
+        Args:
+            routing_map (torch.Tensor): Local routing map, shape [local_tokens, topk].
+                Boolean or integer tensor mapping each token to its selected experts.
+            probs (torch.Tensor): Local routing probabilities, shape [local_tokens, topk].
+                Normalized weights for each token's selected experts.
+            hidden_states (torch.Tensor): Local hidden states, shape [local_tokens, hidden_dim].
+
+        Returns:
+            dict: A dictionary with the following keys:
+                - "handle": Symmetric memory handle for NVLS ops, or None if
+                  symmetric memory is unavailable.
+                - "routing_map": Raw byte view for the gathered routing map output.
+                - "routing_map_offset": Byte offset of routing_map within the buffer.
+                - "probs": Raw byte view for the gathered probs output.
+                - "probs_offset": Byte offset of probs within the buffer.
+                - "hidden_states": Raw byte view for the gathered hidden states output.
+                - "hidden_states_offset": Byte offset of hidden_states within the buffer.
+                When allocation fails, all tensor views are None and offsets are 0.
+        """
+        _NONE = {
+            "handle": None,
+            "routing_map": None,
+            "routing_map_offset": 0,
+            "probs": None,
+            "probs_offset": 0,
+            "hidden_states": None,
+            "hidden_states_offset": 0,
+        }
+
+        local_tokens = probs.size(0)
+        global_tokens = local_tokens * self.ep_size
+        topk = probs.size(-1)
+        hidden_dim = hidden_states.size(-1)
+
+        result = get_global_symmetric_memory_buffer_ep().maybe_get_tensors(
+            [
+                (global_tokens * topk, routing_map.dtype),
+                (global_tokens * topk, probs.dtype),
+                (global_tokens * hidden_dim, hidden_states.dtype),
+            ]
+        )
+
+        if result["handle"] is None:
+            return _NONE
+
+        (rm_buf, rm_off), (p_buf, p_off), (hs_buf, hs_off) = result["tensors"]
+        return {
+            "handle": result["handle"],
+            "routing_map": rm_buf,
+            "routing_map_offset": rm_off,
+            "probs": p_buf,
+            "probs_offset": p_off,
+            "hidden_states": hs_buf,
+            "hidden_states_offset": hs_off,
+        }
+
+    def _maybe_allocate_rs_buffer(self, x: torch.Tensor) -> dict:
+        """Allocate a symmetric memory buffer for reduce-scatter input.
+
+        The buffer has the same shape and dtype as x so that x can be copied
+        into it before the NVLS reduce-scatter kernel.
+
+        Args:
+            x (torch.Tensor): The global hidden states to be reduce-scattered,
+                shape [global_tokens, hidden_dim].
+
+        Returns:
+            dict: A dictionary with keys "handle" (symmetric memory handle, or
+                None if unavailable) and "tensor" (the allocated buffer, or None).
+        """
+        symm_mem_buffer = get_global_symmetric_memory_buffer_ep().maybe_get_tensor(
+            list(x.size()), dtype=x.dtype
+        )
+        return symm_mem_buffer
+
+    def token_dispatch(self, hidden_states, probs):
+        """Gathers tokens from all EP ranks using AllGather.
+
+        Performs all-gather on routing_map (stored in self.routing_map), probs,
+        and hidden_states so that every rank holds the full global view.
+        Uses latency-optimized fused NVLS multimem_all_gather on Hopper+ GPUs
+        with BF16 when symmetric memory is available. Falls back to NCCL otherwise.
+
+        Args:
+            hidden_states (torch.Tensor): Local hidden states,
+                shape [local_tokens, hidden_dim].
+            probs (torch.Tensor): Local routing probabilities,
+                shape [local_tokens, topk]. Normalized weights for each token's
+                selected experts.
+
+        Returns:
+            tuple: (hidden_states, probs) gathered across all EP ranks.
+                - hidden_states (torch.Tensor): Shape [global_tokens, hidden_dim].
+                - probs (torch.Tensor): Shape [global_tokens, topk].
+                Also updates self.routing_map in-place to the gathered
+                shape [global_tokens, topk].
+        """
+        if self.ep_size == 1:
+            return hidden_states, probs
+
+        # 1. Check inputs only: if inputs are 16-byte divisible,
+        #  outputs (world_size * input) are too.
+        nvls_eligible = self.triton_nvls_kernels_allowed and are_tensors_nvls_eligible(
+            hidden_states, probs, self.routing_map
+        )
+        ag_buffers = None
+
+        if nvls_eligible:
+            # 2. Now attempt to allocate symmetric memory buffers for
+            # all-gather outputs. If allocation fails, fallback to NCCL.
+            ag_buffers = self._maybe_allocate_ag_buffers(self.routing_map, probs, hidden_states)
+
+        # 3. Can use NVLS if eligible and buffers allocated successfully (handle is not None)
+        can_use_nvls = nvls_eligible and ag_buffers["handle"] is not None
+
+        if can_use_nvls:
+            # Capture shapes for reshaping after all-gather
+            # Output shape: [local_tokens * ep_size, dim]
+            local_tokens = probs.size(0)
+            global_tokens = local_tokens * self.ep_size
+            topk = probs.size(1)
+            hidden_dim = hidden_states.size(1)
+            routing_map_dtype = self.routing_map.dtype
+            probs_dtype = probs.dtype
+            hidden_dtype = hidden_states.dtype
+
+            # Fused NVLS all-gather: single kernel launch + single barrier for all 3 tensors
+            multimem_all_gather_fused(
+                ag_buffers["routing_map"].view(
+                    torch.bfloat16
+                ),  # .view does not change the underlying data
+                self.routing_map.view(torch.bfloat16),
+                ag_buffers["routing_map_offset"],
+                ag_buffers["probs"].view(torch.bfloat16),
+                probs.view(torch.bfloat16),
+                ag_buffers["probs_offset"],
+                ag_buffers["hidden_states"].view(torch.bfloat16),
+                hidden_states.view(torch.bfloat16),
+                ag_buffers["hidden_states_offset"],
+                ag_buffers["handle"],
+            )
+            self.routing_map = (
+                ag_buffers["routing_map"].view(routing_map_dtype).view(global_tokens, topk)
+            )
+            probs = ag_buffers["probs"].view(probs_dtype).view(global_tokens, topk)
+            hidden_states = (
+                ag_buffers["hidden_states"].view(hidden_dtype).view(global_tokens, hidden_dim)
+            )
+        else:
+            # Fallback to NCCL for all tensors
+            with torch.no_grad():
+                self.routing_map = gather_from_sequence_parallel_region(
+                    self.routing_map, group=self.tp_ep_group
+                )
+            probs = gather_from_sequence_parallel_region(probs, group=self.tp_ep_group)
+            hidden_states = gather_from_sequence_parallel_region(
+                hidden_states, group=self.tp_ep_group
+            )
+
+        return hidden_states, probs
+
+    def dispatch_postprocess(self, hidden_states, probs):
+        """Pass-through: returns inputs directly without permutation.
+
+        Unlike the training dispatcher, this does not permute tokens or compute
+        tokens_per_expert. The downstream InferenceGroupedMLP (FlashInfer /
+        CUTLASS fused MoE kernel) operates directly on the routing map stored
+        in self.routing_map.
+
+        Args:
+            hidden_states (torch.Tensor): Gathered hidden states,
+                shape [global_tokens, hidden_dim].
+            probs (torch.Tensor): Gathered routing probabilities,
+                shape [global_tokens, topk].
+
+        Returns:
+            tuple: (hidden_states, tokens_per_expert, probs) where
+                tokens_per_expert is always None.
+        """
+        return hidden_states, None, probs
+
+    def combine_preprocess(self, expert_output):
+        """Pass-through: InferenceGroupedMLP already produces unpermuted output.
+
+        No unpermutation is needed because dispatch_postprocess did not permute
+        the tokens in the first place.
+
+        Args:
+            expert_output (torch.Tensor): Output from InferenceGroupedMLP,
+                shape [global_tokens, hidden_dim].
+
+        Returns:
+            torch.Tensor: The input tensor unchanged.
+        """
+        return expert_output
+
+    def token_combine(self, hidden_states):
+        """Combines expert outputs across EP ranks using Reduce-Scatter.
+
+        Reduces the global expert output (summing contributions from each rank)
+        and scatters the result so each rank receives its local token slice.
+        Uses latency-optimized NVLS multimem_reduce_scatter on Hopper+ GPUs
+        with BF16 when symmetric memory is available. Falls back to NCCL otherwise.
+
+        Args:
+            hidden_states (torch.Tensor): Combined expert output after routing
+                weights have been applied, shape [global_tokens, hidden_dim].
+
+        Returns:
+            torch.Tensor: Local slice of the reduced output,
+                shape [local_tokens, hidden_dim] where
+                local_tokens = global_tokens // ep_size.
+        """
+        if self.ep_size == 1:
+            return hidden_states
+
+        # Compute output shape first — check NVLS eligibility on the output,
+        # since if the smaller output is 16-byte divisible, the input is too.
+        output_shape = list(hidden_states.size())
+        output_shape[0] = hidden_states.size(0) // self.ep_size
+        output = torch.empty(output_shape, dtype=hidden_states.dtype, device=hidden_states.device)
+
+        # Check output only: if output is 16-byte divisible, input (world_size * output) is too.
+        nvls_eligible = self.triton_nvls_kernels_allowed and are_tensors_nvls_eligible(output)
+        rs_buffer = None
+
+        if nvls_eligible:
+            rs_buffer = self._maybe_allocate_rs_buffer(hidden_states)
+
+        can_use_nvls = nvls_eligible and rs_buffer["handle"] is not None
+
+        if can_use_nvls:
+            # Copy input to symmetric memory for reduce-scatter
+            rs_buffer["tensor"].copy_(hidden_states)
+
+            # Use latency-optimized NVLS reduce-scatter
+            multimem_reduce_scatter(output, rs_buffer["tensor"], rs_buffer["handle"])
+            return output
+        else:
+            # Fallback to NCCL
+            hidden_states = reduce_scatter_to_sequence_parallel_region(
+                hidden_states, group=self.tp_ep_group
+            )
+            return hidden_states
diff --git a/megatron/core/transformer/multi_latent_attention.py b/megatron/core/transformer/multi_latent_attention.py
index 4b3f876a978..fcf53a46eae 100644
--- a/megatron/core/transformer/multi_latent_attention.py
+++ b/megatron/core/transformer/multi_latent_attention.py
@@ -16,6 +16,7 @@
 
 
 from megatron.core import tensor_parallel
+from megatron.core.dist_checkpointing.mapping import ShardedObject
 from megatron.core.extensions.transformer_engine import split_te_layernorm_column_parallel_linear
 from megatron.core.models.common.embeddings import (
     RotaryEmbedding,
@@ -39,7 +40,12 @@
 from megatron.core.transformer.torch_norm import LayerNormBuilder
 from megatron.core.transformer.transformer_config import MLATransformerConfig
 from megatron.core.typed_torch import apply_module
-from megatron.core.utils import deprecate_inference_params, get_pg_size, is_te_min_version
+from megatron.core.utils import (
+    deprecate_inference_params,
+    get_pg_size,
+    is_te_min_version,
+    make_tp_sharded_tensor_for_checkpoint,
+)
 
 try:
     from megatron.core.fusions.fused_mla_yarn_rope_apply import (
@@ -54,6 +60,7 @@
 try:
     from megatron.core.extensions.transformer_engine import (
         TEColumnParallelLinear,
+        TELayerNormColumnParallelLinear,
         TELinear,
         set_save_original_input,
     )
@@ -61,7 +68,13 @@
 
     HAVE_TE = True
 except ImportError:
-    TEColumnParallelLinear, TELinear, Linear, set_save_original_input = None, None, None, None
+    (
+        TEColumnParallelLinear,
+        TELayerNormColumnParallelLinear,
+        TELinear,
+        Linear,
+        set_save_original_input,
+    ) = (None, None, None, None, None)
     HAVE_TE = False
 
 
@@ -78,6 +91,7 @@ class MLASelfAttentionSubmodules:
     linear_q_up_proj: Union[ModuleSpec, type] = None
     linear_kv_down_proj: Union[ModuleSpec, type] = None
     linear_kv_up_proj: Union[ModuleSpec, type] = None
+    linear_qkv_down_proj: Union[ModuleSpec, type] = None
     core_attention: Union[ModuleSpec, type] = None
     linear_proj: Union[ModuleSpec, type] = None
 
@@ -509,6 +523,34 @@ def __init__(
             eps=self.config.layernorm_epsilon,
         )
 
+    def _qkv_down_projection(self, hidden_states):
+        """Unfused q/kv down projection path."""
+        if self.config.q_lora_rank is not None:
+            # if linear_q_down_proj is ColumnParallelLinear:
+            #     q_compressed: [s, b, q_lora_rank / TP]
+            # elif linear_q_down_proj is Linear:
+            #     q_compressed: [s / TP, b, q_lora_rank]
+            q_compressed, _ = self.linear_q_down_proj(hidden_states)
+
+            # When output is sharded (ColumnParallelLinear), two things are needed to be
+            # identical to a normal Linear.
+            #   1. Manually gather output to restore output dim q_lora_rank;
+            #   2. Scatter sequence back to s / TP if sequence-parallel since it was
+            #      gathered by ColumnParallelLinear.
+            if q_compressed.size(-1) != self.config.q_lora_rank:
+                q_compressed = gather_from_tensor_model_parallel_region(q_compressed)
+                if self.config.sequence_parallel:
+                    q_compressed = scatter_to_sequence_parallel_region(q_compressed)
+        else:
+            q_compressed = hidden_states
+
+        # if linear_kv_down_proj is ColumnParallelLinear:
+        #     kv_combined: [s, b, (kv_lora_rank + qk_pos_emb_head_dim) / TP]
+        # elif linear_kv_down_proj is Linear:
+        #     kv_combined: [s / TP, b, (kv_lora_rank + qk_pos_emb_head_dim)]
+        kv_combined, _ = self.linear_kv_down_proj(hidden_states)
+        return q_compressed, kv_combined
+
     def get_query_key_value_tensors(
         self,
         hidden_states,
@@ -578,30 +620,7 @@ def get_query_key_value_tensors(
         # =========================================
         # QKV down projection and layernorm
         # =========================================
-        if self.config.q_lora_rank is not None:
-            # if linear_q_down_proj is ColumnParallelLinear:
-            #     q_compressed: [s, b, q_lora_rank / TP]
-            # elif linear_q_down_proj is Linear:
-            #     q_compressed: [s / TP, b, q_lora_rank]
-            q_compressed, _ = self.linear_q_down_proj(hidden_states)
-
-            # When output is sharded (ColumnParallelLinear), two things are needed to be
-            # identical to a normal Linear.
-            #   1. Manually gather output to restore output dim q_lora_rank;
-            #   2. Scatter sequence back to s / TP if sequence-parallel since it was
-            #      gathered by ColumnParallelLinear.
-            if q_compressed.size(-1) != self.config.q_lora_rank:
-                q_compressed = gather_from_tensor_model_parallel_region(q_compressed)
-                if self.config.sequence_parallel:
-                    q_compressed = scatter_to_sequence_parallel_region(q_compressed)
-        else:
-            q_compressed = hidden_states
-
-        # if linear_kv_down_proj is ColumnParallelLinear:
-        #     kv_combined: [s, b, (kv_lora_rank + qk_pos_emb_head_dim) / TP]
-        # elif linear_kv_down_proj is Linear:
-        #     kv_combined: [s / TP, b, (kv_lora_rank + qk_pos_emb_head_dim)]
-        kv_combined, _ = self.linear_kv_down_proj(hidden_states)
+        q_compressed, kv_combined = self._qkv_down_projection(hidden_states)
         if kv_combined.size(-1) != self.config.kv_lora_rank + self.config.qk_pos_emb_head_dim:
             # kv_combined: [s, b, (kv_lora_rank + qk_pos_emb_head_dim)]
             kv_combined = gather_from_tensor_model_parallel_region(kv_combined)
@@ -1095,3 +1114,217 @@ def _clip_kv_proj_weight(self, weight):
         )
 
         return weight_kv_updated
+
+
+class FusedMLASelfAttention(MLASelfAttention):
+    """MLA self-attention with fused q/kv down projection."""
+
+    def __init__(
+        self,
+        config: MLATransformerConfig,
+        submodules: MLASelfAttentionSubmodules,
+        layer_number: int,
+        attn_mask_type=AttnMaskType.padding,
+        cp_comm_type: Optional[str] = None,
+        pg_collection: Optional[ProcessGroupCollection] = None,
+    ):
+        if pg_collection is None:
+            pg_collection = ProcessGroupCollection.use_mpu_process_groups()
+
+        MultiLatentAttention.__init__(
+            self,
+            config=config,
+            submodules=submodules,
+            layer_number=layer_number,
+            attn_mask_type=attn_mask_type,
+            attention_type="self",
+            cp_comm_type=cp_comm_type,
+            pg_collection=pg_collection,
+        )
+
+        assert self.config.q_lora_rank is not None, (
+            "FusedMLASelfAttention requires q_lora_rank to be set; "
+            "fallback to MLASelfAttention for q_lora_rank=None."
+        )
+
+        qkv_down_proj_kwargs = {}
+        if submodules.linear_qkv_down_proj in [TELinear]:
+            qkv_down_proj_kwargs['parallel_mode'] = 'duplicated'
+        elif submodules.linear_qkv_down_proj in [
+            Linear,
+            TEColumnParallelLinear,
+            ColumnParallelLinear,
+            TELayerNormColumnParallelLinear,
+        ]:
+            qkv_down_proj_kwargs['gather_output'] = False
+        else:
+            raise ValueError(f"Unsupported linear_qkv_down_proj: {submodules.linear_qkv_down_proj}")
+
+        self.linear_qkv_down_proj = build_module(
+            submodules.linear_qkv_down_proj,
+            self.config.hidden_size,
+            self.config.q_lora_rank + self.config.kv_lora_rank + self.config.qk_pos_emb_head_dim,
+            config=self.config,
+            init_method=self.config.init_method,
+            bias=False,
+            skip_bias_add=False,
+            is_expert=False,
+            tp_comm_buffer_name='qkv_down_proj',
+            skip_weight_param_allocation=False,
+            tp_group=(
+                pg_collection.tp
+                if qkv_down_proj_kwargs.get('parallel_mode') != 'duplicated'
+                else None
+            ),
+            **qkv_down_proj_kwargs,
+        )
+
+        self.linear_q_up_proj = build_module(
+            submodules.linear_q_up_proj,
+            self.config.q_lora_rank,
+            self.config.num_attention_heads * self.q_head_dim,
+            config=self.config,
+            init_method=self.config.init_method,
+            gather_output=False,
+            bias=False,
+            skip_bias_add=False,
+            is_expert=False,
+            tp_comm_buffer_name='q_up_proj',
+            tp_group=pg_collection.tp,
+        )
+
+        self.linear_kv_up_proj = build_module(
+            submodules.linear_kv_up_proj,
+            self.config.kv_lora_rank,
+            self.config.num_attention_heads * (self.config.qk_head_dim + self.config.v_head_dim),
+            config=self.config,
+            init_method=self.config.init_method,
+            gather_output=False,
+            bias=False,
+            skip_bias_add=False,
+            is_expert=False,
+            tp_comm_buffer_name='kv_up_proj',
+            tp_group=pg_collection.tp,
+        )
+
+        self.q_layernorm = submodules.q_layernorm(
+            hidden_size=self.config.q_lora_rank,
+            config=self.config,
+            eps=self.config.layernorm_epsilon,
+        )
+        self.kv_layernorm = submodules.kv_layernorm(
+            hidden_size=self.config.kv_lora_rank,
+            config=self.config,
+            eps=self.config.layernorm_epsilon,
+        )
+
+    def _qkv_down_projection(self, hidden_states):
+        """Fused q/kv down projection path."""
+        qkv, _ = self.linear_qkv_down_proj(hidden_states)
+        q_compressed, kv_combined = torch.split(
+            qkv,
+            [self.config.q_lora_rank, self.config.kv_lora_rank + self.config.qk_pos_emb_head_dim],
+            dim=-1,
+        )
+        return q_compressed, kv_combined
+
+    def sharded_state_dict(self, prefix: str = "", sharded_offsets: tuple = (), metadata=None):
+        """Return a sharded state dict compatible with pre-fusion checkpoints."""
+        sharded_state_dict = super().sharded_state_dict(prefix, sharded_offsets, metadata)
+
+        def _clone_sharded_object_with_key(obj: ShardedObject, new_key: str) -> ShardedObject:
+            return ShardedObject(
+                key=new_key,
+                data=obj.data,
+                global_shape=obj.global_shape,
+                global_offset=obj.global_offset,
+                replica_id=obj.replica_id,
+            )
+
+        fused_prefix = f"{prefix}linear_qkv_down_proj."
+
+        fused_extra_keys = [
+            k
+            for k in sharded_state_dict.keys()
+            if k.startswith(fused_prefix) and "_extra_state" in k
+        ]
+        for fused_extra_key in fused_extra_keys:
+            suffix = fused_extra_key[len(fused_prefix) :]
+            q_extra_key = f"{prefix}linear_q_down_proj.{suffix}"
+            kv_extra_key = f"{prefix}linear_kv_down_proj.{suffix}"
+            fused_obj = sharded_state_dict.get(fused_extra_key)
+            if isinstance(fused_obj, ShardedObject):
+                sharded_state_dict[q_extra_key] = _clone_sharded_object_with_key(
+                    fused_obj, q_extra_key
+                )
+                sharded_state_dict[kv_extra_key] = _clone_sharded_object_with_key(
+                    fused_obj, kv_extra_key
+                )
+            elif fused_obj is not None:
+                sharded_state_dict[q_extra_key] = fused_obj
+                sharded_state_dict[kv_extra_key] = fused_obj
+
+        for key in list(sharded_state_dict.keys()):
+            if key.startswith(fused_prefix):
+                del sharded_state_dict[key]
+
+        fused_weight = self.linear_qkv_down_proj.weight
+        total_out = (
+            self.config.q_lora_rank + self.config.kv_lora_rank + self.config.qk_pos_emb_head_dim
+        )
+        tp_size = get_pg_size(self.tp_group)
+
+        if fused_weight.size(0) == total_out:
+            q_split = self.config.q_lora_rank
+            kv_split = self.config.kv_lora_rank + self.config.qk_pos_emb_head_dim
+        else:
+            assert (
+                self.config.q_lora_rank % tp_size == 0
+            ), "q_lora_rank must be divisible by tensor-parallel size"
+            assert (
+                self.config.kv_lora_rank + self.config.qk_pos_emb_head_dim
+            ) % tp_size == 0, (
+                "kv_lora_rank + qk_pos_emb_head_dim must be divisible by tensor-parallel size"
+            )
+            q_split = self.config.q_lora_rank // tp_size
+            kv_split = (self.config.kv_lora_rank + self.config.qk_pos_emb_head_dim) // tp_size
+
+        if q_split + kv_split != fused_weight.size(0):
+            raise ValueError(
+                "Unexpected fused qkv-down weight shape: "
+                f"got {tuple(fused_weight.size())}, expected dim0 {q_split + kv_split}"
+            )
+
+        q_weight, kv_weight = torch.split(fused_weight, [q_split, kv_split], dim=0)
+
+        q_key = f"{prefix}linear_q_down_proj.weight"
+        kv_key = f"{prefix}linear_kv_down_proj.weight"
+
+        sharded_state_dict[q_key] = make_tp_sharded_tensor_for_checkpoint(
+            tensor=q_weight, key=q_key, tp_axis=0, prepend_offsets=sharded_offsets
+        )
+        sharded_state_dict[kv_key] = make_tp_sharded_tensor_for_checkpoint(
+            tensor=kv_weight, key=kv_key, tp_axis=0, prepend_offsets=sharded_offsets
+        )
+
+        return sharded_state_dict
+
+    def _load_from_state_dict(self, state_dict, prefix, *args, **kwargs):
+        """Load state dict with automatic unfused->fused conversion."""
+        q_key = f"{prefix}linear_q_down_proj.weight"
+        kv_key = f"{prefix}linear_kv_down_proj.weight"
+        fused_key = f"{prefix}linear_qkv_down_proj.weight"
+
+        def _as_tensor(x):
+            return x.data if hasattr(x, 'data') else x
+
+        if fused_key not in state_dict and q_key in state_dict and kv_key in state_dict:
+            q_weight = _as_tensor(state_dict[q_key])
+            kv_weight = _as_tensor(state_dict[kv_key])
+            state_dict[fused_key] = torch.cat([q_weight, kv_weight], dim=0)
+            del state_dict[q_key]
+            del state_dict[kv_key]
+            state_dict.pop(f"{prefix}linear_q_down_proj.bias", None)
+            state_dict.pop(f"{prefix}linear_kv_down_proj.bias", None)
+
+        return super()._load_from_state_dict(state_dict, prefix, *args, **kwargs)
diff --git a/megatron/core/transformer/multi_token_prediction.py b/megatron/core/transformer/multi_token_prediction.py
index 7b8a764b813..9afd32e6d37 100755
--- a/megatron/core/transformer/multi_token_prediction.py
+++ b/megatron/core/transformer/multi_token_prediction.py
@@ -612,6 +612,44 @@ def set_loss_scale(scale: torch.Tensor):
         MTPLossAutoScaler.main_loss_backward_scale = scale
 
 
+def compute_mtp_inference_logits(
+    hidden_states: Tensor,
+    mtp_num_layers: int,
+    output_layer: Callable,
+    output_weight: Optional[Tensor],
+    runtime_gather_output: Optional[bool],
+) -> tuple:
+    """Compute MTP logits for inference mode.
+
+    Splits the concatenated hidden states and generates logits for each MTP layer.
+
+    Args:
+        hidden_states (Tensor): Concatenated hidden states from main + MTP layers.
+        mtp_num_layers (int): Number of MTP layers.
+        output_layer (Callable): Output layer method to compute logits.
+        output_weight (Optional[Tensor]): Optional output weight for shared embeddings.
+        runtime_gather_output (Optional[bool]): Whether to gather output at runtime.
+
+    Returns:
+        tuple: (hidden_states, mtp_logits_cache) where hidden_states is the main hidden
+            states and mtp_logits_cache is a tensor of shape
+            [mtp_num_layers, batch_size, vocab_size].
+    """
+    hidden_states_list = torch.chunk(hidden_states, 1 + mtp_num_layers, dim=0)
+    hidden_states = hidden_states_list[0]
+    mtp_inference_logits = []
+    for mtp_layer_number in range(mtp_num_layers):
+        mtp_logits, _ = output_layer(
+            hidden_states_list[mtp_layer_number + 1],
+            weight=output_weight,
+            runtime_gather_output=runtime_gather_output,
+        )
+        # mtp logits shape [b, 1, vocab size]
+        mtp_inference_logits.append(mtp_logits.squeeze(1).unsqueeze(0))
+    mtp_logits_cache = torch.cat(mtp_inference_logits, dim=0)
+    return hidden_states, mtp_logits_cache
+
+
 def process_mtp_loss(
     hidden_states: Tensor,
     labels: Tensor,
@@ -624,6 +662,7 @@ def process_mtp_loss(
     config: TransformerConfig,
     cp_group: Optional[torch.distributed.ProcessGroup] = None,
     packed_seq_params: Optional[PackedSeqParams] = None,
+    scale_logits_fn: Optional[Callable[[Tensor], Tensor]] = None,
 ) -> Tensor:
     """Process Multi-Token Prediction (MTP) loss computation.
 
@@ -642,6 +681,8 @@ def process_mtp_loss(
         config (TransformerConfig): Model configuration containing mtp_num_layers etc.
         cp_group (Optional[ProcessGroup]): Context parallelism process group.
         packed_seq_params (Optional[PackedSeqParams]): Packed sequence parameters.
+        scale_logits_fn (Optional[Callable[[Tensor], Tensor]]): Optional function to
+            scale logits before loss computation (e.g., MuP output scaling).
 
     Returns:
         Tensor: Updated hidden states after MTP loss processing (first chunk only).
@@ -682,6 +723,8 @@ def process_mtp_loss(
             )
         else:
             mtp_logits, _ = output_layer(**output_layer_kwargs)
+            if scale_logits_fn is not None:
+                mtp_logits = scale_logits_fn(mtp_logits)
             mtp_loss = compute_language_model_loss(mtp_labels, mtp_logits)
         mtp_loss = loss_mask * mtp_loss
         if is_training:
@@ -818,11 +861,13 @@ def __init__(
         # 2. GPT path: single TransformerLayer
         if mtp_layer_pattern is not None and mamba_submodules is not None:
             from megatron.core.ssm.mamba_block import MambaStack
+            from megatron.core.ssm.mamba_hybrid_layer_allocation import validate_segment_layers
 
             self.mtp_model_layer = MambaStack(
                 config=self.config,
                 submodules=mamba_submodules,
-                hybrid_override_pattern=mtp_layer_pattern,
+                layer_type_list=validate_segment_layers(mtp_layer_pattern),
+                pp_layer_offset=0,
                 pre_process=True,  # Always receives input from eh_proj
                 post_layer_norm=False,  # MTP has its own final_layernorm
                 post_process=True,  # MTP layer is self-contained
@@ -1000,6 +1045,53 @@ def _postprocess(self, hidden_states: torch.Tensor):
 
         return hidden_states
 
+    def forward_single_position(
+        self,
+        hidden_states: Tensor,
+        next_token_ids: Tensor,
+        position_ids: Tensor,
+        embedding: Callable,
+        attention_mask: Optional[Tensor] = None,
+        rotary_pos_emb: Optional[Tensor] = None,
+        rotary_pos_cos: Optional[Tensor] = None,
+        rotary_pos_sin: Optional[Tensor] = None,
+        inference_params=None,
+        packed_seq_params: Optional[PackedSeqParams] = None,
+        sequence_len_offset: Optional[Tensor] = None,
+    ) -> Tensor:
+        """Forward for single positions without roll_tensor (speculative decoding).
+
+        Unlike the regular forward which rolls input_ids to get the next token's
+        embedding, this method directly takes the correct next_token_ids. This is
+        used in speculative decoding where the correct next token is known after
+        verification.
+
+        Args:
+            hidden_states (Tensor): Hidden states at positions of interest [N, B, H].
+            next_token_ids (Tensor): The correct next token IDs [B, N].
+            position_ids (Tensor): Position IDs for the next tokens [B, N].
+            embedding (Callable): The embedding module.
+
+        Returns:
+            Tensor: MTP hidden states [N, B, H].
+        """
+        decoder_input = embedding(input_ids=next_token_ids, position_ids=position_ids)
+        hidden_states = make_viewless_tensor(
+            inp=hidden_states, requires_grad=False, keep_graph=False
+        )
+        hidden_states = self._proj_and_transformer_layer(
+            hidden_states=hidden_states,
+            decoder_input=decoder_input,
+            attention_mask=attention_mask,
+            rotary_pos_emb=rotary_pos_emb,
+            rotary_pos_cos=rotary_pos_cos,
+            rotary_pos_sin=rotary_pos_sin,
+            inference_params=inference_params,
+            packed_seq_params=packed_seq_params,
+            sequence_len_offset=sequence_len_offset,
+        )
+        return hidden_states
+
     def _checkpointed_forward(self, forward_func, *args, **kwargs):
         def checkpoint_handler():
             """Determines whether to use the `te_checkpoint` or `tensor_parallel.checkpoint`"""
diff --git a/megatron/core/transformer/transformer_block.py b/megatron/core/transformer/transformer_block.py
index e9bd52f34b4..5e0730a9759 100755
--- a/megatron/core/transformer/transformer_block.py
+++ b/megatron/core/transformer/transformer_block.py
@@ -159,7 +159,7 @@ def get_num_layers_to_build(
 
         assert (
             num_layers % config.pipeline_model_parallel_size == 0
-        ), "num_layers should be divisible by pipeline_model_parallel_size"
+        ), f"{num_layers=} should be divisible by {config.pipeline_model_parallel_size=}"
         num_layers_per_pipeline_rank = num_layers // config.pipeline_model_parallel_size
 
     vp_size = config.virtual_pipeline_model_parallel_size
diff --git a/megatron/core/transformer/transformer_config.py b/megatron/core/transformer/transformer_config.py
index f55de2ae2ff..08284f94c6c 100644
--- a/megatron/core/transformer/transformer_config.py
+++ b/megatron/core/transformer/transformer_config.py
@@ -1,6 +1,7 @@
 # Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 
 import logging
+import math
 import warnings
 from dataclasses import dataclass, field
 from typing import Callable, List, Literal, Optional, Tuple, Union
@@ -22,6 +23,7 @@
     init_method_normal,
     is_te_min_version,
     is_torch_min_version,
+    mup_scaled_init_method_normal,
     scaled_init_method_normal,
 )
 
@@ -69,7 +71,7 @@ class TransformerConfig(ModelParallelConfig):
     """Use a single MTP layer repeatedly instead of multiple separate layers."""
 
     mtp_hybrid_override_pattern: Optional[str] = None
-    """DEPRECATED: Use unified hybrid_override_pattern instead.
+    """DEPRECATED: Use unified hybrid_layer_pattern instead.
     Legacy argument for loading old checkpoints.
     Force a specific hybrid layer pattern for MTP layers.
     """
@@ -324,7 +326,8 @@ class TransformerConfig(ModelParallelConfig):
     output_layer_init_method: Optional[Callable] = None
     """Method to initialize weights of the output layer of both attention and MLP blocks. If None,
     will be set to megatron.core.utils.scaled_init_method_normal(init_method_std) which is torch nn
-    init normal with mean=0.0 and std=init_method_std / math.sqrt(2.0 * num_layers)."""
+    init normal with mean=0.0 and std=init_method_std / math.sqrt(2.0 * num_layers).
+    Note: this does not control vocab readout/unembedding initialization."""
 
     init_method_std: float = 0.02
     """Standard deviation of the zero mean normal for the default initialization method, not used if
@@ -351,6 +354,59 @@ class TransformerConfig(ModelParallelConfig):
     training of very large models. This feature is only works when megatron fsdp is turned on.
     """
 
+    ####################
+    # MuP (Maximal Update Parameterization)
+    ####################
+    use_mup: bool = False
+    """
+    Enable Maximal Update Parameterization (MuP) for hyperparameter transfer across
+    model widths. When enabled, learning rates and initialization are scaled according
+    to the width multiplier to ensure consistent training dynamics.
+    """
+
+    mup_width_mult: float = 1.0
+    """
+    Width multiplier for MuP scaling, computed as hidden_size / mup_base_hidden_size.
+    This value is automatically computed in __post_init__ when use_mup is enabled.
+    """
+
+    mup_base_hidden_size: Optional[int] = None
+    """
+    Base hidden size for MuP width scaling. This is the reference width from which
+    scaling factors are computed. Defaults to hidden_size if not specified (base model
+    case where width_mult=1.0). Set this to your base/proxy model's hidden size when
+    scaling up.
+    """
+
+    mup_embedding_mult: float = 1.0
+    """
+    Multiplier for embedding layer output. Applied after the embedding lookup.
+    Default: 1.0 (no scaling).
+    """
+
+    mup_output_mult: float = 1.0
+    """
+    Multiplier for output logits before softmax. When MuP is enabled and this is left
+    at 1.0, it is auto-set to 1/mup_width_mult to keep output variance stable across
+    widths. Override to customize output scaling.
+    Default: 1.0.
+    """
+
+    mup_base_head_dim: Optional[float] = None
+    """
+    Base head dimension for MuP attention scaling. When set,
+    softmax_scale = sqrt(mup_base_head_dim) / (kv_channels ** mup_attn_scale_power).
+    Set to base model's d_head (e.g., 64) to match standard 1/sqrt(d_head) scaling
+    at the base width, ensuring non-MuP compatibility for that specific value.
+    """
+
+    mup_attn_scale_power: float = 1.0
+    """
+    Power for attention scaling: softmax_scale = 1 / (kv_channels ** mup_attn_scale_power).
+    0.5 = standard attention (1/sqrt(d_head)), 1.0 = MuP attention (1/d_head).
+    Default: 1.0 (MuP scaling when use_mup is True). Set to 0.5 for standard scaling.
+    """
+
     ####################
     # mixed-precision
     ####################
@@ -395,6 +451,9 @@ class TransformerConfig(ModelParallelConfig):
     fused_single_qkv_rope: bool = False
     """If set, avoid splitting QKV before ROPE forward and avoid concatenating ROPE dgrads."""
 
+    fused_residual_rmsnorm: bool = False
+    """If True, fuses residual connection and RMSNorm backward pass when TE is used."""
+
     ####################
     # activation recomputation
     ####################
@@ -681,10 +740,6 @@ class TransformerConfig(ModelParallelConfig):
     GEMM feature introduced since CUTLASS 2.8 (https://github.com/fanshiqing/grouped_gemm).
     """
 
-    moe_use_legacy_grouped_gemm: bool = False
-    """Use legacy GroupedMLP rather than TEGroupedMLP.
-    Note: The legacy one will be deprecated soon."""
-
     moe_aux_loss_coeff: Union[float, List[float]] = 0.0
     """Scaling coefficient for the aux loss. A starting value of 1e-2 is recommended.
     If a list of load balancing types is provided for `moe_router_load_balancing_type`,
@@ -903,6 +958,13 @@ class TransformerConfig(ModelParallelConfig):
     inference_fuse_tp_communication: bool = False
     """ If true, uses a fused reduce-scatter-residual-norm-allgather kernel during inference. """
 
+    inference_disable_triton_nvls_kernels: bool = False
+    """ If true, disables the use of Triton NVLS kernels during inference. """
+
+    inference_disable_torch_grouped_mm: bool = False
+    """ If true, disables torch._grouped_mm in InferenceGroupedMLP, 
+    falling back to TE GroupedGEMM. """
+
     mrope_section: Optional[List[int]] = None
     """ Multimodal rope section is for channel dimension of temporal, height and width
     in rope calculation. """
@@ -1143,6 +1205,31 @@ def __post_init__(self):
         if self.expert_model_parallel_size > 1 and self.num_moe_experts is None:
             raise ValueError("num_moe_experts must be non None to use expert-parallel.")
 
+        if self.transformer_impl == "inference_optimized" and self.num_moe_experts is not None:
+            if self.expert_tensor_parallel_size > 1:
+                raise ValueError(
+                    "Inference-optimized MoE layers does not support expert tensor parallelism."
+                )
+            if self.moe_expert_capacity_factor is not None:
+                raise ValueError("Inference-optimized MoE layers only support dropless MoE ")
+            if self.moe_router_padding_for_quantization:
+                raise ValueError(
+                    "Inference-optimized MoE layers do not support padded "
+                    "routing map for quantization."
+                )
+            if self.moe_router_dtype != "fp32":
+                raise ValueError(
+                    "Inference-optimized MoE requires --moe-router-dtype=fp32 "
+                    "to avoid costly dtype conversions during decode."
+                )
+            if self.gated_linear_unit and self.cuda_graph_impl != "none":
+                raise ValueError(
+                    "Inference-optimized MoE does not yet support CUDA graphs with gated "
+                    "linear units (SwiGLU/GeGLU) due to differences in weight layouts "
+                    "between the FlashInfer kernel and mcore. Either disable CUDA graphs "
+                    "(--cuda-graph-impl=none) or use a non-gated activation (e.g. squared_relu)."
+                )
+
         if self.num_moe_experts is not None and self.num_moe_experts <= 0:
             raise ValueError("num_moe_experts must be non-negative.")
 
@@ -1655,6 +1742,12 @@ def __post_init__(self):
                     "to True and use_te_activation_func to False."
                 )
 
+        if self.fused_residual_rmsnorm:
+            if self.normalization != "RMSNorm":
+                raise ValueError(
+                    "fused_residual_rmsnorm is only supported when normalization is RMSNorm."
+                )
+
         if self.use_te_activation_func:
             if self.activation_func not in (F.gelu, F.silu, F.relu):
                 raise ValueError(
@@ -1698,7 +1791,48 @@ def __post_init__(self):
         if self.multi_latent_attention and self.rotary_interleaved:
             raise ValueError("rotary_interleaved does not work with multi_latent_attention.")
 
-        # Set the embedding init method
+        # MuP (Maximal Update Parameterization) configuration
+        if self.use_mup:
+            # Default base_hidden_size to hidden_size (base model case, width_mult=1.0)
+            if self.mup_base_hidden_size is None:
+                self.mup_base_hidden_size = self.hidden_size
+            assert self.mup_base_hidden_size > 0, "--mup-base-hidden-size must be positive."
+            # Compute width multiplier
+            self.mup_width_mult = self.hidden_size / self.mup_base_hidden_size
+
+            # MuP attention scaling: 1/d_head instead of 1/sqrt(d_head).
+            if self.softmax_scale is None:
+                base_head_scale = (
+                    1.0 if self.mup_base_head_dim is None else self.mup_base_head_dim**0.5
+                )
+                self.softmax_scale = base_head_scale / (self.kv_channels**self.mup_attn_scale_power)
+
+            # MuP output scaling: scale logits by 1/width_mult to keep outputs O(1).
+            # Only auto-set if user hasn't explicitly configured it.
+            if self.mup_output_mult == 1.0 and self.mup_width_mult != 1.0:
+                self.mup_output_mult = 1.0 / self.mup_width_mult
+
+            overridden_init_methods = []
+            if self.init_method is not None:
+                overridden_init_methods.append("init_method")
+            if self.output_layer_init_method is not None:
+                overridden_init_methods.append("output_layer_init_method")
+            if overridden_init_methods:
+                overridden_init_methods_text = " and ".join(overridden_init_methods)
+                verb = "is" if len(overridden_init_methods) == 1 else "are"
+                warnings.warn(
+                    "use_mup is enabled, but custom "
+                    + overridden_init_methods_text
+                    + f" {verb} set. This may break MuP initialization assumptions.",
+                    UserWarning,
+                )
+
+        # Set the embedding init method.
+        # NOTE: This block must run AFTER the MuP block above but BEFORE the init_method
+        # block below. When MuP is enabled and init_method is None (the common case),
+        # embedding_init_method is set here using the unscaled init_method_std, while
+        # init_method (set below) gets MuP width-scaling. This ordering ensures embeddings
+        # use the base (unscaled) initialization as required by MuP.
         if self.embedding_init_method_std is None:
             # By default, use the same init std as you use for every other non-output layer.
             self.embedding_init_method_std = self.init_method_std
@@ -1716,14 +1850,29 @@ def __post_init__(self):
                 self.embedding_init_method = self.init_method
 
         if self.init_method is None:
-            self.init_method = init_method_normal(self.init_method_std)
+            if self.use_mup:
+                # MuP: scale std by 1/sqrt(width_mult).
+                self.init_method = init_method_normal(
+                    self.init_method_std / math.sqrt(self.mup_width_mult)
+                )
+            else:
+                self.init_method = init_method_normal(self.init_method_std)
 
         if self.output_layer_init_method is None:
-            self.output_layer_init_method = scaled_init_method_normal(
-                self.init_method_std,
-                self.num_layers,
-                multiplier=2.0 if not self.is_hybrid_model else 1.0,
-            )
+            if self.use_mup:
+                # MuP: depth and width scaling for output layers.
+                self.output_layer_init_method = mup_scaled_init_method_normal(
+                    self.init_method_std,
+                    self.num_layers,
+                    self.mup_width_mult,
+                    multiplier=2.0 if not self.is_hybrid_model else 1.0,
+                )
+            else:
+                self.output_layer_init_method = scaled_init_method_normal(
+                    self.init_method_std,
+                    self.num_layers,
+                    multiplier=2.0 if not self.is_hybrid_model else 1.0,
+                )
 
         if self.num_moe_experts is not None and self.add_bias_linear:
             assert (
@@ -2053,9 +2202,6 @@ def __post_init__(self):
             assert (
                 self.overlap_moe_expert_parallel_comm
             ), 'overlap_moe_expert_parallel_comm must be enabled when enabling delay_wgrad_compute'
-            assert (
-                not self.moe_use_legacy_grouped_gemm
-            ), 'delay_wgrad_compute is not supported with legacy groupedgemm implementation'
             if self.cuda_graph_impl == "transformer_engine":
                 assert is_te_min_version("2.10.0"), (
                     'TE version >= 2.10.0 is required for delay_wgrad_compute with '
@@ -2151,6 +2297,21 @@ def __post_init__(self):
                 "inference_fuse_tp_communication is only supported "
                 "for inference_optimized transformer implementation."
             )
+            assert (
+                self.num_moe_experts is None
+            ), "--inference-fuse-tp-communication is not supported for MoE models."
+
+        if self.inference_disable_triton_nvls_kernels:
+            assert self.transformer_impl == "inference_optimized", (
+                "inference_disable_triton_nvls_kernels is only supported "
+                "for inference_optimized transformer implementation."
+            )
+
+        if self.inference_disable_torch_grouped_mm:
+            assert self.transformer_impl == "inference_optimized", (
+                "inference_disable_torch_grouped_mm is only supported "
+                "for inference_optimized transformer implementation."
+            )
 
         if self.batch_invariant_mode:
             assert (
@@ -2254,6 +2415,11 @@ class MLATransformerConfig(TransformerConfig):
        This is only for the dynamic inference backend and requires that 
        Flash MLA is installed."""
 
+    mla_down_proj_fusion: bool = False
+    """Enable fused q/kv down-projection and fused input layernorm when backend supports.
+       Otherwise fall back to the unfused MLA.
+    """
+
     def __post_init__(self):
         super().__post_init__()
         if self.multi_latent_attention and self.apply_rope_fusion and self.rope_type != "yarn":
diff --git a/megatron/core/transformer/transformer_layer.py b/megatron/core/transformer/transformer_layer.py
index 61e9bb1535b..aa048485929 100644
--- a/megatron/core/transformer/transformer_layer.py
+++ b/megatron/core/transformer/transformer_layer.py
@@ -281,6 +281,8 @@ def __init__(
         pg_collection: Optional[ProcessGroupCollection] = None,
         vp_stage: Optional[int] = None,
         is_mtp_layer: bool = False,
+        add_layer_offset: bool = True,
+        pp_layer_offset: Optional[int] = None,
     ):
         self.submodules_config = submodules
         super().__init__(config=config, vp_stage=vp_stage)
@@ -294,7 +296,10 @@ def __init__(
         # so they should NOT add the decoder layer offset. The router.py handles MTP layer
         # numbering separately by adding config.num_layers to distinguish MTP layers from decoder
         # layers in the aux loss tracker.
-        if is_mtp_layer:
+        #
+        # When add_layer_offset is False, the caller has already included the correct offset
+        # in layer_number (e.g. when using --hybrid-layer-pattern with fVPP).
+        if is_mtp_layer or not add_layer_offset:
             self.layer_number = layer_number
         else:
             self.layer_number = layer_number + get_transformer_layer_offset(
@@ -314,11 +319,16 @@ def __init__(
         attention_optional_kwargs = {}
         if config.context_parallel_size > 1 and config.cp_comm_type is not None:
             if isinstance(config.cp_comm_type, list):
-                attention_optional_kwargs["cp_comm_type"] = config.cp_comm_type[self.layer_number]
+                # layer_number is 1-indexed, so we need to subtract 1 to get the correct index
+                attention_optional_kwargs["cp_comm_type"] = config.cp_comm_type[
+                    self.layer_number - 1
+                ]
             else:
                 attention_optional_kwargs["cp_comm_type"] = config.cp_comm_type
 
         attention_optional_kwargs["pg_collection"] = pg_collection
+        if pp_layer_offset is not None:
+            attention_optional_kwargs["pp_layer_offset"] = pp_layer_offset
 
         # [Module 2: SelfAttention]
         self.self_attention = build_module(
@@ -359,7 +369,7 @@ def __init__(
         additional_mlp_kwargs = {}
         # import here to avoid circular import
         from megatron.core.extensions.transformer_engine import TEFusedMLP
-        from megatron.core.transformer.moe.experts import GroupedMLP, SequentialMLP, TEGroupedMLP
+        from megatron.core.transformer.moe.experts import SequentialMLP, TEGroupedMLP
         from megatron.core.transformer.moe.moe_layer import MoELayer
 
         # MLP expects tp_group but MoELayer expects pg_collection to be passed in.
@@ -367,7 +377,7 @@ def __init__(
         # The conditional below is to make the logic explicit
         # if submodules.mlp is not a ModuleSpec,we dont have to handle passing additional kwargs
         if isinstance(submodules.mlp, ModuleSpec):
-            if submodules.mlp.module in (MoELayer, GroupedMLP, TEGroupedMLP, SequentialMLP):
+            if submodules.mlp.module in (MoELayer, TEGroupedMLP, SequentialMLP):
                 additional_mlp_kwargs["pg_collection"] = pg_collection
                 # Pass is_mtp_layer flag to MoELayer to distinguish MTP MoE layers.
                 if submodules.mlp.module == MoELayer:
@@ -572,11 +582,6 @@ def _forward_attention(
 
         inference_context = deprecate_inference_params(inference_context, inference_params)
 
-        # Residual connection.
-        residual = hidden_states
-        if self.config.fp32_residual_connection:
-            residual = residual.float()
-
         # Optional Input Layer norm
         if self.recompute_input_layernorm:
             self.input_layernorm_checkpoint = tensor_parallel.CheckpointWithoutOutput()
@@ -588,6 +593,20 @@ def _forward_attention(
             with off_interface(self.offload_attn_norm, hidden_states, "attn_norm") as hidden_states:
                 input_layernorm_output = apply_module(self.input_layernorm)(hidden_states)
 
+        if isinstance(input_layernorm_output, tuple):
+            if len(input_layernorm_output) != 2:
+                raise ValueError(
+                    f"When the output of input_layernorm is a tuple, it is "
+                    f"expected to have 2 elements (output, residual), but "
+                    f"got {len(input_layernorm_output)}"
+                )
+            input_layernorm_output, residual = input_layernorm_output
+        else:
+            residual = hidden_states
+
+        if self.config.fp32_residual_connection:
+            residual = residual.float()
+
         using_fused_tp_inference_kernel = (not self.training) and (
             self.config.inference_fuse_tp_communication
         )
@@ -640,14 +659,23 @@ def _forward_attention(
                 hidden_states, name="attn_norm", forced_released_tensors=[residual]
             )
 
-        # Residual connection.
-        residual = hidden_states
-        if self.config.fp32_residual_connection:
-            residual = residual.float()
-
         # Optional Layer norm after self-attention
         pre_cross_attn_layernorm_output = apply_module(self.pre_cross_attn_layernorm)(hidden_states)
 
+        if isinstance(pre_cross_attn_layernorm_output, tuple):
+            if len(pre_cross_attn_layernorm_output) != 2:
+                raise ValueError(
+                    f"When the output of pre_cross_attn_layernorm_output "
+                    f"is a tuple, it is expected to have 2 elements "
+                    f"(output, residual), but "
+                    f"got {len(pre_cross_attn_layernorm_output)}"
+                )
+            pre_cross_attn_layernorm_output, residual = pre_cross_attn_layernorm_output
+        else:
+            residual = hidden_states
+
+        if self.config.fp32_residual_connection:
+            residual = residual.float()
         # Cross attention.
         attention_output_with_bias = self.cross_attention(
             pre_cross_attn_layernorm_output,
@@ -727,14 +755,24 @@ def _forward_mlp(
             output (Tensor): Transformed hidden states of shape [s, b, h].
         """
 
-        # Residual connection.
-        residual = hidden_states
-        if self.config.fp32_residual_connection:
-            residual = residual.float()
-
         # Optional Layer norm post the cross-attention.
         pre_mlp_layernorm_output = self._forward_pre_mlp_layernorm(hidden_states)
 
+        if isinstance(pre_mlp_layernorm_output, tuple):
+            if len(pre_mlp_layernorm_output) != 2:
+                raise ValueError(
+                    f"When the output of pre_mlp_layernorm is a tuple, it is "
+                    f"expected to have 2 elements (output, residual), but "
+                    f"got {len(pre_mlp_layernorm_output)}"
+                )
+            pre_mlp_layernorm_output, residual = pre_mlp_layernorm_output
+        else:
+            # Residual connection.
+            residual = hidden_states
+
+        if self.config.fp32_residual_connection:
+            residual = residual.float()
+
         nvtx_range_push(suffix="mlp")
         # Potentially chunk the MLP computation during prefill to minimize the peak activation size
         should_chunk_mlp_for_prefill = (
@@ -1142,6 +1180,15 @@ def _te_cuda_graph_replay(self, *args, **kwargs):
                 if not self.is_moe_layer:
                     return residual, None, None, None
                 hidden_states = apply_module(self.pre_mlp_layernorm)(residual)
+                if isinstance(hidden_states, tuple):
+                    if len(hidden_states) != 2:
+                        raise ValueError(
+                            f"When the output of pre_mlp_layernorm is a tuple, it is "
+                            f"expected to have 2 elements (output, residual), but "
+                            f"got {len(hidden_states)}"
+                        )
+                    hidden_states, residual = hidden_states
+
                 shared_expert_output = self.mlp.shared_experts_compute(hidden_states)
                 probs, routing_map = self.mlp.route(hidden_states)
                 hidden_states, probs = self.mlp.preprocess(hidden_states, probs, routing_map)
@@ -1655,6 +1702,62 @@ def __init__(self, *args, **kwargs):
 
         super().__init__(*args, **kwargs)
 
+    def _should_call_local_cudagraph(self, *args, **kwargs):
+        """
+        Controls whether the full-layer cudagraph_manager captures the entire forward call
+        as a single graph. Returns False to skip full-layer capture and route through _forward_mlp.
+
+        MoE layers have two cudagraph modes:
+        - Full-layer (use_partial_cudagraphs=False): the full-layer cudagraph_manager captures
+          the forward pass as one graph. This is used during inference.
+        - Partial (use_partial_cudagraphs=True): the full-layer manager is bypassed (returns
+          False), and _forward_mlp routes through cudagraph_manager_router and
+          cudagraph_manager_postprocess, which are monkey-patched onto _forward_mlp_router
+          and _forward_mlp_postprocess by CudaGraphManager.__init__. The expert dispatch
+          in between runs eagerly. This is used during training.
+        """
+        if self.use_partial_cudagraphs:
+            return False
+        if self.config.cuda_graph_impl != "local":
+            return False
+        return super()._should_call_local_cudagraph(*args, **kwargs)
+
+    def transition_cudagraph_scope(self, mode):
+        """Transition between full-layer and partial CUDA graph capture.
+
+        Args:
+            mode: 'full' for inference (full-layer capture) or 'partial' for training
+            (router + postprocess captured, expert dispatch runs eagerly).
+        """
+        from megatron.core.transformer.cuda_graphs import CudaGraphManager
+
+        if mode == 'partial':
+            self.use_partial_cudagraphs = True
+            self.moe_layer_recompute = (
+                self.config.recompute_granularity == 'selective'
+                and "moe" in self.config.recompute_modules
+                and self.config.cuda_graph_impl == "local"
+            )
+            if not hasattr(self, '_router_dtoh_event'):
+                self._router_dtoh_event = torch.cuda.Event()
+            if not hasattr(self, 'cudagraph_manager_router'):
+                self.cudagraph_manager_router = CudaGraphManager(
+                    self.config, self, function_name="_forward_mlp_router"
+                )
+            if not hasattr(self, 'cudagraph_manager_postprocess'):
+                self.cudagraph_manager_postprocess = CudaGraphManager(
+                    self.config, self, function_name="_forward_mlp_postprocess"
+                )
+        elif mode == 'full':
+            self.use_partial_cudagraphs = False
+            self.mlp.fwd_execution_map = ["route", "expert_compute", "postprocess"]
+            assert hasattr(self, 'cudagraph_manager'), (
+                "MoETransformerLayer missing full cudagraph_manager; "
+                "expected it to be created at __init__ with scope = [] "
+            )
+        else:
+            raise ValueError(f"Unknown MoE cudagraph mode: {mode}, expected 'full' or 'partial'")
+
     def create_mcore_cudagraph_manager(self, config):
         """
         Initializes the CUDA graph manager(s) for the MoE layer.
@@ -1673,21 +1776,7 @@ def create_mcore_cudagraph_manager(self, config):
             CudaGraphScope.moe_router in self.config.cuda_graph_scope
             or CudaGraphScope.moe_preprocess in self.config.cuda_graph_scope
         ):
-            # full MoE layer recompute with partial_cudagraphs. If not partial cudagraphs, MoE
-            # layer recompute is handled by the moe_layer.MoELayer class
-            self.moe_layer_recompute = (
-                self.config.recompute_granularity == 'selective'
-                and "moe" in self.config.recompute_modules
-                and self.config.cuda_graph_impl == "local"
-            )
-
-            self.use_partial_cudagraphs = True
-            self.cudagraph_manager_router = CudaGraphManager(
-                self.config, self, function_name="_forward_mlp_router"
-            )
-            self.cudagraph_manager_postprocess = CudaGraphManager(
-                self.config, self, function_name="_forward_mlp_postprocess"
-            )
+            self.transition_cudagraph_scope('partial')
 
     def _forward_mlp_router(self, hidden_states, padding_mask=None):
         """
@@ -1697,11 +1786,22 @@ def _forward_mlp_router(self, hidden_states, padding_mask=None):
         This method is isolated so it can be captured by `cudagraph_manager_router`.
         """
 
-        residual = hidden_states
-        if self.config.fp32_residual_connection:
-            residual = residual.float()
         self.mlp.fwd_execution_map = "route"
         pre_mlp_layernorm_output = self._forward_pre_mlp_layernorm(hidden_states)
+        if isinstance(pre_mlp_layernorm_output, tuple):
+            if len(pre_mlp_layernorm_output) != 2:
+                raise ValueError(
+                    f"When the output of pre_mlp_layernorm is a tuple, it is "
+                    f"expected to have 2 elements (output, residual), but "
+                    f"got {len(pre_mlp_layernorm_output)}"
+                )
+            pre_mlp_layernorm_output, residual = pre_mlp_layernorm_output
+        else:
+            residual = hidden_states
+
+        if self.config.fp32_residual_connection:
+            residual = residual.float()
+
         router_outputs = self.mlp(
             pre_mlp_layernorm_output, intermediate_tensors=(), padding_mask=padding_mask
         )
@@ -1741,6 +1841,12 @@ def _forward_mlp_postprocess(self, residual, output, shared_expert_output, mlp_b
 
         """
 
+        # Restore token dispatcher attributes. During graph warmup, the router capture leaves these
+        # attrs pointing into cudagraph pool memory; restoring them here ensures the postprocess
+        # graph captures with valid pointers.
+        for name, attr in self.token_dispatcher_attrs.items():
+            setattr(self.mlp.token_dispatcher, name, attr)
+
         self.mlp.fwd_execution_map = "postprocess"
         output = self.mlp(None, intermediate_tensors=(output, shared_expert_output))
         return self._forward_post_mlp((output, mlp_bias), residual)
@@ -1766,6 +1872,16 @@ def _forward_mlp_partial_cudagraphs(
             residual, hidden_states, probs, shared_expert_output = self._forward_mlp_router(
                 hidden_states, padding_mask=padding_mask
             )
+
+            # After the router graph replays, the captured .copy_() operations that update
+            # self.token_dispatcher_attrs via `_maybe_dtoh_and_synchronize` are queued on the
+            # current stream but may not have completed. Record an event after the router
+            # graph and wait on it, so we block only until the router's D2H copies complete.
+            self._router_dtoh_event.record()
+            self._router_dtoh_event.synchronize()
+            for name, attr in self.token_dispatcher_attrs.items():
+                setattr(self.mlp.token_dispatcher, name, attr)
+
             expert_output, mlp_bias = self._forward_mlp_expert_compute(hidden_states, probs)
             return self._forward_mlp_postprocess(
                 residual, expert_output, shared_expert_output, mlp_bias
diff --git a/megatron/core/transformer/utils.py b/megatron/core/transformer/utils.py
index 3b2b8668252..d1df4898d18 100644
--- a/megatron/core/transformer/utils.py
+++ b/megatron/core/transformer/utils.py
@@ -434,6 +434,22 @@ def toggle_cuda_graphs(model, set_to="none"):
                             delattr(module[0], "cudagraph_manager")
 
 
+def transition_moe_cudagraphs(model, scope: str):
+    """
+    Switch MoE layers to the given cudagraph scope. Flips between 'partial' and 'full'.
+
+    Args:
+        model: The model with MoE layers which will be transitioned.
+        scope: 'partial' for training (router + postprocess captured, expert dispatch eager)
+               or 'full' for inference (full-layer graph capture).
+    """
+    from megatron.core.transformer.transformer_layer import MoETransformerLayer
+
+    for module in model.modules():
+        if isinstance(module, MoETransformerLayer):
+            module.transition_cudagraph_scope(scope)
+
+
 def is_layer_window_attention(
     window_size: Optional[Tuple[int, int]], window_attn_skip_freq: int | list, layer_number: int
 ) -> bool:
diff --git a/megatron/core/utils.py b/megatron/core/utils.py
index 14c783ab0dc..ca03a9a1af2 100644
--- a/megatron/core/utils.py
+++ b/megatron/core/utils.py
@@ -310,28 +310,6 @@ def __getattribute__(self, attr):
     return validator
 
 
-def get_torch_version():
-    """Get pytorch version from __version__; if not available use pip's. Use caching."""
-
-    if not HAVE_PACKAGING:
-        raise ImportError(
-            "packaging is not installed. Please install it with `pip install packaging`."
-        )
-
-    def get_torch_version_str():
-        import torch
-
-        if hasattr(torch, "__version__"):
-            return str(torch.__version__)
-        else:
-            return version("torch")
-
-    global _torch_version
-    if _torch_version is None:
-        _torch_version = PkgVersion(get_torch_version_str())
-    return _torch_version
-
-
 def get_te_version():
     """Get TE version from __version__; if not available use pip's. Use caching."""
     if not HAVE_PACKAGING:
@@ -355,8 +333,11 @@ def get_te_version_str():
             return version("transformer-engine")
 
     global _te_version
-    if _te_version is None and HAVE_TE:
-        _te_version = PkgVersion(get_te_version_str())
+    if _te_version is None:
+        if HAVE_TE:
+            _te_version = PkgVersion(get_te_version_str())
+        else:
+            _te_version = PkgVersion("0.0.0")
     return _te_version
 
 
@@ -728,6 +709,44 @@ def _allocate(self, numel, dtype) -> torch.Tensor:
         required_bytes = numel * torch.tensor([], dtype=dtype).element_size()
         return self.symm_buffer[0:required_bytes].view(dtype).view(numel)
 
+    def maybe_get_tensors(self, tensor_specs, alignment=16):
+        """
+        Pack multiple tensors contiguously in the symmetric buffer with alignment.
+
+        Each tensor's starting offset is aligned to `alignment` bytes (default 16
+        for 128-bit multimem access).
+
+        Args:
+            tensor_specs: list of (numel, dtype) tuples.
+            alignment: byte alignment for each tensor's start offset (default 16).
+
+        Returns:
+            {"handle": None, "tensors": None} if unavailable or insufficient space.
+            {"handle": symm_mem_hdl, "tensors": [(raw_byte_view, byte_offset), ...]}
+            on success, where raw_byte_view is a uint8 slice of the buffer.
+        """
+        _NONE_RESULT = {"handle": None, "tensors": None}
+        if self.symm_mem_hdl is None:
+            return _NONE_RESULT
+
+        # Compute aligned byte sizes and running offsets
+        slices = []
+        current_offset = 0
+        for numel, dtype in tensor_specs:
+            nbytes = numel * torch.tensor([], dtype=dtype).element_size()
+            aligned_nbytes = ((nbytes + alignment - 1) // alignment) * alignment
+            slices.append((current_offset, nbytes))
+            current_offset += aligned_nbytes
+
+        if not self._can_allocate(current_offset, torch.uint8):
+            return _NONE_RESULT
+
+        tensors = []
+        for offset, nbytes in slices:
+            tensors.append((self.symm_buffer[offset : offset + nbytes], offset))
+
+        return {"handle": self.symm_mem_hdl, "tensors": tensors}
+
     def maybe_get_tensor(self, tensor_shape, dtype):
         """
         Returns (potentially) a sub-tensor from the self.symm_buffer for the given shape.
@@ -862,6 +881,26 @@ def scaled_init_method_normal(sigma, num_layers, multiplier=2.0):
     return functools.partial(torch.nn.init.normal_, mean=0.0, std=std)
 
 
+def mup_scaled_init_method_normal(sigma, num_layers, width_mult, multiplier=2.0):
+    """MuP scaled init method for output layers: N(0, sigma / (sqrt(2*L) * sqrt(m))).
+
+    Combines the standard scaled initialization (for output projection layers)
+    with MuP width scaling. This ensures that both depth and width scaling
+    are accounted for in the initialization.
+
+    Args:
+        sigma (float): Base standard deviation for initialization.
+        num_layers (int): Number of transformer layers.
+        width_mult (float): Width multiplier (hidden_size / base_hidden_size).
+        multiplier (float): Multiplier for depth scaling (default: 2.0).
+
+    Returns:
+        Callable: Initialization function for torch.nn.init.
+    """
+    std = sigma / (math.sqrt(multiplier * num_layers) * math.sqrt(width_mult))
+    return functools.partial(torch.nn.init.normal_, mean=0.0, std=std)
+
+
 def log_on_each_pipeline_stage(
     logger: logging.Logger,
     *args: Any,
diff --git a/megatron/inference/utils.py b/megatron/inference/utils.py
index b8a277fb9e1..ec8f1088be1 100644
--- a/megatron/inference/utils.py
+++ b/megatron/inference/utils.py
@@ -12,6 +12,8 @@
     InferenceConfig,
     KVCacheManagementMode,
     MambaInferenceStateConfig,
+    PrefixCachingCoordinatorPolicy,
+    PrefixCachingEvictionPolicy,
 )
 from megatron.core.inference.contexts import DynamicInferenceContext
 from megatron.core.inference.engines import DynamicInferenceEngine
@@ -173,6 +175,13 @@ def add_inference_args(parser: ArgumentParser) -> ArgumentParser:
         help="To minimize the output file size of larger runs, only write the "
         "results of every `n` requests.",
     )
+    group.add_argument(
+        "--output-request-events",
+        action='store_true',
+        default=False,
+        help="Include request events (lifecycle + per-token block allocator metrics) "
+        "in the JSON output.",
+    )
     group.add_argument(
         "--prompt-file",
         help='Jsonl file containing input prompts, where each item (i.e., line) '
@@ -215,9 +224,15 @@ def add_inference_args(parser: ArgumentParser) -> ArgumentParser:
         type=int,
         default=None,
         help="Suspend and resume the dynamic engine every "
-        "`suspend_resume_interval` steps. This is used to tet the suspend/resume "
+        "`suspend_resume_interval` requests. This is used to test the suspend/resume "
         "system.",
     )
+    group.add_argument(
+        "--suspend-timeout",
+        type=float,
+        default=0.0,
+        help="Seconds to sleep while the engine is suspended (simulates a training step).",
+    )
     group.add_argument(
         "--inference-repeat-n",
         type=int,
@@ -230,7 +245,25 @@ def add_inference_args(parser: ArgumentParser) -> ArgumentParser:
         default=False,
         help="If true, only run throughput check without verifying outputs.",
     )
-
+    group.add_argument(
+        "--drain-between-batches",
+        action='store_true',
+        default=False,
+        help="Process requests in batches, draining all active requests between batches.",
+    )
+    group.add_argument(
+        "--batch-boundaries",
+        type=str,
+        default=None,
+        help="Comma-separated list of request indices where each batch starts. "
+        "Used with --drain-between-batches.",
+    )
+    group.add_argument(
+        "--coordinator-schedule-output-path",
+        type=str,
+        default=None,
+        help="Path to write coordinator request scheduling decisions as JSON",
+    )
     return parser
 
 
@@ -259,7 +292,11 @@ def get_inference_config_from_model_and_args(model: MegatronModule, args):
     if args.inference_dynamic_batching_max_requests is not None:
         max_sequence_length = max(max_sequence_length, max_batch_size)
 
-    mamba_inference_state_config = MambaInferenceStateConfig.from_model(model)
+    mamba_inference_state_config = MambaInferenceStateConfig.from_model(
+        model,
+        conv_states_dtype=args.mamba_inference_conv_states_dtype,
+        ssm_states_dtype=args.mamba_inference_ssm_states_dtype,
+    )
     pg_collection = get_attr_wrapped_model(model, "pg_collection")
 
     # Get inference logging configuration from args
@@ -304,11 +341,16 @@ def get_inference_config_from_model_and_args(model: MegatronModule, args):
         mamba_inference_state_config=mamba_inference_state_config,
         pg_collection=pg_collection,
         use_flashinfer_fused_rope=args.use_flashinfer_fused_rope,
-        materialize_only_last_token_logits=not args.return_log_probs,
+        materialize_only_last_token_logits=(not args.return_log_probs and args.num_speculative_tokens == 0),
+        track_generated_token_events=args.inference_dynamic_batching_track_generated_token_events,
         track_paused_request_events=args.inference_dynamic_batching_track_paused_request_events,
         enable_chunked_prefill=args.enable_chunked_prefill,
+        enable_prefix_caching=args.inference_dynamic_batching_enable_prefix_caching,
+        prefix_caching_eviction_policy=PrefixCachingEvictionPolicy(args.inference_dynamic_batching_prefix_caching_eviction_policy),
+        prefix_caching_coordinator_policy=PrefixCachingCoordinatorPolicy(args.inference_dynamic_batching_prefix_caching_coordinator_policy),
         metrics_writer=metrics_writer,
         logging_step_interval=args.inference_logging_step_interval,
+        num_speculative_tokens=args.num_speculative_tokens,
     )
 
 
diff --git a/megatron/legacy/indexer.py b/megatron/legacy/indexer.py
deleted file mode 100644
index 179e00e6cdf..00000000000
--- a/megatron/legacy/indexer.py
+++ /dev/null
@@ -1,130 +0,0 @@
-# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
-import sys
-import time
-import torch
-import torch.distributed as dist
-
-from megatron.training import get_args, print_rank_0
-from megatron.core import mpu
-from megatron.training.checkpointing import load_biencoder_checkpoint
-from megatron.legacy.data.orqa_wiki_dataset import get_open_retrieval_wiki_dataset
-from megatron.legacy.data.orqa_wiki_dataset import get_open_retrieval_batch
-from megatron.legacy.data.biencoder_dataset_utils import get_one_epoch_dataloader
-from megatron.legacy.data.realm_index import detach, OpenRetreivalDataStore
-from megatron.legacy.model.biencoder_model import get_model_provider
-from megatron.training import get_model
-
-
-class IndexBuilder(object):
-    """
-    Object for taking one pass over a dataset and creating a BlockData of its
-    embeddings
-    """
-    def __init__(self):
-        args = get_args()
-        self.model = None
-        self.dataloader = None
-        self.evidence_embedder_obj = None
-        self.biencoder_shared_query_context_model = \
-            args.biencoder_shared_query_context_model
-
-        # need to know whether we're using a REALM checkpoint (args.load)
-        # or ICT checkpoint
-        assert not (args.load and args.ict_load)
-
-        self.log_interval = args.indexer_log_interval
-        self.batch_size = args.indexer_batch_size
-
-        self.load_attributes()
-        self.is_main_builder = mpu.get_data_parallel_rank() == 0
-        self.num_total_builders = mpu.get_data_parallel_world_size()
-        self.iteration = self.total_processed = 0
-
-    def load_attributes(self):
-        """
-        Load the necessary attributes: model, dataloader and empty BlockData
-        """
-        only_context_model = True
-        if self.biencoder_shared_query_context_model:
-            only_context_model = False
-
-        model = get_model(get_model_provider(only_context_model=\
-            only_context_model, biencoder_shared_query_context_model=\
-            self.biencoder_shared_query_context_model))
-
-        self.model = load_biencoder_checkpoint(model,
-                only_context_model=only_context_model)
-
-        assert len(self.model) == 1
-        self.model[0].eval()
-
-        self.dataset = get_open_retrieval_wiki_dataset()
-        self.dataloader = iter(get_one_epoch_dataloader(self.dataset, \
-            self.batch_size))
-
-        self.evidence_embedder_obj = OpenRetreivalDataStore( \
-            load_from_path=False)
-
-    def track_and_report_progress(self, batch_size):
-        """
-        Utility function for tracking progress
-        """
-        self.iteration += 1
-        self.total_processed += batch_size * self.num_total_builders
-        if self.is_main_builder and self.iteration % self.log_interval == 0:
-            print('Batch {:10d} | Total {:10d}'.format(self.iteration,
-                self.total_processed), flush=True)
-
-    def build_and_save_index(self):
-        """
-        Goes through one epoch of the dataloader and adds all data to this
-        instance's BlockData.
-
-        The copy of BlockData is saved as a shard, which when run in a
-        distributed setting will be consolidated by the rank 0 process
-        and saved as a final pickled BlockData.
-        """
-        assert len(self.model) == 1
-        unwrapped_model = self.model[0]
-
-        while not hasattr(unwrapped_model, 'embed_text'):
-            unwrapped_model = unwrapped_model.module
-
-        while True:
-            try:
-                # batch also has query_tokens and query_pad_data
-                row_id, context_tokens, context_mask, context_types, \
-                    context_pad_mask = get_open_retrieval_batch( \
-                    self.dataloader)
-            except (StopIteration, IndexError):
-                break
-
-            # TODO: can we add with torch.no_grad() to reduce memory usage
-            # detach, separate fields and add to BlockData
-            assert context_mask.dtype == torch.bool
-            context_logits = unwrapped_model.embed_text(
-                unwrapped_model.context_model, context_tokens, context_mask,
-                context_types)
-
-            context_logits = detach(context_logits)
-            row_id = detach(row_id)
-
-            self.evidence_embedder_obj.add_block_data(row_id, context_logits)
-            self.track_and_report_progress(batch_size=len(row_id))
-
-        # This process signals to finalize its shard and then synchronize with
-        # the other processes
-        self.evidence_embedder_obj.save_shard()
-        torch.distributed.barrier()
-        del self.model
-
-        # rank 0 process builds the final copy
-        if self.is_main_builder:
-            self.evidence_embedder_obj.merge_shards_and_save()
-            # make sure that every single piece of data was embedded
-            assert len(self.evidence_embedder_obj.embed_data) == \
-                len(self.dataset)
-        self.evidence_embedder_obj.clear()
-
-        # complete building the final copy
-        torch.distributed.barrier()
diff --git a/megatron/post_training/model_builder.py b/megatron/post_training/model_builder.py
index 7d38041a7a3..085d188e811 100644
--- a/megatron/post_training/model_builder.py
+++ b/megatron/post_training/model_builder.py
@@ -106,6 +106,11 @@ def _load_teacher_model_config(checkpoint_path: str) -> Namespace:
     del args_dict["kv_channels"]  # not recalculated if present
     args_dict.update(config)
 
+    # Backward compat: old checkpoints have hybrid_override_pattern but not hybrid_layer_pattern
+    if (args_dict.get('hybrid_override_pattern') is not None
+            and args_dict.get('hybrid_layer_pattern') is None):
+        args_dict['hybrid_layer_pattern'] = args_dict['hybrid_override_pattern']
+
     return Namespace(**args_dict)
 
 
@@ -114,13 +119,10 @@ def _load_teacher_model(config, config_raw: Namespace, model_kwargs: Dict[str, A
     args = get_args()
 
     if config.is_hybrid_model:
-        # These parameters are not part of the TransformerConfig and need to be passed separately.
-        if "hybrid_override_pattern" in config_raw:
-            model_kwargs["hybrid_override_pattern"] = config_raw.hybrid_override_pattern
-        if "hybrid_attention_ratio" in config_raw:
-            model_kwargs["hybrid_attention_ratio"] = config_raw.hybrid_attention_ratio
-        if "hybrid_mlp_ratio" in config_raw:
-            model_kwargs["hybrid_mlp_ratio"] = config_raw.hybrid_mlp_ratio
+        # This parameter is not part of the TransformerConfig and needs to be passed separately.
+        # Note: hybrid_override_pattern is remapped to hybrid_layer_pattern in
+        # _load_teacher_model_config, so config_raw.hybrid_layer_pattern is always set here.
+        model_kwargs["hybrid_layer_pattern"] = config_raw.hybrid_layer_pattern
 
         teacher = MCoreMambaModel(config=config, **model_kwargs)
     else:
@@ -257,7 +259,7 @@ def modelopt_gpt_mamba_builder(
             "pg_collection": pg_collection,
         }
         model = MCoreGPTModel(config=config, **model_kwargs)
-    elif args.export_model_type == "MambaModel" or args.is_hybrid_model:
+    elif args.export_model_type == "MambaModel" or getattr(args, 'hybrid_layer_pattern', None) is not None:
         from megatron.core.post_training.modelopt.mamba.model_specs import get_mamba_stack_modelopt_spec
 
         if args.export_default_te_spec and args.export_te_mcore_model:
@@ -275,10 +277,8 @@ def modelopt_gpt_mamba_builder(
             "mamba_stack_spec": mamba_stack_spec,
             "vocab_size": args.padded_vocab_size,
             "max_sequence_length": args.max_position_embeddings,
+            "hybrid_layer_pattern": args.hybrid_layer_pattern,
             "pre_process": pre_process,
-            "hybrid_attention_ratio": args.hybrid_attention_ratio,
-            "hybrid_mlp_ratio": args.hybrid_mlp_ratio,
-            "hybrid_override_pattern": args.hybrid_override_pattern,
             "post_process": post_process,
             "fp16_lm_cross_entropy": args.fp16_lm_cross_entropy,
             "parallel_output": True,
diff --git a/megatron/rl/agent/api.py b/megatron/rl/agent/api.py
index dd52c7277bf..310ef106f1e 100644
--- a/megatron/rl/agent/api.py
+++ b/megatron/rl/agent/api.py
@@ -48,6 +48,10 @@ class Rollout(AgentBaseModel):
     reward: float = None
     env_id: str = ''
     problem_id: str | None = None
+    policy_staleness: list[list[int]]
+    kv_cache_staleness: list[list[int]]
+    completed_at_step: list[int]
+    num_evictions: list[int]
 
 
 class TokenRollout(AgentBaseModel):
@@ -59,6 +63,10 @@ class TokenRollout(AgentBaseModel):
     logprobs: list[list[float]] | None = None
     env_id: str = ''
     problem_id: str | None = None
+    policy_staleness: list[list[int]]
+    kv_cache_staleness: list[list[int]]
+    completed_at_step: list[int]
+    num_evictions: list[int]
 
 
 class ContrastiveRollout(AgentBaseModel):
diff --git a/megatron/rl/agent/reward_only_agent.py b/megatron/rl/agent/reward_only_agent.py
index 4099406a98a..a945dd885e3 100644
--- a/megatron/rl/agent/reward_only_agent.py
+++ b/megatron/rl/agent/reward_only_agent.py
@@ -104,6 +104,10 @@ async def rollout_from_response(
                 generation_mask=[generation_mask],
                 env_id=self.env_id,
                 problem_id=golden['problem_id'] if 'problem_id' in golden else None,
+                policy_staleness=[response.policy_staleness],
+                kv_cache_staleness=[response.kv_cache_staleness],
+                completed_at_step=[response.completed_at_step],
+                num_evictions=[response.num_evictions],
             )
         else:
             rollout = Rollout(
@@ -111,6 +115,10 @@ async def rollout_from_response(
                 reward=await self.get_reward(response_text, golden),
                 env_id=self.env_id,
                 problem_id=golden['problem_id'] if 'problem_id' in golden else None,
+                policy_staleness=[response.policy_staleness],
+                kv_cache_staleness=[response.kv_cache_staleness],
+                completed_at_step=[response.completed_at_step],
+                num_evictions=[response.num_evictions],
             )
 
         return rollout
diff --git a/megatron/rl/inference/api.py b/megatron/rl/inference/api.py
index 87f6b87f908..95e24a55e73 100644
--- a/megatron/rl/inference/api.py
+++ b/megatron/rl/inference/api.py
@@ -23,3 +23,7 @@ class InferenceResponse(BaseModel):
     token_ids: list[int] | None = None
     prompt_length: int | None = None
     logprobs: list[float] | None = None
+    policy_staleness: list[int]
+    kv_cache_staleness: list[int]
+    completed_at_step: int
+    num_evictions: int
diff --git a/megatron/rl/inference/megatron.py b/megatron/rl/inference/megatron.py
index ee31a16eeaa..e609f376461 100644
--- a/megatron/rl/inference/megatron.py
+++ b/megatron/rl/inference/megatron.py
@@ -3,11 +3,19 @@
 import asyncio
 import logging
 
+import httpx
 import torch.distributed as dist
+from openai import AsyncOpenAI, DefaultAioHttpClient
 from pydantic import PrivateAttr
 
+try:
+    import h2  # noqa: F401
+    use_http2 = True
+except ImportError:
+    use_http2 = False
+
 from megatron.core.inference.config import KVCacheManagementMode
-from megatron.core.inference.engines.dynamic_engine import DynamicInferenceEngine
+from megatron.core.inference.engines.dynamic_engine import DynamicInferenceEngine, EngineState
 from megatron.core.inference.inference_client import InferenceClient
 from megatron.core.models.gpt.gpt_model import GPTModel
 from megatron.core.utils import log_single_rank
@@ -23,7 +31,7 @@
 from ..server.api import InferenceServer
 
 logger = logging.getLogger(__name__)
-
+logging.getLogger("httpx").setLevel(logging.WARNING)
 
 class MegatronLocal(InferenceServer, ReturnsTokens, ReturnsRaw):
     """Interface to use MCoreEngine directly as an inference engine."""
@@ -31,21 +39,19 @@ class MegatronLocal(InferenceServer, ReturnsTokens, ReturnsRaw):
     host: str
     port: int
 
-    _server_task: asyncio.Task = PrivateAttr(None)
     _client: InferenceClient = PrivateAttr(None)
     _inference_engine: DynamicInferenceEngine = PrivateAttr(None)
     _rl_kv_cache_management_mode: KVCacheManagementMode = PrivateAttr(None)
+    _openai_client: AsyncOpenAI = PrivateAttr(None)
 
     async def base_generate(self, request: InferenceRequest) -> InferenceResponse:
-
-        assert self._server_task is not None, "Inference server is not initialized"
         tokenizer = get_tokenizer()
         args = get_args()
 
-        from openai import AsyncOpenAI
-        client = AsyncOpenAI(base_url=f"http://{self.host}:{self.port}", api_key="NONE")
+        # Use the shared, optimized client instead of spinning up a new one
+        client = self._openai_client
 
-        # Things that may be problematic when doign this switch
+        # Things that may be problematic when doing this switch
         # - Add BOS token
         # - Skip prompt logprobs
         response = await client.chat.completions.create(
@@ -70,6 +76,10 @@ async def base_generate(self, request: InferenceRequest) -> InferenceResponse:
             token_ids=choice.prompt_token_ids + choice.generation_token_ids,
             logprobs=choice.generation_log_probs,
             prompt_length=len(choice.prompt_token_ids),
+            policy_staleness=choice.policy_staleness,
+            kv_cache_staleness=choice.kv_cache_staleness,
+            completed_at_step=args.curr_iteration,
+            num_evictions=getattr(choice, 'num_evictions', 0),
         )
 
     @classmethod
@@ -93,44 +103,90 @@ async def launch(cls, model: GPTModel, **kwargs):
         )
 
         if dist.get_rank() == 0:
-            from megatron.core.inference.text_generation_server.dynamic_text_gen_server.flask_server import run_flask_server_on_client
-            loop = asyncio.get_event_loop()
+            from megatron.core.inference.text_generation_server.dynamic_text_gen_server import start_text_gen_server
+
             client = InferenceClient(inference_coordinator_address=dp_addr)
-            await client.start()
-            server_task = loop.create_task(run_flask_server_on_client(
-                client=client,
+            client.start()
+
+            start_text_gen_server(
+                coordinator_addr=dp_addr,
                 tokenizer=inference_engine.controller.tokenizer,
-                flask_port=kwargs.get('port', 8294),
+                rank=dist.get_rank(),
+                server_port=kwargs.get('port', 8294),
                 parsers=[],
                 verbose=kwargs.get('verbose', False),
-            ))
+            )
         else:
             client = None
-            server_task = None
-            
+
         launched_server = cls(**kwargs)
         launched_server._client = client
-        launched_server._server_task = server_task
         launched_server._inference_engine = inference_engine
         launched_server._rl_kv_cache_management_mode = KVCacheManagementMode(
             args.rl_kv_cache_management_mode
         )
 
+        concurrency_limit = args.grpo_prompts_per_step * args.grpo_group_size * args.rl_parallel_generation_tasks
+        custom_limits = httpx.Limits(
+            max_connections=concurrency_limit,
+            max_keepalive_connections=concurrency_limit,
+        )
+        http_client = DefaultAioHttpClient(
+            timeout=None,
+            limits=custom_limits,
+            http2=use_http2
+        )
+
+        launched_server._openai_client = AsyncOpenAI(
+            base_url=f"http://{launched_server.host}:{launched_server.port}",
+            api_key="NONE",
+            http_client=http_client
+        )
+
         return launched_server
 
     async def kill(self):
+        # Gracefully close the shared OpenAI client connections
+        if self._openai_client is not None:
+            await self._openai_client.close()
+
         if dist.get_rank() == 0:
-            await self._client.stop_engines()
-        await self._inference_engine.stopped.wait()
+            self._client.pause_engines()
+        await self._inference_engine.wait_until(EngineState.PAUSED)
+
+        if dist.get_rank() == 0:
+            self._client.stop_engines()
+        await self._inference_engine.wait_until(EngineState.STOPPED)
+
+        if dist.get_rank() == 0:
+            self._client.shutdown_coordinator()
+            self._client.stop()
+
+        if dist.get_rank() == 0:
+            from megatron.core.inference.text_generation_server.dynamic_text_gen_server import stop_text_gen_server
+            stop_text_gen_server()
+
+    def increment_staleness(self):
+        if dist.get_rank() == 0:
+            self._client.increment_staleness()
 
     async def suspend(self):
+        if dist.get_rank() == 0:
+            self._client.pause_engines()
+        await self._inference_engine.wait_until(EngineState.PAUSED)
+
         if dist.get_rank() == 0:
             self._client.suspend_engines()
-        await self._inference_engine.paused.wait()
-        self._inference_engine.suspend()
+        await self._inference_engine.wait_until(EngineState.SUSPENDED)
 
     async def resume(self):
+        if self._inference_engine._state_events[EngineState.RUNNING].is_set():
+            return
+
         if dist.get_rank() == 0:
             self._client.resume_engines()
-        await self._inference_engine.running.wait()
-        self._inference_engine.resume()
+        await self._inference_engine.wait_until(EngineState.RESUMED)
+
+        if dist.get_rank() == 0:
+            self._client.unpause_engines()
+        await self._inference_engine.wait_until(EngineState.RUNNING)
diff --git a/megatron/rl/rl_utils.py b/megatron/rl/rl_utils.py
index 46436add1da..d68a0330989 100644
--- a/megatron/rl/rl_utils.py
+++ b/megatron/rl/rl_utils.py
@@ -24,6 +24,7 @@
 from torch.utils.tensorboard import SummaryWriter
 
 from megatron.core import mpu
+from megatron.core.packed_seq_params import PackedSeqParams
 from megatron.core.full_cuda_graph import FullCudaGraphWrapper
 from megatron.core.models.common.language_module.language_module import LanguageModule
 from megatron.core.num_microbatches_calculator import reconfigure_num_microbatches_calculator
@@ -32,14 +33,20 @@
 from megatron.core.pipeline_parallel.utils import is_pp_last_stage, get_pp_last_rank
 from megatron.core.rerun_state_machine import RerunDataIterator
 from megatron.core.tokenizers import MegatronTokenizer
+from megatron.core.tokenizers.text.libraries.huggingface_tokenizer import HuggingFaceTokenizer
 from megatron.core.transformer.cuda_graphs import _CudagraphGlobalRecord
 from megatron.core.transformer.enums import CudaGraphScope
-from megatron.core.transformer.utils import toggle_cuda_graphs
+from megatron.core.transformer.utils import (
+    toggle_cuda_graphs,
+    transition_moe_cudagraphs,
+)
+from megatron.core.inference.utils import set_decode_expert_padding
 from megatron.core.resharding.refit import swap_model_weights
 from megatron.core.inference.unified_memory import (
     advise_managed_module_parameters_preferred_location,
     prefetch_managed_module_parameters,
 )
+from megatron.core.inference.utils import device_memory_summary
 from megatron.core.utils import get_asyncio_loop, log_single_rank
 from megatron.rl.sequence_packing_utils import (
     get_microbatch_dataloader,
@@ -117,16 +124,19 @@ def _torch_saver_swap_inference_model(*, to_cpu: bool) -> None:
             "(see https://github.com/fzyzcjy/torch_memory_saver)"
         )
 
+    tag = "rl_inference_model"
     if to_cpu:
         if not _INFERENCE_MODEL_IS_PAUSED:
-            torch_memory_saver.pause("rl_inference_model")
+            print_rank_0(f"torch_memory_saver: pausing {tag}, before: {device_memory_summary()}")
+            torch_memory_saver.pause(tag)
             _INFERENCE_MODEL_IS_PAUSED = True
-            print_rank_0("[Rank 0] offloaded RL inference model weights to CPU using torch_memory_saver")
+            print_rank_0(f"torch_memory_saver: paused  {tag}, after:  {device_memory_summary()}")
     else:
         if _INFERENCE_MODEL_IS_PAUSED:
-            torch_memory_saver.resume("rl_inference_model")
+            print_rank_0(f"torch_memory_saver: resuming {tag}, before: {device_memory_summary()}")
+            torch_memory_saver.resume(tag)
             _INFERENCE_MODEL_IS_PAUSED = False
-            print_rank_0("[Rank 0] restored RL inference model weights to GPU using torch_memory_saver")
+            print_rank_0(f"torch_memory_saver: resumed  {tag}, after:  {device_memory_summary()}")
 
 
 def _maybe_prefetch_separate_inference_model_weights(model_core, *, to_cpu: bool) -> None:
@@ -275,6 +285,10 @@ class RolloutStats:
     min_inf_prob: None | float
     max_inf_prob: None | float
     mean_inf_prob: None | float
+    policy_staleness: list[list[int]]
+    kv_cache_staleness: list[list[int]]
+    completed_at_steps: list[list[int]]
+    num_evictions: list[list[int]]
 
 
 # Runtime state container for RL-specific data that shouldn't be checkpointed
@@ -432,7 +446,7 @@ def get_inference_interface(args, loop, model):
                 model[0],
                 host='0.0.0.0',
                 port=8294,
-                verbose=args.inference_flask_server_logging)
+                verbose=args.inference_text_gen_server_logging)
         )
     return _INFERENCE_INTERFACE
 
@@ -508,9 +522,8 @@ def get_environment_rollouts(
         inference_model = model
 
     inference_pg_collection = get_attr_wrapped_model(inference_model[0], "pg_collection")
-    assert (
-        n_prompts % get_pg_size(inference_pg_collection.ep) == 0
-    ), "n_prompts must be divisible by data_parallel_world_size"
+    pg_size = get_pg_size(inference_pg_collection.ep)
+    assert (n_prompts % pg_size == 0), f"{n_prompts=} must be divisible by {pg_size=}"
 
     with nvtx_range("rollout-collection"):
         loop = get_asyncio_loop()
@@ -520,6 +533,7 @@ def get_environment_rollouts(
             args.cuda_graph_impl,
             False, # offload optimizer during rollout collection is handled above
             training_model=model if has_separate_inference_model else None,
+            increment_staleness_on_suspend=True,
         ) as inference_interface:
 
             with nvtx_range("inference-setup"):
@@ -640,13 +654,28 @@ def get_logprobs(model, tokens, position_ids, no_grad=False, sequence_packing=Fa
     """
 
     args = get_args()
-    # Ensure packed_seq_params is always provided for CUDA graph signature consistency
-    if packed_seq_params is None and sequence_packing:
-        packed_seq_params = get_default_packed_seq_params(
-            seq_length=tokens.shape[1],
-            max_sequences_per_bin=args.rl_sequence_packing_max_sequences_per_bin,
-            device=tokens.device,
-        )
+    # Ensure packed_seq_params is always provided for CUDA graph signature consistency.
+    # When sequence_packing is enabled, construct from packing config (max_sequences_per_bin).
+    # When sequence_packing is disabled, construct a single-sequence default so the CUDA
+    # graph signature matches the training forward_step in train_rl.py.
+    # This is necessary because reference logprobs steps will reuse the training forward graph.
+    if packed_seq_params is None:
+        if sequence_packing:
+            packed_seq_params = get_default_packed_seq_params(
+                seq_length=tokens.shape[1],
+                max_sequences_per_bin=args.rl_sequence_packing_max_sequences_per_bin,
+                device=tokens.device,
+            )
+        else:
+            cu_seqlens = torch.tensor([0, tokens.shape[1]], dtype=torch.int32, device=tokens.device)
+            packed_seq_params = PackedSeqParams(
+                qkv_format='thd',
+                cu_seqlens_q=cu_seqlens,
+                cu_seqlens_kv=cu_seqlens,
+                max_seqlen_q=tokens.shape[1],
+                max_seqlen_kv=tokens.shape[1],
+                total_tokens=tokens.shape[1],
+            )
 
     nvtx_range = get_nvtx_range()
 
@@ -733,11 +762,19 @@ def compute_group_stats(
     env_ids = []
     group_reward_ids = []
     num_turns = [] # num_turns per traj
+    all_policy_staleness = []
+    all_kv_cache_staleness = []
+    all_completed_at_steps = []
+    all_num_evictions = []
     for group in rollouts:
         group_rewards = []
         group_traj_lengths = []
         group_turn_lengths = []
         group_num_turns = []
+        group_policy_staleness = []
+        group_kv_staleness = []
+        group_completed_at_steps = []
+        group_num_evictions = []
         for rollout in group:
             if isinstance(rollout, TokenRollout):
                 for turn_traj in rollout.trajectory:
@@ -758,6 +795,14 @@ def compute_group_stats(
             roll_turn_lens = [len(t) for t in rollout.trajectory]
             group_turn_lengths.extend(roll_turn_lens)
             group_traj_lengths.append(sum(roll_turn_lens))
+            group_policy_staleness.extend(s for turn in rollout.policy_staleness for s in turn)
+            group_kv_staleness.extend(s for turn in rollout.kv_cache_staleness for s in turn)
+            group_completed_at_steps.extend(rollout.completed_at_step)
+            group_num_evictions.append(sum(rollout.num_evictions))
+        all_policy_staleness.append(group_policy_staleness)
+        all_kv_cache_staleness.append(group_kv_staleness)
+        all_completed_at_steps.append(group_completed_at_steps)
+        all_num_evictions.append(group_num_evictions)
         traj_lens.append(group_traj_lengths)
         turn_lens.append(group_turn_lengths)
         env_ids.append(group[0].env_id) # All rollouts in a group share the env_id by design.
@@ -785,10 +830,44 @@ def compute_group_stats(
         min_inf_prob=None,
         max_inf_prob=None,
         mean_inf_prob=None,
+        policy_staleness=all_policy_staleness,
+        kv_cache_staleness=all_kv_cache_staleness,
+        completed_at_steps=all_completed_at_steps,
+        num_evictions=all_num_evictions,
     )
     return stats
 
 
+def compute_true_staleness(
+    per_token_staleness: list[list[int]],
+    completed_at_steps: list[list[int]],
+    turn_lens: list[list[int]],
+    current_iteration: int,
+) -> list[int]:
+    """Compute true per-token staleness by adding the completion gap.
+
+    Args:
+        per_token_staleness: Grouped flat list of per-token raw staleness values.
+        completed_at_steps: Grouped list of per-turn completion steps.
+        turn_lens: Grouped list of per-turn token counts.
+        current_iteration: Current training iteration.
+
+    Returns:
+        Flat list of true staleness values (one per token across all groups).
+    """
+    result = []
+    for group_staleness, group_completed, group_turn_lens in zip(
+        per_token_staleness, completed_at_steps, turn_lens
+    ):
+        token_idx = 0
+        for completed_at, num_tokens in zip(group_completed, group_turn_lens):
+            gap = current_iteration - completed_at
+            for _ in range(num_tokens):
+                result.append(group_staleness[token_idx] + gap)
+                token_idx += 1
+    return result
+
+
 def prep_wandb_metrics(
         wandb_writer: wandb_run.Run,
         traj_lens: List[List[int]],
@@ -796,8 +875,13 @@ def prep_wandb_metrics(
         rewards: List[List[float]],
         num_turns: List[List[int]],
         advantages: List[float],
+        policy_staleness: List[List[int]],
+        kv_cache_staleness: List[List[int]],
+        num_evictions: List[List[int]],
+        completed_at_steps: List[List[int]],
+        current_iteration: int,
         example_group: list[TokenRollout | Rollout] | None = None,
-        tokenizer: MegatronTokenizer | None = None
+        tokenizer: MegatronTokenizer | None = None,
     ):
 
     """Make a wandb-parseable dictionary of metrics for logging.
@@ -809,8 +893,13 @@ def prep_wandb_metrics(
         rewards: Grouped list of rewards.
         num_turns: Grouped list of number of turns in the trajectories.
         advantages: Flattened list of advantages.
+        policy_staleness: Grouped list of per-token policy staleness.
+        kv_cache_staleness: Grouped list of per-token KV cache staleness.
+        num_evictions: Grouped list of per-rollout number of evictions.
+        completed_at_steps: Grouped list of per-turn completed at steps.
+        current_iteration: Current training iteration.
+        example_group: A list of rollouts of one group to log examples of trajectories.
         tokenizer: Tokenizer to untokenize trajectories for logging.
-        example_groups: A list of rollouts of one group to log examples of trajectories.
     """
 
     group_table = wandb_writer.Table(
@@ -818,6 +907,11 @@ def prep_wandb_metrics(
         data=[[np.mean(g), np.std(g)] for g in rewards],
     )
 
+    true_policy_staleness = compute_true_staleness(
+        policy_staleness, completed_at_steps, turn_lens, current_iteration)
+    true_kv_staleness = compute_true_staleness(
+        kv_cache_staleness, completed_at_steps, turn_lens, current_iteration)
+
     metrics = {
             'group_means_hist': wandb_writer.plot.histogram(
                 group_table, 'group_means', 'Group Means'
@@ -837,6 +931,14 @@ def prep_wandb_metrics(
                 ),
                 'advantages', 'Advantages'
             ),
+            'rollout_table': wandb_writer.Table(
+                columns=['reward', 'traj_length', 'num_evictions'],
+                data=list(zip(
+                    [r for g in rewards for r in g],
+                    [l for g in traj_lens for l in g],
+                    [e for g in num_evictions for e in g],
+                )),
+            ),
             'mean_turn_length': np.mean([np.mean(g) for g in turn_lens]),
             'mean_turn_length_std': np.mean([np.std(g) for g in turn_lens]),
             'max_turn_length': max([max(g) for g in turn_lens]),
@@ -852,6 +954,15 @@ def prep_wandb_metrics(
             'mean_advantage': np.mean(advantages),
             'nonzero_groups_ratio': np.count_nonzero(advantages)
             / len(advantages),
+            'mean_policy_staleness': np.mean(true_policy_staleness),
+            'max_policy_staleness': max(true_policy_staleness),
+            'min_policy_staleness': min(true_policy_staleness),
+            'mean_kv_cache_staleness': np.mean(true_kv_staleness),
+            'max_kv_cache_staleness': max(true_kv_staleness),
+            'min_kv_cache_staleness': min(true_kv_staleness),
+            'total_eviction_count': sum([sum(g) for g in num_evictions]),
+            'max_num_evictions': max([max(g) for g in num_evictions]),
+            'mean_completion_gap': np.mean([current_iteration - s for g in completed_at_steps for s in g]),
     }
     if example_group:
         if tokenizer is None:
@@ -910,8 +1021,15 @@ def maybe_log_training_metrics(
     rewards = group_stats.rewards
     num_turns = group_stats.num_turns
     advantages = group_stats.advantages
-    metrics = metrics | prep_wandb_metrics(wandb_writer=wandb_writer, 
-        traj_lens=traj_lens, turn_lens=turn_lens, rewards=rewards, num_turns=num_turns, advantages=advantages)  
+    policy_staleness = group_stats.policy_staleness
+    kv_cache_staleness = group_stats.kv_cache_staleness
+    num_evictions = group_stats.num_evictions
+    completed_at_steps = group_stats.completed_at_steps
+
+    metrics = metrics | prep_wandb_metrics(wandb_writer=wandb_writer,
+        traj_lens=traj_lens, turn_lens=turn_lens, rewards=rewards, num_turns=num_turns, advantages=advantages,
+        policy_staleness=policy_staleness, kv_cache_staleness=kv_cache_staleness, num_evictions=num_evictions,
+        completed_at_steps=completed_at_steps, current_iteration=current_iteration)
     env_stats = lambda cont, idx: [cont[i] for i in idx]
     group_turn_counts = [sum(nt) for nt in num_turns]
 
@@ -925,11 +1043,16 @@ def maybe_log_training_metrics(
             end = st + group_turn_counts[i]
             env_advantages.extend(advantages[st:end])
 
-        env_metrics = prep_wandb_metrics(wandb_writer=wandb_writer, traj_lens=env_stats(traj_lens, env_idx), 
-            turn_lens=env_stats(turn_lens, env_idx), 
+        env_metrics = prep_wandb_metrics(wandb_writer=wandb_writer, traj_lens=env_stats(traj_lens, env_idx),
+            turn_lens=env_stats(turn_lens, env_idx),
             rewards=env_stats(rewards, env_idx),
             num_turns=env_stats(num_turns, env_idx),
             advantages=env_advantages,
+            policy_staleness=env_stats(policy_staleness, env_idx),
+            kv_cache_staleness=env_stats(kv_cache_staleness, env_idx),
+            num_evictions=env_stats(num_evictions, env_idx),
+            completed_at_steps=env_stats(completed_at_steps, env_idx),
+            current_iteration=current_iteration,
             example_group=example_groups[env_id],
             tokenizer=tokenizer,
         )
@@ -957,16 +1080,17 @@ def prepare_trajectories(
     # Track counts for each environment ID
     env_id_counts = Counter()
 
-    DEFAULT_PAD_TOKENS = ['<|finetune_right_pad_id|>']
+    DEFAULT_PAD_TOKENS = ['<|finetune_right_pad_id|>', '<SPECIAL_999>']
 
     if tokenizer.library == "huggingface":
+        tokenizer : HuggingFaceTokenizer
         if not tokenizer.pad:
             for pad_token in DEFAULT_PAD_TOKENS:
-                if pad_token in tokenizer.vocab:
+                if pad_token in tokenizer._tokenizer.tokenizer.get_vocab():
                     log_single_rank(
                         logger, logging.INFO, f"Updating tokenizer pad token to {pad_token}"
                     )
-                    tokenizer._tokenizer.pad_token_id = tokenizer.vocab[pad_token]
+                    tokenizer._tokenizer.pad_token = pad_token
                     break
             else:
                 raise ValueError("No pad token found in tokenizer vocabulary")
@@ -1153,7 +1277,7 @@ def prepare_data_for_update(
     tokenizer: MegatronTokenizer,
     sequence_packing: bool,
     is_correction: bool,
-) -> RerunDataIterator:
+) -> tuple[RerunDataIterator, RolloutStats, dict]:
     """Extract data for the update from raw rollouts.
 
     Args:
@@ -1165,7 +1289,7 @@ def prepare_data_for_update(
         is_correction: Prepare data for IS correction if True.
 
     Returns:
-        Cycled iterator over dataset batches. In GRPO we might want to go over the same data multiple times.
+        Tuple of (cycled iterator over dataset batches, group stats, example groups per env).
     """
     args = get_args()
     nvtx_range = get_nvtx_range()
@@ -1271,14 +1395,12 @@ def prepare_data_for_update(
         with torch.no_grad(), nvtx_range("compute_logprobs", time=True):
             # Before we can update the model, we need to get the logprobs for the \pi_{old} model.
 
-            # Wrap forward_backward_func for Full iteration CUDA graph
             forward_backward_func = get_forward_backward_func()
             if args.cuda_graph_impl == "local" and CudaGraphScope.full_iteration in args.cuda_graph_scope:
                 forward_backward_func = FullCudaGraphWrapper(
                     forward_backward_func, cuda_graph_warmup_steps=args.cuda_graph_warmup_steps
                 )
 
-
             dtype = (
                 torch.bfloat16 if args.bf16 else (torch.float16 if args.fp16 else torch.float32)
             )
@@ -1418,15 +1540,7 @@ def prepare_data_for_update(
                 loader = DataLoader(data, batch_size=args.micro_batch_size)
 
 
-        with nvtx_range("log-wandb-tb"):
-            maybe_log_training_metrics(
-                group_stats=group_stats,
-                current_iteration=args.curr_iteration,
-                tokenizer=tokenizer,
-                example_groups=example_groups,
-            )
-
-    return RerunDataIterator(itertools.cycle(loader))
+    return RerunDataIterator(itertools.cycle(loader)), group_stats, example_groups
 
 
 def get_grpo_data_iterator(
@@ -1466,27 +1580,38 @@ def get_grpo_data_iterator(
         RerunDataIterator for the current training step
     """
     runtime_state = get_rl_runtime_state()
+    tokenizer = get_tokenizer()
 
     # We collect new rollouts when we've gone over the collected data 'grpo_iterations' times.
-    global_batches_per_collection = (grpo_prompts_per_step * grpo_group_size) // global_batch_size 
+    global_batches_per_collection = (grpo_prompts_per_step * grpo_group_size) // global_batch_size
     if (
         buffered_rollouts is None or
-        iteration == runtime_state.last_collection_iteration + 
+        iteration == runtime_state.last_collection_iteration +
         (grpo_iterations * global_batches_per_collection)
     ):
 
-        buffered_rollouts = get_environment_rollouts(
+        rollouts = get_environment_rollouts(
             model, inference_model, optimizer, grpo_prompts_per_step, grpo_group_size
         )
-        buffered_rollouts = prepare_data_for_update(model=model, 
-            ref_state_dict=ref_state_dict, 
-            rollouts=buffered_rollouts,
-            tokenizer=get_tokenizer(),
+        buffered_rollouts, group_stats, example_groups = prepare_data_for_update(
+            model=model,
+            ref_state_dict=ref_state_dict,
+            rollouts=rollouts,
+            tokenizer=tokenizer,
             sequence_packing=sequence_packing,
             is_correction=is_correction,
-            )
+        )
+        runtime_state.group_stats = group_stats
+        runtime_state.example_groups = example_groups
         runtime_state.reset_iteration_counters(iteration)
 
+    maybe_log_training_metrics(
+        group_stats=runtime_state.group_stats,
+        current_iteration=iteration,
+        tokenizer=tokenizer,
+        example_groups=runtime_state.example_groups,
+    )
+
     return buffered_rollouts
 
 
@@ -1704,6 +1829,7 @@ def megatron_rl_inference_mode(
     cuda_graph_impl: str,
     offload_optimizer_during_inference: bool,
     training_model: Optional[list[LanguageModule]] = None,
+    increment_staleness_on_suspend: bool = False,
 ):
     """Manage the model inference context when collecting rollouts.
 
@@ -1725,9 +1851,17 @@ def megatron_rl_inference_mode(
 
     logger.debug(f"[{dist.get_rank()}] Entering inference mode")
 
+    # Change cudagraph scope for inference (empty list = full-layer capture)
+    model[0].config.cuda_graph_scope = []
+    model[0].config.cuda_graph_impl = "local"
+
     # If we get a lower precision wrapper, we go one object deeper.
     lang_module = model[0].module.module if hasattr(model[0].module, "module") else model[0].module
 
+    # Switch MoE layers to full CUDA graph capture for inference
+    if args.rl_training_cuda_graphs and args.num_experts is not None:
+        transition_moe_cudagraphs(lang_module, 'full')
+
     lang_module.eval()
     # If this is a separate RL inference model with offloading enabled, ensure weights are on GPU
     # before any CUDA-graph capture/replay or inference. This is a no-op if already on GPU.
@@ -1768,10 +1902,28 @@ def megatron_rl_inference_mode(
 
         with nvtx_range("suspend-engine"):
             loop.run_until_complete(inference_interface.suspend())
+            if increment_staleness_on_suspend:
+                inference_interface.increment_staleness()
 
         if cuda_graph_impl != "none" and not args.rl_training_cuda_graphs:
             toggle_cuda_graphs(lang_module, 'none')
 
+        # Reset drop_and_pad leaked from inference decode
+        set_decode_expert_padding(unwrap_model(model[0]), set_to=False)
+
+        # Restore partial capture cudagraph scope for training if this is MoE
+        if args.num_experts is not None:
+            model[0].config.cuda_graph_scope = [
+                CudaGraphScope.mamba,
+                CudaGraphScope.attn,
+                CudaGraphScope.moe_router,
+                CudaGraphScope.moe_preprocess,
+            ]
+
+        # Switch MoE layers to partial CUDA graph capture for training
+        if args.rl_training_cuda_graphs and args.num_experts is not None:
+            transition_moe_cudagraphs(lang_module, 'partial')
+
         # If this is a separate RL inference model, prefetch weights back to CPU so they
         # don't consume GPU memory during training.
         with nvtx_range("prefetch-inference-model-weights-to-cpu"):
@@ -1797,6 +1949,13 @@ def megatron_rl_inference_mode(
 
 def rl_inference_interface_shutdown():
     global _INFERENCE_INTERFACE
+    global _ROLLOUT_GENERATOR
+
+    if _ROLLOUT_GENERATOR is not None:
+        loop = get_asyncio_loop()
+        loop.run_until_complete(_ROLLOUT_GENERATOR.aclose())
+        _ROLLOUT_GENERATOR = None
+
     if _INFERENCE_INTERFACE is not None:
         loop = get_asyncio_loop()
         loop.run_until_complete(_INFERENCE_INTERFACE.kill())
diff --git a/megatron/rl/sequence_packing_utils.py b/megatron/rl/sequence_packing_utils.py
index b641ecd85d0..1285d0926f8 100644
--- a/megatron/rl/sequence_packing_utils.py
+++ b/megatron/rl/sequence_packing_utils.py
@@ -428,6 +428,7 @@ def get_default_packed_seq_params(seq_length: int, max_sequences_per_bin: int, d
         cu_seqlens_kv_padded=None,
         max_seqlen_q=seq_length,
         max_seqlen_kv=seq_length,
+        total_tokens=seq_length,
     )
 
 def create_packed_seq_params(packing_context: PackingContext):
@@ -503,6 +504,7 @@ def create_packed_seq_params_for_bin(
         cu_seqlens_kv_padded=None,
         max_seqlen_q=max_seqlen,
         max_seqlen_kv=max_seqlen,
+        total_tokens=bin_size,
     )
 
 
diff --git a/megatron/rl/server/api.py b/megatron/rl/server/api.py
index 528e6e880dc..6635b3d48dc 100644
--- a/megatron/rl/server/api.py
+++ b/megatron/rl/server/api.py
@@ -18,7 +18,7 @@ async def launch(cls) -> Self:
     async def suspend(self):
         pass
 
-    def resume(self):
+    async def resume(self):
         pass
 
     async def kill(self):
diff --git a/megatron/training/arguments.py b/megatron/training/arguments.py
index c1bb0f8ac0d..3ff6342ff5b 100644
--- a/megatron/training/arguments.py
+++ b/megatron/training/arguments.py
@@ -406,38 +406,8 @@ def validate_args(args, defaults={}):
                         "installed. See https://github.com/fzyzcjy/torch_memory_saver."
                     )
 
-        # When using different EP sizes for inference and training (EP refit), the legacy
-        # GroupedMLP is not supported. Only SequentialMLP or TEGroupedMLP can be used.
-        if (
-            args.rl_inference_expert_model_parallel_size is not None
-            and args.rl_inference_expert_model_parallel_size != args.expert_model_parallel_size
-        ):
-            assert not args.moe_use_legacy_grouped_gemm, (
-                "Legacy GroupedMLP (--moe-use-legacy-grouped-gemm) is not supported when using "
-                "different expert parallelism sizes for inference and training. "
-                "Use SequentialMLP (default when --moe-grouped-gemm is not set) or "
-                "TEGroupedMLP (--moe-grouped-gemm without --moe-use-legacy-grouped-gemm)."
-            )
-
         args.grpo_samples_per_iteration = args.grpo_prompts_per_step * args.grpo_group_size
-        num_generated_samples_per_inference_iteration = (
-            args.grpo_samples_per_iteration * args.grpo_iterations)
-
-        # Ensure that the number of prompts we collect is a multiple of the global batch size.
-        # TODO: Make this account for batch size rampup?
-        assert num_generated_samples_per_inference_iteration % args.global_batch_size == 0, \
-            f"grpo_group_size * grpo_prompts_per_step * grpo_iterations should be divisible by global_batch_size"
 
-        # For now only exit/checkpoint on iterations where we generate data. We don't currently
-        # have a way to checkpoint the generated data.
-        num_training_iterations_per_inference_iteration = (
-            num_generated_samples_per_inference_iteration // args.global_batch_size)
-        if args.exit_interval is not None:
-            assert args.exit_interval % num_training_iterations_per_inference_iteration == 0, \
-                f"exit_interval should be divisible by number of global batches per inference iteration."
-        if args.save_interval is not None:
-            assert args.save_interval % num_training_iterations_per_inference_iteration == 0, \
-                f"save_interval should be divisible by number of global batches per inference iteration."
         if args.rl_use_sequence_packing:
             assert args.micro_batch_size == 1, \
                 "micro_batch_size must be 1 when using sequence packing. To increase compute per micro batch increase the sequence length."
@@ -554,35 +524,150 @@ def validate_args(args, defaults={}):
         print_rank_0('setting global batch size to {}'.format(args.global_batch_size))
     assert args.global_batch_size > 0
 
-    # === MTP validation ===
-    # Deprecation warnings for legacy MTP arguments
+    if args.perform_rl_step:
+        num_generated_samples_per_inference_iteration = (
+            args.grpo_samples_per_iteration * args.grpo_iterations)
+
+        # Ensure that the number of prompts we collect is a multiple of the global batch size.
+        # TODO: Make this account for batch size rampup?
+        assert num_generated_samples_per_inference_iteration % args.global_batch_size == 0, \
+            f"grpo_group_size * grpo_prompts_per_step * grpo_iterations should be divisible by global_batch_size"
+
+        # For now only exit/checkpoint on iterations where we generate data. We don't currently
+        # have a way to checkpoint the generated data.
+        num_training_iterations_per_inference_iteration = (
+            num_generated_samples_per_inference_iteration // args.global_batch_size)
+        if args.exit_interval is not None:
+            assert args.exit_interval % num_training_iterations_per_inference_iteration == 0, \
+                f"exit_interval should be divisible by number of global batches per inference iteration."
+        if args.save_interval is not None:
+            assert args.save_interval % num_training_iterations_per_inference_iteration == 0, \
+                f"save_interval should be divisible by number of global batches per inference iteration."
+
+    # === Hybrid layer pattern: deprecation handling and validation ===
+
+    # Backward compat: --hybrid-override-pattern is deprecated in favor of --hybrid-layer-pattern
+    used_hybrid_override_pattern = False
+    if args.hybrid_override_pattern is not None:
+        assert args.hybrid_layer_pattern is None, (
+            '--hybrid-override-pattern and --hybrid-layer-pattern cannot both be specified. '
+            '--hybrid-override-pattern is deprecated; use --hybrid-layer-pattern instead.'
+        )
+        warn_rank_0(
+            "--hybrid-override-pattern is deprecated. Use --hybrid-layer-pattern instead.",
+            args.rank,
+        )
+        args.hybrid_layer_pattern = args.hybrid_override_pattern
+        used_hybrid_override_pattern = True
+
     if args.mtp_hybrid_override_pattern is not None:
         warn_rank_0(
             "--mtp-hybrid-override-pattern is deprecated. "
-            "For new hybrid models with MTP models, use unified --hybrid-override-pattern instead. "
+            "For new hybrid models with MTP, use unified --hybrid-layer-pattern instead. "
             "Example: 'M*M*/MM/MM' means main='M*M*', MTP pattern='MM' with 2 depths. "
             "This argument is kept only for loading old checkpoints.",
             args.rank,
         )
 
-    # Backward compatibility: convert legacy mtp_hybrid_override_pattern to unified format
-    from megatron.core.ssm.mamba_hybrid_layer_allocation import Symbols, parse_hybrid_pattern
+    from megatron.core.ssm.mamba_hybrid_layer_allocation import (
+        Symbols, parse_hybrid_pattern, get_hybrid_total_layer_count,
+        get_hybrid_total_pipeline_segment_count,
+    )
     sep = Symbols.MTP_SEPARATOR
+
+    # Backward compat: convert legacy mtp_hybrid_override_pattern to unified format
     if (
-        getattr(args, 'mtp_hybrid_override_pattern', None) is not None
+        args.mtp_hybrid_override_pattern is not None
         and args.mtp_num_layers is not None
         and args.mtp_num_layers > 0
-        and (args.hybrid_override_pattern is None or sep not in args.hybrid_override_pattern)
+        and (args.hybrid_layer_pattern is None or sep not in args.hybrid_layer_pattern)
     ):
-        main_pattern = args.hybrid_override_pattern or ''
+        main_pattern = args.hybrid_layer_pattern or ''
         mtp_pattern = args.mtp_hybrid_override_pattern
-        args.hybrid_override_pattern = main_pattern + sep + sep.join([mtp_pattern] * args.mtp_num_layers)
+        args.hybrid_layer_pattern = main_pattern + sep + sep.join([mtp_pattern] * args.mtp_num_layers)
         args.mtp_hybrid_override_pattern = None
-        print_rank_0(f"Converted legacy MTP pattern to unified: {args.hybrid_override_pattern}")
+        print_rank_0(f"Converted legacy MTP pattern to unified: {args.hybrid_layer_pattern}")
+
+    if args.hybrid_layer_pattern is not None:
+        # Derive num_layers from pattern; hybrid_layer_pattern always overrides --num-layers when
+        # both are present (e.g. when loading from checkpoint with --use-checkpoint-args).
+        num_layers_in_pattern = get_hybrid_total_layer_count(args.hybrid_layer_pattern)
+        if args.num_layers is not None and args.num_layers != num_layers_in_pattern:
+            warn_rank_0(
+                f'--hybrid-layer-pattern is set; ignoring --num-layers ({args.num_layers}) and '
+                f'using the layer count derived from the pattern ({num_layers_in_pattern}).',
+                args.rank,
+            )
+        args.num_layers = num_layers_in_pattern
+
+        # first/last pipeline num layers are incompatible with pipe-separated patterns
+        # (the pipe separators already define the pipeline layout explicitly), but are
+        # allowed for pipe-free patterns where they control uneven PP splitting.
+        has_pipes = Symbols.PIPE in args.hybrid_layer_pattern.split(sep)[0]
+        if has_pipes:
+            assert args.decoder_first_pipeline_num_layers is None, (
+                'If --hybrid-layer-pattern contains pipe separators, '
+                '--decoder-first-pipeline-num-layers should not be specified '
+                'as the pipeline layout is explicitly defined.'
+            )
+            assert args.decoder_last_pipeline_num_layers is None, (
+                'If --hybrid-layer-pattern contains pipe separators, '
+                '--decoder-last-pipeline-num-layers should not be specified '
+                'as the pipeline layout is explicitly defined.'
+            )
+        assert args.num_layers_per_virtual_pipeline_stage is None, (
+            '--num-layers-per-virtual-pipeline-stage should not be used with '
+            '--hybrid-layer-pattern. To specify virtual pipelining, describe a number of '
+            'pipeline segments in --hybrid-layer-pattern that is a multiple of '
+            '--pipeline-model-parallel-size greater than 1'
+        )
+        assert args.num_virtual_stages_per_pipeline_rank is None, (
+            '--num-virtual-stages-per-pipeline-rank should not be used with '
+            '--hybrid-layer-pattern. Virtual pipeline stages are derived from the '
+            'number of | segments in the pattern.'
+        )
+        assert args.pipeline_model_parallel_layout is None, (
+            '--pipeline-model-parallel-layout should not be used with --hybrid-layer-pattern. '
+            'Pipeline stage layout is defined by | separators in the pattern.'
+        )
+        assert not args.account_for_embedding_in_pipeline_split, (
+            '--account-for-embedding-in-pipeline-split should not be used with '
+            '--hybrid-layer-pattern. Pipeline stage layout is defined by | separators '
+            'in the pattern.'
+        )
+        assert not args.account_for_loss_in_pipeline_split, (
+            '--account-for-loss-in-pipeline-split should not be used with '
+            '--hybrid-layer-pattern. Pipeline stage layout is defined by | separators '
+            'in the pattern.'
+        )
+
+        # Derive VPP from pipe segments in the pattern
+        hybrid_pipeline_segments = get_hybrid_total_pipeline_segment_count(
+            args.hybrid_layer_pattern
+        )
+        if hybrid_pipeline_segments == 1 and args.transformer_pipeline_model_parallel_size > 1:
+            # No pipes in pattern -- PP will be handled by select_pipeline_segment
+            # at model init time (for backwards compatibility).
+            args.virtual_pipeline_model_parallel_size = None
+        else:
+            assert hybrid_pipeline_segments % args.transformer_pipeline_model_parallel_size == 0, (
+                'The number of hybrid pipeline segments described by --hybrid-layer-pattern must '
+                'be evenly divisible by --pipeline-model-parallel-size. '
+                f'Got {hybrid_pipeline_segments} segments and '
+                f'{args.transformer_pipeline_model_parallel_size} pipeline parallel size.'
+            )
+            if hybrid_pipeline_segments > args.transformer_pipeline_model_parallel_size:
+                # Must be set here in order to assign virtual parallel ranks in
+                # training.py/get_model
+                args.virtual_pipeline_model_parallel_size = (
+                    hybrid_pipeline_segments // args.transformer_pipeline_model_parallel_size
+                )
+            else:
+                args.virtual_pipeline_model_parallel_size = None
 
     # Infer mtp_num_layers from unified pattern
-    if args.hybrid_override_pattern and sep in args.hybrid_override_pattern:
-        parsed = parse_hybrid_pattern(args.hybrid_override_pattern)
+    if args.hybrid_layer_pattern and sep in args.hybrid_layer_pattern:
+        parsed = parse_hybrid_pattern(args.hybrid_layer_pattern)
         if parsed.mtp_pattern and parsed.mtp_num_depths > 0:
             inferred_mtp_num_layers = parsed.mtp_num_depths
             if args.mtp_num_layers is None:
@@ -590,7 +675,8 @@ def validate_args(args, defaults={}):
             elif args.mtp_num_layers != inferred_mtp_num_layers:
                 warn_rank_0(
                     f"--mtp-num-layers ({args.mtp_num_layers}) conflicts with "
-                    f"MTP depth count ({inferred_mtp_num_layers}) in pattern '{args.hybrid_override_pattern}'. "
+                    f"MTP depth count ({inferred_mtp_num_layers}) in pattern "
+                    f"'{args.hybrid_layer_pattern}'. "
                     f"Using the inferred value ({inferred_mtp_num_layers}).",
                     args.rank
                 )
@@ -605,14 +691,14 @@ def validate_args(args, defaults={}):
         )
 
     # Validate MTP args for hybrid vs non-hybrid models
-    if args.is_hybrid_model:
+    if args.hybrid_layer_pattern is not None:
         # Mamba/hybrid model MTP validation
-        if args.mtp_num_layers and not (args.hybrid_override_pattern and sep in args.hybrid_override_pattern):
+        if args.mtp_num_layers and not (args.hybrid_layer_pattern and sep in args.hybrid_layer_pattern):
             # Hybrid model wants MTP but no unified pattern - check for legacy args
             if args.mtp_hybrid_override_pattern is None:
                 warn_rank_0(
                     "Hybrid model with --mtp-num-layers but no MTP pattern. "
-                    "Use unified --hybrid-override-pattern with '/' separator (e.g., 'M*M*/MM/MM') "
+                    "Use unified --hybrid-layer-pattern with '/' separator (e.g., 'M*M*/MM/MM') "
                     "or legacy --mtp-hybrid-override-pattern for old checkpoints.",
                     args.rank
                 )
@@ -625,8 +711,8 @@ def validate_args(args, defaults={}):
                 "This argument will be ignored.",
                 args.rank
             )
-    # === End of MTP validation ===
-    
+    # === End of hybrid layer pattern: deprecation handling and validation ===
+
     # Uneven virtual pipeline parallelism
     assert (
         int(args.num_layers_per_virtual_pipeline_stage is not None)
@@ -681,12 +767,14 @@ def validate_args(args, defaults={}):
         if args.virtual_pipeline_model_parallel_size == 1:
             args.virtual_pipeline_model_parallel_size = None
     else:
-        args.virtual_pipeline_model_parallel_size = None
+        # Only set VPP to None if it wasn't already derived from --hybrid-layer-pattern
+        if args.hybrid_layer_pattern is None:
+            args.virtual_pipeline_model_parallel_size = None
 
         if args.decoder_first_pipeline_num_layers is None and args.decoder_last_pipeline_num_layers is None:
             # Divisibility check not applicable for T5 models which specify encoder_num_layers
-            # and decoder_num_layers.
-            if args.num_layers is not None:
+            # and decoder_num_layers, or for hybrid models using --hybrid-layer-pattern.
+            if args.num_layers is not None and args.hybrid_layer_pattern is None:
                 num_layers = args.num_layers
 
                 if args.account_for_embedding_in_pipeline_split:
@@ -723,8 +811,9 @@ def validate_args(args, defaults={}):
     )
 
     if args.overlap_param_gather:
-        assert args.use_distributed_optimizer or args.use_megatron_fsdp, \
-            '--overlap-param-gather only supported with distributed optimizer or megatron fsdp'
+        assert args.use_distributed_optimizer or args.use_megatron_fsdp \
+            or args.optimizer == 'dist_muon', \
+            '--overlap-param-gather only supported with distributed optimizer, megatron fsdp, or dist_muon'
         assert args.overlap_grad_reduce, \
             'Must use --overlap-param-gather with --overlap-grad-reduce'
         assert not args.use_legacy_models, \
@@ -770,6 +859,7 @@ def validate_args(args, defaults={}):
         assert not args.use_dist_ckpt, \
             '--overlap-param-gather-with-optimizer-step not supported with distributed checkpointing yet'
 
+    # Map string data-type to torch.dtype.
     dtype_map = {
         'fp32': torch.float32, 'bf16': torch.bfloat16, 'fp16': torch.float16, 'fp8': torch.uint8,
     }
@@ -779,6 +869,14 @@ def validate_args(args, defaults={}):
     args.main_params_dtype = map_dtype(args.main_params_dtype)
     args.exp_avg_dtype = map_dtype(args.exp_avg_dtype)
     args.exp_avg_sq_dtype = map_dtype(args.exp_avg_sq_dtype)
+    args.mamba_inference_conv_states_dtype = map_dtype(args.mamba_inference_conv_states_dtype)
+    args.mamba_inference_ssm_states_dtype = map_dtype(args.mamba_inference_ssm_states_dtype)
+
+    args.megatron_fsdp_main_params_dtype = map_dtype(args.megatron_fsdp_main_params_dtype)
+    args.megatron_fsdp_main_grads_dtype = map_dtype(args.megatron_fsdp_main_grads_dtype)
+    args.megatron_fsdp_grad_comm_dtype = map_dtype(args.megatron_fsdp_grad_comm_dtype)
+    if args.grad_reduce_in_bf16:
+        args.megatron_fsdp_grad_comm_dtype = torch.bfloat16
 
     if args.fp8_param_gather:
         assert args.use_distributed_optimizer or args.use_torch_fsdp2 or args.use_megatron_fsdp or not torch.is_grad_enabled(), \
@@ -885,7 +983,8 @@ def validate_args(args, defaults={}):
         args.consumed_train_bins = 0
 
     # Iteration-based training.
-    if args.train_iters:
+    # Skip these checks when skip_train is set: LR config is irrelevant.
+    if args.train_iters and not args.skip_train:
         # If we use iteration-based training, make sure the
         # sample-based options are off.
         assert args.train_samples is None, \
@@ -901,7 +1000,7 @@ def validate_args(args, defaults={}):
                 'can only specify one of lr-warmup-fraction and lr-warmup-iters'
 
     # Sample-based training.
-    if args.train_samples:
+    if args.train_samples and not args.skip_train:
         # If we use sample-based training, make sure the
         # iteration-based options are off.
         assert args.train_iters is None, \
@@ -1285,13 +1384,7 @@ def validate_args(args, defaults={}):
                 'Disabling --async-save.'
             )
             args.async_save = False
-        elif args.dist_ckpt_workers > 1:
-            warn_rank_0(
-                'async ckpt forks processes for parallel writing which may introduce '
-                'instability on checkpoints. Consider using --dist-ckpt-workers=1 in case of '
-                'issues.'
-            )
-
+        
     # Inference args
     if args.inference_batch_times_seqlen_threshold > -1:
         assert args.pipeline_model_parallel_size > 1, \
@@ -1304,7 +1397,7 @@ def validate_args(args, defaults={}):
         assert args.inference_dynamic_batching_buffer_size_gb is not None
         assert args.inference_dynamic_batching_block_size % 256 == 0, "block size should be a multiple of 256"
 
-    if args.cuda_graph_impl == "local" and args.expert_model_parallel_size > 1:
+    if args.cuda_graph_impl == "local" and args.expert_model_parallel_size > 1 and args.transformer_impl != "inference_optimized":
        assert args.moe_pad_experts_for_cuda_graph_inference, \
         "--moe-pad-experts-for-cuda-graph-inference must be set when using CUDA graphs with expert parallelism"
 
@@ -1319,9 +1412,15 @@ def validate_args(args, defaults={}):
             warn_rank_0('enabling --no-load-rng for upcycling.')
 
     # --skip-train checks.
-    if args.skip_train and not args.no_load_optim:
+    # In RL inference-only mode, --no-load-optim is user-controlled: it determines whether the
+    # optimizer is created (needed for --rl-offload-optimizer-during-inference) or skipped entirely.
+    if args.skip_train and not args.perform_rl_step and not args.no_load_optim:
         args.no_load_optim = True
         warn_rank_0('enabling --no-load-optim when skipping training.')
+    if args.skip_train and args.perform_rl_step and args.no_load_optim and args.rl_offload_optimizer_during_inference:
+        assert False, \
+            '--no-load-optim with --skip-train --perform-rl-step skips the optimizer; ' \
+            '--rl-offload-optimizer-during-inference is incompatible (no optimizer to offload).'
 
     # emerging optimizer check
     if args.optimizer not in ('sgd', 'adam'):
@@ -1337,9 +1436,11 @@ def validate_args(args, defaults={}):
             args.use_layer_wise_distributed_optimizer = True
             args.use_distributed_optimizer = False
 
-        # TODO: remove these checks once we support them
-        assert not args.overlap_grad_reduce, "Muon optimizer does not support overlap grad reduce for now."
-        assert not args.overlap_param_gather, "Muon optimizer does not support overlap param gather for now."
+        if args.optimizer == 'muon':
+            assert not args.overlap_grad_reduce, "Muon optimizer does not support overlap grad reduce. Use dist_muon instead."
+            assert not args.overlap_param_gather, "Muon optimizer does not support overlap param gather. Use dist_muon instead."
+
+        assert not args.use_distributed_optimizer, "Muon optimizer does not support distributed optimizer for now."
         assert not args.use_torch_fsdp2, "Muon optimizer does not support Torch-FSDP2 for now."
         assert not args.use_megatron_fsdp, "Muon optimizer does not support Megatron-FSDP for now."
         assert args.ckpt_format in ["torch", "torch_dist"], "Muon optimizer supports torch and torch_dist checkpoint format."
@@ -1450,13 +1551,15 @@ def validate_args(args, defaults={}):
     
     if args.multi_latent_attention:
         assert not args.group_query_attention, "Group query attention is mutually exclusive with multi latent attention."
+        
+    if args.mla_down_proj_fusion:
+        assert args.multi_latent_attention, "--mla-down-proj-fusion requires --multi-latent-attention"
 
     # MoE latent projections
     if args.moe_latent_size is not None:
         assert args.moe_latent_size > 0, "MoE latent projection dimension has to be greater than zero."
         assert args.num_experts is not None, "MoE latent projections are applicable only for MoE models."
         assert not args.use_legacy_models, "MoE latent projections are only supported for mcore models."
-        assert not args.moe_use_legacy_grouped_gemm, "MoE latent projection is not supported yet with legacy grouped GEMM."
 
     if args.tiktoken_special_tokens and not args.tokenizer_special_tokens:
         warn_rank_0(
@@ -1560,8 +1663,8 @@ def core_transformer_config_from_args(args, config_class=None):
 
     if len(args.cp_comm_type) == 1:
         kw_args['cp_comm_type'] = args.cp_comm_type[0]
-    if args.is_hybrid_model:
-        kw_args['is_hybrid_model'] = args.is_hybrid_model
+    if args.hybrid_layer_pattern is not None:
+        kw_args['is_hybrid_model'] = True
 
     kw_args['inference_sampling_seed'] = args.seed
 
@@ -1717,10 +1820,35 @@ def _add_inference_args(parser):
                        '1) allocate `memory_buffer` in unified memory. '
                        'Eventually, additional levels will be included to '
                        'control other tensors within the context.')
-    # TODO(ksanthanam): Clean this up in future PR
     group.add_argument('--enable-chunked-prefill', dest='enable_chunked_prefill',
                        action='store_true', default=False,
                        help="Enable chunked prefill (disabled by default)")
+    group.add_argument('--num-speculative-tokens', type=int, default=0,
+                       help='Number of speculative tokens generated during decode')
+    group.add_argument('--inference-dynamic-batching-prefix-caching',
+                       dest='inference_dynamic_batching_enable_prefix_caching',
+                       action=argparse.BooleanOptionalAction,
+                       default=False,
+                       help='Enable/disable prefix caching for dynamic batching inference. '
+                       'When disabled, KV cache blocks cannot be shared between '
+                       'requests with identical prompt prefixes.')
+    group.add_argument('--inference-dynamic-batching-prefix-caching-eviction-policy',
+                       type=str, default='ref_zero',
+                       choices=['ref_zero', 'lru'],
+                       dest='inference_dynamic_batching_prefix_caching_eviction_policy',
+                       help='Eviction policy for prefix caching blocks. '
+                       '"ref_zero" (default) immediately returns blocks to the '
+                       'free pool when ref_count hits 0. "lru" keeps blocks '
+                       'cached and evicts via LRU only when space is needed.')
+    group.add_argument('--inference-dynamic-batching-prefix-caching-coordinator-policy',
+                       type=str, default='first_prefix_block',
+                       choices=['longest_prefix', 'first_prefix_block', 'round_robin'],
+                       dest='inference_dynamic_batching_prefix_caching_coordinator_policy',
+                       help='Coordinator routing policy for prefix caching. '
+                       '"first_prefix_block" (default) routes based on the first '
+                       'block hash only. "longest_prefix" routes to the rank with '
+                       'the longest matching prefix. "round_robin" ignores prefix '
+                       'affinity and cycles through ranks.')
     group.add_argument('--inference-dynamic-batching-cuda-graph-max-tokens',
                        type=int, default=16384,
                        help='Maximum number of tokens to capture in a cuda graph.')
@@ -1730,13 +1858,19 @@ def _add_inference_args(parser):
     group.add_argument('--inference-logging-step-interval', type=int, default=0,
                        help='Step interval for logging inference metrics. '
                             'Default to 0 to disable inference logging.')
-    group.add_argument('--inference-flask-server-logging', action=argparse.BooleanOptionalAction,
+    group.add_argument('--inference-text-gen-server-logging', action=argparse.BooleanOptionalAction,
                        required=False, default=False,
-                       help='Enable per-request logging in the Flask inference server.')
+                       help='Enable per-request logging in the inference text generation server.')
     group.add_argument('--inference-wandb-logging', action=argparse.BooleanOptionalAction,
                        required=False, default=False, help='Enable inference wandb logging.')
     group.add_argument("--inference-coordinator-port", type=int,
                        help="This port will be used to setup the inference coordinator on node-0")
+    group.add_argument('--mamba-inference-conv-states-dtype', type=str,
+                       choices=['bf16', 'fp16', 'fp32'], default='bf16',
+                       help='Dtype for the Mamba inference conv states tensor')
+    group.add_argument('--mamba-inference-ssm-states-dtype', type=str,
+                       choices=['bf16', 'fp16', 'fp32'], default='bf16',
+                       help='Dtype for the Mamba inference SSM states tensor')
     return parser
 
 
@@ -1906,7 +2040,7 @@ def _add_network_size_args(parser):
     return parser
 
 def _add_straggler_detector_args(parser):
-    from megatron.training.resilience_config import StragglerDetectionConfig
+    from megatron.training.config import StragglerDetectionConfig
 
     straggler_factory = ArgumentGroupFactory(StragglerDetectionConfig)
     group = straggler_factory.build_group(parser, "straggler")
@@ -2014,7 +2148,7 @@ def _add_ft_package_args(parser):
 
 
 def _add_logging_args(parser):
-    from megatron.training.training_config import LoggerConfig
+    from megatron.training.config import LoggerConfig
 
     log_factory = ArgumentGroupFactory(LoggerConfig, exclude = ["log_throughput_to_tensorboard", "throughput_window_size", "memory_keys", "log_l2_norm_grad_to_tensorboard", "log_runtime_to_tensorboard", "runtime_time_unit", "filter_warnings", "modules_to_filter", "set_level_for_all_loggers", "save_config_filepath"])
     group = log_factory.build_group(parser, title="logging")
@@ -2115,7 +2249,7 @@ def _add_rl_args(parser):
                             'persist: leave KV cache in GPU memory (default), '
                             'offload: offload KV cache to CPU during training, '
                             'recompute: deallocate KV cache and recompute from scratch each cycle')
-    group.add_argument('--rl-persist-cuda-graphs', action=argparse.BooleanOptionalAction, type=bool, default=True,
+    group.add_argument('--rl-persist-cuda-graphs', action=argparse.BooleanOptionalAction, type=bool, default=False,
                        help='Persist CUDA graphs when the inference engine is suspended. '
                             'If False, CUDA graphs are deleted on suspend and re-captured on resume.')
     group.add_argument('--rl-partial-rollouts', action=argparse.BooleanOptionalAction, default=False,
@@ -2196,11 +2330,14 @@ def _add_rl_args(parser):
                         help='Number of parallel generation tasks for RL inference.')
     group.add_argument('--rl-skip-bos-token', action=argparse.BooleanOptionalAction, type=bool, default=False,
                         help='Skip BOS token at the beginning of the sequences. Default is False.')
+    group.add_argument('--rl-inference-parsers', nargs='*', default=[],
+                       help='List of response parsers to enable for RL inference '
+                            '(e.g. --rl-inference-parsers deepseek-r1-reasoning qwen3-coder-tool).')
     return parser
 
 def _add_training_args(parser):
-    from megatron.training.training_config import TrainingConfig
-    from megatron.training.common_config import ProfilingConfig
+    from megatron.training.config import TrainingConfig
+    from megatron.training.config import ProfilingConfig
 
     prof_factory = ArgumentGroupFactory(ProfilingConfig, exclude=["record_shapes", "nvtx_ranges"])
     prof_group = prof_factory.build_group(parser, "profiling")
@@ -2309,7 +2446,7 @@ def _add_training_args(parser):
 
 
 def _add_rerun_machine_args(parser):
-    from megatron.training.resilience_config import RerunStateMachineConfig
+    from megatron.training.config import RerunStateMachineConfig
 
     rerun_factory = ArgumentGroupFactory(RerunStateMachineConfig, exclude=["check_for_nan_in_loss"])
     group = rerun_factory.build_group(parser, "rerun engine")
@@ -2318,7 +2455,7 @@ def _add_rerun_machine_args(parser):
 
 
 def _add_initialization_args(parser):
-    from megatron.training.common_config import RNGConfig
+    from megatron.training.config import RNGConfig
 
     rng_factory = ArgumentGroupFactory(RNGConfig)
     group = rng_factory.build_group(parser, "RNG and initialization")
@@ -2330,7 +2467,7 @@ def _add_initialization_args(parser):
 
 
 def _add_learning_rate_args(parser):
-    from megatron.training.training_config import SchedulerConfig
+    from megatron.training.config import SchedulerConfig
 
     sched_factory = ArgumentGroupFactory(SchedulerConfig, exclude=["no_weight_decay_cond_type"])
     group = sched_factory.build_group(parser, title="learning rate and weight decay")
@@ -2355,7 +2492,7 @@ def _add_learning_rate_args(parser):
 
 
 def _add_checkpointing_args(parser):
-    from megatron.training.training_config import CheckpointConfig
+    from megatron.training.config import CheckpointConfig
 
     ckpt_factory = ArgumentGroupFactory(CheckpointConfig, exclude=["most_recent_k", "save_tokenizer_assets", "save_optim", "save_rng", "load_optim", "load_rng"])
     group = ckpt_factory.build_group(parser, "checkpointing")
@@ -2416,7 +2553,7 @@ def _add_mixed_precision_args(parser):
 
 
 def _add_distributed_args(parser):
-    from megatron.training.common_config import DistributedInitConfig
+    from megatron.training.config import DistributedInitConfig
 
     dist_init_factory = ArgumentGroupFactory(DistributedInitConfig)
     group = dist_init_factory.build_group(parser, "distributed init")
@@ -2535,7 +2672,7 @@ def _add_distributed_args(parser):
 
 
 def _add_validation_args(parser):
-    from megatron.training.training_config import ValidationConfig
+    from megatron.training.config import ValidationConfig
 
     val_factory = ArgumentGroupFactory(ValidationConfig)
     group = val_factory.build_group(parser, "validation")
@@ -2913,6 +3050,13 @@ def _add_mla_args(parser):
                        help="Mscale all dimensions for YaRN RoPE in multi-latent attention.")
     group.add_argument('--cache-mla-latents', action='store_true', default=False,
                        help="If set caches the mla down projected latents with mla flash decode.")
+    group.add_argument(
+        '--mla-down-proj-fusion',
+        action='store_true',
+        default=False,
+        help="Enable fused q/kv down-projection and fused input layernorm when backend supports. "
+             "Otherwise fall back to the unfused MLA.",
+    )
 
     return parser
 
@@ -2992,32 +3136,28 @@ def _add_experimental_args(parser):
                        'To use local spec specify local as the argument.'
                        'For more details, see the model class, '
                        '`transformer_block.py`, or `transformer_layer.py`')
-    group.add_argument('--hybrid-attention-ratio', type=float, default=0.0,
-                       help='Ratio of attention layers to total layers, in the '
-                       'range [0.0, 1.0].')
-    group.add_argument('--hybrid-mlp-ratio', type=float, default=0.0,
-                       help='Ratio of mlp layers to total layers, in the '
-                       'range [0.0, 1.0].')
+    group.add_argument('--hybrid-layer-pattern', type=str, default=None,
+                       help='Specify a hybrid layer pattern using M (mamba), * (attention), '
+                       '- (mlp), E (moe). Use | to define pipeline stage boundaries for '
+                       'flexible virtual pipeline parallel (fVPP). Use / to separate MTP '
+                       'patterns. Example: "M-M-|M-M*-|M-M-|M-M*-" or "M-M-|M-M*-/MM/MM". '
+                       'When this flag is used, it is the sole indicator that a hybrid model '
+                       'is being run.')
     group.add_argument('--hybrid-override-pattern', type=str, default=None,
-                       help='Force a specific hybrid layer pattern. The value'
-                            'should be a string of characters chosen from'
-                            'core.ssm.mamba_hybrid_layer_allocation.Symbols.'
-                            'If a value greater than 0.0 is supplied to any of the '
-                            'hybrid ratio arguments, then the number of each type'
-                            'of layer in the override pattern must match number in'
-                            'the overidden pattern')
+                       help='Deprecated. Use --hybrid-layer-pattern instead. '
+                       'If specified, its value will be forwarded to --hybrid-layer-pattern.')
     group.add_argument('--yaml-cfg', type=str, default=None,
                        help = 'Config file to add additional arguments')
 
-    # Args of precision-aware optimizer
+    # Args of precision-aware optimizer.
     group.add_argument('--use-precision-aware-optimizer', action='store_true',
                        help='Use the precision-aware optimizer in TransformerEngine, which allows '
                        'setting the main params and optimizer states to lower precision, such as '
                        'fp16, bf16 and fp8.')
     group.add_argument('--main-grads-dtype', default='fp32', choices=['fp32', 'bf16'],
-                       help='Dtype of main grads when enabling precision-aware-optimizer')
+                       help='Dtype of main grads when enabling precision-aware-optimizer.')
     group.add_argument('--main-params-dtype', default='fp32', choices=['fp32', 'fp16'],
-                       help='Dtype of main params when enabling precision-aware-optimizer')
+                       help='Dtype of main params when enabling precision-aware-optimizer.')
     group.add_argument('--exp-avg-dtype', default='fp32', choices=['fp32', 'fp16', 'bf16', 'fp8'],
                        help='Dtype of exp_avg (1st moment in adam optimizer) when enabling '
                             'precision-aware-optimizer. This dtype is used for storing the '
@@ -3028,6 +3168,18 @@ def _add_experimental_args(parser):
                             'precision-aware-optimizer. This dtype is used for storing the '
                             'optimizer state in memory during training but does not affect '
                             'the precision in the kernel computation.')
+
+    # Megatron-FSDP Arguments
+    group.add_argument('--megatron-fsdp-main-params-dtype', default='fp32', choices=['fp32', 'bf16', 'fp16'],
+                       help="Data type for the main weight buffer utilized for distributed optimization "
+                            "and quantization with Megatron-FSDP.")
+    group.add_argument('--megatron-fsdp-main-grads-dtype', default='fp32', choices=['fp32', 'bf16', 'fp16'],
+                       help="Data type for the main gradient buffer utilized for distributed optimization "
+                            "with Megatron-FSDP.")
+    group.add_argument("--megatron-fsdp-grad-comm-dtype", default='fp32', choices=['fp32', 'fp16', 'bf16'],
+                        help="When using Megatron-FSDP, this controls the data-type used when communicating "
+                             "model gradients during FSDP.")
+    
     return parser
 
 
diff --git a/megatron/training/async_utils.py b/megatron/training/async_utils.py
index 6cf0c1e5586..c49b82af6dc 100644
--- a/megatron/training/async_utils.py
+++ b/megatron/training/async_utils.py
@@ -5,9 +5,13 @@
 the async checkpoint save calls.
 """
 import logging
+import time
 
 from megatron.core.dist_checkpointing.strategies.async_utils import AsyncCallsQueue, AsyncRequest
-from megatron.core.dist_checkpointing.strategies.filesystem_async import _results_queue
+from megatron.core.dist_checkpointing.strategies.cached_metadata_filesystem_reader import (
+    CachedMetadataFileSystemReader,
+)
+from megatron.core.dist_checkpointing.strategies.filesystem_async import _results_queue, get_write_results_queue
 from megatron.training import get_args
 from megatron.training.utils import print_rank_0
 
@@ -18,11 +22,26 @@
 _async_calls_queue = AsyncCallsQueue()
 
 
-def init_persistent_async_worker():
+def init_persistent_async_worker(rank: int, mp_mode: str = 'spawn'):
     global _async_calls_queue
+    args = get_args()
     # Recreate the async_calls_queue for persistent worker
     # This duplicate step is for backward compatiblity
+    time_start = time.time()
+    if rank == 0:
+        print(f"init_persistent_async_worker: {rank}, Starting Async Caller", flush=True)
     _async_calls_queue = AsyncCallsQueue(persistent=True)
+    # initialize the persistent caller with QoS priorities from args
+    AsyncCallsQueue.warmup_persistent_caller(
+        rank,
+        mp_mode,
+        cpu_priority=args.async_ckpt_cpu_priority,
+        io_priority=args.async_ckpt_io_priority,
+    )
+    # initialize ckpt write results queue
+    get_write_results_queue('fork')
+    if rank == 0:
+        print(f"init_persistent_async_worker: rank {rank}, Async Caller Started in {time.time() - time_start} seconds", flush=True)
 
 
 def schedule_async_save(async_request: AsyncRequest):
@@ -35,7 +54,7 @@ def schedule_async_save(async_request: AsyncRequest):
 
 
 def maybe_finalize_async_save(blocking: bool = False, terminate=False):
-    """Finalizes active async save calls.
+    """Finalizes active async save calls and cleans up deletion processes.
 
     Args:
         blocking (bool, optional): if True, will wait until all active requests
@@ -53,6 +72,11 @@ def maybe_finalize_async_save(blocking: bool = False, terminate=False):
 
     _async_calls_queue.maybe_finalize_async_calls(blocking, no_dist=False)
 
+    # Clean up finished deletion processes to prevent zombies
+    # Import here to avoid circular dependency
+    from .checkpointing import finalize_deletion_processes
+    finalize_deletion_processes(blocking=blocking or terminate)
+
     if terminate:
         _async_calls_queue.close()
 
@@ -68,6 +92,7 @@ def is_empty_async_queue() -> bool:
 
 def reset_persistent_async_worker():
     global _async_calls_queue, _results_queue
+    
     if _async_calls_queue is not None:
         _async_calls_queue.close(abort=True)
         del _async_calls_queue
@@ -76,3 +101,4 @@ def reset_persistent_async_worker():
         del _results_queue
     _results_queue = None
     _async_calls_queue = None
+    CachedMetadataFileSystemReader.clear_metadata_cache()
diff --git a/megatron/training/checkpointing.py b/megatron/training/checkpointing.py
index d9204f9007d..497b0bb844f 100644
--- a/megatron/training/checkpointing.py
+++ b/megatron/training/checkpointing.py
@@ -3,6 +3,7 @@
 """Input/output checkpointing."""
 
 import contextlib
+import multiprocessing
 import os
 import random
 import shutil
@@ -39,6 +40,7 @@
 from ..core.dist_checkpointing.utils import _clean_metadata_for_serialization
 from . import ft_integration, wandb_utils
 from .async_utils import is_empty_async_queue, schedule_async_save
+from megatron.core.dist_checkpointing.strategies.async_utils import AsyncRequest, _disable_gc
 from .global_vars import get_args
 from .one_logger_utils import on_save_checkpoint_start, on_save_checkpoint_success
 from .utils import append_to_progress_log, is_last_rank, print_rank_0, unwrap_model
@@ -70,6 +72,32 @@
 logger = getLogger(__name__)
 _NON_PERSISTENT_CKPT_SUBDIR = 'non_persistent'
 
+# Track deletion processes to prevent zombies
+_deletion_processes = []
+
+def finalize_deletion_processes(blocking=False):
+    """Clean up deletion processes to prevent zombie processes.
+
+    Args:
+        blocking (bool): If True, waits for all deletion processes to complete.
+                        If False, only joins processes that have already finished.
+
+    Note: Deletion processes are daemon processes (auto-terminate if parent dies),
+    but we still need to join() them to reap zombie processes when they complete normally.
+    The daemon flag and join() serve different purposes:
+    - daemon=True: Auto-terminate if parent process dies abruptly
+    - join(): Reap zombie processes after normal completion
+    """
+    global _deletion_processes
+    finished = []
+    for proc in _deletion_processes:
+        if not proc.is_alive() or blocking:
+            logger.debug(f"Joining deletion process {proc.pid} (blocking={blocking}, is_alive={proc.is_alive()})")
+            proc.join()
+            finished.append(proc)
+    for proc in finished:
+        _deletion_processes.remove(proc)
+
 def set_checkpoint_version(value):
     global _CHECKPOINT_VERSION
     if _CHECKPOINT_VERSION is not None:
@@ -360,27 +388,8 @@ def get_rng_state(ckpt_format: str, tp_group: torch.distributed.ProcessGroup, pp
         pp_size = get_pg_size(pp_group)
         tp_rank = get_pg_rank(tp_group)
         tp_size = get_pg_size(tp_group)
-        ep_size = mpu.get_expert_model_parallel_world_size()
-
-        if ep_size > 1:
-            # Shard RNG by PP, TP, DP when using expert parallelism.
-            dp_rank = mpu.get_data_parallel_rank(with_context_parallel=True)
-            dp_size = mpu.get_data_parallel_world_size(with_context_parallel=True)
-            rng_state_list = ShardedObject(
-                'rng_state',
-                rng_state_list,
-                (pp_size, tp_size, dp_size),
-                (pp_rank, tp_rank, dp_rank),
-                replica_id=0,
-            )
-        else:
-            rng_state_list = ShardedObject(
-                'rng_state',
-                rng_state_list,
-                (pp_size, tp_size),
-                (pp_rank, tp_rank),
-                replica_id=mpu.get_data_parallel_rank(with_context_parallel=True),
-            )
+        rng_state_list = ShardedObject('rng_state', rng_state_list, (pp_size, tp_size), (pp_rank, tp_rank),
+                                       replica_id=mpu.get_data_parallel_rank(with_context_parallel=True))
     elif ckpt_format == "fsdp_dtensor":
         pp_rank = mpu.get_pipeline_model_parallel_rank()
         tp_rank = mpu.get_tensor_model_parallel_rank()
@@ -743,22 +752,6 @@ def iter_finalize_fn():
                     append_to_progress_log(f'Saved async checkpoint\tIteration: {iteration}',
                                            barrier=False)
 
-                def delete_checkpoint(args, iteration_to_delete):
-                    checkpoint_name = get_checkpoint_name(args.save, iteration=iteration_to_delete,
-                                                          return_base_dir=True)
-                    try:
-                        shutil.rmtree(checkpoint_name)  # TODO: Make this work with MSC remote paths?
-                        print_rank_0(f"  [{datetime.now().strftime('%Y-%m-%d %H:%M:%S.%f')}] successfully "
-                                     f"deleted checkpoint from iteration {iteration_to_delete:7d} "
-                                     f"at {args.save}")
-                        if args.log_progress:
-                            append_to_progress_log(f'Deleted checkpoint\tIteration: {iteration_to_delete}', barrier=False)
-                    except Exception as e:
-                        print_rank_0(f'  encountered exception "{e}" when trying to delete checkpoint from '
-                                     f'iteration {iteration_to_delete:7d} at {args.save}')
-                        # Any exception encountered in checkpoint deletion can be ignored and is not fatal.
-                        pass
-
                 if save_retain_interval is not None:
                     if prev_iteration > 0 and prev_iteration != iteration and prev_iteration % save_retain_interval != 0:
                         checkpoint_name = get_checkpoint_name(args.save, iteration=prev_iteration,
@@ -769,7 +762,23 @@ def delete_checkpoint(args, iteration_to_delete):
                                          f'at {args.save} since it is a symbolic link')
                         else:
                             # Asynchronous version of delete_checkpoint(args, iteration_to_delete=prev_iteration).
-                            threading.Thread(target=delete_checkpoint, args=(args, prev_iteration,)).start()
+                            # Use multiprocessing to delete checkpoint in background
+                            if args.async_save:
+                                # Clean up any finished deletion processes before starting a new one
+                                finalize_deletion_processes(blocking=False)
+                                ctx = multiprocessing.get_context('fork')
+                                delete_process = ctx.Process(
+                                    target=_async_delete_checkpoint_impl,
+                                    args=(args.save, prev_iteration, args.log_progress, True,
+                                          args.async_ckpt_cpu_priority, args.async_ckpt_io_priority),
+                                    daemon=True
+                                )
+                                delete_process.start()
+                                # Track the process so we can join it later to prevent zombies
+                                _deletion_processes.append(delete_process)
+                            else:
+                                th = threading.Thread(target=_async_delete_checkpoint_impl, args=(args.save, prev_iteration, args.log_progress))
+                                th.start()
 
         if args.async_save:
             assert async_save_request is not None
@@ -813,6 +822,42 @@ def wandb_finalize_fn():
 
     ft_integration.on_checkpointing_end(is_async_finalization=False)
 
+@_disable_gc()
+def _async_delete_checkpoint_impl(save_path, iteration_to_delete, log_progress=False, lower_priority=False,
+                                  cpu_priority=None, io_priority=None):
+    """Module-level function for async checkpoint deletion.
+    
+    This function can be pickled and executed by the async worker process.
+    Note: This is only called from rank 0, so we use regular print() instead of print_rank_0()
+    since torch.distributed won't be initialized in the async worker process.
+    
+    Args:
+        save_path (str): Path to the checkpoints directory
+        iteration_to_delete (int): Iteration number of checkpoint to delete
+        log_progress (bool): Whether to log progress
+        lower_priority (bool): If True, set process QoS (e.g. nice, ionice) so deletion doesn't contend with training.
+        cpu_priority (int): Nice value for CPU when lower_priority is True (from args.async_ckpt_cpu_priority).
+        io_priority (int): I/O class when lower_priority is True (from args.async_ckpt_io_priority).
+    """
+    if lower_priority:
+        from megatron.core.dist_checkpointing.strategies.async_utils import _set_process_qos
+        _set_process_qos(cpu_priority=cpu_priority, io_priority=io_priority)
+
+    checkpoint_name = get_checkpoint_name(save_path, iteration=iteration_to_delete,
+                                         return_base_dir=True)
+    try:
+        shutil.rmtree(checkpoint_name)  # TODO: Make this work with MSC remote paths?
+        print(f'  successfully deleted checkpoint from iteration {iteration_to_delete:7d} '
+              f'at {save_path}', flush=True)
+        if log_progress:
+            append_to_progress_log(f'Deleted checkpoint\tIteration: {iteration_to_delete}', barrier=False)
+    except Exception as e:
+        print(f'  encountered exception "{e}" when trying to delete checkpoint from '
+              f'iteration {iteration_to_delete:7d} at {save_path}', flush=True)
+        # Any exception encountered in checkpoint deletion can be ignored and is not fatal.
+        pass
+
+
 def cleanup_old_non_persistent_checkpoint(save_dir, leave_ckpt_num=1, do_async=False):
     if torch.distributed.is_initialized() and torch.distributed.get_rank() != 0:
         return
@@ -1130,7 +1175,9 @@ def _load_global_dist_base_checkpoint(
         )
 
     checkpoint_name = get_checkpoint_name(load_dir, iteration, release, return_base_dir=True)
-    load_strategy = get_default_load_sharded_strategy(checkpoint_name)
+    load_strategy = get_default_load_sharded_strategy(
+        checkpoint_name, cache_metadata=args.ckpt_assume_constant_structure
+    )
     # NOTE: `args.ckpt_fully_parallel_load` applies to both persistent and non-persistent checkpoints.
     if args.ckpt_fully_parallel_load:
         if args.ckpt_fully_parallel_load_process_group == 'dp':
@@ -1393,6 +1440,18 @@ def load_args_from_checkpoint(
             checkpoint_args, 'add_bias_linear', not getattr(checkpoint_args, 'disable_bias_linear')
         )
 
+    # Backward compat: old checkpoints have hybrid_override_pattern but not hybrid_layer_pattern
+    if (getattr(checkpoint_args, 'hybrid_override_pattern', None) is not None
+            and getattr(checkpoint_args, 'hybrid_layer_pattern', None) is None):
+        setattr(
+            checkpoint_args, 'hybrid_layer_pattern',
+            getattr(checkpoint_args, 'hybrid_override_pattern'),
+        )
+        # num_layers is now derived from hybrid_layer_pattern in validate_args, and should not be
+        # set at the same time as hybrid_layer_pattern.
+        if hasattr(checkpoint_args, 'num_layers'):
+            setattr(checkpoint_args, 'num_layers', None)
+
     def _set_arg(arg_name, old_arg_name=None, force=False):
         if not force and getattr(args, arg_name, None) is not None:
             return
@@ -1435,18 +1494,15 @@ def _set_arg(arg_name, old_arg_name=None, force=False):
     _set_arg('attention_dropout', force=True)
     _set_arg('hidden_dropout', force=True)
 
-    _set_arg('hybrid_override_pattern', force=True)
-
     # Legacy MTP pattern for old checkpoints
     _set_arg('mtp_hybrid_override_pattern', force=True)
     _set_arg('mtp_num_layers', force=True)
     _set_arg('mtp_use_repeated_layer', force=True)
 
     _set_arg('spec', force=True)
-    _set_arg('hybrid_attention_ratio', force=True)
-    _set_arg('hybrid_mlp_ratio', force=True)
 
     _set_arg('num_experts', force=True)
+    _set_arg('mtp_num_layers', force=True)
     _set_arg('moe_layer_freq', force=True)
     if getattr(checkpoint_args, 'num_experts', None) is not None:
         _set_arg('moe_ffn_hidden_size', force=True)
@@ -1466,7 +1522,9 @@ def _set_arg(arg_name, old_arg_name=None, force=False):
     _set_arg('mamba_head_dim', force=True)
     _set_arg('mamba_num_groups', force=True)
     _set_arg('mamba_num_heads', force=True)
-    _set_arg('is_hybrid_model', force=True)
+    # We need to be able to override hybrid_layer_pattern from the command-line so that different
+    # pipelining can be specified when re-loading a model (e.g. for inference or post-training).
+    _set_arg('hybrid_layer_pattern')
 
     # Heterogeneous args.
     _set_arg('heterogeneous_layers_config_path', force=True)
diff --git a/megatron/training/config/__init__.py b/megatron/training/config/__init__.py
new file mode 100644
index 00000000000..3d346ddd8fe
--- /dev/null
+++ b/megatron/training/config/__init__.py
@@ -0,0 +1,18 @@
+# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
+
+from megatron.training.config.common_config import (
+    RNGConfig,
+    ProfilingConfig,
+    DistributedInitConfig,
+)
+from megatron.training.config.training_config import (
+    TrainingConfig,
+    ValidationConfig,
+    SchedulerConfig,
+    LoggerConfig,
+    CheckpointConfig,
+)
+from megatron.training.config.resilience_config import (
+    RerunStateMachineConfig,
+    StragglerDetectionConfig,
+)
diff --git a/megatron/training/common_config.py b/megatron/training/config/common_config.py
similarity index 85%
rename from megatron/training/common_config.py
rename to megatron/training/config/common_config.py
index eb7e6313dec..ebf7afe96bc 100644
--- a/megatron/training/common_config.py
+++ b/megatron/training/config/common_config.py
@@ -131,5 +131,23 @@ class DistributedInitConfig:
     distributed_timeout_seconds_after_init: int | None = None
     """Timeout in seconds for process groups after initialization. This timeout is applied to all process groups after initialization and the first iteration completes."""
 
+    flight_recorder_dump_path: str | None = None
+    """Path for NCCL flight recorder trace dumps. Sets TORCH_FR_DUMP_TEMP_FILE and TORCH_NCCL_DEBUG_INFO_TEMP_FILE env variables before distributed init."""
+
+    flight_recorder_trace_buffer_size: int = 2000
+    """Size of the NCCL flight recorder trace buffer (TORCH_NCCL_TRACE_BUFFER_SIZE)."""
+
+    flight_recorder_dump_on_timeout: bool = True
+    """Dump flight recorder traces on NCCL timeout (TORCH_NCCL_DUMP_ON_TIMEOUT)."""
+
+    flight_recorder_include_stack_trace: bool = False
+    """Include stack traces in flight recorder dumps (TORCH_INCLUDE_STACK_TRACE)."""
+
+    flight_recorder_include_only_active: bool = True
+    """Include only active operations in flight recorder dumps (TORCH_INCLUDE_ONLY_ACTIVE)."""
+
+    flight_recorder_extra_dump_on_exec: bool = True
+    """Enable extra flight recorder dump on execution (TORCH_NCCL_EXTRA_DUMP_ON_EXEC)."""
+
     disable_jit_fuser: bool = False
     """Disable the JIT fuser."""
diff --git a/megatron/training/resilience_config.py b/megatron/training/config/resilience_config.py
similarity index 100%
rename from megatron/training/resilience_config.py
rename to megatron/training/config/resilience_config.py
diff --git a/megatron/training/training_config.py b/megatron/training/config/training_config.py
similarity index 97%
rename from megatron/training/training_config.py
rename to megatron/training/config/training_config.py
index 526a2e7ee59..fcc875b62a6 100644
--- a/megatron/training/training_config.py
+++ b/megatron/training/config/training_config.py
@@ -1,7 +1,7 @@
 # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
 from dataclasses import dataclass, field
 import signal
-from typing import Literal
+from typing import Literal, Optional
 
 @dataclass(kw_only=True)
 class TrainingConfig:
@@ -446,6 +446,14 @@ class CheckpointConfig:
     worker thread/process for handling async saves. When disabled, uses temporal workers that are
     created and destroyed for each save operation."""
 
+    async_ckpt_cpu_priority: int = 10
+    """CPU nice value target (0-19, higher = lower priority) for the async checkpoint writer process.
+    If it exceeds 19, it will be set to 19. If the current nice value is greater than the target, it will be left unchanged.
+    Only applies when using persistent ckpt worker."""
+
+    async_ckpt_io_priority: Optional[int] = 3
+    """I/O scheduling class (0-3, 3=idle) for the async checkpoint writer process."""
+
     ckpt_fully_parallel_load: bool = False
     """Apply full load parallelization across DP for distributed checkpoints."""
 
diff --git a/megatron/training/initialize.py b/megatron/training/initialize.py
index a5c757ca41b..4cd64190dba 100644
--- a/megatron/training/initialize.py
+++ b/megatron/training/initialize.py
@@ -83,9 +83,6 @@ def initialize_megatron(
         load_args_from_checkpoint(args, load_arg='pretrained_checkpoint')
         load_args_from_checkpoint(args)
 
-    if args.async_save and args.use_persistent_ckpt_worker:
-        init_persistent_async_worker()
-
     if args.yaml_cfg is not None:
         args = validate_yaml(args, args_defaults)
     else:
@@ -98,6 +95,9 @@ def initialize_megatron(
     # set logging level
     setup_logging()
 
+    if args.async_save and args.use_persistent_ckpt_worker:
+        init_persistent_async_worker(args.rank, 'forkserver')
+
     # init rerun state
     def state_save_func():
         return {'rng_tracker_states': tensor_parallel.get_cuda_rng_tracker().get_states()}
@@ -343,6 +343,41 @@ def _initialize_distributed(get_embedding_ranks, get_position_embedding_ranks, s
         if args.cuda_graph_impl == "transformer_engine":
             torch.cuda.set_stream(torch.cuda.Stream())
 
+        # Set flight recorder env vars if specified.
+        # Priority: pre-existing environment variable > MLM argument.
+        # All vars follow the same setdefault semantics: if already set in the
+        # environment we warn and keep the user's value; otherwise we apply the
+        # value derived from the MLM argument / flag.
+        # The block is also triggered when either path env var is already set
+        # so that the remaining defaults are applied consistently.
+        _fr_path = (
+            args.flight_recorder_dump_path
+            or os.environ.get('TORCH_FR_DUMP_TEMP_FILE')
+            or os.environ.get('TORCH_NCCL_DEBUG_INFO_TEMP_FILE')
+        )
+        if _fr_path is not None:
+            _fr_env_defaults = {
+                'TORCH_FR_DUMP_TEMP_FILE': _fr_path,
+                'TORCH_NCCL_DEBUG_INFO_TEMP_FILE': _fr_path,
+                'TORCH_NCCL_TRACE_BUFFER_SIZE': str(args.flight_recorder_trace_buffer_size),
+                'TORCH_NCCL_DUMP_ON_TIMEOUT': str(int(args.flight_recorder_dump_on_timeout)),
+                'TORCH_INCLUDE_STACK_TRACE': str(int(args.flight_recorder_include_stack_trace)),
+                'TORCH_INCLUDE_ONLY_ACTIVE': str(int(args.flight_recorder_include_only_active)),
+                'TORCH_NCCL_EXTRA_DUMP_ON_EXEC': str(int(args.flight_recorder_extra_dump_on_exec)),
+            }
+            for _var, _default in _fr_env_defaults.items():
+                if _var in os.environ:
+                    warn_rank_0(
+                        f"Flight recorder: environment variable {_var} is already set to "
+                        f"'{os.environ[_var]}'; ignoring config value '{_default}'."
+                    )
+                else:
+                    os.environ[_var] = _default
+            print_rank_0(
+                "Flight recorder env vars:\n"
+                + "\n".join(f"  {k}={os.environ[k]}" for k in _fr_env_defaults)
+            )
+
         # Call the init process
         init_process_group_kwargs = {
             'backend': args.distributed_backend,
diff --git a/megatron/training/theoretical_memory_usage.py b/megatron/training/theoretical_memory_usage.py
index 7d4043b62d7..d3b40e8d533 100644
--- a/megatron/training/theoretical_memory_usage.py
+++ b/megatron/training/theoretical_memory_usage.py
@@ -4,7 +4,7 @@
 
 
 import math
-from .utils import print_rank_0
+from .utils import is_hybrid_model, print_rank_0
 
 NUM_BYTES_IN_MEGABYTE = 1024 * 1024
 
@@ -338,7 +338,7 @@ def compute_activation_memory_without_sp(args, num_microbatches, verbose=False):
 
 
 def report_theoretical_memory(args, num_microbatches=None, verbose=False):
-    if args.is_hybrid_model:
+    if is_hybrid_model(args):
         print("Theoretical memory footprints not yet supported for hybrid Mamba-Transformer models.")
         return
 
diff --git a/megatron/training/training.py b/megatron/training/training.py
index c5715e96aed..aeda6bcfa9d 100644
--- a/megatron/training/training.py
+++ b/megatron/training/training.py
@@ -101,7 +101,7 @@ def set_startup_timestamps(program_start=None, main_entry=None):
     is_vp_first_stage,
     is_vp_last_stage,
 )
-from megatron.core.optimizer import get_standard_config_overrides
+from megatron.core.optimizer import get_mup_config_overrides, get_standard_config_overrides
 from megatron.training.checkpointing import load_checkpoint
 from megatron.training.checkpointing import save_checkpoint, save_grads
 from megatron.training.checkpointing import checkpoint_exists
@@ -140,7 +140,7 @@ def set_startup_timestamps(program_start=None, main_entry=None):
 from megatron.training.initialize import initialize_megatron
 from megatron.training.initialize import write_args_to_tensorboard
 from megatron.training.initialize import set_jit_fusion_options
-from megatron.training.utils import get_batch_on_this_cp_rank, get_batch_on_this_tp_rank
+from megatron.training.utils import get_batch_on_this_cp_rank, get_batch_on_this_tp_rank, is_hybrid_model
 from megatron.training.datasets.data_samplers import build_pretraining_data_loader
 from megatron.core.datasets.data_schedule import DynamicCPDataLoaderWrapper
 from megatron.core.optimizer_param_scheduler import OptimizerParamScheduler
@@ -614,9 +614,14 @@ def transformer_flops():
         return total_floating_point_operations
 
     # Main entrypoint for FLOPs calculation.
-    if args.is_hybrid_model:
+    if is_hybrid_model(args):
         # Calculate the number of each type of layer.
-        num_attn_layers, num_mamba_layers, num_mlp_layers, num_moe_layers = calculate_layer_counts()
+        from operator import itemgetter
+
+        from megatron.core.ssm.mamba_hybrid_layer_allocation import Symbols, get_hybrid_layer_counts
+        num_attn_layers, num_mamba_layers, num_mlp_layers, num_moe_layers = itemgetter(
+            Symbols.ATTENTION, Symbols.MAMBA, Symbols.MLP, Symbols.MOE
+        )(get_hybrid_layer_counts(args.hybrid_layer_pattern))
 
         mtp_num_layers = args.mtp_num_layers
         if mtp_num_layers is None:
@@ -1094,11 +1099,15 @@ def pretrain(
         # Add job name to the wandb config to make it easier to run more singleton dependency jobs.
         wandb_writer.config.update({'slurm_job_name': os.getenv("SLURM_JOB_NAME", "N/A")})
 
-    if not args.skip_train:
-        print_rank_0('training ...')
+    if not args.skip_train or args.perform_rl_step:
+        if args.skip_train:
+            print_rank_0('RL inference-only mode (--skip-train --perform-rl-step) ...')
+        else:
+            print_rank_0('training ...')
 
         iteration = 0
-        if args.do_train and args.train_iters > 0:
+        args.curr_iteration = iteration
+        if args.do_train and (args.train_iters or 0) > 0:
             iteration, num_floating_point_operations_so_far = train(
                 forward_step_func,
                 model,
@@ -1115,7 +1124,7 @@ def pretrain(
 
         print_datetime('after training is done')
 
-        if args.save and iteration != 0 and iteration % args.save_interval != 0:
+        if not args.skip_train and args.save and iteration != 0 and iteration % args.save_interval != 0:
             save_checkpoint(
                 iteration,
                 model,
@@ -1138,7 +1147,7 @@ def pretrain(
 
     if args.do_valid:
         prefix = f'iteration {iteration} on validation set'
-        if getattr(args, 'perform_rl_step', False):
+        if args.perform_rl_step:
             rl_eval_model = model
             rl_training_model = None
             if inference_model is not None:
@@ -1193,7 +1202,7 @@ def pretrain(
         {'app_finish_time': one_logger_utils.get_timestamp_in_ms()}
     )
 
-    if getattr(args, 'perform_rl_step', False):
+    if args.perform_rl_step:
         rl_utils.rl_inference_interface_shutdown()
 
     ft_integration.shutdown()
@@ -1395,6 +1404,7 @@ def build_model():
             # Set bucket_size to infinity if overlap_grad_reduce is False.
             if not ddp_config.overlap_grad_reduce:
                 ddp_config.bucket_size = None
+
         # Setup stream for ddp initialization. The side-stream may be necessary for cuda graph
         #  capture support with DDP, but we sync it with the current stream to avoid races.
         ddp_stream = torch.cuda.Stream()
@@ -1402,15 +1412,23 @@ def build_model():
         ddp_stream.wait_stream(torch.cuda.current_stream())
         # Make ddp_stream start after whatever the default stream already queued
         with torch.cuda.stream(ddp_stream):
+            # To pass kwargs unique to specific DDP classes.
+            dp_init_kwargs = {}
+            if args.use_megatron_fsdp:
+                # Also pass the mixed-precision arguments for Megatron-FSDP only.
+                dp_init_kwargs["main_params_dtype"] = args.megatron_fsdp_main_params_dtype
+                dp_init_kwargs["main_grads_dtype"] = args.megatron_fsdp_main_grads_dtype
+                dp_init_kwargs["grad_comm_dtype"] = args.megatron_fsdp_grad_comm_dtype
+
             model = [
                 DP(
                     config=config,
                     ddp_config=ddp_config,
                     module=model_chunk,
-                    # Turn off bucketing for model_chunk 2 onwards, since communication for these
-                    # model chunks is overlapped with compute anyway.
-                    disable_bucketing=(model_chunk_idx > 0)
-                    or args.overlap_param_gather_with_optimizer_step,
+                    # Turn off bucketing for model_chunk 2 onwards, since communication
+                    # for these model chunks is overlapped with compute anyway.
+                    disable_bucketing=(model_chunk_idx > 0) or args.overlap_param_gather_with_optimizer_step,
+                    **dp_init_kwargs,
                 )
                 for (model_chunk_idx, model_chunk) in enumerate(model)
             ]
@@ -1508,16 +1526,35 @@ def setup_model_and_optimizer(
     timers = get_timers()
     one_logger = get_one_logger()
 
-    wrap_with_ddp = not args.skip_train
+    # Skip optimizer when not training. In RL inference-only mode (skip_train + perform_rl_step),
+    # --no-load-optim controls whether the optimizer is skipped (saving memory) or created
+    # (required for --rl-offload-optimizer-during-inference).
+    skip_optimizer = args.skip_train and (not args.perform_rl_step or args.no_load_optim)
+    wrap_with_ddp = not skip_optimizer
     model = get_model(model_provider_func, model_type, wrap_with_ddp=wrap_with_ddp)
     unwrapped_model = unwrap_model(model)
 
     one_logger and one_logger.log_metrics({"app_build_optimzer_start_time": one_logger_utils.get_timestamp_in_ms()})
-    if args.skip_train:
+    if skip_optimizer:
         optimizer, opt_param_scheduler = None, None
+        # In RL inference-only mode, train_iters must still be set despite having no optimizer.
+        if args.perform_rl_step:
+            update_train_iters(args)
     else:
         config, config_overrides = get_megatron_optimizer_config(args)
         config.timers = timers
+        if getattr(args, "use_mup", False):
+            model_config_source = (
+                unwrapped_model[0] if isinstance(unwrapped_model, list) else unwrapped_model
+            )
+            model_config = get_model_config(model_config_source)
+            mup_overrides = get_mup_config_overrides(
+                config=config,
+                mup_width_mult=model_config.mup_width_mult,
+                optimizer_type=config.optimizer,
+            )
+            if mup_overrides:
+                config_overrides = {**(config_overrides or {}), **mup_overrides}
 
         optimizer = get_megatron_optimizer(
             config,
@@ -1945,7 +1982,7 @@ def training_log(
             'forward-backward-send-forward-backward-recv',
         ])
     # Add timers from RL loop if needed.
-    if getattr(args, 'perform_rl_step', False):
+    if args.perform_rl_step:
         timers_to_log.extend(['rollout-collection', 'inference-setup', 'collect-rollouts', 'postrollout-gc-collect',
                               'sync-rollouts', 'prepare-data-for-update', 'compute-group-stats',
                               'prepare-trajectories', 'get-ltor-masks-and-position-ids', 'create-logprobs-dataloader',
@@ -2021,7 +2058,7 @@ def training_log(
             writer.add_scalar('params-norm vs samples', params_norm, args.consumed_train_samples)
             if wandb_writer:
                 wandb_writer.log({'params-norm': params_norm}, iteration)
-        if getattr(args, 'perform_rl_step', False):
+        if args.perform_rl_step:
             grpo_collection_iteration = iteration // (args.grpo_iterations * ( ( args.grpo_samples_per_iteration )// args.global_batch_size ))
             writer.add_scalar('grpo_collection_iteration', grpo_collection_iteration, iteration)
             if wandb_writer:
@@ -2056,8 +2093,13 @@ def training_log(
         if args.moe_z_loss_coeff is not None:
             track_names.append("z_loss")
 
-        if args.is_hybrid_model:
-            layers = args.hybrid_override_pattern.count('E')
+        if is_hybrid_model(args):
+            from operator import itemgetter
+
+            from megatron.core.ssm.mamba_hybrid_layer_allocation import (
+                Symbols, get_hybrid_layer_counts,
+            )
+            layers = itemgetter(Symbols.MOE)(get_hybrid_layer_counts(args.hybrid_layer_pattern))
         else:
             layers = args.num_layers
 
@@ -2147,14 +2189,15 @@ def training_log(
                 wandb_writer.log({'iter-energy/gpu': energy}, iteration)
                 wandb_writer.log({'power/gpu': power}, iteration)
         # Decoupled_learning_rate should be not None only on first and last pipeline stage.
-        log_string += f' learning rate: {learning_rate:.6E} |'
+        if learning_rate is not None:
+            log_string += f' learning rate: {learning_rate:.6E} |'
         log_string += f' global batch size: {batch_size:5d} |'
         for key in total_loss_dict:
             if key not in [advanced_iters_key, skipped_iters_key, nan_iters_key]:
                 avg = total_loss_dict[key].item() / float(
                     max(1, total_loss_dict[advanced_iters_key])
                 )
-                if avg > 0.0:
+                if avg >= 0.0:
                     log_string += ' {}: {:.6E} |'.format(key, avg)
                 if should_reset:
                     total_loss_dict[key] = torch.tensor([0.0], dtype=torch.float, device='cuda')
@@ -2269,6 +2312,10 @@ def save_checkpoint_and_time(
     timers = get_timers()
     energy_monitor = get_energy_monitor()
 
+    # Synchronize forward pre-hook state before checkpoint save to avoid race conditions
+    if should_disable_forward_pre_hook(args):
+        force_param_sync(model)
+
     # Stop timer to get accurate train interval time and exclude checkpointing duration
     timers('interval-time').stop()
     if args.log_energy:
@@ -2280,8 +2327,13 @@ def save_checkpoint_and_time(
 
     # Log E2E metrics before save-checkpoint
     one_logger_utils.track_e2e_metrics()
-    if should_disable_forward_pre_hook(args):
-        force_param_sync(model)
+    # Free overlap param-gather buffers and release cached GPU memory so
+    # that the async checkpoint worker process has enough GPU headroom for
+    # D2H tensor transfers.
+    for model_chunk in model:
+        if hasattr(model_chunk, 'free_overlap_buffers'):
+            model_chunk.free_overlap_buffers()
+    torch.cuda.empty_cache()
 
     global num_checkpoints_memory_reported, MAX_NUM_CHECKPOINTS_MEMORY_REPORTED
     should_report_memory = num_checkpoints_memory_reported < MAX_NUM_CHECKPOINTS_MEMORY_REPORTED
@@ -2527,47 +2579,54 @@ def train(
     args = get_args()
     timers = get_timers()
 
-    if getattr(args, 'perform_rl_step', False):
+    if args.perform_rl_step:
         assert has_rl_utils, "RL cannot run without the megatron.rl package"
 
     # Additional variable initialization for RL training
-    if getattr(args, 'perform_rl_step', False):
-        print_rank_0("> Loading pretrained checkpoint for reference weights in RL training...")
-        load, finetune, no_load_optim = args.load, args.finetune, args.no_load_optim
-        args.no_load_optim = True
-
-        # Load pretrained checkpoint
-        args.load = None
-        args.finetune = True
-        load_checkpoint(
-                model,
-                None,  # Don't load optimizer state
-                None,  # Don't load scheduler state
-                checkpointing_context=checkpointing_context,
-                skip_load_to_model_and_opt=HAVE_FSDP2
-                and getattr(args, "use_torch_fsdp2", False)
-                and args.ckpt_format == "torch_dist",
-            )
-        ref_state_dict = {k: (v.cpu() if v is not None else v) for k, v in model[0].state_dict().items()}
+    if args.perform_rl_step:
+        if args.skip_train:
+            # In inference-only mode, use current weights as reference.
+            print_rank_0("> RL inference-only: using current weights as reference.")
+            ref_state_dict = {
+                k: (v.cpu() if v is not None else v) for k, v in model[0].state_dict().items()
+            }
+        else:
+            print_rank_0("> Loading pretrained checkpoint for reference weights in RL training...")
+            load, finetune, no_load_optim = args.load, args.finetune, args.no_load_optim
+            args.no_load_optim = True
+
+            # Load pretrained checkpoint
+            args.load = None
+            args.finetune = True
+            load_checkpoint(
+                    model,
+                    None,  # Don't load optimizer state
+                    None,  # Don't load scheduler state
+                    checkpointing_context=checkpointing_context,
+                    skip_load_to_model_and_opt=HAVE_FSDP2
+                    and getattr(args, "use_torch_fsdp2", False)
+                    and args.ckpt_format == "torch_dist",
+                )
+            ref_state_dict = {k: (v.cpu() if v is not None else v) for k, v in model[0].state_dict().items()}
 
-        # Reload RL training checkpoint weights
-        args.load = load
-        args.finetune = finetune
-        print_rank_0("> Reloading RL training checkpoint...")
-        load_checkpoint(
-                model,
-                None,
-                None,
-                checkpointing_context=checkpointing_context,
-                skip_load_to_model_and_opt=HAVE_FSDP2
-                and getattr(args, "use_torch_fsdp2", False)
-                and args.ckpt_format == "torch_dist",
-            )
+            # Reload RL training checkpoint weights
+            args.load = load
+            args.finetune = finetune
+            print_rank_0("> Reloading RL training checkpoint...")
+            load_checkpoint(
+                    model,
+                    None,
+                    None,
+                    checkpointing_context=checkpointing_context,
+                    skip_load_to_model_and_opt=HAVE_FSDP2
+                    and getattr(args, "use_torch_fsdp2", False)
+                    and args.ckpt_format == "torch_dist",
+                )
 
-        args.no_load_optim = no_load_optim
+            args.no_load_optim = no_load_optim
 
     # IMPORTANT FIX: For RL training, reinitialize the microbatch calculator with the correct configuration
-    if getattr(args, 'perform_rl_step', False):
+    if args.perform_rl_step:
         print_rank_0("> Reinitializing microbatch calculator for GRPO training...")
         from megatron.core.num_microbatches_calculator import (
             destroy_num_microbatches_calculator,
@@ -2640,7 +2699,7 @@ def train(
     num_floating_point_operations_so_far = args.num_floating_point_operations_so_far
 
     # Setup some training config params.
-    config.grad_scale_func = optimizer.scale_loss
+    config.grad_scale_func = optimizer.scale_loss if optimizer is not None else None
     config.timers = timers
     if isinstance(model[0], (megatron_FSDP, DDP)) and args.overlap_grad_reduce:
         assert config.no_sync_func is None, (
@@ -2892,7 +2951,10 @@ def trace_handler(p):
         # For GRPO, we keep the data for a few epochs. DeepSeekMath paper calls this number $\mu$.
         # It is similar to a PPO epoch.
 
-        if getattr(args, 'perform_rl_step', False):
+        if args.perform_rl_step:
+            if optimizer is None:
+                # Release stale CUDA cached memory before inference.
+                torch.cuda.empty_cache()
             with torch.no_grad():
                 train_data_iterator = rl_utils.get_grpo_data_iterator(
                     model, inference_model, optimizer, iteration, ref_state_dict,
@@ -2908,22 +2970,35 @@ def trace_handler(p):
                 # we use previously-generated data for an update.
                 buffered_rollouts = train_data_iterator
 
-        ft_integration.on_training_step_start()
-        (
-            loss_dict,
-            skipped_iter,
-            should_checkpoint,
-            should_exit,
-            exit_code,
-            grad_norm,
-            num_zeros_in_grad,
-            max_attention_logit,
-            seqlen_sum_this_global_batch, 
-            seqlen_squared_sum_this_global_batch,
-        ) = train_step(
-            forward_step_func, train_data_iterator, model, optimizer, opt_param_scheduler, config, forward_backward_func, iteration=iteration
-        )
-        ft_integration.on_training_step_end()
+        if args.skip_train:
+            # RL inference-only mode: skip gradient updates, just collect rollouts.
+            loss_dict = {}
+            skipped_iter = 0
+            should_checkpoint = False
+            should_exit = False
+            exit_code = 0
+            grad_norm = 0.0
+            num_zeros_in_grad = 0
+            max_attention_logit = None
+            seqlen_sum_this_global_batch = 0
+            seqlen_squared_sum_this_global_batch = 0
+        else:
+            ft_integration.on_training_step_start()
+            (
+                loss_dict,
+                skipped_iter,
+                should_checkpoint,
+                should_exit,
+                exit_code,
+                grad_norm,
+                num_zeros_in_grad,
+                max_attention_logit,
+                seqlen_sum_this_global_batch,
+                seqlen_squared_sum_this_global_batch,
+            ) = train_step(
+                forward_step_func, train_data_iterator, model, optimizer, opt_param_scheduler, config, forward_backward_func, iteration=iteration
+            )
+            ft_integration.on_training_step_end()
         if should_checkpoint:
             save_checkpoint_and_time(
                 iteration,
@@ -2980,7 +3055,7 @@ def trace_handler(p):
                     if pad_buf is not None:
                         pad_buf.manual_buffer_registration()
 
-        if getattr(args, 'perform_rl_step', False) and args.rl_use_sequence_packing:
+        if args.perform_rl_step and args.rl_use_sequence_packing:
             iteration_sequences = rl_utils.get_iteration_sequence_count(args)
             # Track bins separately for packed mode
             bin_count = (
@@ -3012,7 +3087,7 @@ def trace_handler(p):
         num_floating_point_operations_since_last_log_event += num_floating_point_operations_in_batch
 
         # Logging.
-        if not optimizer.is_stub_optimizer:
+        if optimizer is not None and not optimizer.is_stub_optimizer:
             loss_scale = optimizer.get_loss_scale().item()
         else:
             loss_scale = 1.0
@@ -3020,7 +3095,10 @@ def trace_handler(p):
 
         if args.log_params_norm:
             params_norm = calc_params_l2_norm(model)
-        learning_rate = get_canonical_lr_for_logging(optimizer.param_groups)
+        if optimizer is not None:
+            learning_rate = get_canonical_lr_for_logging(optimizer.param_groups)
+        else:
+            learning_rate = None
         report_memory_flag = training_log(
             loss_dict,
             total_loss_dict,
@@ -3053,7 +3131,7 @@ def trace_handler(p):
                 gc.collect()
             prefix = f'iteration {iteration}'
             timers('eval-time', log_level=0).start(barrier=True)
-            if getattr(args, 'perform_rl_step', False):
+            if args.perform_rl_step:
                 rl_eval_model = model
                 rl_training_model = None
                 # If separate inference and training models, swap training weights
@@ -3158,7 +3236,7 @@ def trace_handler(p):
             wandb_writer.finish()
         ft_integration.shutdown()
         one_logger_utils.finish()
-        if getattr(args, 'perform_rl_step', False):
+        if args.perform_rl_step:
             rl_utils.rl_inference_interface_shutdown()
         sys.exit(exit_code)
 
@@ -3350,8 +3428,6 @@ def evaluate(
 
     rerun_state_machine.set_mode(rerun_mode)
 
-    rerun_state_machine.set_mode(rerun_mode)
-
     return total_loss_dict, collected_non_loss_data, False
 
 
@@ -3536,12 +3612,12 @@ def build_train_valid_test_data_loaders(build_train_valid_test_datasets_provider
     if is_distributed or mpu.get_tensor_model_parallel_rank() == 0:
 
         # Build datasets and dataloders.
-        if getattr(args, 'perform_rl_step', True):
+        if args.perform_rl_step:
             # we don't need to build any dataloaders for RL training
             train_dataloader = None
             valid_dataloaders = None
             test_dataloader = None
-            do_train = args.train_iters > 0
+            do_train = (args.train_iters or 0) > 0
             do_valid = (args.full_validation or args.eval_iters > 0)
             do_test = (args.full_validation or args.eval_iters > 0)
 
@@ -3584,9 +3660,6 @@ def build_train_valid_test_data_loaders(build_train_valid_test_datasets_provider
     args.do_train = getattr(args, "do_train", False) or flags[0].item()
     args.do_valid = getattr(args, "do_valid", False) or flags[1].item()
     args.do_test = getattr(args, "do_test", False) or flags[2].item()
-    if getattr(args, 'perform_rl_step', False):
-        args.to_test = False
-
     return train_dataloader, valid_dataloaders, test_dataloader
 
 
@@ -3667,4 +3740,8 @@ def _get_iterator(dataloader_type, dataloader):
 
 def should_disable_forward_pre_hook(args):
     """Block forward pre-hook for certain configurations."""
-    return not args.use_megatron_fsdp and args.use_distributed_optimizer and args.overlap_param_gather
+    return (
+        not args.use_megatron_fsdp
+        and (args.use_distributed_optimizer or 'dist' in args.optimizer)
+        and args.overlap_param_gather
+    )
diff --git a/megatron/training/utils.py b/megatron/training/utils.py
index 7844b450136..4a54cf65cbb 100644
--- a/megatron/training/utils.py
+++ b/megatron/training/utils.py
@@ -433,6 +433,11 @@ def print_rank_last(message):
         print(message, flush=True)
 
 
+def is_hybrid_model(args):
+    """Returns True if the model is a hybrid Mamba-Transformer model."""
+    return args.hybrid_layer_pattern is not None
+
+
 def is_first_or_last_pipeline_stage(vp_stage):
     """Return True if on first or last pipeline stage, taking into account virtual
     pipeline parallelism."""
diff --git a/pretrain_mamba.py b/pretrain_mamba.py
index 037f1817d99..048d40f82a5 100644
--- a/pretrain_mamba.py
+++ b/pretrain_mamba.py
@@ -236,7 +236,7 @@ def forward_step(data_iterator, model: MambaModel):
     if cu_seqlens is None:
         packed_seq_params = None
     else:
-        # TODO(duncan): This class seems overly complex for what needs to be conveyed
+        total_tokens = tokens.size(1) if tokens is not None else labels.size(1)
         packed_seq_params = PackedSeqParams(
             qkv_format="thd",
             cu_seqlens_q=cu_seqlens,
@@ -245,6 +245,7 @@ def forward_step(data_iterator, model: MambaModel):
             cu_seqlens_kv_padded=None,
             max_seqlen_q=max_seqlen,
             max_seqlen_kv=max_seqlen,
+            total_tokens=total_tokens,
         )
 
     timers('batch-generator').stop()
diff --git a/pyproject.toml b/pyproject.toml
index 7ce7e3e17c6..0095a929215 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -8,7 +8,12 @@ build-backend = "setuptools.build_meta"
 include-package-data = true
 
 [tool.setuptools.packages.find]
-include = ["megatron.core", "megatron.core.*"]
+include = [
+    "megatron.core",
+    "megatron.core.*",
+    "megatron.training",
+    "megatron.training.*",
+]
 
 [tool.setuptools.dynamic]
 version = { attr = "megatron.core.package_info.__version__" }
@@ -63,6 +68,16 @@ Download = "https://github.com/NVIDIA/Megatron-LM/releases"
 Homepage = "https://github.com/NVIDIA/Megatron-LM"
 
 [project.optional-dependencies]
+training = [
+    "flask-restful",
+    "sentencepiece",
+    "tiktoken",
+    "wandb",
+    "transformers",
+    "accelerate",
+]
+
+### 'mlm' group is deprecated. please use 'training' instead ###
 mlm = [
     "flask-restful",
     "sentencepiece",
@@ -74,7 +89,7 @@ mlm = [
 
 dev = [
     "nvidia-modelopt[torch]; sys_platform != 'darwin'",
-    "transformer-engine[pytorch,core_cu13]>=2.9.0a0,<2.12.0",
+    "transformer-engine[pytorch,core_cu13]",
     "nvidia-resiliency-ext",
     "tqdm",
     "einops~=0.8",
@@ -91,12 +106,14 @@ dev = [
     "flashinfer-python~=0.5.0",
     "wget",
     "onnxscript",
-    "fastapi~=0.50",                                          # Forcing a little bit more recent version of fastapi to be compatible with pydantic 2.0
+    "fastapi~=0.50",                                    # Forcing a little bit more recent version of fastapi to be compatible with pydantic 2.0
     "datasets",
     "emerging_optimizers; python_version >= '3.12'",
     "flask[async]",
     "hypercorn",
-    "openai",
+    "quart",
+    "openai[aiohttp]",
+    "orjson",
 ]
 
 lts = [
@@ -126,6 +143,7 @@ test = [
     "wrapt",
     "pytest==8.3.5",
     "pytest-mock",
+    "mock",
     "pytest-cov",
     "pytest-random-order",
     "pytest-asyncio",
@@ -160,7 +178,7 @@ linting = [
     "pylint==3.2.6",
 ]
 ci = ["python-gitlab", "slack-sdk", "pandas"]
-no_pypi_wheels = ["emerging_optimizers; python_version >= '3.12'", "fast-hadamard-transform"]
+no_pypi_wheels = ["emerging_optimizers; python_version >= '3.12'"]
 
 [tool.uv]
 default-groups = ["linting", "build", "test"]
@@ -233,8 +251,10 @@ markers = [
 concurrency = ["thread", "multiprocessing"]
 omit = [
     "/tmp/*",
-    "/workspace/tests/*",
+    "/opt/megatron-lm/tests/*",
+    "/opt/megatron-lm/tools/*",
     "/usr/local/lib/python3.12/dist-packages/*",
+    "/opt/megatron-lm/_remote_module_non_scriptable",
 ]
 parallel = true
 sigterm = false
diff --git a/tests/functional_tests/python_test_utils/test_inference_regular_pipeline.py b/tests/functional_tests/python_test_utils/test_inference_regular_pipeline.py
index 93bfa6e1299..165e17b102b 100644
--- a/tests/functional_tests/python_test_utils/test_inference_regular_pipeline.py
+++ b/tests/functional_tests/python_test_utils/test_inference_regular_pipeline.py
@@ -15,6 +15,7 @@
 _NON_REQUEST_TOP_LEVEL_KEYS = {
     # System-level metrics
     "throughput",
+    "lifetime_prefill_token_count",
     # Peak memory metrics (added by inference scripts; optionally checked if present in golden values)
     "mem-max-allocated-bytes",
 }
@@ -55,6 +56,9 @@ def test_inference_pipeline(
         model_config_content = f3.read()
 
     metrics = yaml.safe_load(model_config_content)["METRICS"]
+    if not metrics:
+        print("No metrics defined in model_config.yaml, skipping validation.")
+        return
 
     output_groundtruth = json.loads(golden_values_content)
 
@@ -130,6 +134,17 @@ def test_inference_pipeline(
                 )
         output_groundtruth.pop("mem-max-allocated-bytes")
 
+    lptc_key = "lifetime_prefill_token_count"
+    if lptc_key in output_groundtruth and lptc_key not in metrics:
+        # metrics does not have lifetime_prefill_token_count, so ignore it
+        output_groundtruth.pop(lptc_key)
+    elif lptc_key in metrics:
+        # Ground truth does not have lifetime_prefill_token_count, so ignore it
+        metrics.pop(lptc_key)
+    elif lptc_key in output_groundtruth and lptc_key in metrics:
+        # TODO: Compare liftime_prefill_token_count to groundtruth
+        pass
+
     for request_id, groundtruth_results in output_groundtruth.items():
         current_results = output_current[request_id]
 
diff --git a/tests/functional_tests/shell_test_utils/_run_training.sh b/tests/functional_tests/shell_test_utils/_run_training.sh
index 72fd187d19d..8f848a24add 100644
--- a/tests/functional_tests/shell_test_utils/_run_training.sh
+++ b/tests/functional_tests/shell_test_utils/_run_training.sh
@@ -124,8 +124,8 @@ else
                 value=$(echo "$value" | sed 's/^\[//;s/\]$//')
                 TRAINING_PARAMS_FROM_CONFIG+="$key $value "
 
-            # Case: contains spaces
-            elif [[ "$value" == *" "* ]]; then
+            # Case: contains spaces or shell metacharacters
+            elif [[ "$value" == *" "* || "$value" == *"|"* || "$value" == *"("* || "$value" == *")"* ]]; then
                 TRAINING_PARAMS_FROM_CONFIG+="$key \"$value\" "
             # Case: default
             else
diff --git a/tests/functional_tests/test_cases/bert/bert_mcore_tp1_pp2/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/bert/bert_mcore_tp1_pp2/golden_values_dev_dgx_h100.json
index b9b1236875c..3cd46711c32 100644
--- a/tests/functional_tests/test_cases/bert/bert_mcore_tp1_pp2/golden_values_dev_dgx_h100.json
+++ b/tests/functional_tests/test_cases/bert/bert_mcore_tp1_pp2/golden_values_dev_dgx_h100.json
@@ -4,56 +4,56 @@
         "end_step": 50,
         "step_interval": 1,
         "values": {
-            "1": 10.47723,
-            "2": 10.47576,
-            "3": 10.46809,
-            "4": 10.47326,
-            "5": 10.47148,
-            "6": 10.46049,
-            "7": 10.46357,
-            "8": 10.47334,
-            "9": 10.48063,
-            "10": 10.46319,
-            "11": 10.47102,
-            "12": 10.45502,
-            "13": 10.44665,
-            "14": 10.451,
-            "15": 10.48846,
-            "16": 10.4509,
-            "17": 10.44648,
-            "18": 10.44272,
-            "19": 10.43057,
-            "20": 10.44534,
-            "21": 10.41771,
-            "22": 10.38656,
-            "23": 10.39328,
-            "24": 10.37849,
-            "25": 10.35466,
-            "26": 10.35965,
-            "27": 10.34523,
-            "28": 10.33556,
-            "29": 10.25418,
-            "30": 10.23008,
-            "31": 10.14093,
-            "32": 10.13603,
-            "33": 10.13936,
-            "34": 10.11381,
-            "35": 10.08888,
-            "36": 10.09238,
-            "37": 10.06851,
-            "38": 10.0466,
-            "39": 9.97582,
-            "40": 9.93764,
-            "41": 9.90872,
-            "42": 9.84882,
-            "43": 9.85772,
-            "44": 9.7925,
-            "45": 9.80329,
-            "46": 9.70285,
-            "47": 9.73423,
-            "48": 9.70106,
-            "49": 9.69966,
-            "50": 9.70252
+            "1": 10.60641,
+            "2": 10.59358,
+            "3": 10.60634,
+            "4": 10.59643,
+            "5": 10.60761,
+            "6": 10.59317,
+            "7": 10.58967,
+            "8": 10.59921,
+            "9": 10.5983,
+            "10": 10.58354,
+            "11": 10.58822,
+            "12": 10.58724,
+            "13": 10.58739,
+            "14": 10.58492,
+            "15": 10.59569,
+            "16": 10.5771,
+            "17": 10.56463,
+            "18": 10.57446,
+            "19": 10.57211,
+            "20": 10.56815,
+            "21": 10.55538,
+            "22": 10.50412,
+            "23": 10.49141,
+            "24": 10.4805,
+            "25": 10.4668,
+            "26": 10.47242,
+            "27": 10.46337,
+            "28": 10.46183,
+            "29": 10.41063,
+            "30": 10.318,
+            "31": 10.27509,
+            "32": 10.23156,
+            "33": 10.24371,
+            "34": 10.18787,
+            "35": 10.21126,
+            "36": 10.17085,
+            "37": 10.15203,
+            "38": 10.12971,
+            "39": 10.09881,
+            "40": 10.05552,
+            "41": 9.99652,
+            "42": 9.93929,
+            "43": 9.92324,
+            "44": 9.87817,
+            "45": 9.85239,
+            "46": 9.79434,
+            "47": 9.77988,
+            "48": 9.75602,
+            "49": 9.79748,
+            "50": 9.75571
         }
     },
     "num-zeros": {
@@ -61,56 +61,56 @@
         "end_step": 50,
         "step_interval": 1,
         "values": {
-            "1": 2137.0,
-            "2": 1618.0,
-            "3": 1561.0,
-            "4": 1871.0,
-            "5": 1983.0,
-            "6": 1565.0,
-            "7": 2779.0,
-            "8": 2108.0,
-            "9": 2008.0,
-            "10": 2086.0,
-            "11": 2534.0,
-            "12": 1686.0,
-            "13": 2120.0,
-            "14": 2814.0,
-            "15": 1735.0,
-            "16": 2535.0,
-            "17": 2409.0,
-            "18": 2345.0,
-            "19": 2374.0,
-            "20": 2743.0,
-            "21": 2039.0,
-            "22": 2925.0,
-            "23": 2630.0,
-            "24": 2821.0,
-            "25": 2366.0,
-            "26": 2633.0,
-            "27": 2921.0,
-            "28": 2760.0,
-            "29": 2635.0,
-            "30": 2614.0,
-            "31": 2073.0,
-            "32": 2275.0,
-            "33": 2130.0,
-            "34": 2185.0,
-            "35": 2312.0,
-            "36": 2789.0,
-            "37": 2937.0,
-            "38": 2652.0,
-            "39": 2929.0,
-            "40": 3348.0,
-            "41": 1812.0,
-            "42": 1441.0,
-            "43": 1726.0,
-            "44": 2437.0,
-            "45": 3263.0,
-            "46": 2813.0,
-            "47": 2668.0,
-            "48": 3411.0,
-            "49": 3174.0,
-            "50": 2441.0
+            "1": 2208.0,
+            "2": 2609.0,
+            "3": 2409.0,
+            "4": 2431.0,
+            "5": 2807.0,
+            "6": 2571.0,
+            "7": 1640.0,
+            "8": 2187.0,
+            "9": 2256.0,
+            "10": 2274.0,
+            "11": 2387.0,
+            "12": 2741.0,
+            "13": 2254.0,
+            "14": 2109.0,
+            "15": 2732.0,
+            "16": 2497.0,
+            "17": 2618.0,
+            "18": 2666.0,
+            "19": 2662.0,
+            "20": 2300.0,
+            "21": 2429.0,
+            "22": 2863.0,
+            "23": 2413.0,
+            "24": 2435.0,
+            "25": 2437.0,
+            "26": 2516.0,
+            "27": 2760.0,
+            "28": 2513.0,
+            "29": 2564.0,
+            "30": 2580.0,
+            "31": 3102.0,
+            "32": 2972.0,
+            "33": 2628.0,
+            "34": 2701.0,
+            "35": 3079.0,
+            "36": 3209.0,
+            "37": 3103.0,
+            "38": 2619.0,
+            "39": 2558.0,
+            "40": 2267.0,
+            "41": 3542.0,
+            "42": 3538.0,
+            "43": 3502.0,
+            "44": 3660.0,
+            "45": 3684.0,
+            "46": 2736.0,
+            "47": 2050.0,
+            "48": 3406.0,
+            "49": 3358.0,
+            "50": 3608.0
         }
     },
     "mem-allocated-bytes": {
@@ -118,56 +118,56 @@
         "end_step": 50,
         "step_interval": 1,
         "values": {
-            "1": 3405920768.0,
-            "2": 3405920768.0,
-            "3": 3405920768.0,
-            "4": 3405920768.0,
-            "5": 3405920768.0,
-            "6": 3405920768.0,
-            "7": 3405920768.0,
-            "8": 3405920768.0,
-            "9": 3405920768.0,
-            "10": 3405920768.0,
-            "11": 3405920768.0,
-            "12": 3405920768.0,
-            "13": 3405920768.0,
-            "14": 3405920768.0,
-            "15": 3405920768.0,
-            "16": 3405920768.0,
-            "17": 3405920768.0,
-            "18": 3405920768.0,
-            "19": 3405920768.0,
-            "20": 3405920768.0,
-            "21": 3405920768.0,
-            "22": 3405920768.0,
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-            "47": 3405920768.0,
-            "48": 3405920768.0,
-            "49": 3405920768.0,
-            "50": 3405920768.0
+            "1": 3434522112.0,
+            "2": 3434522112.0,
+            "3": 3435308544.0,
+            "4": 3434522112.0,
+            "5": 3435308544.0,
+            "6": 3434522112.0,
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+            "10": 3435308544.0,
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+            "15": 3434522112.0,
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+            "20": 3435308544.0,
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+            "31": 3435308544.0,
+            "32": 3434522112.0,
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+            "38": 3435308544.0,
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+            "40": 3435308544.0,
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+            "48": 3435308544.0,
+            "49": 3435308544.0,
+            "50": 3434522112.0
         }
     },
     "mem-max-allocated-bytes": {
@@ -175,56 +175,56 @@
         "end_step": 50,
         "step_interval": 1,
         "values": {
-            "1": 4195575808.0,
-            "2": 5662015488.0,
-            "3": 5662015488.0,
-            "4": 5662015488.0,
-            "5": 5662015488.0,
-            "6": 5662015488.0,
-            "7": 5662015488.0,
-            "8": 5662015488.0,
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diff --git a/tests/functional_tests/test_cases/bert/bert_mcore_tp1_pp4_vp2/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/bert/bert_mcore_tp1_pp4_vp2/golden_values_dev_dgx_h100.json
index 30fa7e80d5a..bb8be591b74 100644
--- a/tests/functional_tests/test_cases/bert/bert_mcore_tp1_pp4_vp2/golden_values_dev_dgx_h100.json
+++ b/tests/functional_tests/test_cases/bert/bert_mcore_tp1_pp4_vp2/golden_values_dev_dgx_h100.json
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diff --git a/tests/functional_tests/test_cases/bert/bert_mcore_tp2_pp2/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/bert/bert_mcore_tp2_pp2/golden_values_dev_dgx_h100.json
index 7a21f7ae2f9..361dbf99a7c 100644
--- a/tests/functional_tests/test_cases/bert/bert_mcore_tp2_pp2/golden_values_dev_dgx_h100.json
+++ b/tests/functional_tests/test_cases/bert/bert_mcore_tp2_pp2/golden_values_dev_dgx_h100.json
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diff --git a/tests/functional_tests/test_cases/bert/bert_mcore_tp2_pp2_local_spec/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/bert/bert_mcore_tp2_pp2_local_spec/golden_values_dev_dgx_h100.json
index d034c6bf7d8..a975f6a69c9 100644
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diff --git a/tests/functional_tests/test_cases/bert/bert_mcore_tp2_pp2_resume_torch_dist/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/bert/bert_mcore_tp2_pp2_resume_torch_dist/golden_values_dev_dgx_h100.json
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diff --git a/tests/functional_tests/test_cases/bert/bert_mcore_tp2_pp2_resume_torch_dist/golden_values_dev_dgx_h100_2nd.json b/tests/functional_tests/test_cases/bert/bert_mcore_tp2_pp2_resume_torch_dist/golden_values_dev_dgx_h100_2nd.json
index a5b9c2f1ab2..7990f924ec5 100644
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+++ b/tests/functional_tests/test_cases/bert/bert_mcore_tp2_pp2_resume_torch_dist/golden_values_dev_dgx_h100_2nd.json
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diff --git a/tests/functional_tests/test_cases/bert/bert_mcore_tp2_pp2_resume_torch_dist_local_spec/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/bert/bert_mcore_tp2_pp2_resume_torch_dist_local_spec/golden_values_dev_dgx_h100.json
index fe766022589..ca483519042 100644
--- a/tests/functional_tests/test_cases/bert/bert_mcore_tp2_pp2_resume_torch_dist_local_spec/golden_values_dev_dgx_h100.json
+++ b/tests/functional_tests/test_cases/bert/bert_mcore_tp2_pp2_resume_torch_dist_local_spec/golden_values_dev_dgx_h100.json
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diff --git a/tests/functional_tests/test_cases/bert/bert_mcore_tp4_pp1/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/bert/bert_mcore_tp4_pp1/golden_values_dev_dgx_h100.json
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diff --git a/tests/functional_tests/test_cases/bert/bert_release_sm/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/bert/bert_release_sm/golden_values_dev_dgx_gb200.json
index 2774c3e5d17..812804fe033 100644
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 }
\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/bert/bert_release_sm/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/bert/bert_release_sm/golden_values_dev_dgx_h100.json
index 481cd5d6f2c..d1ae85719d6 100644
--- a/tests/functional_tests/test_cases/bert/bert_release_sm/golden_values_dev_dgx_h100.json
+++ b/tests/functional_tests/test_cases/bert/bert_release_sm/golden_values_dev_dgx_h100.json
@@ -4,407 +4,407 @@
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     },
     "iteration-time": {
@@ -1656,7 +1656,7 @@
             "85": "nan",
             "90": "nan",
             "95": "nan",
-            "100": 0.6788,
+            "100": 0.58989,
             "105": "nan",
             "110": "nan",
             "115": "nan",
@@ -1676,7 +1676,7 @@
             "185": "nan",
             "190": "nan",
             "195": "nan",
-            "200": 0.25176,
+            "200": 0.26173,
             "205": "nan",
             "210": "nan",
             "215": "nan",
@@ -1696,7 +1696,7 @@
             "285": "nan",
             "290": "nan",
             "295": "nan",
-            "300": 0.28405,
+            "300": 0.26925,
             "305": "nan",
             "310": "nan",
             "315": "nan",
@@ -1716,7 +1716,7 @@
             "385": "nan",
             "390": "nan",
             "395": "nan",
-            "400": 0.2554,
+            "400": 0.2606,
             "405": "nan",
             "410": "nan",
             "415": "nan",
@@ -1736,7 +1736,7 @@
             "485": "nan",
             "490": "nan",
             "495": "nan",
-            "500": 0.25856,
+            "500": 0.25904,
             "505": "nan",
             "510": "nan",
             "515": "nan",
@@ -1756,7 +1756,7 @@
             "585": "nan",
             "590": "nan",
             "595": "nan",
-            "600": 0.27163,
+            "600": 0.27358,
             "605": "nan",
             "610": "nan",
             "615": "nan",
@@ -1776,7 +1776,7 @@
             "685": "nan",
             "690": "nan",
             "695": "nan",
-            "700": 0.24758,
+            "700": 0.30298,
             "705": "nan",
             "710": "nan",
             "715": "nan",
@@ -1796,7 +1796,7 @@
             "785": "nan",
             "790": "nan",
             "795": "nan",
-            "800": 0.28282,
+            "800": 0.29186,
             "805": "nan",
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@@ -1816,7 +1816,7 @@
             "885": "nan",
             "890": "nan",
             "895": "nan",
-            "900": 0.25896,
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             "905": "nan",
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             "915": "nan",
@@ -1836,7 +1836,7 @@
             "985": "nan",
             "990": "nan",
             "995": "nan",
-            "1000": 0.26503,
+            "1000": 0.27405,
             "1005": "nan",
             "1010": "nan",
             "1015": "nan",
@@ -1856,7 +1856,7 @@
             "1085": "nan",
             "1090": "nan",
             "1095": "nan",
-            "1100": 0.288,
+            "1100": 0.27684,
             "1105": "nan",
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             "1115": "nan",
@@ -1876,7 +1876,7 @@
             "1185": "nan",
             "1190": "nan",
             "1195": "nan",
-            "1200": 0.28612,
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             "1205": "nan",
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@@ -1896,7 +1896,7 @@
             "1285": "nan",
             "1290": "nan",
             "1295": "nan",
-            "1300": 0.2819,
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             "1305": "nan",
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             "1315": "nan",
@@ -1916,7 +1916,7 @@
             "1385": "nan",
             "1390": "nan",
             "1395": "nan",
-            "1400": 0.26349,
+            "1400": 0.25948,
             "1405": "nan",
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@@ -1936,7 +1936,7 @@
             "1485": "nan",
             "1490": "nan",
             "1495": "nan",
-            "1500": 0.27004,
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             "1505": "nan",
             "1510": "nan",
             "1515": "nan",
@@ -1956,7 +1956,7 @@
             "1585": "nan",
             "1590": "nan",
             "1595": "nan",
-            "1600": 0.27881,
+            "1600": 0.27844,
             "1605": "nan",
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             "1615": "nan",
@@ -1976,7 +1976,7 @@
             "1685": "nan",
             "1690": "nan",
             "1695": "nan",
-            "1700": 0.27532,
+            "1700": 0.27607,
             "1705": "nan",
             "1710": "nan",
             "1715": "nan",
@@ -1996,7 +1996,7 @@
             "1785": "nan",
             "1790": "nan",
             "1795": "nan",
-            "1800": 0.26438,
+            "1800": 0.27527,
             "1805": "nan",
             "1810": "nan",
             "1815": "nan",
@@ -2016,7 +2016,7 @@
             "1885": "nan",
             "1890": "nan",
             "1895": "nan",
-            "1900": 0.26337,
+            "1900": 0.27903,
             "1905": "nan",
             "1910": "nan",
             "1915": "nan",
@@ -2036,7 +2036,7 @@
             "1985": "nan",
             "1990": "nan",
             "1995": "nan",
-            "2000": 0.26296
+            "2000": 0.31051
         }
     }
 }
\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_gb200/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_gb200/model_config.yaml
new file mode 100644
index 00000000000..73edd1d93aa
--- /dev/null
+++ b/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_gb200/model_config.yaml
@@ -0,0 +1,100 @@
+ENV_VARS:
+  CUDA_DEVICE_MAX_CONNECTIONS: 1
+  NVTE_ALLOW_NONDETERMINISTIC_ALGO: 1
+  NON_DETERMINSTIC_RESULTS: 1
+  NVTE_NORM_FWD_USE_CUDNN: 1
+  NVTE_NORM_BWD_USE_CUDNN: 1
+  NVTE_FUSED_ATTN: 1
+  PYTORCH_CUDA_ALLOC_CONF: expandable_segments:True
+  USE_MNNVL: 1
+TEST_TYPE: "release"
+MODEL_ARGS:
+  # Distributed args
+  --distributed-timeout-minutes: 60
+  --tensor-model-parallel-size: 4
+  --pipeline-model-parallel-size: 2
+  --num-layers-per-virtual-pipeline-stage: 8
+  --use-distributed-optimizer: true
+  --overlap-grad-reduce: true
+  --overlap-param-gather: true
+  --tp-comm-overlap: true
+  # Training args
+  --use-mcore-models: true
+  --sequence-parallel: true
+  --disable-bias-linear: true
+  --micro-batch-size: 4
+  --rampup-batch-size: "[384 384 97656250]"
+  --global-batch-size: 1152
+  --train-samples: 19531250
+  --manual-gc: true
+  --cross-entropy-loss-fusion: true
+  --cross-entropy-fusion-impl: te
+  # Transformer Engine args
+  --transformer-impl: transformer_engine
+  # Data args
+  --data-cache-path: ${DATA_CACHE_PATH}
+  --tokenizer-type: GPTSentencePieceTokenizer
+  --tokenizer-model: ${DATA_PATH}/utils/nemotron_2_256k.model
+  --data-path: $DATA_BLEND
+  --split: 99,1,0
+  --no-mmap-bin-files: true
+  --num-workers: 6
+  # Add network size args
+  --apply-layernorm-1p: true
+  --untie-embeddings-and-output-weights: true
+  --position-embedding-type: rope
+  --rotary-percent: 0.5
+  --squared-relu: true
+  --num-layers: 32
+  --hidden-size: 6144
+  --num-attention-heads: 48
+  --group-query-attention: true
+  --num-query-groups: 8
+  --seq-length: 4096
+  --max-position-embeddings: 4096
+  # Add regularization args
+  --attention-dropout: 0.0
+  --hidden-dropout: 0.0
+  --clip-grad: 1.0
+  --weight-decay: 0.1
+  # Add learning rate args
+  --lr-decay-samples: 1949218748
+  --lr-warmup-samples: 3906252
+  --lr: 4.5e-4
+  --min-lr: 4.5e-5
+  --decoupled-lr: 5.0e-4
+  --decoupled-min-lr: 4.5e-5
+  --lr-decay-style: cosine
+  --adam-beta1: 0.9
+  --adam-beta2: 0.95
+  # Add validation args
+  --eval-iters: 32
+  --eval-interval: 2000
+  # Add checkpointing args
+  --save: ${CHECKPOINT_SAVE_PATH}
+  --load: ${CHECKPOINT_LOAD_PATH}
+  --save-interval: 1000
+  --save-retain-interval: 5000
+  # Add initialization args
+  --init-method-std: 0.0134
+  # Add logging args
+  --log-timers-to-tensorboard: true
+  --log-memory-to-tensorboard: true
+  --log-num-zeros-in-grad: true
+  --log-params-norm: true
+  --log-validation-ppl-to-tensorboard: true
+  --log-throughput: true
+  --log-interval: 100
+  --tensorboard-dir: ${TENSORBOARD_PATH}
+  --wandb-project: megatron-core-release-runs
+  --wandb-entity: adlr
+  --wandb-exp-name: ${WANDB_EXPERIMENT}
+  # Add mixed precision args
+  --bf16: true
+  --exit-interval: 51000
+  --wandb-save-dir: ${WANDB_SAVE_PATH}
+METRICS:
+  - "iteration-time"
+  - "lm loss"
+  - "mem-allocated-bytes"
+  - "mem-max-allocated-bytes"
diff --git a/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_sm_gb200/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_sm_gb200/golden_values_dev_dgx_gb200.json
new file mode 100644
index 00000000000..e05f23ef303
--- /dev/null
+++ b/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_sm_gb200/golden_values_dev_dgx_gb200.json
@@ -0,0 +1,13042 @@
+{
+    "lm loss": {
+        "start_step": 1,
+        "end_step": 13000,
+        "step_interval": 5,
+        "values": {
+            "1": 13.00341,
+            "5": 12.95282,
+            "10": 12.10778,
+            "15": 11.54743,
+            "20": 10.38064,
+            "25": 9.96943,
+            "30": 9.61583,
+            "35": 9.35993,
+            "40": 9.18388,
+            "45": 9.04655,
+            "50": 8.88708,
+            "55": 8.73169,
+            "60": 8.64595,
+            "65": 8.56844,
+            "70": 8.47431,
+            "75": 8.29862,
+            "80": 8.15455,
+            "85": 8.13037,
+            "90": 8.03999,
+            "95": 7.94008,
+            "100": 7.81135,
+            "105": 7.68743,
+            "110": 7.59148,
+            "115": 7.48955,
+            "120": 7.46567,
+            "125": 7.41631,
+            "130": 7.2757,
+            "135": 7.23718,
+            "140": 7.1914,
+            "145": 7.04633,
+            "150": 7.18433,
+            "155": 7.03577,
+            "160": 6.94043,
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+            "170": 6.81691,
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_sm_gb200/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_sm_gb200/golden_values_dev_dgx_h100.json
new file mode 100644
index 00000000000..02bcf7fe698
--- /dev/null
+++ b/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_sm_gb200/golden_values_dev_dgx_h100.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_sm_gb200/golden_values_lts_dgx_a100.json b/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_sm_gb200/golden_values_lts_dgx_a100.json
new file mode 100644
index 00000000000..379036a5d12
--- /dev/null
+++ b/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_sm_gb200/golden_values_lts_dgx_a100.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_sm_gb200/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_sm_gb200/model_config.yaml
new file mode 100644
index 00000000000..0b4255a7599
--- /dev/null
+++ b/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_sm_gb200/model_config.yaml
@@ -0,0 +1,100 @@
+ENV_VARS:
+  CUDA_DEVICE_MAX_CONNECTIONS: 1
+  NVTE_ALLOW_NONDETERMINISTIC_ALGO: 1
+  NON_DETERMINSTIC_RESULTS: 1
+  NVTE_NORM_FWD_USE_CUDNN: 1
+  NVTE_NORM_BWD_USE_CUDNN: 1
+  NVTE_FUSED_ATTN: 1
+  PYTORCH_CUDA_ALLOC_CONF: expandable_segments:True
+  USE_MNNVL: 1
+TEST_TYPE: "release"
+MODEL_ARGS:
+  # Distributed args
+  --distributed-timeout-minutes: 60
+  --tensor-model-parallel-size: 4
+  --pipeline-model-parallel-size: 2
+  --num-layers-per-virtual-pipeline-stage: 8
+  --use-distributed-optimizer: true
+  --overlap-grad-reduce: true
+  --overlap-param-gather: true
+  --tp-comm-overlap: true
+  # Training args
+  --use-mcore-models: true
+  --sequence-parallel: true
+  --disable-bias-linear: true
+  --micro-batch-size: 4
+  --rampup-batch-size: "[384 384 97656250]"
+  --global-batch-size: 1152
+  --train-samples: 19531250
+  --manual-gc: true
+  --cross-entropy-loss-fusion: true
+  --cross-entropy-fusion-impl: te
+  # Transformer Engine args
+  --transformer-impl: transformer_engine
+  # Data args
+  --data-cache-path: ${DATA_CACHE_PATH}
+  --tokenizer-type: GPTSentencePieceTokenizer
+  --tokenizer-model: ${DATA_PATH}/utils/nemotron_2_256k.model
+  --data-path: $DATA_BLEND
+  --split: 99,1,0
+  --no-mmap-bin-files: true
+  --num-workers: 6
+  # Add network size args
+  --apply-layernorm-1p: true
+  --untie-embeddings-and-output-weights: true
+  --position-embedding-type: rope
+  --rotary-percent: 0.5
+  --squared-relu: true
+  --num-layers: 32
+  --hidden-size: 6144
+  --num-attention-heads: 48
+  --group-query-attention: true
+  --num-query-groups: 8
+  --seq-length: 4096
+  --max-position-embeddings: 4096
+  # Add regularization args
+  --attention-dropout: 0.0
+  --hidden-dropout: 0.0
+  --clip-grad: 1.0
+  --weight-decay: 0.1
+  # Add learning rate args
+  --lr-decay-samples: 1949218748
+  --lr-warmup-samples: 3906252
+  --lr: 4.5e-4
+  --min-lr: 4.5e-5
+  --decoupled-lr: 5.0e-4
+  --decoupled-min-lr: 4.5e-5
+  --lr-decay-style: cosine
+  --adam-beta1: 0.9
+  --adam-beta2: 0.95
+  # Add validation args
+  --eval-iters: 32
+  --eval-interval: 2000
+  # Add checkpointing args
+  --save: ${CHECKPOINT_SAVE_PATH}
+  --load: ${CHECKPOINT_LOAD_PATH}
+  --save-interval: 1000
+  --save-retain-interval: 5000
+  # Add initialization args
+  --init-method-std: 0.0134
+  # Add logging args
+  --log-timers-to-tensorboard: true
+  --log-memory-to-tensorboard: true
+  --log-num-zeros-in-grad: true
+  --log-params-norm: true
+  --log-validation-ppl-to-tensorboard: true
+  --log-throughput: true
+  --log-interval: 100
+  --tensorboard-dir: ${TENSORBOARD_PATH}
+  --wandb-project: megatron-core-release-runs
+  --wandb-entity: adlr
+  --wandb-exp-name: ${WANDB_EXPERIMENT}
+  # Add mixed precision args
+  --bf16: true
+  --exit-interval: 13000
+  --wandb-save-dir: ${WANDB_SAVE_PATH}
+METRICS:
+  - "iteration-time"
+  - "lm loss"
+  - "mem-allocated-bytes"
+  - "mem-max-allocated-bytes"
diff --git a/tests/functional_tests/test_cases/gpt/gpt3_7b_tp1_pp4_memory_speed/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_7b_tp1_pp4_memory_speed/golden_values_dev_dgx_gb200.json
index 0174aaf4684..21b63e8effd 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_7b_tp1_pp4_memory_speed/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_7b_tp1_pp4_memory_speed/golden_values_dev_dgx_gb200.json
@@ -6,29 +6,29 @@
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     },
     "num-zeros": {
@@ -36,31 +36,31 @@
         "end_step": 25,
         "step_interval": 1,
         "values": {
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     },
     "mem-allocated-bytes": {
@@ -133,29 +133,29 @@
         "step_interval": 1,
         "values": {
             "1": "nan",
-            "2": 3.93489,
+            "2": 10.37368,
             "3": "nan",
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_7b_tp1_pp4_memory_speed/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/gpt/gpt3_7b_tp1_pp4_memory_speed/golden_values_dev_dgx_h100.json
index 2ed3bf0784f..1719b6e9578 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_7b_tp1_pp4_memory_speed/golden_values_dev_dgx_h100.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_7b_tp1_pp4_memory_speed/golden_values_dev_dgx_h100.json
@@ -6,29 +6,29 @@
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     },
     "num-zeros": {
@@ -36,31 +36,31 @@
         "end_step": 25,
         "step_interval": 1,
         "values": {
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     },
     "mem-allocated-bytes": {
@@ -101,30 +101,30 @@
         "step_interval": 1,
         "values": {
             "1": 52730814464.0,
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         }
     },
     "iteration-time": {
@@ -133,29 +133,29 @@
         "step_interval": 1,
         "values": {
             "1": "nan",
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+            "2": 9.40588,
             "3": "nan",
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     }
diff --git a/tests/functional_tests/test_cases/gpt/gpt3_7b_tp4_pp1_memory_speed/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_7b_tp4_pp1_memory_speed/golden_values_dev_dgx_gb200.json
index ca51cd1bcb3..dc762c9b513 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_7b_tp4_pp1_memory_speed/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_7b_tp4_pp1_memory_speed/golden_values_dev_dgx_gb200.json
@@ -6,29 +6,29 @@
         "values": {
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     },
     "num-zeros": {
@@ -36,31 +36,31 @@
         "end_step": 25,
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     },
     "mem-allocated-bytes": {
@@ -133,29 +133,29 @@
         "step_interval": 1,
         "values": {
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_dist_optimizer_fim_dataset/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_dist_optimizer_fim_dataset/golden_values_dev_dgx_gb200.json
index bac18297ae6..1597a12fc00 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_dist_optimizer_fim_dataset/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_dist_optimizer_fim_dataset/golden_values_dev_dgx_gb200.json
@@ -4,56 +4,56 @@
         "end_step": 50,
         "step_interval": 1,
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_dist_optimizer_fim_dataset/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_dist_optimizer_fim_dataset/golden_values_dev_dgx_h100.json
index 036b53dabb1..3fc7359ad8a 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_dist_optimizer_fim_dataset/golden_values_dev_dgx_h100.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_dist_optimizer_fim_dataset/golden_values_dev_dgx_h100.json
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_dist_optimizer_no_mmap_bin_files/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_dist_optimizer_no_mmap_bin_files/golden_values_dev_dgx_gb200.json
index 8bcd3aa91d5..045c3d3b47a 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_dist_optimizer_no_mmap_bin_files/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_dist_optimizer_no_mmap_bin_files/golden_values_dev_dgx_gb200.json
@@ -7,52 +7,52 @@
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@@ -61,56 +61,56 @@
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 }
\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_mup/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_mup/model_config.yaml
new file mode 100644
index 00000000000..811b88718f9
--- /dev/null
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_mup/model_config.yaml
@@ -0,0 +1,57 @@
+ENV_VARS:
+  CUDA_DEVICE_MAX_CONNECTIONS: 1
+  NVTE_ALLOW_NONDETERMINISTIC_ALGO: 0
+  NCCL_ALGO: Ring
+  CUBLAS_WORKSPACE_CONFIG: :4096:8
+MODEL_ARGS:
+  --num-layers: 12
+  --hidden-size: 512
+  --num-attention-heads: 8
+  --log-params-norm: true
+  --log-num-zeros-in-grad: true
+  --log-validation-ppl-to-tensorboard: true
+  --log-timers-to-tensorboard: true
+  --tensorboard-dir: ${TENSORBOARD_PATH}
+  --micro-batch-size: 4
+  --global-batch-size: 32
+  --seq-length: 1024
+  --max-position-embeddings: 1024
+  --train-iters: 50
+  --timing-log-level: 0
+  --lr-decay-iters: 320000
+  --save: ${CHECKPOINT_SAVE_PATH}
+  --load: ${CHECKPOINT_LOAD_PATH}
+  --data-path: ${DATA_PATH}/text/the_pile/shard00/my-gpt3_00_text_document
+  --vocab-file: ${DATA_PATH}/text/the_pile/shard00/bpe/vocab.json
+  --merge-file: ${DATA_PATH}/text/the_pile/shard00/bpe/merges.txt
+  --split: 949,50,1
+  --distributed-backend: nccl
+  --lr: 0.00015
+  --lr-decay-style: cosine
+  --min-lr: 1.0e-5
+  --weight-decay: 1e-2
+  --clip-grad: 1.0
+  --lr-warmup-fraction: .01
+  --log-interval: 1
+  --save-interval: 10000
+  --eval-interval: 1000
+  --eval-iters: 10
+  --transformer-impl: transformer_engine
+  --tensor-model-parallel-size: 1
+  --pipeline-model-parallel-size: 1
+  --use-distributed-optimizer: true
+  --no-mmap-bin-files: true
+  --deterministic-mode: true
+  --no-gradient-accumulation-fusion: true
+  --attention-softmax-in-fp32: true
+  --use-mcore-models: true
+  --ckpt-format: torch_dist
+  --dist-ckpt-strictness: log_all
+  --data-cache-path: ${DATA_CACHE_PATH}
+  --bf16: true
+  --attention-backend: unfused
+  --log-memory-to-tensorboard: true
+  # MuP (Maximal Update Parameterization) args
+  --use-mup: true
+  --mup-base-hidden-size: 256
+TEST_TYPE: regular
diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_resume_torch_dist_dist_optimizer/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_resume_torch_dist_dist_optimizer/golden_values_dev_dgx_gb200.json
index eb5a06ac1fc..63661ce44ba 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_resume_torch_dist_dist_optimizer/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_resume_torch_dist_dist_optimizer/golden_values_dev_dgx_gb200.json
@@ -6,104 +6,104 @@
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     "num-zeros": {
@@ -111,106 +111,106 @@
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_resume_torch_dist_dist_optimizer/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_resume_torch_dist_dist_optimizer/golden_values_dev_dgx_h100.json
index 131bcbe928e..63e2f03839f 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_resume_torch_dist_dist_optimizer/golden_values_dev_dgx_h100.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_resume_torch_dist_dist_optimizer/golden_values_dev_dgx_h100.json
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_resume_torch_dist_uniform_full_recompute/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_resume_torch_dist_uniform_full_recompute/golden_values_dev_dgx_h100.json
index 05b11c3c8ee..0f5dd5066ab 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_resume_torch_dist_uniform_full_recompute/golden_values_dev_dgx_h100.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_resume_torch_dist_uniform_full_recompute/golden_values_dev_dgx_h100.json
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\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp2_resume_torch_dist_rope_embeddings/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp2_resume_torch_dist_rope_embeddings/golden_values_dev_dgx_h100.json
index 1f743e8c2e8..bfff8b2dafd 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp2_resume_torch_dist_rope_embeddings/golden_values_dev_dgx_h100.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp2_resume_torch_dist_rope_embeddings/golden_values_dev_dgx_h100.json
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_resume_torch_dist_disable_bias_linear/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_resume_torch_dist_disable_bias_linear/golden_values_dev_dgx_gb200.json
index 63c74381364..947616883e3 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_resume_torch_dist_disable_bias_linear/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_resume_torch_dist_disable_bias_linear/golden_values_dev_dgx_gb200.json
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_resume_torch_dist_persistent_disable_bias_linear/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_resume_torch_dist_persistent_disable_bias_linear/golden_values_dev_dgx_gb200.json
index a9a12874e97..463885b1cba 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_resume_torch_dist_persistent_disable_bias_linear/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_resume_torch_dist_persistent_disable_bias_linear/golden_values_dev_dgx_gb200.json
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_resume_torch_dist_swiglu/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_resume_torch_dist_swiglu/golden_values_dev_dgx_gb200.json
index 1fc5ef869c5..fe86682f4bc 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_resume_torch_dist_swiglu/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_resume_torch_dist_swiglu/golden_values_dev_dgx_gb200.json
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_resume_torch_dist_swiglu/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_resume_torch_dist_swiglu/golden_values_dev_dgx_h100.json
index 42889e09b26..0f203186fb7 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_resume_torch_dist_swiglu/golden_values_dev_dgx_h100.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_resume_torch_dist_swiglu/golden_values_dev_dgx_h100.json
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1/golden_values_dev_dgx_gb200.json
index 2610b7fe2f4..65904c58aca 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1/golden_values_dev_dgx_gb200.json
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_dist_optimizer_overlap_grad_reduce_param_gather_overlap_optimizer/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_dist_optimizer_overlap_grad_reduce_param_gather_overlap_optimizer/golden_values_dev_dgx_gb200.json
index 10988c85257..3029be3a708 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_dist_optimizer_overlap_grad_reduce_param_gather_overlap_optimizer/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_dist_optimizer_overlap_grad_reduce_param_gather_overlap_optimizer/golden_values_dev_dgx_gb200.json
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_dist_optimizer_overlap_grad_reduce_param_gather_overlap_optimizer/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_dist_optimizer_overlap_grad_reduce_param_gather_overlap_optimizer/golden_values_dev_dgx_h100.json
index ea2f72181ea..732b7f7f1de 100644
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+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_dist_optimizer_overlap_grad_reduce_param_gather_overlap_optimizer/golden_values_dev_dgx_h100.json
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\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_resume_torch_decoupled_lr/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_resume_torch_decoupled_lr/golden_values_dev_dgx_gb200.json
index a34edb3389a..4ce33e9f3b9 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_resume_torch_decoupled_lr/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_resume_torch_decoupled_lr/golden_values_dev_dgx_gb200.json
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\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_resume_torch_dist_calculate_per_token_loss/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_resume_torch_dist_calculate_per_token_loss/golden_values_dev_dgx_gb200.json
index 0758fd3a8cf..64f3b7e5f89 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_resume_torch_dist_calculate_per_token_loss/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_resume_torch_dist_calculate_per_token_loss/golden_values_dev_dgx_gb200.json
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce/golden_values_dev_dgx_gb200.json
index 28843c12217..a3165b8856a 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce/golden_values_dev_dgx_gb200.json
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce/golden_values_dev_dgx_h100.json
index 82352c11781..893627e9928 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce/golden_values_dev_dgx_h100.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce/golden_values_dev_dgx_h100.json
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_tunable_overlap/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_tunable_overlap/golden_values_dev_dgx_gb200.json
index 8a90b6fb7df..5e655485d52 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_tunable_overlap/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_tunable_overlap/golden_values_dev_dgx_gb200.json
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_tunable_overlap/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_tunable_overlap/golden_values_dev_dgx_h100.json
index 399a2c50a8d..3535401ed62 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_tunable_overlap/golden_values_dev_dgx_h100.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_tunable_overlap/golden_values_dev_dgx_h100.json
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_uneven_pipeline/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_uneven_pipeline/golden_values_dev_dgx_gb200.json
index da72109d85d..b59f2eab3b3 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_uneven_pipeline/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_uneven_pipeline/golden_values_dev_dgx_gb200.json
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\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2/golden_values_dev_dgx_h100.json
index 5b1ee17f8f6..eaa28ef7c26 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2/golden_values_dev_dgx_h100.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2/golden_values_dev_dgx_h100.json
@@ -7,52 +7,52 @@
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 }
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_calculate_per_token_loss/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_calculate_per_token_loss/golden_values_dev_dgx_h100.json
index f5628621ad5..e8497a14105 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_calculate_per_token_loss/golden_values_dev_dgx_h100.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_calculate_per_token_loss/golden_values_dev_dgx_h100.json
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_etp4_calculate_per_token_loss_dp_last/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_etp4_calculate_per_token_loss_dp_last/golden_values_dev_dgx_h100.json
index a470bf65873..95a3aa7d78e 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_etp4_calculate_per_token_loss_dp_last/golden_values_dev_dgx_h100.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_etp4_calculate_per_token_loss_dp_last/golden_values_dev_dgx_h100.json
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 }
\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_etp4_dp_last/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_etp4_dp_last/golden_values_dev_dgx_h100.json
index f78c3deb59d..77286bef5b8 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_etp4_dp_last/golden_values_dev_dgx_h100.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_etp4_dp_last/golden_values_dev_dgx_h100.json
@@ -7,52 +7,52 @@
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_mla/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_mla/golden_values_dev_dgx_gb200.json
index 31729dd5fe5..047db9b77c3 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_mla/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_mla/golden_values_dev_dgx_gb200.json
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist/golden_values_dev_dgx_gb200.json
index caa1e54ee64..b7ced9b3ff9 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist/golden_values_dev_dgx_gb200.json
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist_ddp_average_in_collective/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist_ddp_average_in_collective/golden_values_dev_dgx_gb200.json
index 27eb21de0f0..09e346c2bca 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist_ddp_average_in_collective/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist_ddp_average_in_collective/golden_values_dev_dgx_gb200.json
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_zp_z3_resume_fsdp_dtensor/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_zp_z3_resume_fsdp_dtensor/golden_values_dev_dgx_gb200.json
index 7dd5b31f34f..2471c5117cc 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_zp_z3_resume_fsdp_dtensor/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_zp_z3_resume_fsdp_dtensor/golden_values_dev_dgx_gb200.json
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_zp_z3_resume_fsdp_dtensor/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_zp_z3_resume_fsdp_dtensor/golden_values_dev_dgx_h100.json
index feb49a01aad..6c88449234e 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_zp_z3_resume_fsdp_dtensor/golden_values_dev_dgx_h100.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_zp_z3_resume_fsdp_dtensor/golden_values_dev_dgx_h100.json
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp4_pp1_dist_optimizer_overlap_grad_reduce_param_gather/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp4_pp1_dist_optimizer_overlap_grad_reduce_param_gather/golden_values_dev_dgx_gb200.json
index 85eca8a168b..5662faa717f 100644
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+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp4_pp1_dist_optimizer_overlap_grad_reduce_param_gather/golden_values_dev_dgx_gb200.json
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_tp1_pp2/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_tp1_pp2/golden_values_dev_dgx_gb200.json
index 063c93b8168..f6f7f91fb03 100644
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_tp1_pp4/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_tp1_pp4/golden_values_dev_dgx_h100.json
index 4aa2800617e..b12d4c68133 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_tp1_pp4/golden_values_dev_dgx_h100.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_tp1_pp4/golden_values_dev_dgx_h100.json
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_weekly_mcore_tp2_pp2_current_scaling_native_fp8_tp_pp_sp_tp_overlap/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_weekly_mcore_tp2_pp2_current_scaling_native_fp8_tp_pp_sp_tp_overlap/golden_values_dev_dgx_gb200.json
index 701a1e8451e..ac1685e7437 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_weekly_mcore_tp2_pp2_current_scaling_native_fp8_tp_pp_sp_tp_overlap/golden_values_dev_dgx_gb200.json
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\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/gpt/gpt3_weekly_mcore_tp2_pp2_current_scaling_native_fp8_tp_pp_sp_tp_overlap/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/gpt/gpt3_weekly_mcore_tp2_pp2_current_scaling_native_fp8_tp_pp_sp_tp_overlap/golden_values_dev_dgx_h100.json
index 8353ea30868..15d82b3a27f 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_weekly_mcore_tp2_pp2_current_scaling_native_fp8_tp_pp_sp_tp_overlap/golden_values_dev_dgx_h100.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_weekly_mcore_tp2_pp2_current_scaling_native_fp8_tp_pp_sp_tp_overlap/golden_values_dev_dgx_h100.json
@@ -4,2006 +4,2006 @@
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 }
\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/gpt/gpt3_weekly_mcore_tp4_cp2_current_scaling_native_fp8_tp_sp_cp_tp_overlap/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_weekly_mcore_tp4_cp2_current_scaling_native_fp8_tp_sp_cp_tp_overlap/golden_values_dev_dgx_gb200.json
index 477c5e1e929..695e6c48261 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_weekly_mcore_tp4_cp2_current_scaling_native_fp8_tp_sp_cp_tp_overlap/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_weekly_mcore_tp4_cp2_current_scaling_native_fp8_tp_sp_cp_tp_overlap/golden_values_dev_dgx_gb200.json
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_weekly_mcore_tp4_cp2_current_scaling_native_fp8_tp_sp_cp_tp_overlap/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/gpt/gpt3_weekly_mcore_tp4_cp2_current_scaling_native_fp8_tp_sp_cp_tp_overlap/golden_values_dev_dgx_h100.json
index a5003a9a581..c97e14dbfc3 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_weekly_mcore_tp4_cp2_current_scaling_native_fp8_tp_sp_cp_tp_overlap/golden_values_dev_dgx_h100.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_weekly_mcore_tp4_cp2_current_scaling_native_fp8_tp_sp_cp_tp_overlap/golden_values_dev_dgx_h100.json
@@ -6,2004 +6,2004 @@
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+            "1932": 5.31665,
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+            "1939": 5.34792,
+            "1940": 5.73239,
+            "1941": 5.35344,
+            "1942": 5.34736,
+            "1943": 5.34529,
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+            "1945": 5.35461,
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+            "1948": 5.35061,
+            "1949": 5.34214,
+            "1950": 5.34188,
+            "1951": 5.76619,
+            "1952": 5.35525,
+            "1953": 5.36166,
+            "1954": 5.34642,
+            "1955": 5.34997,
+            "1956": 5.34383,
+            "1957": 5.34251,
+            "1958": 5.35039,
+            "1959": 5.32345,
+            "1960": 5.70915,
+            "1961": 5.32029,
+            "1962": 5.68241,
+            "1963": 5.65728,
+            "1964": 5.32803,
+            "1965": 5.36096,
+            "1966": 5.71921,
+            "1967": 5.32613,
+            "1968": 5.33584,
+            "1969": 5.3418,
+            "1970": 5.32655,
+            "1971": 5.32373,
+            "1972": 6.61849,
+            "1973": 5.69259,
+            "1974": 5.32121,
+            "1975": 5.34514,
+            "1976": 5.33539,
+            "1977": 5.33024,
+            "1978": 5.71369,
+            "1979": 5.32844,
+            "1980": 5.33303,
+            "1981": 5.30736,
+            "1982": 5.32579,
+            "1983": 5.31773,
+            "1984": 5.33124,
+            "1985": 5.32303,
+            "1986": 5.34222,
+            "1987": 5.35372,
+            "1988": 5.35724,
+            "1989": 5.36871,
+            "1990": 5.38439,
+            "1991": 5.37115,
+            "1992": 5.3544,
+            "1993": 5.34048,
+            "1994": 5.33628,
+            "1995": 5.327,
+            "1996": 5.75054,
+            "1997": 5.33898,
+            "1998": 5.33139,
+            "1999": 5.32919,
+            "2000": 5.33263
         }
     }
 }
\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/gpt/gpt_dynamic_inference_tp1_pp8_dp1_583m_logitsmatch_zmq/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt_dynamic_inference_tp1_pp8_dp1_583m_logitsmatch_zmq/model_config.yaml
index bd34c11fc24..345fc250694 100644
--- a/tests/functional_tests/test_cases/gpt/gpt_dynamic_inference_tp1_pp8_dp1_583m_logitsmatch_zmq/model_config.yaml
+++ b/tests/functional_tests/test_cases/gpt/gpt_dynamic_inference_tp1_pp8_dp1_583m_logitsmatch_zmq/model_config.yaml
@@ -42,9 +42,6 @@ MODEL_ARGS:
   --top_k: 1
   --return-log-probs: true
   --num-tokens-to-generate: 30
-  --inference-dynamic-batching-max-requests-override: 8 # hardcode decode padding tokens to 7 for reproducibility
-  --inference-dynamic-batching-buffer-guaranteed-fraction: 0
-  --inference-dynamic-batching-buffer-overflow-factor: 0.2
   --inference-dynamic-batching-buffer-size-gb: 20
   --dist-ckpt-strictness: log_unexpected
   --inference-ckpt-non-strict: true # To handle the extra_state errors
diff --git a/tests/functional_tests/test_cases/gpt/gpt_dynamic_inference_tp2_pp2_dp2_583m_logitsmatch_zmq/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/gpt/gpt_dynamic_inference_tp2_pp2_dp2_583m_logitsmatch_zmq/golden_values_dev_dgx_h100.json
index 55d6955055a..3af1d61504a 100644
--- a/tests/functional_tests/test_cases/gpt/gpt_dynamic_inference_tp2_pp2_dp2_583m_logitsmatch_zmq/golden_values_dev_dgx_h100.json
+++ b/tests/functional_tests/test_cases/gpt/gpt_dynamic_inference_tp2_pp2_dp2_583m_logitsmatch_zmq/golden_values_dev_dgx_h100.json
@@ -1,158 +1,158 @@
 {
     "0": {
         "input_prompt": "Time travel to 2008, and go to a bar or a club or one of the myriad disco-basements on the Lower East Side that does not quite know which of those it is. Dance awkwardly in a room full of other glittered-up nerds, and wait for something to happen, buoyed on the feeling that this is the big swollen heart of life, that this is New York like the movies.",
-        "generated_text": " And then you get to the end of the movie, and you realize that this is not New York at all. This is New York at the end",
+        "generated_text": " And that this is the place where you can be yourself, and be yourself in the most beautiful way. And that this is the place where you can",
         "generated_tokens": [
             3060,
-            2430,
-            1636,
-            2012,
-            1317,
-            1278,
-            2362,
-            1307,
+            1455,
+            1593,
+            1395,
             1278,
-            16070,
+            3535,
+            2478,
+            1636,
+            1710,
+            1402,
+            14019,
             1044,
             1321,
-            1636,
-            23067,
+            1402,
+            14019,
+            1294,
+            1278,
+            2725,
+            15568,
+            3039,
+            1046,
+            3060,
             1455,
             1593,
             1395,
-            1605,
-            3140,
-            5152,
-            1513,
-            1747,
-            1046,
-            2409,
-            1395,
-            3140,
-            5152,
-            1513,
             1278,
-            2362
+            3535,
+            2478,
+            1636,
+            1710
         ],
-        "latency": 44.73653959017247,
+        "latency": 56.18305364251137,
         "logprobs": [
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-            -2.7523813247680664,
-            -4.628502368927002,
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-            -1.7354254722595215,
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-            -2.0520384311676025,
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+            -0.6682117581367493,
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+            -0.483473539352417,
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+            -0.0857735350728035,
+            -0.13640964031219482,
+            -0.06942988932132721
         ]
     }
 }
diff --git a/tests/functional_tests/test_cases/gpt/gpt_dynamic_inference_tp2_pp2_dp2_583m_logitsmatch_zmq/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt_dynamic_inference_tp2_pp2_dp2_583m_logitsmatch_zmq/model_config.yaml
index 13d409c5968..3b55b09e82e 100644
--- a/tests/functional_tests/test_cases/gpt/gpt_dynamic_inference_tp2_pp2_dp2_583m_logitsmatch_zmq/model_config.yaml
+++ b/tests/functional_tests/test_cases/gpt/gpt_dynamic_inference_tp2_pp2_dp2_583m_logitsmatch_zmq/model_config.yaml
@@ -23,6 +23,7 @@ MODEL_ARGS:
   --distributed-backend: nccl
   --log-interval: 1
   --transformer-impl: inference_optimized
+  --inference-fuse-tp-communication: true
   --sequence-parallel: true
   --tensor-model-parallel-size: 2
   --pipeline-model-parallel-size: 2
@@ -42,9 +43,6 @@ MODEL_ARGS:
   --top_k: 1
   --return-log-probs: true
   --num-tokens-to-generate: 30
-  --inference-dynamic-batching-max-requests-override: 8 # hardcode decode padding tokens to 7 for reproducibility
-  --inference-dynamic-batching-buffer-guaranteed-fraction: 0
-  --inference-dynamic-batching-buffer-overflow-factor: 0.2
   --inference-dynamic-batching-buffer-size-gb: 20
   --dist-ckpt-strictness: log_unexpected
   --inference-ckpt-non-strict: true # To handle the extra_state errors
diff --git a/tests/functional_tests/test_cases/gpt/gpt_dynamic_inference_tp8_pp1_dp1_583m_logitsmatch_zmq/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt_dynamic_inference_tp8_pp1_dp1_583m_logitsmatch_zmq/model_config.yaml
index 8d5779a5099..88a3e40a193 100644
--- a/tests/functional_tests/test_cases/gpt/gpt_dynamic_inference_tp8_pp1_dp1_583m_logitsmatch_zmq/model_config.yaml
+++ b/tests/functional_tests/test_cases/gpt/gpt_dynamic_inference_tp8_pp1_dp1_583m_logitsmatch_zmq/model_config.yaml
@@ -51,9 +51,6 @@ MODEL_ARGS:
   --incoming-requests-per-step: 32
   --use-flashinfer-fused-rope: true
   --inference-logging-step-interval: 1
-  --cuda-graph-impl: local 
-  --inference-dynamic-batching-max-requests: 128
-  --inference-dynamic-batching-num-cuda-graphs: 2
 METRICS:
   - "generated_tokens"
   - "logprobs"
diff --git a/tests/functional_tests/test_cases/hybrid/hybrid_dynamic_inference_tp1_pp1_dp8_583m/model_config.yaml b/tests/functional_tests/test_cases/hybrid/hybrid_dynamic_inference_tp1_pp1_dp8_583m/model_config.yaml
index 0232bcb30bf..b1ffaaa0147 100644
--- a/tests/functional_tests/test_cases/hybrid/hybrid_dynamic_inference_tp1_pp1_dp8_583m/model_config.yaml
+++ b/tests/functional_tests/test_cases/hybrid/hybrid_dynamic_inference_tp1_pp1_dp8_583m/model_config.yaml
@@ -22,19 +22,17 @@ MODEL_ARGS:
   --pipeline-model-parallel-size: 1
   --expert-model-parallel-size: 1
   --use-mcore-models: true
-  --is-hybrid-model: true
   --model-provider: mamba
   --init-method-std: 0.0198
   --untie-embeddings-and-output-weights: true
   --disable-bias-linear: true
   --init-method-std: 0.014
   --position-embedding-type: none
-  --num-layers: 50
   --hidden-size: 2048
   --ffn-hidden-size: 11264
   --num-attention-heads: 16
   --kv-channels: 128
-  --hybrid-override-pattern: M-M-M-M*-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M-
+  --hybrid-layer-pattern: M-M-M-M*-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M-
   --spec: megatron.core.models.mamba.mamba_layer_specs mamba_stack_spec
   --normalization: RMSNorm
   --swiglu: true
diff --git a/tests/functional_tests/test_cases/hybrid/hybrid_dynamic_inference_tp1_pp1_dp8_583m_chunked_prefill/model_config.yaml b/tests/functional_tests/test_cases/hybrid/hybrid_dynamic_inference_tp1_pp1_dp8_583m_chunked_prefill/model_config.yaml
index 7ff5911a877..36148fb30c9 100644
--- a/tests/functional_tests/test_cases/hybrid/hybrid_dynamic_inference_tp1_pp1_dp8_583m_chunked_prefill/model_config.yaml
+++ b/tests/functional_tests/test_cases/hybrid/hybrid_dynamic_inference_tp1_pp1_dp8_583m_chunked_prefill/model_config.yaml
@@ -22,19 +22,17 @@ MODEL_ARGS:
   --pipeline-model-parallel-size: 1
   --expert-model-parallel-size: 1
   --use-mcore-models: true
-  --is-hybrid-model: true
   --model-provider: mamba
   --init-method-std: 0.0198
   --untie-embeddings-and-output-weights: true
   --disable-bias-linear: true
   --init-method-std: 0.014
   --position-embedding-type: none
-  --num-layers: 50
   --hidden-size: 2048
   --ffn-hidden-size: 11264
   --num-attention-heads: 16
   --kv-channels: 128
-  --hybrid-override-pattern: M-M-M-M*-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M-
+  --hybrid-layer-pattern: M-M-M-M*-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M-
   --spec: megatron.core.models.mamba.mamba_layer_specs mamba_stack_spec
   --normalization: RMSNorm
   --swiglu: true
diff --git a/tests/functional_tests/test_cases/hybrid/hybrid_mr_mcore_te_tp1_pp1_cp1_dgx_a100_1N8G/model_config.yaml b/tests/functional_tests/test_cases/hybrid/hybrid_mr_mcore_te_tp1_pp1_cp1_dgx_a100_1N8G/model_config.yaml
index 22d0cbaa3bf..ddb776d0aee 100644
--- a/tests/functional_tests/test_cases/hybrid/hybrid_mr_mcore_te_tp1_pp1_cp1_dgx_a100_1N8G/model_config.yaml
+++ b/tests/functional_tests/test_cases/hybrid/hybrid_mr_mcore_te_tp1_pp1_cp1_dgx_a100_1N8G/model_config.yaml
@@ -4,14 +4,12 @@ ENV_VARS:
   NCCL_ALGO: Ring
   CUBLAS_WORKSPACE_CONFIG: :4096:8
 MODEL_ARGS:
-  --num-layers: 44
   --hidden-size: 1024
   --num-attention-heads: 16
   --group-query-attention: true
   --num-query-groups: 8
-  --hybrid-override-pattern: M-M-M-M*-M-M-M-M*-M-M-M-M*-M-M-M-M*-M-M-M-M-
+  --hybrid-layer-pattern: M-M-M-M*-M-M-M-M*-M-M-M-M*-M-M-M-M*-M-M-M-M-
   --spec: "[megatron.core.models.mamba.mamba_layer_specs mamba_stack_spec]"
-  --is-hybrid-model: true
   --log-params-norm: true
   --log-num-zeros-in-grad: true
   --log-validation-ppl-to-tensorboard: true
diff --git a/tests/functional_tests/test_cases/hybrid/hybrid_mr_mcore_te_tp1_pp2_vpp2_cp1_dgx_a100_1N8G/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/hybrid/hybrid_mr_mcore_te_tp1_pp2_vpp2_cp1_dgx_a100_1N8G/golden_values_dev_dgx_h100.json
new file mode 100644
index 00000000000..b3308e04b06
--- /dev/null
+++ b/tests/functional_tests/test_cases/hybrid/hybrid_mr_mcore_te_tp1_pp2_vpp2_cp1_dgx_a100_1N8G/golden_values_dev_dgx_h100.json
@@ -0,0 +1,287 @@
+{
+    "lm loss": {
+        "start_step": 1,
+        "end_step": 50,
+        "step_interval": 1,
+        "values": {
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+            "2": 10.98656,
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+            "35": 9.68183,
+            "36": 9.56636,
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+            "40": 9.32825,
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+            "48": 9.03541,
+            "49": 8.57937,
+            "50": 9.11692
+        }
+    },
+    "num-zeros": {
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+        "end_step": 50,
+        "step_interval": 1,
+        "values": {
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+    },
+    "mem-max-allocated-bytes": {
+        "start_step": 1,
+        "end_step": 50,
+        "step_interval": 1,
+        "values": {
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+    },
+    "iteration-time": {
+        "start_step": 1,
+        "end_step": 50,
+        "step_interval": 1,
+        "values": {
+            "1": "nan",
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+        }
+    }
+}
\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/hybrid/hybrid_mr_mcore_te_tp1_pp2_vpp2_cp1_dgx_a100_1N8G/model_config.yaml b/tests/functional_tests/test_cases/hybrid/hybrid_mr_mcore_te_tp1_pp2_vpp2_cp1_dgx_a100_1N8G/model_config.yaml
new file mode 100644
index 00000000000..3f2a25be6b4
--- /dev/null
+++ b/tests/functional_tests/test_cases/hybrid/hybrid_mr_mcore_te_tp1_pp2_vpp2_cp1_dgx_a100_1N8G/model_config.yaml
@@ -0,0 +1,59 @@
+ENV_VARS:
+  CUDA_DEVICE_MAX_CONNECTIONS: 1
+  NVTE_ALLOW_NONDETERMINISTIC_ALGO: 1
+  NCCL_ALGO: Ring
+  CUBLAS_WORKSPACE_CONFIG: :4096:8
+MODEL_ARGS:
+  --hidden-size: 1024
+  --num-attention-heads: 16
+  --group-query-attention: true
+  --num-query-groups: 8
+  --hybrid-layer-pattern: M-M-M-M*-M-|M-M-M*-M-M-|M-M*-M-M-M-|M*-M-M-M-M-
+  --spec: "[megatron.core.models.mamba.mamba_layer_specs mamba_stack_spec]"
+  --log-params-norm: true
+  --log-num-zeros-in-grad: true
+  --log-validation-ppl-to-tensorboard: true
+  --log-timers-to-tensorboard: true
+  --tensorboard-dir: ${TENSORBOARD_PATH}
+  --micro-batch-size: 4
+  --global-batch-size: 32
+  --seq-length: 1024
+  --max-position-embeddings: 1024
+  --train-iters: 50
+  --timing-log-level: 0
+  --lr-decay-iters: 320000
+  --save: ${CHECKPOINT_SAVE_PATH}
+  --load: ${CHECKPOINT_LOAD_PATH}
+  --data-path: ${DATA_PATH}/text/the_pile/shard00/my-gpt3_00_text_document
+  --vocab-file: ${DATA_PATH}/text/the_pile/shard00/bpe/vocab.json
+  --merge-file: ${DATA_PATH}/text/the_pile/shard00/bpe/merges.txt
+  --split: 949,50,1
+  --distributed-backend: nccl
+  --lr: 0.00015
+  --lr-decay-style: cosine
+  --min-lr: 1.0e-5
+  --weight-decay: 1e-2
+  --clip-grad: 1.0
+  --lr-warmup-fraction: .01
+  --log-interval: 1
+  --save-interval: 10000
+  --eval-interval: 1000
+  --eval-iters: 10
+  --transformer-impl: transformer_engine
+  --tensor-model-parallel-size: 1
+  --pipeline-model-parallel-size: 2
+  --use-distributed-optimizer: true
+  --overlap-grad-reduce: true
+  --overlap-param-gather: true
+  --check-weight-hash-across-dp-replicas-interval: 10
+  --ckpt-fully-parallel-load: true
+  --no-gradient-accumulation-fusion: true
+  --attention-softmax-in-fp32: true
+  --use-mcore-models: true
+  --ckpt-format: torch_dist
+  --dist-ckpt-optim-fully-reshardable: true
+  --data-cache-path: ${DATA_CACHE_PATH}
+  --bf16: true
+  --attention-backend: unfused
+  --log-memory-to-tensorboard: true
+TEST_TYPE: regular
diff --git a/tests/functional_tests/test_cases/hybrid/hybrid_mr_mcore_te_tp1_pp4_cp1_dgx_a100_1N8G/golden_values_dev_dgx_a100.json b/tests/functional_tests/test_cases/hybrid/hybrid_mr_mcore_te_tp1_pp4_cp1_dgx_a100_1N8G/golden_values_dev_dgx_a100.json
index db5414bfb90..fe4e1b47237 100644
--- a/tests/functional_tests/test_cases/hybrid/hybrid_mr_mcore_te_tp1_pp4_cp1_dgx_a100_1N8G/golden_values_dev_dgx_a100.json
+++ b/tests/functional_tests/test_cases/hybrid/hybrid_mr_mcore_te_tp1_pp4_cp1_dgx_a100_1N8G/golden_values_dev_dgx_a100.json
@@ -1 +1,287 @@
-{"lm loss": {"start_step": 1, "end_step": 50, "step_interval": 5, "values": {"1": 10.81478, "5": 10.8517, "10": 10.78749, "15": 10.79506, "20": 10.69119, "25": 10.52293, "30": 10.34604, "35": 10.26168, "40": 10.07199, "45": 9.8098, "50": 9.88336}}, "num-zeros": {"start_step": 1, "end_step": 50, "step_interval": 5, "values": {"1": 1549.0, "5": 1915.0, "10": 1391.0, "15": 1773.0, "20": 1615.0, "25": 1748.0, "30": 1877.0, "35": 1915.0, "40": 2111.0, "45": 2009.0, "50": 2347.0}}, "mem-allocated-bytes": {"start_step": 1, "end_step": 50, "step_interval": 5, "values": {"1": 522846720.0, "5": 522846720.0, "10": 522846720.0, "15": 522846720.0, "20": 522846720.0, "25": 522846720.0, "30": 522846720.0, "35": 522846720.0, "40": 522846720.0, "45": 522846720.0, "50": 522846720.0}}, "mem-max-allocated-bytes": {"start_step": 1, "end_step": 50, "step_interval": 5, "values": {"1": 3768846848.0, "5": 3912608256.0, "10": 3912608256.0, "15": 3912608256.0, "20": 3912608256.0, "25": 3912608256.0, "30": 3912608256.0, "35": 3912608256.0, "40": 3912608256.0, "45": 3912608256.0, "50": 3912608256.0}}, "iteration-time": {"start_step": 1, "end_step": 50, "step_interval": 5, "values": {"1": 14.36782, "5": 0.18832, "10": 0.16735, "15": 0.16595, "20": 0.16466, "25": 0.16564, "30": 0.16594, "35": 0.16362, "40": 0.16524, "45": 0.16382, "50": 0.16329}}}
\ No newline at end of file
+{
+    "lm loss": {
+        "start_step": 1,
+        "end_step": 50,
+        "step_interval": 1,
+        "values": {
+            "1": 11.00877,
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+        }
+    },
+    "num-zeros": {
+        "start_step": 1,
+        "end_step": 50,
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diff --git a/tests/functional_tests/test_cases/hybrid/hybrid_mr_mcore_te_tp1_pp4_cp1_dgx_a100_1N8G/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/hybrid/hybrid_mr_mcore_te_tp1_pp4_cp1_dgx_a100_1N8G/golden_values_dev_dgx_h100.json
new file mode 100644
index 00000000000..fe4e1b47237
--- /dev/null
+++ b/tests/functional_tests/test_cases/hybrid/hybrid_mr_mcore_te_tp1_pp4_cp1_dgx_a100_1N8G/golden_values_dev_dgx_h100.json
@@ -0,0 +1,287 @@
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diff --git a/tests/functional_tests/test_cases/hybrid/hybrid_mr_mcore_te_tp1_pp4_cp1_dgx_a100_1N8G/golden_values_lts_dgx_a100.json b/tests/functional_tests/test_cases/hybrid/hybrid_mr_mcore_te_tp1_pp4_cp1_dgx_a100_1N8G/golden_values_lts_dgx_a100.json
index dc393d0dffc..fe4e1b47237 100644
--- a/tests/functional_tests/test_cases/hybrid/hybrid_mr_mcore_te_tp1_pp4_cp1_dgx_a100_1N8G/golden_values_lts_dgx_a100.json
+++ b/tests/functional_tests/test_cases/hybrid/hybrid_mr_mcore_te_tp1_pp4_cp1_dgx_a100_1N8G/golden_values_lts_dgx_a100.json
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+}
\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/hybrid/hybrid_mr_mcore_te_tp1_pp4_cp1_dgx_a100_1N8G/model_config.yaml b/tests/functional_tests/test_cases/hybrid/hybrid_mr_mcore_te_tp1_pp4_cp1_dgx_a100_1N8G/model_config.yaml
index 0983337becc..8cecc7de2ed 100644
--- a/tests/functional_tests/test_cases/hybrid/hybrid_mr_mcore_te_tp1_pp4_cp1_dgx_a100_1N8G/model_config.yaml
+++ b/tests/functional_tests/test_cases/hybrid/hybrid_mr_mcore_te_tp1_pp4_cp1_dgx_a100_1N8G/model_config.yaml
@@ -4,14 +4,12 @@ ENV_VARS:
   NCCL_ALGO: Ring
   CUBLAS_WORKSPACE_CONFIG: :4096:8
 MODEL_ARGS:
-  --num-layers: 44
   --hidden-size: 1024
   --num-attention-heads: 16
   --group-query-attention: true
   --num-query-groups: 8
-  --hybrid-override-pattern: M-M-M-M*-M-M-M-M*-M-M-M-M*-M-M-M-M*-M-M-M-M-
+  --hybrid-layer-pattern: M-M-M-M*-M-|M-M-M*-M-M-|M-M*-M-M-M-|M*-M-M-M-M-
   --spec: "[megatron.core.models.mamba.mamba_layer_specs mamba_stack_spec]"
-  --is-hybrid-model: true
   --log-params-norm: true
   --log-num-zeros-in-grad: true
   --log-validation-ppl-to-tensorboard: true
diff --git a/tests/functional_tests/test_cases/hybrid/hybrid_mr_mcore_te_tp2_pp1_cp1_dgx_a100_1N8G/model_config.yaml b/tests/functional_tests/test_cases/hybrid/hybrid_mr_mcore_te_tp2_pp1_cp1_dgx_a100_1N8G/model_config.yaml
index 7f7aac5d78b..79b6fd506bd 100644
--- a/tests/functional_tests/test_cases/hybrid/hybrid_mr_mcore_te_tp2_pp1_cp1_dgx_a100_1N8G/model_config.yaml
+++ b/tests/functional_tests/test_cases/hybrid/hybrid_mr_mcore_te_tp2_pp1_cp1_dgx_a100_1N8G/model_config.yaml
@@ -4,14 +4,12 @@ ENV_VARS:
   NCCL_ALGO: Ring
   CUBLAS_WORKSPACE_CONFIG: :4096:8
 MODEL_ARGS:
-  --num-layers: 44
   --hidden-size: 1024
   --num-attention-heads: 16
   --group-query-attention: true
   --num-query-groups: 8
-  --hybrid-override-pattern: M-M-M-M*-M-M-M-M*-M-M-M-M*-M-M-M-M*-M-M-M-M-
+  --hybrid-layer-pattern: M-M-M-M*-M-M-M-M*-M-M-M-M*-M-M-M-M*-M-M-M-M-
   --spec: "[megatron.core.models.mamba.mamba_layer_specs mamba_stack_spec]"
-  --is-hybrid-model: true
   --log-params-norm: true
   --log-num-zeros-in-grad: true
   --log-validation-ppl-to-tensorboard: true
diff --git a/tests/functional_tests/test_cases/hybrid/hybrid_mr_mcore_te_tp2_pp1_cp4_dgx_a100_1N8G/model_config.yaml b/tests/functional_tests/test_cases/hybrid/hybrid_mr_mcore_te_tp2_pp1_cp4_dgx_a100_1N8G/model_config.yaml
index 93418f580fc..7e16a27960f 100644
--- a/tests/functional_tests/test_cases/hybrid/hybrid_mr_mcore_te_tp2_pp1_cp4_dgx_a100_1N8G/model_config.yaml
+++ b/tests/functional_tests/test_cases/hybrid/hybrid_mr_mcore_te_tp2_pp1_cp4_dgx_a100_1N8G/model_config.yaml
@@ -4,14 +4,12 @@ ENV_VARS:
   NCCL_ALGO: Ring
   CUBLAS_WORKSPACE_CONFIG: :4096:8
 MODEL_ARGS:
-  --num-layers: 44
   --hidden-size: 1024
   --num-attention-heads: 16
   --group-query-attention: true
   --num-query-groups: 8
-  --hybrid-override-pattern: M-M-M-M*-M-M-M-M*-M-M-M-M*-M-M-M-M*-M-M-M-M-
+  --hybrid-layer-pattern: M-M-M-M*-M-M-M-M*-M-M-M-M*-M-M-M-M*-M-M-M-M-
   --spec: "[megatron.core.models.mamba.mamba_layer_specs mamba_stack_spec]"
-  --is-hybrid-model: true
   --log-params-norm: true
   --log-num-zeros-in-grad: true
   --log-validation-ppl-to-tensorboard: true
diff --git a/tests/functional_tests/test_cases/hybrid/hybrid_static_inference_tp1_pp1_2B_cudagraphs/model_config.yaml b/tests/functional_tests/test_cases/hybrid/hybrid_static_inference_tp1_pp1_2B_cudagraphs/model_config.yaml
index 5bc40afede4..26708b32a60 100644
--- a/tests/functional_tests/test_cases/hybrid/hybrid_static_inference_tp1_pp1_2B_cudagraphs/model_config.yaml
+++ b/tests/functional_tests/test_cases/hybrid/hybrid_static_inference_tp1_pp1_2B_cudagraphs/model_config.yaml
@@ -22,19 +22,17 @@ MODEL_ARGS:
   --pipeline-model-parallel-size: 1
   --expert-model-parallel-size: 1
   --use-mcore-models: true
-  --is-hybrid-model: true
   --model-provider: mamba
   --init-method-std: 0.0198
   --untie-embeddings-and-output-weights: true
   --disable-bias-linear: true
   --init-method-std: 0.014
   --position-embedding-type: none
-  --num-layers: 50
   --hidden-size: 2048
   --ffn-hidden-size: 11264
   --num-attention-heads: 16
   --kv-channels: 128
-  --hybrid-override-pattern: M-M-M-M*-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M-
+  --hybrid-layer-pattern: M-M-M-M*-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M-
   --spec: megatron.core.models.mamba.mamba_layer_specs mamba_stack_spec
   --normalization: RMSNorm
   --swiglu: true
diff --git a/tests/functional_tests/test_cases/hybrid/hybrid_static_inference_tp1_pp1_2B_logitsmatch/model_config.yaml b/tests/functional_tests/test_cases/hybrid/hybrid_static_inference_tp1_pp1_2B_logitsmatch/model_config.yaml
index b5c3c409605..3964bcb8ecb 100644
--- a/tests/functional_tests/test_cases/hybrid/hybrid_static_inference_tp1_pp1_2B_logitsmatch/model_config.yaml
+++ b/tests/functional_tests/test_cases/hybrid/hybrid_static_inference_tp1_pp1_2B_logitsmatch/model_config.yaml
@@ -22,19 +22,17 @@ MODEL_ARGS:
   --pipeline-model-parallel-size: 1
   --expert-model-parallel-size: 1
   --use-mcore-models: true
-  --is-hybrid-model: true
   --model-provider: mamba
   --init-method-std: 0.0198
   --untie-embeddings-and-output-weights: true
   --disable-bias-linear: true
   --init-method-std: 0.014
   --position-embedding-type: none
-  --num-layers: 50
   --hidden-size: 2048
   --ffn-hidden-size: 11264
   --num-attention-heads: 16
   --kv-channels: 128
-  --hybrid-override-pattern: M-M-M-M*-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M-
+  --hybrid-layer-pattern: M-M-M-M*-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M-
   --spec: megatron.core.models.mamba.mamba_layer_specs mamba_stack_spec
   --normalization: RMSNorm
   --swiglu: true
diff --git a/tests/functional_tests/test_cases/mimo/mimo_vlm_pretrain_convergence_tp1_pp1_cp1_dp8_seq_packing/golden_values_dev.json b/tests/functional_tests/test_cases/mimo/mimo_vlm_pretrain_convergence_tp1_pp1_cp1_dp8_seq_packing/golden_values_dev.json
new file mode 100644
index 00000000000..00ba74777b4
--- /dev/null
+++ b/tests/functional_tests/test_cases/mimo/mimo_vlm_pretrain_convergence_tp1_pp1_cp1_dp8_seq_packing/golden_values_dev.json
@@ -0,0 +1,142 @@
+{
+    "lm loss": {
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+        "end_step": 100,
+        "step_interval": 5,
+        "values": {
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+        "values": {
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+            "95": 2.0,
+            "100": 1.0
+        }
+    },
+    "mem-allocated-bytes": {
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+        "step_interval": 5,
+        "values": {
+            "1": 28620292096.0,
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+        }
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+    "mem-max-allocated-bytes": {
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+        "values": {
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+        }
+    }
+}
\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/mimo/mimo_vlm_pretrain_convergence_tp1_pp1_cp1_dp8_seq_packing/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/mimo/mimo_vlm_pretrain_convergence_tp1_pp1_cp1_dp8_seq_packing/golden_values_dev_dgx_h100.json
new file mode 100644
index 00000000000..581ae01b6a3
--- /dev/null
+++ b/tests/functional_tests/test_cases/mimo/mimo_vlm_pretrain_convergence_tp1_pp1_cp1_dp8_seq_packing/golden_values_dev_dgx_h100.json
@@ -0,0 +1,537 @@
+{
+    "lm loss": {
+        "start_step": 1,
+        "end_step": 100,
+        "step_interval": 1,
+        "values": {
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diff --git a/tests/functional_tests/test_cases/mimo/mimo_vlm_pretrain_convergence_tp1_pp1_cp1_dp8_seq_packing/model_config.yaml b/tests/functional_tests/test_cases/mimo/mimo_vlm_pretrain_convergence_tp1_pp1_cp1_dp8_seq_packing/model_config.yaml
new file mode 100644
index 00000000000..2e86278fa67
--- /dev/null
+++ b/tests/functional_tests/test_cases/mimo/mimo_vlm_pretrain_convergence_tp1_pp1_cp1_dp8_seq_packing/model_config.yaml
@@ -0,0 +1,65 @@
+ENV_VARS:
+  CUDA_DEVICE_MAX_CONNECTIONS: 1
+  NVTE_ALLOW_NONDETERMINISTIC_ALGO: 0
+  NCCL_ALGO: Ring
+  CUBLAS_WORKSPACE_CONFIG: :4096:8
+  ARTIFACTS_ROOT: /workspace/checkpoints
+MODEL_ARGS:
+  --num-layers: 32
+  --hidden-size: 4096
+  --attention-dropout: 0.0
+  --hidden-dropout: 0.0
+  --num-attention-heads: 32
+  --log-params-norm: true
+  --log-num-zeros-in-grad: true
+  --log-validation-ppl-to-tensorboard: true
+  --log-timers-to-tensorboard: true
+  --tensorboard-dir: ${TENSORBOARD_PATH}
+  --micro-batch-size: 1
+  --global-batch-size: 32
+  --total-seq-length: 3200
+  --packing-buffer-size: 4
+  --seq-length: 4096
+  --max-position-embeddings: 4096
+  --train-iters: 100
+  --timing-log-level: 2
+  --lr-decay-iters: 2200
+  --save: ${CHECKPOINT_SAVE_PATH}
+  --load: ${CHECKPOINT_LOAD_PATH}
+  --tokenizer-type: HuggingFaceTokenizer
+  --tokenizer-model: llava-hf/llava-1.5-7b-hf
+  --distributed-backend: nccl
+  --lr: 0.001
+  --lr-decay-style: cosine
+  --min-lr: 2.0e-5
+  --lr-warmup-iters: 150
+  --log-interval: 1
+  --save-interval: 200
+  --eval-interval: 20000
+  --eval-iters: 30
+  --tensor-model-parallel-size: 1
+  --pipeline-model-parallel-size: 1
+  --context-parallel-size: 1
+  --transformer-impl: transformer_engine
+  --bf16: true
+  --no-gradient-accumulation-fusion: true
+  --init-method-std: 0.006
+  --adam-beta1: 0.9
+  --adam-beta2: 0.95
+  --dataset-provider: llava_vlm
+  --model-provider: llava_vlm
+  --deterministic-mode: true
+  --log-memory-to-tensorboard: true
+  --dataloader-type: external
+  --data-path: ${DATA_PATH}/mixed/mcore_mimo_vlm/llava_pretrain_energon
+  --language-model-checkpoint: ${CHECKPOINT_LOAD_PATH}/model/vicuna_7b_pyt/dcp/mcore-v1.5_fp32/weights
+  --auto-detect-ckpt-format: true
+  --accumulate-allreduce-grads-in-fp32: true
+  --position-embedding-type: rope
+TEST_TYPE: frozen-start
+METRICS:
+  - "iteration-time"
+  - "lm loss"
+  - "num-zeros"
+  - "mem-allocated-bytes"
+  - "mem-max-allocated-bytes"
diff --git a/tests/functional_tests/test_cases/mimo/mimo_vlm_pretrain_convergence_tp1_pp1_cp2_dp8/golden_values_dev.json b/tests/functional_tests/test_cases/mimo/mimo_vlm_pretrain_convergence_tp1_pp1_cp2_dp8/golden_values_dev.json
new file mode 100644
index 00000000000..d60b063b7cc
--- /dev/null
+++ b/tests/functional_tests/test_cases/mimo/mimo_vlm_pretrain_convergence_tp1_pp1_cp2_dp8/golden_values_dev.json
@@ -0,0 +1,142 @@
+{
+    "lm loss": {
+        "start_step": 1,
+        "end_step": 100,
+        "step_interval": 5,
+        "values": {
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+            "90": 3.40519,
+            "95": 3.38313,
+            "100": 3.34749
+        }
+    },
+    "num-zeros": {
+        "start_step": 1,
+        "end_step": 100,
+        "step_interval": 5,
+        "values": {
+            "1": 47.0,
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diff --git a/tests/functional_tests/test_cases/mimo/mimo_vlm_pretrain_convergence_tp1_pp1_cp2_dp8/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/mimo/mimo_vlm_pretrain_convergence_tp1_pp1_cp2_dp8/golden_values_dev_dgx_h100.json
new file mode 100644
index 00000000000..a50cbc39e27
--- /dev/null
+++ b/tests/functional_tests/test_cases/mimo/mimo_vlm_pretrain_convergence_tp1_pp1_cp2_dp8/golden_values_dev_dgx_h100.json
@@ -0,0 +1,537 @@
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diff --git a/tests/functional_tests/test_cases/mimo/mimo_vlm_pretrain_convergence_tp1_pp1_cp2_dp8/model_config.yaml b/tests/functional_tests/test_cases/mimo/mimo_vlm_pretrain_convergence_tp1_pp1_cp2_dp8/model_config.yaml
new file mode 100644
index 00000000000..37c55e4cd93
--- /dev/null
+++ b/tests/functional_tests/test_cases/mimo/mimo_vlm_pretrain_convergence_tp1_pp1_cp2_dp8/model_config.yaml
@@ -0,0 +1,65 @@
+ENV_VARS:
+  CUDA_DEVICE_MAX_CONNECTIONS: 1
+  NVTE_ALLOW_NONDETERMINISTIC_ALGO: 0
+  NCCL_ALGO: Ring
+  CUBLAS_WORKSPACE_CONFIG: :4096:8
+  ARTIFACTS_ROOT: /workspace/checkpoints
+MODEL_ARGS:
+  --num-layers: 32
+  --hidden-size: 4096
+  --attention-dropout: 0.0
+  --hidden-dropout: 0.0
+  --num-attention-heads: 32
+  --log-params-norm: true
+  --log-num-zeros-in-grad: true
+  --log-validation-ppl-to-tensorboard: true
+  --log-timers-to-tensorboard: true
+  --tensorboard-dir: ${TENSORBOARD_PATH}
+  --micro-batch-size: 8
+  --global-batch-size: 128
+  --total-seq-length: 2048
+  --seq-length: 4096
+  --max-position-embeddings: 4096
+  --train-iters: 100
+  --timing-log-level: 2
+  --lr-decay-iters: 2200
+  --save: ${CHECKPOINT_SAVE_PATH}
+  --load: ${CHECKPOINT_LOAD_PATH}
+  --tokenizer-type: HuggingFaceTokenizer
+  --tokenizer-model: llava-hf/llava-1.5-7b-hf
+  --distributed-backend: nccl
+  --lr: 0.001
+  --lr-decay-style: cosine
+  --min-lr: 2.0e-5
+  --lr-warmup-iters: 150
+  --log-interval: 1
+  --save-interval: 200
+  --eval-interval: 20000
+  --eval-iters: 30
+  --tensor-model-parallel-size: 1
+  --pipeline-model-parallel-size: 1
+  --context-parallel-size: 2
+  --transformer-impl: transformer_engine
+  --attention-softmax-in-fp32: true
+  --bf16: true
+  --no-gradient-accumulation-fusion: true
+  --init-method-std: 0.006
+  --adam-beta1: 0.9
+  --adam-beta2: 0.95
+  --dataset-provider: llava_vlm
+  --model-provider: llava_vlm
+  --deterministic-mode: true
+  --log-memory-to-tensorboard: true
+  --dataloader-type: external
+  --data-path: ${DATA_PATH}/mixed/mcore_mimo_vlm/llava_pretrain_energon
+  --language-model-checkpoint: ${CHECKPOINT_LOAD_PATH}/model/vicuna_7b_pyt/dcp/mcore-v1.5_fp32/weights
+  --auto-detect-ckpt-format: true
+  --accumulate-allreduce-grads-in-fp32: true
+  --position-embedding-type: rope
+TEST_TYPE: frozen-start
+METRICS:
+  - "iteration-time"
+  - "lm loss"
+  - "num-zeros"
+  - "mem-allocated-bytes"
+  - "mem-max-allocated-bytes"
diff --git a/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp1pp4emp16etp1cp1_gb_200_release_sm/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp1pp4emp16etp1cp1_gb_200_release_sm/golden_values_dev_dgx_gb200.json
deleted file mode 100644
index 5e4de4d06ee..00000000000
--- a/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp1pp4emp16etp1cp1_gb_200_release_sm/golden_values_dev_dgx_gb200.json
+++ /dev/null
@@ -1,11492 +0,0 @@
-{
-    "lm loss": {
-        "start_step": 1,
-        "end_step": 9535,
-        "step_interval": 5,
-        "values": {
-            "1": 13.90637,
-            "5": 13.89465,
-            "10": 13.87132,
-            "15": 13.85789,
-            "20": 13.75898,
-            "25": 13.72214,
-            "30": 13.42091,
-            "35": 13.34203,
-            "40": 13.25256,
-            "45": 13.16108,
-            "50": 12.57591,
-            "55": 12.39253,
-            "60": 12.21628,
-            "65": 12.0505,
-            "70": 11.86941,
-            "75": 11.52687,
-            "80": 11.33205,
-            "85": 11.1146,
-            "90": 10.96645,
-            "95": 10.81121,
-            "100": 10.58355,
-            "105": 10.46089,
-            "110": 10.25095,
-            "115": 10.04008,
-            "120": 9.91015,
-            "125": 9.76938,
-            "130": 9.68146,
-            "135": 9.61196,
-            "140": 9.38399,
-            "145": 9.37863,
-            "150": 9.21742,
-            "155": 9.14491,
-            "160": 9.06631,
-            "165": 8.94936,
-            "170": 8.90341,
-            "175": 8.85991,
-            "180": 8.71416,
-            "185": 8.75178,
-            "190": 8.62925,
-            "195": 8.63671,
-            "200": 8.52092,
-            "205": 8.43087,
-            "210": 8.39184,
-            "215": 8.43945,
-            "220": 8.30747,
-            "225": 8.32272,
-            "230": 8.30667,
-            "235": 8.23045,
-            "240": 8.18417,
-            "245": 8.16512,
-            "250": 8.10245,
-            "255": 8.10897,
-            "260": 8.00768,
-            "265": 7.99256,
-            "270": 7.94369,
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\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp1pp4emp16etp1cp1_release/model_config.yaml b/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp1pp4emp16etp1cp1_release/model_config.yaml
index ab618ab915c..e504bcb1320 100644
--- a/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp1pp4emp16etp1cp1_release/model_config.yaml
+++ b/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp1pp4emp16etp1cp1_release/model_config.yaml
@@ -19,7 +19,7 @@ MODEL_ARGS:
   --distributed-timeout-minutes: 60
   --tensor-model-parallel-size: 2
   --pipeline-model-parallel-size: 4
-  --pipeline-model-parallel-layout: Et*2\\|\\(tt\\|\\)*5t\\|tmL # Et*2|(tt|)*5t|tmL
+  --pipeline-model-parallel-layout: Et*2|(tt|)*5t|tmL
   --expert-model-parallel-size: 16
   --context-parallel-size: 1
   --expert-tensor-parallel-size: 1
diff --git a/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp1pp4emp16etp1cp1_release_sm/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp1pp4emp16etp1cp1_release_sm/golden_values_dev_dgx_h100.json
index f3a45c077a1..7128b0fe3b7 100644
--- a/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp1pp4emp16etp1cp1_release_sm/golden_values_dev_dgx_h100.json
+++ b/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp1pp4emp16etp1cp1_release_sm/golden_values_dev_dgx_h100.json
@@ -4,1914 +4,1914 @@
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 }
\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp1pp4emp16etp1cp1_release_sm/model_config.yaml b/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp1pp4emp16etp1cp1_release_sm/model_config.yaml
index ab1df7f1d1e..49cca71a596 100644
--- a/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp1pp4emp16etp1cp1_release_sm/model_config.yaml
+++ b/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp1pp4emp16etp1cp1_release_sm/model_config.yaml
@@ -19,7 +19,7 @@ MODEL_ARGS:
   --distributed-timeout-minutes: 60
   --tensor-model-parallel-size: 2
   --pipeline-model-parallel-size: 4
-  --pipeline-model-parallel-layout: Et*2\\|\\(tt\\|\\)*5t\\|tmL # Et*2|(tt|)*5t|tmL
+  --pipeline-model-parallel-layout: Et*2|(tt|)*5t|tmL
   --expert-model-parallel-size: 16
   --context-parallel-size: 1
   --expert-tensor-parallel-size: 1
diff --git a/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp1pp4emp16etp1cp1_gb_200_release/model_config.yaml b/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp2pp2emp16etp1cp1_gb_200_release/model_config.yaml
similarity index 95%
rename from tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp1pp4emp16etp1cp1_gb_200_release/model_config.yaml
rename to tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp2pp2emp16etp1cp1_gb_200_release/model_config.yaml
index a77d456506c..4452ad22987 100644
--- a/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp1pp4emp16etp1cp1_gb_200_release/model_config.yaml
+++ b/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp2pp2emp16etp1cp1_gb_200_release/model_config.yaml
@@ -13,16 +13,16 @@ ENV_VARS:
   NON_DETERMINSTIC_RESULTS: 1
   NVSHMEM_IB_ENABLE_IBGDA: 0
   CUDA_DEVICE_MAX_CONNECTIONS: 1
-  NUM_OF_HYBRID_EP_RANKS_PER_NVLINK_DOMAIN: 16
+  NUM_OF_HYBRID_EP_RANKS_PER_NVLINK_DOMAIN: 8
   USE_MNNVL: 1
 TEST_TYPE: "release"
 MODEL_ARGS:
   # Distributed args
   --distributed-timeout-minutes: 60
   --tensor-model-parallel-size: 2
-  --pipeline-model-parallel-size: 4
-  --pipeline-model-parallel-layout: Et*2\\|\\(tt\\|\\)*5t\\|tmL # Et*2|(tt|)*5t|tmL
-  --expert-model-parallel-size: 16
+  --pipeline-model-parallel-size: 2
+  --pipeline-model-parallel-layout: "Et*7|t*7mL"
+  --expert-model-parallel-size: 8
   --context-parallel-size: 1
   --expert-tensor-parallel-size: 1
   --use-distributed-optimizer: true
@@ -34,7 +34,7 @@ MODEL_ARGS:
   --sequence-parallel: true
   --use-flash-attn: true
   --disable-bias-linear: true
-  --micro-batch-size: 1
+  --micro-batch-size: 4
   --global-batch-size: 512
   --train-samples: 24414062
   --exit-duration-in-mins: 220
diff --git a/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp2pp2emp16etp1cp1_gb_200_release_sm/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp2pp2emp16etp1cp1_gb_200_release_sm/golden_values_dev_dgx_gb200.json
new file mode 100644
index 00000000000..f1fe2600baf
--- /dev/null
+++ b/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp2pp2emp16etp1cp1_gb_200_release_sm/golden_values_dev_dgx_gb200.json
@@ -0,0 +1,11492 @@
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+            "9525": 7.54771,
+            "9530": 7.62317,
+            "9535": 7.59545
+        }
+    }
+}
\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp1pp4emp16etp1cp1_gb_200_release_sm/model_config.yaml b/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp2pp2emp16etp1cp1_gb_200_release_sm/model_config.yaml
similarity index 94%
rename from tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp1pp4emp16etp1cp1_gb_200_release_sm/model_config.yaml
rename to tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp2pp2emp16etp1cp1_gb_200_release_sm/model_config.yaml
index beb3633b510..36cd34bf752 100644
--- a/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp1pp4emp16etp1cp1_gb_200_release_sm/model_config.yaml
+++ b/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp2pp2emp16etp1cp1_gb_200_release_sm/model_config.yaml
@@ -13,16 +13,16 @@ ENV_VARS:
   NON_DETERMINSTIC_RESULTS: 1
   NVSHMEM_IB_ENABLE_IBGDA: 0
   CUDA_DEVICE_MAX_CONNECTIONS: 1
-  NUM_OF_HYBRID_EP_RANKS_PER_NVLINK_DOMAIN: 16
+  NUM_OF_HYBRID_EP_RANKS_PER_NVLINK_DOMAIN: 8
   USE_MNNVL: 1
 TEST_TYPE: "release"
 MODEL_ARGS:
   # Distributed args
   --distributed-timeout-minutes: 60
   --tensor-model-parallel-size: 2
-  --pipeline-model-parallel-size: 4
-  --pipeline-model-parallel-layout: Et*2\\|\\(tt\\|\\)*5t\\|tmL # Et*2|(tt|)*5t|tmL
-  --expert-model-parallel-size: 16
+  --pipeline-model-parallel-size: 2
+  --pipeline-model-parallel-layout: "Et*7|t*7mL"
+  --expert-model-parallel-size: 8
   --context-parallel-size: 1
   --expert-tensor-parallel-size: 1
   --use-distributed-optimizer: true
@@ -34,7 +34,7 @@ MODEL_ARGS:
   --sequence-parallel: true
   --use-flash-attn: true
   --disable-bias-linear: true
-  --micro-batch-size: 1
+  --micro-batch-size: 4
   --global-batch-size: 512
   --train-samples: 24414062
   --exit-duration-in-mins: 220
@@ -94,7 +94,7 @@ MODEL_ARGS:
   --adam-beta2: 0.95
 
   # Add MoE args
-  --num-experts: 64 # local 4 + 1 shared, EP16
+  --num-experts: 64 # local 8 + 1 shared, EP8
   --moe-layer-freq: ([0]*3+[1]*11)
   --moe-ffn-hidden-size: 2048
   --moe-shared-expert-intermediate-size: 2048
diff --git a/tests/functional_tests/test_cases/mixtral/mixtral_8x22b_tp2pp8ep8vpp1_release/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/mixtral/mixtral_8x22b_tp2pp8ep8vpp1_release/golden_values_dev_dgx_h100.json
index d87affad78d..65b1c44baec 100644
--- a/tests/functional_tests/test_cases/mixtral/mixtral_8x22b_tp2pp8ep8vpp1_release/golden_values_dev_dgx_h100.json
+++ b/tests/functional_tests/test_cases/mixtral/mixtral_8x22b_tp2pp8ep8vpp1_release/golden_values_dev_dgx_h100.json
@@ -1 +1,192 @@
-{"lm loss": {"start_step": 1, "end_step": 150, "step_interval": 5, "values": {"1": 1.73921, "5": 1.76233, "10": 1.71016, "15": 1.71693, "20": 1.70925, "25": 1.65675, "30": 1.70044, "35": 1.59874, "40": 1.61213, "45": 1.61385, "50": 1.5751, "55": 1.58708, "60": 1.58154, "65": 1.57132, "70": 1.55148, "75": 1.57899, "80": 1.56981, "85": 1.56007, "90": 1.57537, "95": 1.56237, "100": 1.535, "105": 1.52638, "110": 1.54277, "115": 1.52584, "120": 1.53289, "125": 1.53919, "130": 1.57159, "135": 1.52419, "140": 1.53203, "145": 1.50764, "150": 1.53393}}, "num-zeros": {"start_step": 1, "end_step": 150, "step_interval": 5, "values": {"1": 45157344.0, "5": 48253032.0, "10": 45832968.0, "15": 48763376.0, "20": 49512520.0, "25": 49548924.0, "30": 46404436.0, "35": 46939256.0, "40": 46914044.0, "45": 47307180.0, "50": 46042360.0, "55": 50101696.0, "60": 47000412.0, "65": 49045092.0, "70": 45219292.0, "75": 46238708.0, "80": 48393620.0, "85": 46355312.0, "90": 48178600.0, "95": 47343576.0, "100": 47841512.0, "105": 49604120.0, "110": 49585476.0, "115": 43063424.0, "120": 44880640.0, "125": 47404780.0, "130": 48068596.0, "135": 48338664.0, "140": 47951836.0, "145": 45426960.0, "150": 48922204.0}}, "mem-allocated-bytes": {"start_step": 1, "end_step": 150, "step_interval": 5, "values": {"1": 22504325120.0, "5": 22504327168.0, "10": 22504327168.0, "15": 22504327168.0, "20": 22504327168.0, "25": 22504327168.0, "30": 22504327168.0, "35": 22504327168.0, "40": 22504327168.0, "45": 22504327168.0, "50": 22504327168.0, "55": 22504327168.0, "60": 22504327168.0, "65": 22504327168.0, "70": 22504327168.0, "75": 22504327168.0, "80": 22504327168.0, "85": 22504327168.0, "90": 22504327168.0, "95": 22504327168.0, "100": 22504327168.0, "105": 22504327168.0, "110": 22504327168.0, "115": 22504327168.0, "120": 22504327168.0, "125": 22504327168.0, "130": 22504327168.0, "135": 22504327168.0, "140": 22504327168.0, "145": 22504327168.0, "150": 22504327168.0}}, "mem-max-allocated-bytes": {"start_step": 1, "end_step": 150, "step_interval": 5, "values": {"1": 26732187648.0, "5": 27730509824.0, "10": 27929333760.0, "15": 27929333760.0, "20": 27929333760.0, "25": 27929333760.0, "30": 27929333760.0, "35": 27929333760.0, "40": 28029356032.0, "45": 28029356032.0, "50": 28029356032.0, "55": 28029356032.0, "60": 28030414848.0, "65": 28030414848.0, "70": 28030414848.0, "75": 28030414848.0, "80": 28030414848.0, "85": 28030414848.0, "90": 28030414848.0, "95": 28030414848.0, "100": 28030414848.0, "105": 28030414848.0, "110": 28030414848.0, "115": 28030414848.0, "120": 28030414848.0, "125": 28030414848.0, "130": 28030414848.0, "135": 28030414848.0, "140": 28030414848.0, "145": 28030414848.0, "150": 28030414848.0}}, "iteration-time": {"start_step": 1, "end_step": 150, "step_interval": 5, "values": {"1": 141.97864, "5": 6.34884, "10": 7.2048, "15": 6.66177, "20": 6.76477, "25": 6.54753, "30": 6.66009, "35": 6.91619, "40": 7.11599, "45": 6.48324, "50": 6.31372, "55": 6.47363, "60": 6.48183, "65": 6.20812, "70": 7.3018, "75": 6.45172, "80": 6.35535, "85": 6.32769, "90": 6.5653, "95": 6.1168, "100": 5.8939, "105": 6.52854, "110": 6.7756, "115": 6.12091, "120": 6.33951, "125": 6.82648, "130": 6.30181, "135": 6.59449, "140": 6.49127, "145": 6.34704, "150": 6.13838}}}
\ No newline at end of file
+{
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+    "mem-allocated-bytes": {
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+}
\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/mixtral/mixtral_8x7b_tp1pp4ep8vpp8_release/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/mixtral/mixtral_8x7b_tp1pp4ep8vpp8_release/golden_values_dev_dgx_h100.json
index f7a6a80a2b7..8f120988635 100644
--- a/tests/functional_tests/test_cases/mixtral/mixtral_8x7b_tp1pp4ep8vpp8_release/golden_values_dev_dgx_h100.json
+++ b/tests/functional_tests/test_cases/mixtral/mixtral_8x7b_tp1pp4ep8vpp8_release/golden_values_dev_dgx_h100.json
@@ -1 +1,242 @@
-{"lm loss": {"start_step": 1, "end_step": 200, "step_interval": 5, "values": {"1": 1.80634, "5": 1.83171, "10": 1.77662, "15": 1.78754, "20": 1.79513, "25": 1.75231, "30": 1.80496, "35": 1.70559, "40": 1.72637, "45": 1.72361, "50": 1.68547, "55": 1.69587, "60": 1.6929, "65": 1.68666, "70": 1.65599, "75": 1.68397, "80": 1.67445, "85": 1.66157, "90": 1.68308, "95": 1.66497, "100": 1.64207, "105": 1.62509, "110": 1.63681, "115": 1.63436, "120": 1.63301, "125": 1.64265, "130": 1.66905, "135": 1.62605, "140": 1.64112, "145": 1.60827, "150": 1.62734, "155": 1.61971, "160": 1.6186, "165": 1.62252, "170": 1.6258, "175": 1.60163, "180": 1.60656, "185": 1.6715, "190": 1.61267, "195": 1.64063, "200": 1.62415}}, "num-zeros": {"start_step": 1, "end_step": 200, "step_interval": 5, "values": {"1": 30107084.0, "5": 32170564.0, "10": 30557572.0, "15": 32510632.0, "20": 33010312.0, "25": 33034532.0, "30": 30938196.0, "35": 31294880.0, "40": 31277928.0, "45": 31540264.0, "50": 30696956.0, "55": 33403104.0, "60": 31335408.0, "65": 32698254.0, "70": 30148036.0, "75": 30827970.0, "80": 32263964.0, "85": 30905140.0, "90": 32120752.0, "95": 31564360.0, "100": 31895836.0, "105": 33071156.0, "110": 33058724.0, "115": 28710640.0, "120": 29922562.0, "125": 31605040.0, "130": 32047268.0, "135": 32227450.0, "140": 31969408.0, "145": 30286728.0, "150": 32616604.0, "155": 33017540.0, "160": 31523356.0, "165": 33046308.0, "170": 31870992.0, "175": 31379856.0, "180": 30557072.0, "185": 31367220.0, "190": 32951800.0, "195": 31584284.0, "200": 30110532.0}}, "mem-allocated-bytes": {"start_step": 1, "end_step": 200, "step_interval": 5, "values": {"1": 19259990016.0, "5": 19259990016.0, "10": 19259990016.0, "15": 19259990016.0, "20": 19259990016.0, "25": 19259990016.0, "30": 19259990016.0, "35": 19259990016.0, "40": 19259990016.0, "45": 19259990016.0, "50": 19259990016.0, "55": 19259990016.0, "60": 19259990016.0, "65": 19259990016.0, "70": 19259990016.0, "75": 19259990016.0, "80": 19259990016.0, "85": 19259990016.0, "90": 19259990016.0, "95": 19259990016.0, "100": 19259990016.0, "105": 19259990016.0, "110": 19259990016.0, "115": 19259990016.0, "120": 19259990016.0, "125": 19259990016.0, "130": 19259990016.0, "135": 19259990016.0, "140": 19259990016.0, "145": 19259990016.0, "150": 19259990016.0, "155": 19259990016.0, "160": 19259990016.0, "165": 19259990016.0, "170": 19259990016.0, "175": 19259990016.0, "180": 19259990016.0, "185": 19259990016.0, "190": 19259990016.0, "195": 19259990016.0, "200": 19259990016.0}}, "mem-max-allocated-bytes": {"start_step": 1, "end_step": 200, "step_interval": 5, "values": {"1": 28930920448.0, "5": 34950066176.0, "10": 34952785920.0, "15": 35008057344.0, "20": 35030818816.0, "25": 35030818816.0, "30": 35043815424.0, "35": 35043815424.0, "40": 35043815424.0, "45": 35043815424.0, "50": 35043815424.0, "55": 35043815424.0, "60": 35043815424.0, "65": 35043815424.0, "70": 35043815424.0, "75": 35043815424.0, "80": 35043815424.0, "85": 35043815424.0, "90": 35043815424.0, "95": 35043815424.0, "100": 35043815424.0, "105": 35043815424.0, "110": 35043815424.0, "115": 35043815424.0, "120": 35043815424.0, "125": 35043815424.0, "130": 35043815424.0, "135": 35043815424.0, "140": 35043815424.0, "145": 35043815424.0, "150": 35043815424.0, "155": 35043815424.0, "160": 35043815424.0, "165": 35043815424.0, "170": 35043815424.0, "175": 35043815424.0, "180": 35043815424.0, "185": 35043815424.0, "190": 35043815424.0, "195": 35043815424.0, "200": 35043815424.0}}, "iteration-time": {"start_step": 1, "end_step": 200, "step_interval": 5, "values": {"1": 98.7801, "5": 4.04517, "10": 3.97416, "15": 3.43908, "20": 3.71676, "25": 3.60356, "30": 3.41843, "35": 3.41826, "40": 3.42692, "45": 3.42386, "50": 3.46431, "55": 3.73202, "60": 3.40706, "65": 3.55895, "70": 3.39731, "75": 3.39689, "80": 3.37554, "85": 3.40303, "90": 3.36983, "95": 3.36802, "100": 3.37332, "105": 3.37588, "110": 3.38044, "115": 3.37542, "120": 3.77014, "125": 3.6352, "130": 3.83078, "135": 3.35818, "140": 3.58233, "145": 3.64723, "150": 3.40026, "155": 3.35303, "160": 3.56001, "165": 3.55831, "170": 3.34244, "175": 3.35938, "180": 3.37525, "185": 3.35005, "190": 3.59691, "195": 3.34804, "200": 3.33378}}}
\ No newline at end of file
+{
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diff --git a/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_resume_torch_dist_te_8experts2parallel_dist_optimizer/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_resume_torch_dist_te_8experts2parallel_dist_optimizer/golden_values_dev_dgx_gb200.json
index 3dd007cc9ec..4c4c8c0c0cf 100644
--- a/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_resume_torch_dist_te_8experts2parallel_dist_optimizer/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_resume_torch_dist_te_8experts2parallel_dist_optimizer/golden_values_dev_dgx_gb200.json
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diff --git a/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_resume_torch_dist_te_8experts2parallel_dist_optimizer/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_resume_torch_dist_te_8experts2parallel_dist_optimizer/golden_values_dev_dgx_h100.json
index 64a0d3b0293..0c807dccff2 100644
--- a/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_resume_torch_dist_te_8experts2parallel_dist_optimizer/golden_values_dev_dgx_h100.json
+++ b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_resume_torch_dist_te_8experts2parallel_dist_optimizer/golden_values_dev_dgx_h100.json
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diff --git a/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_resume_torch_dist_te_8experts2parallel_multi_dist_optimizer_instances/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_resume_torch_dist_te_8experts2parallel_multi_dist_optimizer_instances/golden_values_dev_dgx_gb200.json
index 31167be6de5..160ba7e60e2 100644
--- a/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_resume_torch_dist_te_8experts2parallel_multi_dist_optimizer_instances/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_resume_torch_dist_te_8experts2parallel_multi_dist_optimizer_instances/golden_values_dev_dgx_gb200.json
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\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_resume_torch_dist_te_8experts2parallel_multi_dist_optimizer_instances/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_resume_torch_dist_te_8experts2parallel_multi_dist_optimizer_instances/golden_values_dev_dgx_h100.json
index bf57cfecddc..d4c864e5403 100644
--- a/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_resume_torch_dist_te_8experts2parallel_multi_dist_optimizer_instances/golden_values_dev_dgx_h100.json
+++ b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_resume_torch_dist_te_8experts2parallel_multi_dist_optimizer_instances/golden_values_dev_dgx_h100.json
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diff --git a/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_te_8experts2parallel_ddp_average_in_collective/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_te_8experts2parallel_ddp_average_in_collective/golden_values_dev_dgx_gb200.json
index ce3d79128b1..a6dfbf5a4e0 100644
--- a/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_te_8experts2parallel_ddp_average_in_collective/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_te_8experts2parallel_ddp_average_in_collective/golden_values_dev_dgx_gb200.json
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\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_te_8experts2parallel_ddp_average_in_collective/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_te_8experts2parallel_ddp_average_in_collective/golden_values_dev_dgx_h100.json
index 38498d3139b..a14d81728dc 100644
--- a/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_te_8experts2parallel_ddp_average_in_collective/golden_values_dev_dgx_h100.json
+++ b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_te_8experts2parallel_ddp_average_in_collective/golden_values_dev_dgx_h100.json
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diff --git a/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_te_8experts2parallel_overlap_grad_reduce_param_gather_groupedGEMM/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_te_8experts2parallel_overlap_grad_reduce_param_gather_groupedGEMM/golden_values_dev_dgx_gb200.json
index f62929eef31..54bf682b5f9 100644
--- a/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_te_8experts2parallel_overlap_grad_reduce_param_gather_groupedGEMM/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_te_8experts2parallel_overlap_grad_reduce_param_gather_groupedGEMM/golden_values_dev_dgx_gb200.json
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diff --git a/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_te_8experts2parallel_overlap_grad_reduce_param_gather_groupedGEMM/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_te_8experts2parallel_overlap_grad_reduce_param_gather_groupedGEMM/golden_values_dev_dgx_h100.json
index b626738d63e..fd88a2e7d31 100644
--- a/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_te_8experts2parallel_overlap_grad_reduce_param_gather_groupedGEMM/golden_values_dev_dgx_h100.json
+++ b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_te_8experts2parallel_overlap_grad_reduce_param_gather_groupedGEMM/golden_values_dev_dgx_h100.json
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diff --git a/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_te_a2a_ovlp_8experts_etp1_ep4/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_te_a2a_ovlp_8experts_etp1_ep4/golden_values_dev_dgx_gb200.json
index 682fa44a64d..295fb5f4394 100644
--- a/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_te_a2a_ovlp_8experts_etp1_ep4/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_te_a2a_ovlp_8experts_etp1_ep4/golden_values_dev_dgx_gb200.json
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diff --git a/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_te_a2a_ovlp_8experts_etp1_ep4/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_te_a2a_ovlp_8experts_etp1_ep4/golden_values_dev_dgx_h100.json
index 19b393f6369..196592138ff 100644
--- a/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_te_a2a_ovlp_8experts_etp1_ep4/golden_values_dev_dgx_h100.json
+++ b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_te_a2a_ovlp_8experts_etp1_ep4/golden_values_dev_dgx_h100.json
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 }
\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp2_ep4_etp1_selective_recompute_experimental/model_config.yaml b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp2_ep4_etp1_selective_recompute_experimental/model_config.yaml
index efb1fedf93c..e7971347f02 100644
--- a/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp2_ep4_etp1_selective_recompute_experimental/model_config.yaml
+++ b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp2_ep4_etp1_selective_recompute_experimental/model_config.yaml
@@ -46,7 +46,7 @@ MODEL_ARGS:
   # Add network size args
   --num-layers: 16
   --moe-layer-freq: ([0]*3+[1]*13)
-  --pipeline-model-parallel-layout: Et*3\\|\\(tt\\|\\)*6tmL # Et*3|(tt|)*6tmL
+  --pipeline-model-parallel-layout: Et*3|(tt|)*6tmL
   --hidden-size: 1024
   --ffn-hidden-size: 4096
   --num-attention-heads: 32
diff --git a/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_zp_z3_resume_torch_dist_te_8experts2parallel_top2router/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_zp_z3_resume_torch_dist_te_8experts2parallel_top2router/golden_values_dev_dgx_gb200.json
index 113a491b0ba..5e07fb3bfad 100644
--- a/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_zp_z3_resume_torch_dist_te_8experts2parallel_top2router/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_zp_z3_resume_torch_dist_te_8experts2parallel_top2router/golden_values_dev_dgx_gb200.json
@@ -6,104 +6,104 @@
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@@ -111,106 +111,106 @@
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diff --git a/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_zp_z3_resume_torch_dist_te_8experts2parallel_top2router/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_zp_z3_resume_torch_dist_te_8experts2parallel_top2router/golden_values_dev_dgx_h100.json
index bfbb1e850e1..3459a646ecd 100644
--- a/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_zp_z3_resume_torch_dist_te_8experts2parallel_top2router/golden_values_dev_dgx_h100.json
+++ b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_zp_z3_resume_torch_dist_te_8experts2parallel_top2router/golden_values_dev_dgx_h100.json
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diff --git a/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_cp2_pp2_ep2_te_4experts2parallel/golden_values_dev_dgx_a100.json b/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_cp2_pp2_ep2_te_4experts2parallel/golden_values_dev_dgx_a100.json
index dffbbf25de6..37f74714dfc 100644
--- a/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_cp2_pp2_ep2_te_4experts2parallel/golden_values_dev_dgx_a100.json
+++ b/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_cp2_pp2_ep2_te_4experts2parallel/golden_values_dev_dgx_a100.json
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diff --git a/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_cp2_pp2_ep2_te_4experts2parallel/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_cp2_pp2_ep2_te_4experts2parallel/golden_values_dev_dgx_gb200.json
index bfea64b8438..ce15ba8d8b8 100644
--- a/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_cp2_pp2_ep2_te_4experts2parallel/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_cp2_pp2_ep2_te_4experts2parallel/golden_values_dev_dgx_gb200.json
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\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_cp2_pp2_ep2_te_4experts2parallel_dp_last/golden_values_dev_dgx_a100.json b/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_cp2_pp2_ep2_te_4experts2parallel_dp_last/golden_values_dev_dgx_a100.json
index d2a07cdf1dd..cf306109a97 100644
--- a/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_cp2_pp2_ep2_te_4experts2parallel_dp_last/golden_values_dev_dgx_a100.json
+++ b/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_cp2_pp2_ep2_te_4experts2parallel_dp_last/golden_values_dev_dgx_a100.json
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diff --git a/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_cp2_pp2_ep2_te_4experts2parallel_dp_last/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_cp2_pp2_ep2_te_4experts2parallel_dp_last/golden_values_dev_dgx_gb200.json
index 2bcdb30bc50..27a9727334c 100644
--- a/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_cp2_pp2_ep2_te_4experts2parallel_dp_last/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_cp2_pp2_ep2_te_4experts2parallel_dp_last/golden_values_dev_dgx_gb200.json
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\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_pp2_ep2_etp2_te_4experts2parallel/golden_values_dev_dgx_a100.json b/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_pp2_ep2_etp2_te_4experts2parallel/golden_values_dev_dgx_a100.json
index e3b2e326fda..a10a199ae66 100644
--- a/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_pp2_ep2_etp2_te_4experts2parallel/golden_values_dev_dgx_a100.json
+++ b/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_pp2_ep2_etp2_te_4experts2parallel/golden_values_dev_dgx_a100.json
@@ -6,54 +6,54 @@
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 }
\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_pp2_ep2_etp2_te_4experts2parallel/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_pp2_ep2_etp2_te_4experts2parallel/golden_values_dev_dgx_h100.json
index 6ec10f4f931..c53f8ba9f79 100644
--- a/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_pp2_ep2_etp2_te_4experts2parallel/golden_values_dev_dgx_h100.json
+++ b/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_pp2_ep2_etp2_te_4experts2parallel/golden_values_dev_dgx_h100.json
@@ -6,54 +6,54 @@
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diff --git a/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_pp2_ep2_etp2_te_4experts2parallel_dp_last/golden_values_dev_dgx_a100.json b/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_pp2_ep2_etp2_te_4experts2parallel_dp_last/golden_values_dev_dgx_a100.json
index b4462fc931e..890246c909f 100644
--- a/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_pp2_ep2_etp2_te_4experts2parallel_dp_last/golden_values_dev_dgx_a100.json
+++ b/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_pp2_ep2_etp2_te_4experts2parallel_dp_last/golden_values_dev_dgx_a100.json
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\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_pp2_ep2_etp2_te_4experts2parallel_dp_last/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_pp2_ep2_etp2_te_4experts2parallel_dp_last/golden_values_dev_dgx_h100.json
index 64dc8751e92..0617e255ebc 100644
--- a/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_pp2_ep2_etp2_te_4experts2parallel_dp_last/golden_values_dev_dgx_h100.json
+++ b/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_pp2_ep2_etp2_te_4experts2parallel_dp_last/golden_values_dev_dgx_h100.json
@@ -6,54 +6,54 @@
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diff --git a/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_pp2_ep2_te_4experts2parallel/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_pp2_ep2_te_4experts2parallel/golden_values_dev_dgx_gb200.json
index 3e910ef7869..d3e7b4da4e1 100644
--- a/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_pp2_ep2_te_4experts2parallel/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_pp2_ep2_te_4experts2parallel/golden_values_dev_dgx_gb200.json
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diff --git a/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_pp2_ep2_te_4experts2parallel/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_pp2_ep2_te_4experts2parallel/golden_values_dev_dgx_h100.json
index 8928145fcbb..a6938ff40f2 100644
--- a/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_pp2_ep2_te_4experts2parallel/golden_values_dev_dgx_h100.json
+++ b/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_pp2_ep2_te_4experts2parallel/golden_values_dev_dgx_h100.json
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diff --git a/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_ep8_resume_torch_dist_dist_muon/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_ep8_resume_torch_dist_dist_muon/golden_values_dev_dgx_gb200.json
index d9441fb83aa..f1fdba84cc4 100644
--- a/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_ep8_resume_torch_dist_dist_muon/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_ep8_resume_torch_dist_dist_muon/golden_values_dev_dgx_gb200.json
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diff --git a/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_ep8_resume_torch_dist_dist_muon/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_ep8_resume_torch_dist_dist_muon/golden_values_dev_dgx_h100.json
index ccbece04f60..9320e522e7b 100644
--- a/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_ep8_resume_torch_dist_dist_muon/golden_values_dev_dgx_h100.json
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diff --git a/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_ep8_resume_torch_dist_dist_optimizer/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_ep8_resume_torch_dist_dist_optimizer/golden_values_dev_dgx_h100.json
index b106daa13a1..135841ca0c1 100644
--- a/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_ep8_resume_torch_dist_dist_optimizer/golden_values_dev_dgx_h100.json
+++ b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_ep8_resume_torch_dist_dist_optimizer/golden_values_dev_dgx_h100.json
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diff --git a/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_ep8_resume_torch_dist_muon/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_ep8_resume_torch_dist_muon/golden_values_dev_dgx_gb200.json
index fe8d3f78926..1c1129cfec0 100644
--- a/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_ep8_resume_torch_dist_muon/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_ep8_resume_torch_dist_muon/golden_values_dev_dgx_gb200.json
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diff --git a/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_ep8_resume_torch_dist_muon/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_ep8_resume_torch_dist_muon/golden_values_dev_dgx_h100.json
index 7d62923f634..a00832c0714 100644
--- a/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_ep8_resume_torch_dist_muon/golden_values_dev_dgx_h100.json
+++ b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_ep8_resume_torch_dist_muon/golden_values_dev_dgx_h100.json
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diff --git a/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp2_pp2_ep4_etp1_fine_grained_offloading/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp2_pp2_ep4_etp1_fine_grained_offloading/golden_values_dev_dgx_h100.json
index d5ced620365..623687f52b3 100644
--- a/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp2_pp2_ep4_etp1_fine_grained_offloading/golden_values_dev_dgx_h100.json
+++ b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp2_pp2_ep4_etp1_fine_grained_offloading/golden_values_dev_dgx_h100.json
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 }
\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp2_pp2_ep4_etp1_fine_grained_offloading/model_config.yaml b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp2_pp2_ep4_etp1_fine_grained_offloading/model_config.yaml
index a37dd0dc658..c04d55564a3 100644
--- a/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp2_pp2_ep4_etp1_fine_grained_offloading/model_config.yaml
+++ b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp2_pp2_ep4_etp1_fine_grained_offloading/model_config.yaml
@@ -51,7 +51,7 @@ MODEL_ARGS:
   # Add network size args
   --num-layers: 15
   --moe-layer-freq: ([0]*3+[1]*12)
-  --pipeline-model-parallel-layout: Et*3\\|\\(tt\\|\\)*6mL # Et*3|(tt|)*6mL
+  --pipeline-model-parallel-layout: Et*3|(tt|)*6mL
   --hidden-size: 1024
   --ffn-hidden-size: 4096
   --num-attention-heads: 32
diff --git a/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp2_pp2_ep4_etp1_no_mtp_no_a2a_ovlp_fine_grained_offloading/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp2_pp2_ep4_etp1_no_mtp_no_a2a_ovlp_fine_grained_offloading/golden_values_dev_dgx_h100.json
index 57848f8130e..acfa1b0201d 100644
--- a/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp2_pp2_ep4_etp1_no_mtp_no_a2a_ovlp_fine_grained_offloading/golden_values_dev_dgx_h100.json
+++ b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp2_pp2_ep4_etp1_no_mtp_no_a2a_ovlp_fine_grained_offloading/golden_values_dev_dgx_h100.json
@@ -4,56 +4,56 @@
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@@ -61,56 +61,56 @@
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     }
 }
\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp2_pp2_ep4_etp1_no_mtp_no_a2a_ovlp_fine_grained_offloading/model_config.yaml b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp2_pp2_ep4_etp1_no_mtp_no_a2a_ovlp_fine_grained_offloading/model_config.yaml
index da78378ddae..dbfb29ea48c 100644
--- a/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp2_pp2_ep4_etp1_no_mtp_no_a2a_ovlp_fine_grained_offloading/model_config.yaml
+++ b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp2_pp2_ep4_etp1_no_mtp_no_a2a_ovlp_fine_grained_offloading/model_config.yaml
@@ -51,7 +51,7 @@ MODEL_ARGS:
   # Add network size args
   --num-layers: 15
   --moe-layer-freq: ([0]*3+[1]*12)
-  --pipeline-model-parallel-layout: Et*3\\|\\(tt\\|\\)*6L # Et*3|(tt|)*6L
+  --pipeline-model-parallel-layout: Et*3|(tt|)*6L
   --hidden-size: 1024
   --ffn-hidden-size: 4096
   --num-attention-heads: 32
diff --git a/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp4_ep2_etp2_pp2_resume_torch_dist_dist_optimizer/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp4_ep2_etp2_pp2_resume_torch_dist_dist_optimizer/golden_values_dev_dgx_gb200.json
index a35a7574e59..e4b19929015 100644
--- a/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp4_ep2_etp2_pp2_resume_torch_dist_dist_optimizer/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp4_ep2_etp2_pp2_resume_torch_dist_dist_optimizer/golden_values_dev_dgx_gb200.json
@@ -4,106 +4,106 @@
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@@ -111,106 +111,106 @@
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diff --git a/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp4_ep2_etp2_pp2_resume_torch_dist_dist_optimizer/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp4_ep2_etp2_pp2_resume_torch_dist_dist_optimizer/golden_values_dev_dgx_h100.json
index dc836c3d699..8aa646eca05 100644
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+++ b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp4_ep2_etp2_pp2_resume_torch_dist_dist_optimizer/golden_values_dev_dgx_h100.json
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diff --git a/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp4_ep2_etp2_pp2_scoped_cudagraph/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp4_ep2_etp2_pp2_scoped_cudagraph/golden_values_dev_dgx_gb200.json
index 8710366a4a2..82393aa2372 100644
--- a/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp4_ep2_etp2_pp2_scoped_cudagraph/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp4_ep2_etp2_pp2_scoped_cudagraph/golden_values_dev_dgx_gb200.json
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\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp4_ep2_etp2_pp2_scoped_cudagraph/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp4_ep2_etp2_pp2_scoped_cudagraph/golden_values_dev_dgx_h100.json
index 0954418053d..67ac17c9fe4 100644
--- a/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp4_ep2_etp2_pp2_scoped_cudagraph/golden_values_dev_dgx_h100.json
+++ b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp4_ep2_etp2_pp2_scoped_cudagraph/golden_values_dev_dgx_h100.json
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\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp4_ep2_etp2_pp2_scoped_cudagraph/model_config.yaml b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp4_ep2_etp2_pp2_scoped_cudagraph/model_config.yaml
index f0d1cc0afd3..fd0d79e0986 100644
--- a/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp4_ep2_etp2_pp2_scoped_cudagraph/model_config.yaml
+++ b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp4_ep2_etp2_pp2_scoped_cudagraph/model_config.yaml
@@ -33,7 +33,7 @@ MODEL_ARGS:
   --pipeline-model-parallel-size: 2
   --expert-model-parallel-size: 2
   --expert-tensor-parallel-size: 2
-  --pipeline-model-parallel-layout: Et\\|\\(tt\\|\\)*6mL # Et|(tt|)*6mL
+  --pipeline-model-parallel-layout: Et|(tt|)*6mL
   --sequence-parallel: true
   --num-experts: 8
   --use-distributed-optimizer: true
diff --git a/tests/functional_tests/test_cases/moe/gpt_dynamic_inference_cuda_graphs_pad_tp4_pp1_ep4_16B_logitsmatch/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/moe/gpt_dynamic_inference_cuda_graphs_pad_tp4_pp1_ep4_16B_logitsmatch/golden_values_dev_dgx_h100.json
index 447e404810b..3f022ffde7e 100644
--- a/tests/functional_tests/test_cases/moe/gpt_dynamic_inference_cuda_graphs_pad_tp4_pp1_ep4_16B_logitsmatch/golden_values_dev_dgx_h100.json
+++ b/tests/functional_tests/test_cases/moe/gpt_dynamic_inference_cuda_graphs_pad_tp4_pp1_ep4_16B_logitsmatch/golden_values_dev_dgx_h100.json
@@ -34,7 +34,8 @@
    1394,
    1636
   ],
-  "latency": 0.6956486701965332,
+  "latency": 0.6897497177124023,
+  "ttft": 0.17203974723815918,
   "cuda_graph_request_count_map": {
    "8": 29
   },
@@ -43,253 +44,255 @@
   "prompt_top_n_logprobs": null,
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  },
  "throughput": [
-  1.9407774475980641,
-  40.8327035151158,
-  42.82685786577602,
-  42.814063580843204,
-  42.88001112304976,
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- ]
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+  43.24482453812981
+ ],
+ "mem-max-allocated-bytes": 30516649984,
+ "lifetime_prefill_token_count": 88
 }
\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/moe/gpt_dynamic_inference_tp4_etp1_pp1_ep8_16B_logitsmatch_cudagraph_zmq/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/moe/gpt_dynamic_inference_tp4_etp1_pp1_ep8_16B_logitsmatch_cudagraph_zmq/golden_values_dev_dgx_h100.json
index ab970730d9b..73be1228f97 100644
--- a/tests/functional_tests/test_cases/moe/gpt_dynamic_inference_tp4_etp1_pp1_ep8_16B_logitsmatch_cudagraph_zmq/golden_values_dev_dgx_h100.json
+++ b/tests/functional_tests/test_cases/moe/gpt_dynamic_inference_tp4_etp1_pp1_ep8_16B_logitsmatch_cudagraph_zmq/golden_values_dev_dgx_h100.json
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                 [
                     18,
@@ -21515,8 +21515,8 @@
                     55,
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@@ -21534,14 +21534,14 @@
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                 [
                     39,
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                     6,
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@@ -21558,7 +21558,7 @@
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                     59,
@@ -21580,8 +21580,8 @@
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@@ -21636,8 +21636,8 @@
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@@ -21821,8 +21821,8 @@
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@@ -21925,8 +21925,8 @@
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@@ -21970,7 +21970,7 @@
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                 [
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                     38,
@@ -22130,7 +22130,7 @@
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@@ -22265,8 +22265,8 @@
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@@ -22339,8 +22339,8 @@
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@@ -22388,7 +22388,7 @@
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             [
@@ -22460,8 +22460,8 @@
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@@ -22502,7 +22502,7 @@
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@@ -22596,8 +22596,8 @@
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                 [
@@ -22621,8 +22621,8 @@
                 [
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@@ -22646,8 +22646,8 @@
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@@ -22742,8 +22742,8 @@
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@@ -22767,16 +22767,16 @@
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                     57,
@@ -22789,10 +22789,10 @@
                 [
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                     33,
@@ -22808,21 +22808,21 @@
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@@ -22840,8 +22840,8 @@
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@@ -23244,15 +23244,15 @@
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@@ -23306,26 +23306,26 @@
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@@ -23461,8 +23461,8 @@
                     50,
                     13,
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@@ -23475,8 +23475,8 @@
                 [
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@@ -23550,8 +23550,8 @@
                     15,
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                     4
                 ],
                 [
@@ -23624,7 +23624,7 @@
                     23,
                     43,
                     60,
-                    41
+                    34
                 ],
                 [
                     45,
@@ -23655,8 +23655,8 @@
                     37,
                     40,
                     41,
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+                    54,
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                 ],
                 [
                     48,
@@ -23680,7 +23680,7 @@
                     49,
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                 [
                     51,
@@ -23786,7 +23786,7 @@
                     59,
                     22,
                     15,
-                    46
+                    18
                 ],
                 [
                     8,
@@ -23810,7 +23810,7 @@
                     13,
                     50,
                     14,
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                 ],
                 [
                     54,
@@ -23977,8 +23977,8 @@
                 [
                     47,
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@@ -23996,7 +23996,7 @@
                     36,
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                 ],
                 [
                     5,
@@ -24012,13 +24012,13 @@
                     34,
                     26,
                     42,
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+                    60
                 ],
                 [
                     20,
                     5,
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                     30,
                     33
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@@ -24035,8 +24035,8 @@
                     61,
                     30,
                     14,
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+                    25,
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                 ],
                 [
                     56,
@@ -24044,7 +24044,7 @@
                     2,
                     5,
                     54,
-                    37
+                    44
                 ],
                 [
                     12,
@@ -24072,16 +24072,16 @@
                 ],
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                     17,
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                     6,
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                     29,
                     49,
                     41
                 ],
                 [
                     38,
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                     27
@@ -24100,7 +24100,7 @@
                     35,
                     34,
                     38,
-                    3
+                    55
                 ],
                 [
                     61,
@@ -24165,8 +24165,8 @@
                     13,
                     1,
                     8,
-                    25,
-                    43
+                    43,
+                    25
                 ],
                 [
                     13,
@@ -24185,8 +24185,8 @@
                     56
                 ],
                 [
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@@ -24237,8 +24237,8 @@
                     20,
                     33,
                     14,
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                     17,
@@ -24321,8 +24321,8 @@
                     22
                 ],
                 [
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@@ -24408,7 +24408,7 @@
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@@ -24416,7 +24416,7 @@
                     54,
                     8,
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                 [
                     6,
@@ -24431,23 +24431,23 @@
                     38,
                     23,
                     53,
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-                    17
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                 ],
                 [
                     26,
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                 ],
                 [
@@ -24456,7 +24456,7 @@
                     30,
                     25,
                     20,
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                 ],
                 [
                     18,
@@ -24480,28 +24480,28 @@
                     5,
                     24,
                     37,
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                     47,
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@@ -24511,16 +24511,16 @@
                     52,
                     49,
                     15,
-                    28,
-                    40
+                    40,
+                    63
                 ],
                 [
                     28,
                     39,
                     3,
                     26,
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                 [
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@@ -24532,34 +24532,34 @@
                 ],
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                     41,
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                 ],
                 [
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                     4,
                     34,
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                 [
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                 [
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                 ],
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@@ -24567,8 +24567,8 @@
                     62,
                     6,
                     23,
-                    10,
-                    36
+                    36,
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                 ]
             ],
             [
@@ -24630,11 +24630,11 @@
                 ],
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                     21,
-                    14,
                     59,
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                 ],
                 [
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@@ -24648,17 +24648,17 @@
                     14,
                     53,
                     30,
-                    4,
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+                    4,
                     36
                 ],
                 [
                     26,
                     42,
                     5,
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-                    19
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                 ],
                 [
                     22,
@@ -24698,7 +24698,7 @@
                     24,
                     5,
                     53,
-                    37
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                 ],
                 [
                     12,
@@ -24719,8 +24719,8 @@
                 [
                     19,
                     10,
-                    20,
                     32,
+                    20,
                     18,
                     1
                 ],
@@ -24876,22 +24876,22 @@
                     22,
                     15,
                     5,
-                    46
+                    0
                 ],
                 [
                     7,
                     22,
-                    24,
                     63,
-                    53,
-                    5
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+                    53
                 ],
                 [
                     11,
                     3,
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                     24
                 ],
                 [
@@ -24899,22 +24899,22 @@
                     18,
                     13,
                     39,
-                    0,
-                    17
+                    17,
+                    0
                 ],
                 [
                     62,
                     54,
-                    5,
-                    18,
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                     14
                 ],
                 [
                     19,
                     43,
-                    31,
                     30,
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                     42
                 ],
@@ -24924,7 +24924,7 @@
                     51,
                     34,
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-                    55
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                 ],
                 [
                     59,
@@ -24976,11 +24976,11 @@
                 ],
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                     4,
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                     14,
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-                    11,
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                 ],
                 [
                     47,
@@ -24988,15 +24988,15 @@
                     8,
                     61,
                     13,
-                    9
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                 ],
                 [
                     23,
                     3,
                     27,
                     43,
-                    0,
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                 [
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@@ -25004,7 +25004,7 @@
                     59,
                     62,
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                 ]
             ],
             [
@@ -25078,15 +25078,15 @@
                     23,
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                 ],
                 [
                     53,
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@@ -25102,15 +25102,15 @@
                     60,
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                 ],
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                     24,
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@@ -25132,15 +25132,15 @@
                     56,
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                 ],
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@@ -25150,7 +25150,7 @@
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                     32,
@@ -25182,7 +25182,7 @@
                     30,
                     60,
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-                    1
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@@ -25205,8 +25205,8 @@
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@@ -25312,7 +25312,7 @@
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@@ -25327,8 +25327,8 @@
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@@ -25351,15 +25351,15 @@
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                 ],
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                 ],
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@@ -25383,8 +25383,8 @@
                     53,
                     28,
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-                    12
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@@ -25422,8 +25422,8 @@
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@@ -25432,7 +25432,7 @@
                     28,
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@@ -25457,8 +25457,8 @@
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                     43,
@@ -25536,8 +25536,8 @@
                     57,
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@@ -25578,7 +25578,7 @@
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@@ -25593,14 +25593,14 @@
                     25,
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-                    58
+                    58,
+                    54
                 ],
                 [
                     49,
                     30,
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                     13,
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                     28
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@@ -25641,8 +25641,8 @@
                     13,
                     8,
                     61,
-                    47,
-                    9
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                 [
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@@ -25650,7 +25650,7 @@
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diff --git a/tests/functional_tests/test_cases/moe/gpt_dynamic_inference_tp4_etp1_pp1_ep8_16B_logitsmatch_cudagraph_zmq/model_config.yaml b/tests/functional_tests/test_cases/moe/gpt_dynamic_inference_tp4_etp1_pp1_ep8_16B_logitsmatch_cudagraph_zmq/model_config.yaml
index 5ab6d37f17e..80e2a37c250 100644
--- a/tests/functional_tests/test_cases/moe/gpt_dynamic_inference_tp4_etp1_pp1_ep8_16B_logitsmatch_cudagraph_zmq/model_config.yaml
+++ b/tests/functional_tests/test_cases/moe/gpt_dynamic_inference_tp4_etp1_pp1_ep8_16B_logitsmatch_cudagraph_zmq/model_config.yaml
@@ -79,13 +79,10 @@ MODEL_ARGS:
   --inference-dynamic-batching-buffer-size-gb: 20
   --cuda-graph-impl: local 
   --moe-pad-experts-for-cuda-graph-inference: true
-  --inference-dynamic-batching-buffer-size-gb: 20
-  --inference-dynamic-batching-num-cuda-graphs: 1 
-  --inference-dynamic-batching-max-requests: 512
+  --inference-dynamic-batching-num-cuda-graphs: -1 
+  --inference-dynamic-batching-max-requests: 16
   --inference-logging-step-interval: 1
-  --sequence-parallel: true
   --moe-enable-routing-replay: true
-
 METRICS:
   - "generated_tokens"
   - "logprobs"
diff --git a/tests/functional_tests/test_cases/moe/gpt_dynamic_inference_tp4_etp1_pp1_ep8_16B_logitsmatch_zmq/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/moe/gpt_dynamic_inference_tp4_etp1_pp1_ep8_16B_logitsmatch_zmq/golden_values_dev_dgx_h100.json
index 66c9e3e4121..915f3864fad 100644
--- a/tests/functional_tests/test_cases/moe/gpt_dynamic_inference_tp4_etp1_pp1_ep8_16B_logitsmatch_zmq/golden_values_dev_dgx_h100.json
+++ b/tests/functional_tests/test_cases/moe/gpt_dynamic_inference_tp4_etp1_pp1_ep8_16B_logitsmatch_zmq/golden_values_dev_dgx_h100.json
@@ -34,125 +34,25633 @@
             1394,
             1636
         ],
-        "latency": 28.185462809633464,
+        "latency": 20.171302660994115,
         "logprobs": [
             -10.737512588500977,
-            -3.724862575531006,
-            -2.833397388458252,
-            -1.2464861869812012,
-            -0.2549239993095398,
-            -1.7607988119125366,
-            -2.419379711151123,
-            -1.9533929824829102,
-            -2.1014301776885986,
-            -6.169030666351318,
-            -0.8734959363937378,
-            -2.4733574390411377,
-            -3.4822516441345215,
-            -4.180896759033203,
-            -1.9767613410949707,
-            -1.8347630500793457,
-            -2.2581257820129395,
-            -7.180149078369141,
-            -0.0453881211578846,
-            -1.9841610193252563,
-            -5.015386581420898,
-            -8.827117919921875,
-            -9.885746002197266,
-            -0.8498678207397461,
-            -4.770059585571289,
-            -0.855280339717865,
-            -2.2494924068450928,
-            -0.017164958640933037,
-            -0.03715415671467781,
-            -3.4830124378204346,
-            -8.635110855102539,
-            -1.2520610094070435,
-            -6.62324857711792,
-            -3.639960765838623,
-            -3.664339542388916,
-            -4.182392597198486,
-            -2.1796066761016846,
-            -1.0725229978561401,
-            -0.26311880350112915,
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+                [
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+                [
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+            ]
         ]
     }
 }
\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/moe/gpt_dynamic_inference_tp4_etp1_pp1_ep8_16B_logitsmatch_zmq/model_config.yaml b/tests/functional_tests/test_cases/moe/gpt_dynamic_inference_tp4_etp1_pp1_ep8_16B_logitsmatch_zmq/model_config.yaml
index d62d10db7c1..479cb7a4751 100644
--- a/tests/functional_tests/test_cases/moe/gpt_dynamic_inference_tp4_etp1_pp1_ep8_16B_logitsmatch_zmq/model_config.yaml
+++ b/tests/functional_tests/test_cases/moe/gpt_dynamic_inference_tp4_etp1_pp1_ep8_16B_logitsmatch_zmq/model_config.yaml
@@ -76,9 +76,11 @@ MODEL_ARGS:
   --prompts: "Time travel to 2008, and go to a bar or a club or one of the myriad disco-basements on the Lower East Side that does not quite know which of those it is. Dance awkwardly in a room full of other glittered-up nerds, and wait for something to happen, buoyed on the feeling that this is the big swollen heart of life, that this is New York like the movies."
   --incoming-requests-per-sec: -1 # all requests arrive up front.
   --inference-repeat-n: 8
-  --inference-logging-step-interval: 1 
-  --sequence-parallel: true
-  
+  --inference-dynamic-batching-buffer-size-gb: 20
+  --inference-dynamic-batching-max-requests: 16
+  --inference-logging-step-interval: 1
+  --moe-enable-routing-replay: true
 METRICS:
   - "generated_tokens"
   - "logprobs"
+  - "routing_indices"
diff --git a/tests/functional_tests/test_cases/moe/gpt_dynamic_inference_tp4_etp1_pp1_ep8_16B_logitsmatch_zmq_suspend_resume/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/moe/gpt_dynamic_inference_tp4_etp1_pp1_ep8_16B_logitsmatch_zmq_suspend_resume/golden_values_dev_dgx_h100.json
new file mode 100644
index 00000000000..66c9e3e4121
--- /dev/null
+++ b/tests/functional_tests/test_cases/moe/gpt_dynamic_inference_tp4_etp1_pp1_ep8_16B_logitsmatch_zmq_suspend_resume/golden_values_dev_dgx_h100.json
@@ -0,0 +1,158 @@
+{
+    "0": {
+        "input_prompt": "Time travel to 2008, and go to a bar or a club or one of the myriad disco-basements on the Lower East Side that does not quite know which of those it is. Dance awkwardly in a room full of other glittered-up nerds, and wait for something to happen, buoyed on the feeling that this is the big swollen heart of life, that this is New York like the movies.",
+        "generated_text": " Wait for the moment when the music stops, and the lights come up, and the DJ says, \"I'm going to play a song for you",
+        "generated_tokens": [
+            32844,
+            1394,
+            1278,
+            4735,
+            2200,
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+            -1.2342015504837036,
+            -0.5394397377967834
+        ]
+    }
+}
\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/moe/gpt_dynamic_inference_tp4_etp1_pp1_ep8_16B_logitsmatch_zmq_suspend_resume/model_config.yaml b/tests/functional_tests/test_cases/moe/gpt_dynamic_inference_tp4_etp1_pp1_ep8_16B_logitsmatch_zmq_suspend_resume/model_config.yaml
new file mode 100644
index 00000000000..1f302455440
--- /dev/null
+++ b/tests/functional_tests/test_cases/moe/gpt_dynamic_inference_tp4_etp1_pp1_ep8_16B_logitsmatch_zmq_suspend_resume/model_config.yaml
@@ -0,0 +1,89 @@
+ENV_VARS:
+  CUDA_DEVICE_MAX_CONNECTIONS: 1
+  NVTE_ALLOW_NONDETERMINISTIC_ALGO: 0
+  NCCL_ALGO: Ring
+  CUBLAS_WORKSPACE_CONFIG: :4096:8
+TEST_TYPE: frozen-start
+MODE: inference
+MODEL_ARGS:
+  --log-num-zeros-in-grad: true
+  --log-validation-ppl-to-tensorboard: true
+  --log-timers-to-tensorboard: true
+  --log-memory-to-tensorboard: true
+  --timing-log-level: 0
+  --load: ${CHECKPOINT_LOAD_PATH}/model/deepseek_16b_pyt/dcp/mcore-v1_bf16/checkpoints
+  --tokenizer-model: ${CHECKPOINT_LOAD_PATH}/model/deepseek_16b_pyt/dcp/mcore-v1_bf16/multiMixV8.gpt4o_nc_sd.500000.128k.vocab.json
+  --tokenizer-type: TikTokenizer
+  --tiktoken-pattern: v2
+  --distributed-backend: nccl
+  --log-interval: 1
+  --transformer-impl: transformer_engine
+  --tensor-model-parallel-size: 4
+  --pipeline-model-parallel-size: 1
+  --expert-model-parallel-size: 8
+  --expert-tensor-parallel-size: 1
+  --sequence-parallel: true
+  --use-mcore-models: true
+  --moe-token-dispatcher-type: alltoall
+  --moe-grouped-gemm: true
+  --num-experts: 64
+  --moe-router-topk: 6
+  --moe-z-loss-coeff: 0
+  --moe-router-load-balancing-type: seq_aux_loss
+  --moe-aux-loss-coeff: 1e-3
+  --moe-router-score-function: sigmoid
+  --untie-embeddings-and-output-weights: true
+  --disable-bias-linear: true
+  --init-method-std: 0.014
+  --position-embedding-type: rope
+  --rotary-base: 1000000
+  --rotary-percent: 1.0
+  --num-layers: 27
+  --hidden-size: 2048
+  --moe-ffn-hidden-size: 1408
+  --moe-shared-expert-intermediate-size: 2816
+  --ffn-hidden-size: 10944
+  --num-attention-heads: 16
+  --kv-channels: 128
+  --normalization: RMSNorm
+  --swiglu: true
+  --attention-dropout: 0.0
+  --hidden-dropout: 0.0
+  --seq-length: 4096
+  --max-position-embeddings: 4096
+  --micro-batch-size: 1
+  --ckpt-format: torch_dist
+  --ckpt-fully-parallel-save: true
+  --ckpt-fully-parallel-load: true
+  --ckpt-assume-constant-structure: true
+  --dist-ckpt-strictness: log_unexpected
+  --bf16: true
+  --attention-backend: flash
+  --no-create-attention-mask-in-dataloader: true
+  --num-workers: 8
+  --use-checkpoint-args: true
+  --no-use-tokenizer-model-from-checkpoint-args: true
+  --no-load-optim: true
+  --deterministic-mode: true # moe will use different ops for determinism for inference
+  --save-interval: 2000
+  --temperature: 1.0
+  --top_k: 1
+  --return-log-probs: true
+  --num-tokens-to-generate: 30
+  --max-tokens-to-oom: 3600000
+  --inference-max-seq-length: 4096
+  --output-path: ${INFERENCE_OUTPUT_PATH}
+  --incoming-requests-per-sec: -1 # all requests arrive up front.
+  --inference-repeat-n: 8
+  --inference-dynamic-batching-buffer-size-gb: 20
+  --inference-dynamic-batching-max-requests: 16
+  --inference-logging-step-interval: 1
+  --moe-enable-routing-replay: true
+  --prompt-file: "./tests/functional_tests/test_cases/moe/gpt_dynamic_inference_tp4_etp1_pp1_ep8_16B_logitsmatch_zmq_suspend_resume/prompts.json"
+  --suspend-resume-interval: 16
+  --suspend-timeout: 4.0
+  --rl-kv-cache-management-mode: recompute
+  --inference-dynamic-batching-unified-memory-level: 0
+  --no-rl-persist-cuda-graphs: true
+
+METRICS:
diff --git a/tests/functional_tests/test_cases/moe/gpt_dynamic_inference_tp4_etp1_pp1_ep8_16B_logitsmatch_zmq_suspend_resume/prompts.json b/tests/functional_tests/test_cases/moe/gpt_dynamic_inference_tp4_etp1_pp1_ep8_16B_logitsmatch_zmq_suspend_resume/prompts.json
new file mode 100644
index 00000000000..7dcda2a3fa6
--- /dev/null
+++ b/tests/functional_tests/test_cases/moe/gpt_dynamic_inference_tp4_etp1_pp1_ep8_16B_logitsmatch_zmq_suspend_resume/prompts.json
@@ -0,0 +1,159 @@
+{"text": "The cat sat on the mat and"}
+{"text": "The sun rose in the east,"}
+{"text": "She opened the door and saw"}
+{"text": "He picked up the book,"}
+{"text": "Rain poured down all day,"}
+{"text": "The old house stood"}
+{"text": "Birds sang in the trees,"}
+{"text": "A car drove by,"}
+{"text": "The dog barked loudly,"}
+{"text": "The water was very"}
+{"text": "The capital of France is Paris. It is"}
+{"text": "Water boils at 100 degrees Celsius. This is"}
+{"text": "Humans have five senses: sight, sound, smell, taste, and"}
+{"text": "The Earth revolves around the Sun, taking approximately"}
+{"text": "A triangle has three sides. A square has"}
+{"text": "Computers process information using"}
+{"text": "The Amazon River is the"}
+{"text": "Photosynthesis is the process by which plants"}
+{"text": "Gravity is a force that"}
+{"text": "DNA stands for"}
+{"text": "Once upon a time, there was"}
+{"text": "All's well that ends"}
+{"text": "Every cloud has a silver"}
+{"text": "A stitch in time saves"}
+{"text": "Bite the bullet and"}
+{"text": "Cost an arm and a"}
+{"text": "Hit the road,"}
+{"text": "Let the cat out of the"}
+{"text": "Speak of the devil and"}
+{"text": "The early bird catches the"}
+{"text": "Monday, Tuesday, Wednesday,"}
+{"text": "1, 2, 3, 4,"}
+{"text": "Apple, Banana, Orange,"}
+{"text": "Red, Green, Blue,"}
+{"text": "North, South, East,"}
+{"text": "First, Second, Third,"}
+{"text": "A, B, C, D,"}
+{"text": "January, February, March,"}
+{"text": "Up, Down, Left,"}
+{"text": "Small, Medium, Large,"}
+{"text": "In a small village, nestled among the hills, lived"}
+{"text": "The detective peered through the misty window. Outside,"}
+{"text": "A sudden gust of wind swept through the forest,"}
+{"text": "She woke up with a start, realizing that"}
+{"text": "The ancient map, tattered and worn, showed"}
+{"text": "Far, far away, in a land of dragons and magic,"}
+{"text": "He hesitated at the crossroads, unsure which"}
+{"text": "The ship sailed smoothly across the calm sea,"}
+{"text": "A mysterious package arrived on her doorstep. Inside,"}
+{"text": "The old clock in the hall chimed midnight."}
+{"text": "The sky was a brilliant shade of"}
+{"text": "Her hair was long and flowing, like"}
+{"text": "The smell of freshly baked bread filled"}
+{"text": "The old, gnarled tree stood on the hill, its branches"}
+{"text": "The city lights twinkled in the distance,"}
+{"text": "His voice was deep and resonant,"}
+{"text": "The surface of the lake was smooth as"}
+{"text": "The vibrant colors of the sunset painted"}
+{"text": "The intricate pattern on the rug showed"}
+{"text": "The air was crisp and cool,"}
+{"text": "What do you call a group of"}
+{"text": "How long does it take to"}
+{"text": "Where can one find the most"}
+{"text": "Why is the sky"}
+{"text": "When did the first"}
+{"text": "Who was the last person to"}
+{"text": "Which way should we go to"}
+{"text": "What would happen if"}
+{"text": "How many stars are"}
+{"text": "Where does the river"}
+{"text": "`def main():`"}
+{"text": "`<html><body>`"}
+{"text": "{ \"name\": \"John\", \"age\":"}
+{"text": "`import os`"}
+{"text": "`SELECT * FROM users WHERE`"}
+{"text": "\"Hello?\" she whispered into the darkness. \"Is"}
+{"text": "He said, \"I don't know what to do.\" She replied, \"Well,"}
+{"text": "\"Can you pass the salt?\" asked Tom. Sarah reached for"}
+{"text": "\"Good morning,\" the shopkeeper greeted. \"How may I"}
+{"text": "\"I'm so tired,\" she sighed. \"Me too,\" he agreed, \"I just want to"}
+{"text": "Her laughter was music to"}
+{"text": "The city was a sleeping giant,"}
+{"text": "Time flew like an arrow,"}
+{"text": "His words were daggers,"}
+{"text": "The fog was a thick blanket,"}
+{"text": "The waves crashed like thunder,"}
+{"text": "Hope was a fragile bird,"}
+{"text": "The moon was a silver coin,"}
+{"text": "The wind whispered secrets,"}
+{"text": "Life is a journey, not"}
+{"text": "The quick brown fox jumps over"}
+{"text": "Once bitten, twice"}
+{"text": "As clear as day,"}
+{"text": "The more, the merrier."}
+{"text": "A penny for your"}
+{"text": "You can't have your cake and eat"}
+{"text": "The early bird gets the"}
+{"text": "Between a rock and a hard"}
+{"text": "Don't put all your eggs in one"}
+{"text": "When in Rome, do as the"}
+{"text": "Spring, Summer, Autumn,"}
+{"text": "Do, Re, Mi, Fa,"}
+{"text": "North America, South America, Europe,"}
+{"text": "Circle, Square, Triangle,"}
+{"text": "Tiny, Small, Medium,"}
+{"text": "The train arrived late, causing"}
+{"text": "She carefully placed the delicate vase on"}
+{"text": "He decided to take a long walk to clear"}
+{"text": "The old woman sat by the fire, knitting"}
+{"text": "The news spread quickly throughout the"}
+{"text": "The aroma of coffee filled the air, inviting"}
+{"text": "Beneath the surface of the calm water,"}
+{"text": "He adjusted his glasses and began to read"}
+{"text": "The sound of distant thunder rumbled,"}
+{"text": "The forgotten garden was overgrown with"}
+{"text": "The doctor examined the patient and prescribed"}
+{"text": "The chef prepared a delicious meal, using"}
+{"text": "The engineer designed a new bridge, considering"}
+{"text": "The artist painted a vibrant landscape, capturing"}
+{"text": "The scientist conducted an experiment, observing"}
+{"text": "A group of crows is called a"}
+{"text": "A parliament of owls is a"}
+{"text": "The opposite of hot is"}
+{"text": "The square root of 9 is"}
+{"text": "The longest river in Africa is the"}
+{"text": "The study found that regular exercise can"}
+{"text": "The theory of relativity was proposed by"}
+{"text": "The process of melting ice is called"}
+{"text": "Photosynthesis requires sunlight, water, and"}
+{"text": "The main ingredient in bread is"}
+{"text": "What is the primary function of the"}
+{"text": "How do you define the term"}
+{"text": "Where does the saying 'strike while the iron is hot' come from?"}
+{"text": "Why do leaves change color in the autumn?"}
+{"text": "When was the first personal computer"}
+{"text": "Who invented the light bulb?"}
+{"text": "Which planet is known as the Red Planet?"}
+{"text": "What happens if you mix acid with a base?"}
+{"text": "How many days are in a leap year?"}
+{"text": "Where do migratory birds go in the winter?"}
+{"text": "`function calculateArea(radius) {`"}
+{"text": "`# Python comments start with`"}
+{"text": "`CREATE TABLE employees (`"}
+{"text": "`<?php echo \"Hello, World!\"; ?>`"}
+{"text": "\"Excuse me,\" he coughed, \"but I think you've dropped something.\""}
+{"text": "She retorted, \"I'm not sure what you mean by that.\""}
+{"text": "\"It's freezing in here!\" he exclaimed. \"Can someone please\""}
+{"text": "The customer complained, \"This isn't what I ordered.\" The waiter replied,"}
+{"text": "\"Tell me about your day,\" she encouraged. He began,"}
+{"text": "His memory was a patchwork quilt of"}
+{"text": "The problem was a Gordian knot, impossible to"}
+{"text": "The news hit him like a ton of"}
+{"text": "Her words were a balm to his"}
+{"text": "The traffic moved at a snail's pace on"}
+{"text": "The silence in the room was deafening, a heavy"}
+{"text": "Opportunity knocked loudly on his"}
+{"text": "The city lights were a scatter of diamonds against"}
+{"text": "The mountain stood sentinel over the"}
+{"text": "Reading a good book is like taking a trip without"}
diff --git a/tests/functional_tests/test_cases/moe/gpt_static_inference_cuda_graphs_pad_tp4_pp1_ep4_16B_logitsmatch/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/moe/gpt_static_inference_cuda_graphs_pad_tp4_pp1_ep4_16B_logitsmatch/golden_values_dev_dgx_h100.json
index 0a32a2c875c..9b105e4fa2d 100644
--- a/tests/functional_tests/test_cases/moe/gpt_static_inference_cuda_graphs_pad_tp4_pp1_ep4_16B_logitsmatch/golden_values_dev_dgx_h100.json
+++ b/tests/functional_tests/test_cases/moe/gpt_static_inference_cuda_graphs_pad_tp4_pp1_ep4_16B_logitsmatch/golden_values_dev_dgx_h100.json
@@ -1 +1 @@
-{"1": {"input_prompt": "Time travel to 2008, and go to a bar or a club or one of the myriad disco-basements on the Lower East Side that does not quite know which of those it is. Dance awkwardly in a room full of other glittered-up nerds, and wait for something to happen, buoyed on the feeling that this is the big swollen heart of life, that this is New York like the movies.", "generated_text": " Wait for the moment when the music stops, and the lights come up, and the DJ says, \"I'm going to play a song for you", "generated_tokens": [32844, 1394, 1278, 4735, 2200, 1278, 7146, 30774, 1044, 1321, 1278, 26466, 3930, 2015, 1044, 1321, 1278, 30245, 8223, 1044, 1429, 1073, 4525, 4670, 1317, 3354, 1261, 6947, 1394, 1636], "tpot": [0.4429202973842621, 0.017771262675523758, 0.01790841668844223, 0.016864703968167305, 0.016803359612822533, 0.016811424866318703, 0.016884256154298782, 0.016778528690338135, 0.016986047849059105, 0.016792479902505875, 0.016880257055163383, 0.016891231760382652, 0.016854560002684593, 0.01682988740503788, 0.016822686418890953, 0.01692502386868, 0.016792958602309227, 0.0169671680778265, 0.01693049632012844, 0.0170868169516325, 0.01686326414346695, 0.017535584047436714, 0.0174525436013937, 0.017324192449450493, 0.016914688050746918, 0.01683216169476509, 0.016909919679164886, 0.016884224489331245, 0.017001086845993996, 0.016849694773554802], "latency": 0.9421291537582874, "logprobs": [-10.737512588500977, -3.6907906532287598, -2.8354406356811523, -1.241209626197815, -0.23157396912574768, -1.8544931411743164, -2.305788278579712, -1.9660029411315918, -2.139838695526123, -6.157798767089844, -0.8562318086624146, -2.4214887619018555, -3.5043883323669434, -4.133810043334961, -2.009009838104248, -1.8133208751678467, -2.3323073387145996, -7.160175323486328, -0.040603119879961014, -1.9747259616851807, -5.076613903045654, -8.853288650512695, -9.848663330078125, -0.783089280128479, -4.775578022003174, -0.8501623868942261, -2.353159189224243, -0.019392186775803566, -0.035699184983968735, -3.369636058807373, -8.736637115478516, -1.2479770183563232, -6.668802261352539, -3.84025239944458, -3.774880886077881, -4.180184364318848, -2.2127902507781982, -1.07676362991333, -0.2321961224079132, -0.8445965647697449, -4.720583915710449, -9.144975662231445, -0.013737889938056469, -3.1713855266571045, -1.316046953201294, -3.976555824279785, -0.7929940223693848, -0.0020036876667290926, -2.9234514236450195, -10.630117416381836, -3.2423582077026367, -1.1527093648910522, -4.902451515197754, -0.20881010591983795, -0.06518254429101944, -1.3553434610366821, -2.205620765686035, -4.443068981170654, -0.3349221646785736, -4.0811614990234375, -0.40434733033180237, -0.14260707795619965, -2.7138302326202393, -10.61572551727295, -0.05091002210974693, -3.3788461685180664, -0.8990436792373657, -4.757172584533691, -0.2625967562198639, -2.6857080459594727, -0.8338347673416138, -1.5987446308135986, -5.796599388122559, -17.023239135742188, -2.5919642448425293, -0.1391627937555313, -7.425058841705322, -1.0969927310943604, -2.1373608112335205, -1.5555475950241089, -0.29913192987442017, -5.805688381195068, -0.006563534028828144, -7.741588592529297, -2.729809284210205, -2.989825487136841, -2.937342643737793, -2.452791690826416, -0.39692243933677673, -1.4191737174987793, -2.281113862991333, -0.6101264357566833, -1.3127052783966064, -1.93826162815094, -1.759519100189209, -0.8280774354934692, -0.48737525939941406, -1.2929327487945557, -1.4731515645980835, -1.0149478912353516, -0.402925580739975, -0.4662020802497864, -0.04289804771542549, -1.2809830904006958, -2.1367523670196533, -2.672316074371338, -0.832058310508728, -0.3975365459918976, -2.8649744987487793, -1.5586214065551758, -1.6164027452468872, -0.048774562776088715, -1.3553334474563599, -1.374987006187439, -1.2671791315078735, -1.29192054271698, -0.49132436513900757]}}
\ No newline at end of file
+{"1": {"input_prompt": "Time travel to 2008, and go to a bar or a club or one of the myriad disco-basements on the Lower East Side that does not quite know which of those it is. Dance awkwardly in a room full of other glittered-up nerds, and wait for something to happen, buoyed on the feeling that this is the big swollen heart of life, that this is New York like the movies.", "generated_text": " Wait for the moment when the music stops, and the lights come up, and the DJ says, \"I'm going to play a song for you", "generated_tokens": [32844, 1394, 1278, 4735, 2200, 1278, 7146, 30774, 1044, 1321, 1278, 26466, 3930, 2015, 1044, 1321, 1278, 30245, 8223, 1044, 1429, 1073, 4525, 4670, 1317, 3354, 1261, 6947, 1394, 1636], "tpot": [0.9536803364753723, 0.016934143379330635, 0.016783935949206352, 0.016745438799262047, 0.0167548805475235, 0.01675168052315712, 0.016725631430745125, 0.016744224354624748, 0.016771871596574783, 0.01673772744834423, 0.01671331189572811, 0.016742143779993057, 0.016752831637859344, 0.016735071316361427, 0.016708416864275932, 0.01676086336374283, 0.01672108843922615, 0.01675747148692608, 0.01672768034040928, 0.01673593558371067, 0.01673644781112671, 0.016768928617239, 0.016721663996577263, 0.01674390397965908, 0.016715550795197487, 0.01673286408185959, 0.016974497586488724, 0.016759486868977547, 0.01675366424024105, 0.017026912420988083], "latency": 1.4477943610399961, "logprobs": [-10.737512588500977, -3.6907906532287598, -2.8354406356811523, -1.241209626197815, -0.23157396912574768, -1.8544931411743164, -2.3058578968048096, -1.965078592300415, -2.131650447845459, -6.1445770263671875, -0.8567591905593872, -2.4118287563323975, -3.49544095993042, -4.07368278503418, -1.9927215576171875, -1.8116189241409302, -2.220911979675293, -7.154321193695068, -0.041270580142736435, -1.897834300994873, -5.07651424407959, -8.796056747436523, -9.88559341430664, -0.7177769541740417, -4.775661468505859, -0.8519912362098694, -2.3280630111694336, -0.018436847254633904, -0.038715627044439316, -3.4831454753875732, -8.691339492797852, -1.2537559270858765, -6.647421836853027, -3.785327911376953, -3.7754249572753906, -4.1729302406311035, -2.268207550048828, -1.0762473344802856, -0.22336173057556152, -0.7773433923721313, -4.659046173095703, -9.07835865020752, -0.01364840567111969, -3.1569409370422363, -1.3136285543441772, -3.999577760696411, -0.8146029710769653, -0.0020667400676757097, -2.9257936477661133, -10.559369087219238, -3.301023483276367, -1.1468515396118164, -4.866663455963135, -0.20965954661369324, -0.06276518106460571, -1.3678232431411743, -2.2146267890930176, -4.369752883911133, -0.35328271985054016, -4.076470851898193, -0.39479735493659973, -0.14124885201454163, -2.7151336669921875, -10.645881652832031, -0.05100790411233902, -3.277879238128662, -0.859420657157898, -4.694356918334961, -0.26331964135169983, -2.601013660430908, -0.8340632319450378, -1.5944981575012207, -5.802148818969727, -16.992801666259766, -2.9630136489868164, -0.12174151837825775, -7.421735763549805, -1.090034008026123, -2.115244150161743, -1.592454195022583, -0.297377347946167, -5.607227325439453, -0.006619194056838751, -7.817281723022461, -2.701261043548584, -2.988292932510376, -2.9706215858459473, -2.4563350677490234, -0.3993130624294281, -1.5173310041427612, -2.269473075866699, -0.6111201047897339, -1.313757300376892, -1.9383023977279663, -1.6797527074813843, -0.7714957594871521, -0.49417543411254883, -1.2875804901123047, -1.5885818004608154, -1.018824577331543, -0.4005858302116394, -0.46394026279449463, -0.04470847547054291, -1.2914193868637085, -2.2410547733306885, -2.740159273147583, -0.7651359438896179, -0.4316181242465973, -2.7719383239746094, -1.5367236137390137, -1.652032732963562, -0.051836322993040085, -1.3689777851104736, -1.342658519744873, -1.26646089553833, -1.3113347291946411, -0.5160548686981201]}}
\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/moe/gpt_static_inference_tp1_pp1_ep1_16B_logitsmatch/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/moe/gpt_static_inference_tp1_pp1_ep1_16B_logitsmatch/golden_values_dev_dgx_h100.json
index 0a088a3b4ed..16ce3cb46a0 100644
--- a/tests/functional_tests/test_cases/moe/gpt_static_inference_tp1_pp1_ep1_16B_logitsmatch/golden_values_dev_dgx_h100.json
+++ b/tests/functional_tests/test_cases/moe/gpt_static_inference_tp1_pp1_ep1_16B_logitsmatch/golden_values_dev_dgx_h100.json
@@ -1 +1 @@
-{"0": {"input_prompt": "Time travel to 2008, and go to a bar or a club or one of the myriad disco-basements on the Lower East Side that does not quite know which of those it is. Dance awkwardly in a room full of other glittered-up nerds, and wait for something to happen, buoyed on the feeling that this is the big swollen heart of life, that this is New York like the movies.", "generated_text": " Wait for the moment when the music stops, and the lights come up, and the DJ says, \"I'm going to play a song for you", "generated_tokens": [32844, 1394, 1278, 4735, 2200, 1278, 7146, 30774, 1044, 1321, 1278, 26466, 3930, 2015, 1044, 1321, 1278, 30245, 8223, 1044, 1429, 1073, 4525, 4670, 1317, 3354, 1261, 6947, 1394, 1636], "tpot": [2.715181350708008, 11.115615844726562, 0.08171491324901581, 0.08067888021469116, 0.08026569336652756, 0.08201305568218231, 0.08297079056501389, 0.07990239560604095, 0.07923731207847595, 0.08088396489620209, 0.08342364430427551, 0.07902496308088303, 0.07883366197347641, 0.0821300819516182, 0.07879510521888733, 0.07889033854007721, 0.08096816390752792, 0.07890790700912476, 0.07898931205272675, 0.07891136407852173, 0.07872992008924484, 0.07891993969678879, 0.07908882945775986, 0.08219005167484283, 0.07928377389907837, 0.0791754499077797, 0.08204790204763412, 0.07909702509641647, 0.07994850724935532, 0.0790436789393425], "latency": 16.090769955015276, "logprobs": [-10.748703956604004, -3.675847053527832, -2.8152527809143066, -1.2499192953109741, -0.2585306465625763, -1.7650476694107056, -2.4413700103759766, -1.9855635166168213, -2.1556897163391113, -6.126346588134766, -0.8885424733161926, -2.466485023498535, -3.53129506111145, -4.1022443771362305, -1.973730444908142, -1.8129527568817139, -2.3135061264038086, -7.073224067687988, -0.0406799241900444, -1.9924827814102173, -5.044793128967285, -8.79849910736084, -9.896184921264648, -0.9244536757469177, -4.819119453430176, -0.8409886360168457, -2.3493337631225586, -0.019546041265130043, -0.03429899737238884, -3.486131429672241, -8.708669662475586, -1.2524677515029907, -6.648501396179199, -3.6543850898742676, -3.5817432403564453, -4.293689250946045, -2.213235855102539, -1.026153802871704, -0.22022850811481476, -0.7749938368797302, -4.7083001136779785, -9.260919570922852, -0.013350849971175194, -3.177624464035034, -1.3237272500991821, -3.991711139678955, -0.7711713314056396, -0.0020787552930414677, -2.9259750843048096, -10.556608200073242, -3.0338008403778076, -1.165448546409607, -4.884476184844971, -0.22491267323493958, -0.06299388408660889, -1.2974224090576172, -2.228250503540039, -4.375787258148193, -0.3615659773349762, -4.020719528198242, -0.3728649318218231, -0.16031591594219208, -2.7166409492492676, -10.650144577026367, -0.057426948100328445, -3.3819196224212646, -0.8289875388145447, -4.716109752655029, -0.2623739540576935, -2.6586318016052246, -0.846296489238739, -1.6911215782165527, -5.863524436950684, -17.074047088623047, -2.9786670207977295, -0.12697581946849823, -7.423051834106445, -1.1104215383529663, -2.125497579574585, -1.481943130493164, -0.26388564705848694, -5.852108001708984, -0.006604391150176525, -7.682407379150391, -2.7386088371276855, -2.9692039489746094, -3.0358991622924805, -2.434255838394165, -0.4008456766605377, -1.4501973390579224, -2.3068716526031494, -0.5563173294067383, -1.3114793300628662, -1.9436699151992798, -1.6950371265411377, -0.7694160342216492, -0.504065215587616, -1.2403564453125, -1.5687276124954224, -1.0141794681549072, -0.4076817035675049, -0.48037511110305786, -0.04258028045296669, -1.3669413328170776, -2.1299216747283936, -2.6870312690734863, -0.7604206800460815, -0.39242351055145264, -2.869314432144165, -1.464285135269165, -1.643430471420288, -0.04816753789782524, -1.4055166244506836, -1.3622899055480957, -1.2863339185714722, -1.2075212001800537, -0.5359172821044922]}}
\ No newline at end of file
+{"0": {"input_prompt": "Time travel to 2008, and go to a bar or a club or one of the myriad disco-basements on the Lower East Side that does not quite know which of those it is. Dance awkwardly in a room full of other glittered-up nerds, and wait for something to happen, buoyed on the feeling that this is the big swollen heart of life, that this is New York like the movies.", "generated_text": " Wait for the moment when the music stops, and the lights come up, and the DJ says, \"I'm going to play a song for you", "generated_tokens": [32844, 1394, 1278, 4735, 2200, 1278, 7146, 30774, 1044, 1321, 1278, 26466, 3930, 2015, 1044, 1321, 1278, 30245, 8223, 1044, 1429, 1073, 4525, 4670, 1317, 3354, 1261, 6947, 1394, 1636], "tpot": [2.545225143432617, 0.8161460161209106, 0.08185821026563644, 0.08545549213886261, 0.0806993618607521, 0.08108274638652802, 0.08104605227708817, 0.08051212877035141, 0.08013814687728882, 0.08076290786266327, 0.08076870441436768, 0.08029650896787643, 0.07956188917160034, 0.07908589392900467, 0.07883257418870926, 0.0789686068892479, 0.07868800312280655, 0.0788155198097229, 0.07876496016979218, 0.07869625836610794, 0.07890035212039948, 0.08047452569007874, 0.08083260804414749, 0.08091676980257034, 0.08101955056190491, 0.08092495799064636, 0.08079087734222412, 0.08215625584125519, 0.08282729238271713, 0.08078553527593613], "latency": 5.632539719343185, "logprobs": [-10.748703956604004, -3.6647238731384277, -2.829481363296509, -1.2498102188110352, -0.2529161274433136, -1.8659480810165405, -2.381495237350464, -1.9656293392181396, -2.1487326622009277, -6.079752445220947, -0.854481041431427, -2.4210457801818848, -3.5035147666931152, -4.105377674102783, -1.9736212491989136, -1.812272310256958, -2.2293882369995117, -7.158683776855469, -0.04035309702157974, -1.8912277221679688, -5.030168056488037, -8.83056640625, -9.887261390686035, -0.783703088760376, -4.775330066680908, -0.8503050208091736, -2.3185184001922607, -0.019133294001221657, -0.035927604883909225, -3.367396831512451, -8.677278518676758, -1.249742031097412, -6.646797180175781, -3.7622885704040527, -3.5719683170318604, -4.217543125152588, -2.1486034393310547, -1.0877041816711426, -0.21807751059532166, -0.779091477394104, -4.703271865844727, -9.254538536071777, -0.013438244350254536, -3.1536498069763184, -1.347169041633606, -3.951521873474121, -0.7524824142456055, -0.0020295039284974337, -2.977275848388672, -10.547475814819336, -3.2425384521484375, -1.1805994510650635, -4.853910446166992, -0.20160463452339172, -0.06345974653959274, -1.3051351308822632, -2.185082197189331, -4.38629150390625, -0.36718395352363586, -4.047024726867676, -0.35912153124809265, -0.14624275267124176, -2.715745687484741, -10.57785415649414, -0.057624127715826035, -3.278026819229126, -0.8596221804618835, -4.68856954574585, -0.2627037465572357, -2.6609294414520264, -0.8223639726638794, -1.599161148071289, -5.80098295211792, -16.973386764526367, -2.616450548171997, -0.13072170317173004, -7.462261199951172, -1.082613229751587, -2.122760534286499, -1.5544897317886353, -0.2857922911643982, -5.874238967895508, -0.006397482007741928, -7.670827865600586, -2.7104744911193848, -2.9125661849975586, -3.0234169960021973, -2.3567330837249756, -0.39910370111465454, -1.4856780767440796, -2.293515920639038, -0.6103871464729309, -1.3627440929412842, -1.9288791418075562, -1.683215618133545, -0.7717607021331787, -0.5037432312965393, -1.2485487461090088, -1.559265375137329, -1.0295413732528687, -0.4404847025871277, -0.46411699056625366, -0.04514020308852196, -1.3507211208343506, -2.1256327629089355, -2.687516927719116, -0.759257435798645, -0.41867509484291077, -2.854245185852051, -1.5189608335494995, -1.5710458755493164, -0.05560074374079704, -1.357395052909851, -1.3819431066513062, -1.2458440065383911, -1.2788543701171875, -0.5129148364067078]}}
\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/moe/gpt_static_inference_tp4_pp1_ep4_16B_logitsmatch/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/moe/gpt_static_inference_tp4_pp1_ep4_16B_logitsmatch/golden_values_dev_dgx_h100.json
index 46a451d0b64..9374ead29ab 100644
--- a/tests/functional_tests/test_cases/moe/gpt_static_inference_tp4_pp1_ep4_16B_logitsmatch/golden_values_dev_dgx_h100.json
+++ b/tests/functional_tests/test_cases/moe/gpt_static_inference_tp4_pp1_ep4_16B_logitsmatch/golden_values_dev_dgx_h100.json
@@ -1 +1 @@
-{"0": {"input_prompt": "Time travel to 2008, and go to a bar or a club or one of the myriad disco-basements on the Lower East Side that does not quite know which of those it is. Dance awkwardly in a room full of other glittered-up nerds, and wait for something to happen, buoyed on the feeling that this is the big swollen heart of life, that this is New York like the movies.", "generated_text": " Wait for the moment when the music stops, and the lights come up, and the DJ says, \"I'm going to play a song for you", "generated_tokens": [32844, 1394, 1278, 4735, 2200, 1278, 7146, 30774, 1044, 1321, 1278, 26466, 3930, 2015, 1044, 1321, 1278, 30245, 8223, 1044, 1429, 1073, 4525, 4670, 1317, 3354, 1261, 6947, 1394, 1636], "tpot": [32.35166549682617, 0.833970844745636, 0.09404217451810837, 0.09242991358041763, 0.09451283514499664, 0.09116563200950623, 0.0916728675365448, 0.09169203042984009, 0.0920996442437172, 0.09247440099716187, 0.09316505491733551, 0.09183433651924133, 0.09311366081237793, 0.0922863706946373, 0.09139427542686462, 0.0916166678071022, 0.09881363064050674, 0.09086793661117554, 0.09085418283939362, 0.0913468450307846, 0.0913306251168251, 0.09422652423381805, 0.09134646505117416, 0.09283513575792313, 0.09112297743558884, 0.09120230376720428, 0.09097100794315338, 0.09246265143156052, 0.09317846596240997, 0.09015017747879028], "latency": 35.78577698091976, "logprobs": [-10.737512588500977, -3.6907906532287598, -2.8354406356811523, -1.241209626197815, -0.23157396912574768, -1.8544931411743164, -2.305788278579712, -1.9660029411315918, -2.139838695526123, -6.157798767089844, -0.8562318086624146, -2.4214887619018555, -3.5043883323669434, -4.133810043334961, -2.009009838104248, -1.8133208751678467, -2.3323073387145996, -7.160175323486328, -0.040603119879961014, -1.9747259616851807, -5.076613903045654, -8.853288650512695, -9.848663330078125, -0.783089280128479, -4.775578022003174, -0.8501623868942261, -2.353159189224243, -0.019392186775803566, -0.035699184983968735, -3.369636058807373, -8.736637115478516, -1.2479770183563232, -6.668802261352539, -3.84025239944458, -3.774880886077881, -4.180184364318848, -2.2127902507781982, -1.07676362991333, -0.2321961224079132, -0.8445965647697449, -4.720583915710449, -9.144975662231445, -0.013737889938056469, -3.1713855266571045, -1.316046953201294, -3.976555824279785, -0.7929940223693848, -0.0020036876667290926, -2.9234514236450195, -10.630117416381836, -3.2423582077026367, -1.1527093648910522, -4.902451515197754, -0.20881010591983795, -0.06518254429101944, -1.3553434610366821, -2.205620765686035, -4.443068981170654, -0.3349221646785736, -4.0811614990234375, -0.40434733033180237, -0.14260707795619965, -2.7138302326202393, -10.61572551727295, -0.05091002210974693, -3.3788461685180664, -0.8990436792373657, -4.757172584533691, -0.2625967562198639, -2.6857080459594727, -0.8338347673416138, -1.5987446308135986, -5.796599388122559, -17.023239135742188, -2.5919642448425293, -0.1391627937555313, -7.425058841705322, -1.0969927310943604, -2.1373608112335205, -1.5555475950241089, -0.29913192987442017, -5.805688381195068, -0.006563534028828144, -7.741588592529297, -2.729809284210205, -2.989825487136841, -2.937342643737793, -2.452791690826416, -0.39692243933677673, -1.4191737174987793, -2.281113862991333, -0.6101264357566833, -1.3127052783966064, -1.93826162815094, -1.759519100189209, -0.8280774354934692, -0.48737525939941406, -1.2929327487945557, -1.4731515645980835, -1.0149478912353516, -0.402925580739975, -0.4662020802497864, -0.04289804771542549, -1.2809830904006958, -2.1367523670196533, -2.672316074371338, -0.832058310508728, -0.3975365459918976, -2.8649744987487793, -1.5586214065551758, -1.6164027452468872, -0.048774562776088715, -1.3553334474563599, -1.374987006187439, -1.2671791315078735, -1.29192054271698, -0.49132436513900757]}}
\ No newline at end of file
+{"0": {"input_prompt": "Time travel to 2008, and go to a bar or a club or one of the myriad disco-basements on the Lower East Side that does not quite know which of those it is. Dance awkwardly in a room full of other glittered-up nerds, and wait for something to happen, buoyed on the feeling that this is the big swollen heart of life, that this is New York like the movies.", "generated_text": " Wait for the moment when the music stops, and the lights come up, and the DJ says, \"I'm going to play a song for you", "generated_tokens": [32844, 1394, 1278, 4735, 2200, 1278, 7146, 30774, 1044, 1321, 1278, 26466, 3930, 2015, 1044, 1321, 1278, 30245, 8223, 1044, 1429, 1073, 4525, 4670, 1317, 3354, 1261, 6947, 1394, 1636], "tpot": [13.711557388305664, 0.9041259288787842, 0.09681683033704758, 0.09153660386800766, 0.09111235290765762, 0.09335695952177048, 0.09111097455024719, 0.09149472415447235, 0.09076278656721115, 0.09109959006309509, 0.09105174243450165, 0.09161145985126495, 0.09026294946670532, 0.09116349369287491, 0.09077664464712143, 0.09046704322099686, 0.09124940633773804, 0.09130454063415527, 0.09034591913223267, 0.09066786617040634, 0.0906708836555481, 0.09116076678037643, 0.09075567871332169, 0.09169074892997742, 0.0911671370267868, 0.09112399816513062, 0.09118761122226715, 0.09077664464712143, 0.09082793444395065, 0.09155046194791794], "latency": 17.191568877082318, "logprobs": [-10.737512588500977, -3.6907906532287598, -2.8354406356811523, -1.241209626197815, -0.23157396912574768, -1.8544931411743164, -2.3058578968048096, -1.965078592300415, -2.131650447845459, -6.1445770263671875, -0.8567591905593872, -2.4118287563323975, -3.49544095993042, -4.07368278503418, -1.9927215576171875, -1.8116189241409302, -2.220911979675293, -7.154321193695068, -0.041270580142736435, -1.897834300994873, -5.07651424407959, -8.796056747436523, -9.88559341430664, -0.7177769541740417, -4.775661468505859, -0.8519912362098694, -2.3280630111694336, -0.018436847254633904, -0.038715627044439316, -3.4831454753875732, -8.691339492797852, -1.2537559270858765, -6.647421836853027, -3.785327911376953, -3.7754249572753906, -4.1729302406311035, -2.268207550048828, -1.0762473344802856, -0.22336173057556152, -0.7773433923721313, -4.659046173095703, -9.07835865020752, -0.01364840567111969, -3.1569409370422363, -1.3136285543441772, -3.999577760696411, -0.8146029710769653, -0.0020667400676757097, -2.9257936477661133, -10.559369087219238, -3.301023483276367, -1.1468515396118164, -4.866663455963135, -0.20965954661369324, -0.06276518106460571, -1.3678232431411743, -2.2146267890930176, -4.369752883911133, -0.35328271985054016, -4.076470851898193, -0.39479735493659973, -0.14124885201454163, -2.7151336669921875, -10.645881652832031, -0.05100790411233902, -3.277879238128662, -0.859420657157898, -4.694356918334961, -0.26331964135169983, -2.601013660430908, -0.8340632319450378, -1.5944981575012207, -5.802148818969727, -16.992801666259766, -2.9630136489868164, -0.12174151837825775, -7.421735763549805, -1.090034008026123, -2.115244150161743, -1.592454195022583, -0.297377347946167, -5.607227325439453, -0.006619194056838751, -7.817281723022461, -2.701261043548584, -2.988292932510376, -2.9706215858459473, -2.4563350677490234, -0.3993130624294281, -1.5173310041427612, -2.269473075866699, -0.6111201047897339, -1.313757300376892, -1.9383023977279663, -1.6797527074813843, -0.7714957594871521, -0.49417543411254883, -1.2875804901123047, -1.5885818004608154, -1.018824577331543, -0.4005858302116394, -0.46394026279449463, -0.04470847547054291, -1.2914193868637085, -2.2410547733306885, -2.740159273147583, -0.7651359438896179, -0.4316181242465973, -2.7719383239746094, -1.5367236137390137, -1.652032732963562, -0.051836322993040085, -1.3689777851104736, -1.342658519744873, -1.26646089553833, -1.3113347291946411, -0.5160548686981201]}}
\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/multimodal-llava/multimodal_llava_mcore_te_tp1_pp1/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/multimodal-llava/multimodal_llava_mcore_te_tp1_pp1/golden_values_dev_dgx_h100.json
index f4357530aed..00ed37733ae 100644
--- a/tests/functional_tests/test_cases/multimodal-llava/multimodal_llava_mcore_te_tp1_pp1/golden_values_dev_dgx_h100.json
+++ b/tests/functional_tests/test_cases/multimodal-llava/multimodal_llava_mcore_te_tp1_pp1/golden_values_dev_dgx_h100.json
@@ -6,53 +6,53 @@
         "values": {
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@@ -61,56 +61,56 @@
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@@ -232,56 +232,56 @@
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 }
\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/multimodal-llava/multimodal_llava_mcore_te_tp4_sp_cp2/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/multimodal-llava/multimodal_llava_mcore_te_tp4_sp_cp2/golden_values_dev_dgx_h100.json
index b0c23087659..8fb704f4745 100644
--- a/tests/functional_tests/test_cases/multimodal-llava/multimodal_llava_mcore_te_tp4_sp_cp2/golden_values_dev_dgx_h100.json
+++ b/tests/functional_tests/test_cases/multimodal-llava/multimodal_llava_mcore_te_tp4_sp_cp2/golden_values_dev_dgx_h100.json
@@ -4,56 +4,56 @@
         "end_step": 50,
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\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/t5/t5_11b_mcore_tp4_pp1/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/t5/t5_11b_mcore_tp4_pp1/golden_values_dev_dgx_h100.json
index f4a701a2e4d..92f66b60691 100644
--- a/tests/functional_tests/test_cases/t5/t5_11b_mcore_tp4_pp1/golden_values_dev_dgx_h100.json
+++ b/tests/functional_tests/test_cases/t5/t5_11b_mcore_tp4_pp1/golden_values_dev_dgx_h100.json
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diff --git a/tests/functional_tests/test_cases/t5/t5_release_sm/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/t5/t5_release_sm/golden_values_dev_dgx_gb200.json
index f6470962a57..4a556d7842b 100644
--- a/tests/functional_tests/test_cases/t5/t5_release_sm/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/t5/t5_release_sm/golden_values_dev_dgx_gb200.json
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 }
\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/t5/t5_release_sm/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/t5/t5_release_sm/golden_values_dev_dgx_h100.json
index 91a0cb5d77a..94e5a1b1e47 100644
--- a/tests/functional_tests/test_cases/t5/t5_release_sm/golden_values_dev_dgx_h100.json
+++ b/tests/functional_tests/test_cases/t5/t5_release_sm/golden_values_dev_dgx_h100.json
@@ -6,2005 +6,2005 @@
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-            "9000": 0.16662,
+            "9000": 0.19415,
             "9005": "nan",
             "9010": "nan",
             "9015": "nan",
@@ -9856,7 +9856,7 @@
             "9085": "nan",
             "9090": "nan",
             "9095": "nan",
-            "9100": 0.19752,
+            "9100": 0.18542,
             "9105": "nan",
             "9110": "nan",
             "9115": "nan",
@@ -9876,7 +9876,7 @@
             "9185": "nan",
             "9190": "nan",
             "9195": "nan",
-            "9200": 0.18085,
+            "9200": 0.23006,
             "9205": "nan",
             "9210": "nan",
             "9215": "nan",
@@ -9896,7 +9896,7 @@
             "9285": "nan",
             "9290": "nan",
             "9295": "nan",
-            "9300": 0.16961,
+            "9300": 0.16977,
             "9305": "nan",
             "9310": "nan",
             "9315": "nan",
@@ -9916,7 +9916,7 @@
             "9385": "nan",
             "9390": "nan",
             "9395": "nan",
-            "9400": 0.22047,
+            "9400": 0.17647,
             "9405": "nan",
             "9410": "nan",
             "9415": "nan",
@@ -9936,7 +9936,7 @@
             "9485": "nan",
             "9490": "nan",
             "9495": "nan",
-            "9500": 0.17465,
+            "9500": 0.19239,
             "9505": "nan",
             "9510": "nan",
             "9515": "nan",
@@ -9956,7 +9956,7 @@
             "9585": "nan",
             "9590": "nan",
             "9595": "nan",
-            "9600": 0.17584,
+            "9600": 0.17461,
             "9605": "nan",
             "9610": "nan",
             "9615": "nan",
@@ -9976,7 +9976,7 @@
             "9685": "nan",
             "9690": "nan",
             "9695": "nan",
-            "9700": 0.19429,
+            "9700": 0.19822,
             "9705": "nan",
             "9710": "nan",
             "9715": "nan",
@@ -9996,7 +9996,7 @@
             "9785": "nan",
             "9790": "nan",
             "9795": "nan",
-            "9800": 0.17092,
+            "9800": 0.20847,
             "9805": "nan",
             "9810": "nan",
             "9815": "nan",
@@ -10016,7 +10016,7 @@
             "9885": "nan",
             "9890": "nan",
             "9895": "nan",
-            "9900": 0.17226,
+            "9900": 0.19319,
             "9905": "nan",
             "9910": "nan",
             "9915": "nan",
@@ -10036,7 +10036,7 @@
             "9985": "nan",
             "9990": "nan",
             "9995": "nan",
-            "10000": 0.22092
+            "10000": 0.18154
         }
     }
 }
\ No newline at end of file
diff --git a/tests/test_utils/python_scripts/auto_reminder_github.py b/tests/test_utils/python_scripts/auto_reminder_github.py
index eca1108dcc6..0329b2b23e0 100644
--- a/tests/test_utils/python_scripts/auto_reminder_github.py
+++ b/tests/test_utils/python_scripts/auto_reminder_github.py
@@ -40,6 +40,7 @@ class Reminder:
 class PRReviewTracker:
     EXPERT_REVIEW = "Expert Review"
     FINAL_REVIEW = "Final Review"
+    APPROVED = "Approved"
     EXCLUDED_TEAMS = {"core-adlr", "core-nemo"}
 
     def __init__(
@@ -127,6 +128,11 @@ def get_label_date(self, pr, label: str):
         ]
         return max(dates) if dates else None
 
+    def get_ready_for_review_date(self, pr):
+        """Get the date a PR was marked as ready for review."""
+        dates = [e.created_at for e in pr.as_issue().get_events() if e.event == "ready_for_review"]
+        return max(dates) if dates else None
+
     def days_since(self, date):
         """Calculate days since given date."""
         if not date:
@@ -138,11 +144,22 @@ def days_since(self, date):
     def get_stage(self, pr):
         """Get current review stage."""
         labels = {l.name for l in pr.labels}
-        return self.FINAL_REVIEW if self.FINAL_REVIEW in labels else self.EXPERT_REVIEW
+        if self.APPROVED in labels:
+            return self.APPROVED
+        if self.FINAL_REVIEW in labels:
+            return self.FINAL_REVIEW
+        return self.EXPERT_REVIEW
 
     def get_reviewers(self, pr):
         """Get filtered reviewer emails who haven't approved yet."""
         stage = self.get_stage(pr)
+
+        if stage == self.APPROVED:
+            return (
+                [self.get_user_email(pr.user.login)],
+                "All reviewers have approved. Please merge the PR.",
+            )
+
         org = self.github.get_organization(self.repo.organization.login)
 
         # 1. Get the latest review state for everyone who has submitted a review
@@ -207,9 +224,19 @@ def get_reviewers(self, pr):
         # 8. Handle the original edge cases
         if len(reviewer_emails) == 0:
             if stage == self.EXPERT_REVIEW:
+                # No reviewer activity yet — assignment hasn't completed (e.g. PR just became
+                # ready-for-review). Don't fire a spurious "all approved" message.
+                has_reviewer_activity = bool(
+                    approvers
+                    or non_approving_reviewers
+                    or pending_individuals
+                    or pending_teams_slugs
+                )
+                if not has_reviewer_activity:
+                    return [], "Waiting for reviewers to be assigned."
                 # Assign to PR author
                 reviewer_emails = [self.get_user_email(pr.user.login)]
-                action_message = "All Expert Reviewers approved the PR. Please attach the Final Review label to proceed with the review."
+                action_message = "All Expert Reviewers have approved the PR."
             elif stage == self.FINAL_REVIEW:
                 # Assign to mcore-reviewers who approved
                 try:
@@ -217,7 +244,7 @@ def get_reviewers(self, pr):
                     mcore_members = {m.login for m in mcore_team.get_members()}
                     valid_approvers = approvers & mcore_members
                     reviewer_emails = sorted([self.get_user_email(u) for u in valid_approvers])
-                    action_message = "All Final Reviewers approved the PR. Please ping an Expert or Final Reviewer to merge the PR."
+                    action_message = "All Final Reviewers approved the PR. Please ping the @mcore-oncall to merge the PR."
 
                 except Exception as e:
                     logger.warning(
@@ -229,7 +256,14 @@ def get_reviewers(self, pr):
     def create_reminder(self, pr):
         """Create reminder for PR."""
         stage = self.get_stage(pr)
-        stage_days = self.days_since(self.get_label_date(pr, stage))
+        ready_date = self.get_ready_for_review_date(pr)
+        if stage == self.EXPERT_REVIEW:
+            stage_days = self.days_since(ready_date)
+        elif stage in (self.FINAL_REVIEW, self.APPROVED):
+            stage_days = self.days_since(self.get_label_date(pr, stage))
+        else:
+            stage_days = 0
+        total_review_days = self.days_since(ready_date)
         author_email = self.get_user_email(pr.user.login)
         reviewer_emails, action_message = self.get_reviewers(pr)
         escaped_title = html.escape(pr.title, quote=False)
@@ -241,7 +275,7 @@ def create_reminder(self, pr):
             author=self.get_slack_user_id(author_email),
             priority="P0" if stage_days > 3 else "P1" if stage_days >= 1 else "P2",
             review_stage=stage,
-            total_review_time=self.days_since(self.get_label_date(pr, self.EXPERT_REVIEW)),
+            total_review_time=total_review_days,
             current_stage_time=stage_days,
             reviewers=[self.get_slack_user_id(email) for email in reviewer_emails],
             action_message=action_message,
@@ -256,12 +290,11 @@ def generate_reminders(self):
 
         reminders = []
         for milestone in milestones:
-            # Find issues with the 'Expert Review' or 'Final Review' label
+            # Find all open non-draft PRs with this milestone
             query = (
                 f'repo:"{self.repo.full_name}" '
                 f'milestone:"{milestone.title}" '
-                f'is:open is:pr -is:draft '
-                f'label:"{self.EXPERT_REVIEW}","{self.FINAL_REVIEW}"'
+                f'is:open is:pr -is:draft'
             )
             try:
                 # Use search_issues for a more direct query instead of get_issues + filtering
diff --git a/tests/test_utils/python_scripts/download_coverage_results.py b/tests/test_utils/python_scripts/download_coverage_results.py
index 8bea3d62a83..3f8f92e7f3c 100644
--- a/tests/test_utils/python_scripts/download_coverage_results.py
+++ b/tests/test_utils/python_scripts/download_coverage_results.py
@@ -1,3 +1,4 @@
+# Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 import glob
 import logging
 import os
@@ -35,7 +36,6 @@ def main(pipeline_id: int):
         and pipeline_bridge.downstream_pipeline is not None
     ]
 
-    ASSETS_DIR = pathlib.Path("tmp") / "results" / "iteration=0"
     for pipeline_bridge in pipeline_bridges:
         functional_pipeline = project.pipelines.get(pipeline_bridge.downstream_pipeline['id'])
 
@@ -49,6 +49,7 @@ def main(pipeline_id: int):
             logger.info("Starting with job %s", job.name)
 
             try:
+                shutil.rmtree(pathlib.Path("tmp") / "results", ignore_errors=True)
                 file_name = '__artifacts.zip'
                 with open(file_name, "wb") as f:
                     job.artifacts(streamed=True, action=f.write)
@@ -59,11 +60,15 @@ def main(pipeline_id: int):
                 continue
 
             os.unlink(file_name)
-            restart_dir = os.listdir(pathlib.Path("tmp") / "results" / "iteration=0")[-1]
+            iteration_dir = sorted(
+                (pathlib.Path("tmp") / "results").glob("iteration=*"),
+                key=lambda p: int(p.name.split("=")[1]),
+            )[-1]
+            restart_dir = sorted(os.listdir(iteration_dir))[-1]
             coverage_report_source = list(
                 glob.glob(
                     str(
-                        pathlib.Path(ASSETS_DIR)
+                        iteration_dir
                         / f"{restart_dir}"
                         / "assets"
                         / "basic"
diff --git a/tests/test_utils/python_scripts/download_golden_values.py b/tests/test_utils/python_scripts/download_golden_values.py
index 5d3007cb6c5..a37f7295ecf 100644
--- a/tests/test_utils/python_scripts/download_golden_values.py
+++ b/tests/test_utils/python_scripts/download_golden_values.py
@@ -43,7 +43,6 @@ def download_from_gitlab(pipeline_id: int, only_failing: bool):
         and pipeline_bridge.downstream_pipeline is not None
     ]
 
-    ASSETS_DIR = pathlib.Path("tmp") / "results" / "iteration=0"
     for pipeline_bridge in pipeline_bridges:
         functional_pipeline = project.pipelines.get(pipeline_bridge.downstream_pipeline["id"])
         environment = pipeline_bridge.name[len("functional:run_") :]
@@ -57,6 +56,7 @@ def download_from_gitlab(pipeline_id: int, only_failing: bool):
                 continue
 
             try:
+                shutil.rmtree(pathlib.Path("tmp") / "results", ignore_errors=True)
                 file_name = "__artifacts.zip"
                 with open(file_name, "wb") as f:
                     job.artifacts(streamed=True, action=f.write)
@@ -68,16 +68,18 @@ def download_from_gitlab(pipeline_id: int, only_failing: bool):
                 continue
 
             os.unlink(file_name)
-            restart_dir = os.listdir(pathlib.Path("tmp") / "results" / "iteration=0")[-1]
+            iteration_dir = sorted(
+                (pathlib.Path("tmp") / "results").glob("iteration=*"),
+                key=lambda p: int(p.name.split("=")[1]),
+            )[-1]
+            restart_dir = sorted(os.listdir(iteration_dir))[-1]
+            job_name_hyphenated = job.name.replace("_", "-").lower()
+            assets_basic_dir = iteration_dir / f"{restart_dir}" / "assets" / "basic"
             golden_values_sources = list(
-                (
-                    pathlib.Path(ASSETS_DIR)
-                    / f"{restart_dir}"
-                    / "assets"
-                    / "basic"
-                    / f"{job.name.replace('_', '-').lower()}-{environment.replace('_', '-')}"
-                ).glob("g*.json")
-            )
+                (assets_basic_dir / f"{job_name_hyphenated}-{environment.replace('_', '-')}").glob(
+                    "g*.json"
+                )
+            ) or list((assets_basic_dir / job_name_hyphenated).glob("g*.json"))
 
             if len(golden_values_sources) < 1:
                 logger.info(
diff --git a/tests/test_utils/python_scripts/launch_nemo_run_workload.py b/tests/test_utils/python_scripts/launch_nemo_run_workload.py
index bf4bb37aa20..cdb730fae63 100644
--- a/tests/test_utils/python_scripts/launch_nemo_run_workload.py
+++ b/tests/test_utils/python_scripts/launch_nemo_run_workload.py
@@ -54,6 +54,7 @@ def is_flaky_failure(concat_allranks_logs: str) -> bool:
     "--n-repeat", required=False, type=int, help="Number of times to repeat the workload", default=1
 )
 @click.option("--data-dir", required=False, type=str, help="Data directory of the workload")
+@click.option("--hf-home", required=False, type=str, help="HF home directory of the workload")
 @click.option("--tag", required=False, type=str, help="Tag of the workload")
 @click.option(
     "--enable-lightweight-mode",
@@ -73,6 +74,7 @@ def main(
     container_image,
     n_repeat: int = 1,
     data_dir: Optional[str] = None,
+    hf_home: Optional[str] = None,
     tag: Optional[str] = None,
     enable_lightweight_mode: Optional[bool] = False,
 ):
@@ -107,6 +109,8 @@ def main(
     artifacts.append(f"{os.getcwd()}:/opt/megatron-lm")
     if data_dir:
         artifacts.append(f"{pathlib.Path(data_dir)}:/mnt/artifacts")
+    if hf_home:
+        artifacts.append(f"{pathlib.Path(hf_home)}:/mnt/hf_home")
 
     executor = run.DockerExecutor(
         container_image=container_image,
@@ -122,6 +126,8 @@ def main(
             "CLUSTER": "dgxh100_dgxc",
             "NCCL_DEBUG": "INFO",
             "NCCL_DEBUG_FILE": "/opt/megatron-lm/assets_dir/logs/nccl_debug.log",
+            "HF_HOME": "/mnt/hf_home",
+            "TRANSFORMERS_OFFLINE": "1",
         },
         packager=run.Packager(),
         volumes=artifacts,
diff --git a/tests/test_utils/python_scripts/swap_pr_labels.py b/tests/test_utils/python_scripts/swap_pr_labels.py
index aa5875443f8..03b21f9b323 100644
--- a/tests/test_utils/python_scripts/swap_pr_labels.py
+++ b/tests/test_utils/python_scripts/swap_pr_labels.py
@@ -35,6 +35,7 @@ class Reminder:
 class PRReviewTracker:
     EXPERT_REVIEW = "Expert Review"
     FINAL_REVIEW = "Final Review"
+    APPROVED = "Approved"
     EXCLUDED_TEAMS = {"core-adlr", "core-nemo"}
 
     def __init__(self, token: str, repo_name: str, pr_number: str):
@@ -43,26 +44,111 @@ def __init__(self, token: str, repo_name: str, pr_number: str):
         self.pr = self.repo.get_pull(pr_number)
         self.stage = self.get_stage(self.pr)
         self.org = self.github.get_organization(self.repo.organization.login)
+        self._team_cache = {}
+        self._codeowner_rules = self._parse_codeowners()
+
+    def _parse_codeowners(self):
+        """Parse CODEOWNERS into ordered list of (pattern, teams) rules."""
+        rules = []
+        try:
+            with open(".github/CODEOWNERS") as f:
+                for line in f:
+                    line = line.strip()
+                    if not line or line.startswith("#"):
+                        continue
+                    parts = line.split()
+                    pattern = parts[0]
+                    teams = set()
+                    for part in parts[1:]:
+                        if part.startswith("@NVIDIA/"):
+                            teams.add(part.split("/", 1)[1])
+                    if teams:
+                        rules.append((pattern, teams))
+        except FileNotFoundError:
+            logger.warning("CODEOWNERS file not found")
+        logger.info(f"Parsed {len(rules)} CODEOWNERS rules")
+        return rules
+
+    @staticmethod
+    def _match_file(filepath, pattern):
+        """Check if a file path matches a CODEOWNERS pattern.
+
+        Rules:
+        - Trailing '/' means directory pattern: matches all files under that directory.
+        - Pattern containing '/' is path-relative: exact match or directory prefix.
+        - Pattern without '/' matches the filename component anywhere.
+        """
+        if pattern.endswith('/'):
+            return filepath.startswith(pattern)
+        if '/' in pattern:
+            return filepath == pattern or filepath.startswith(pattern + '/')
+        return filepath == pattern or filepath.endswith('/' + pattern)
+
+    def _get_required_teams(self, pr):
+        """Determine required review teams from CODEOWNERS rules and PR changed files.
+
+        Uses last-match-wins semantics per file, then unions across all files.
+        """
+        required_teams = set()
+        try:
+            changed_files = [f.filename for f in pr.get_files()]
+        except Exception as e:
+            logger.warning(f"Could not get changed files for PR #{pr.number}: {e}")
+            return required_teams
+
+        for filepath in changed_files:
+            matched_teams = None
+            for pattern, teams in self._codeowner_rules:
+                if self._match_file(filepath, pattern):
+                    matched_teams = teams
+            if matched_teams:
+                required_teams.update(matched_teams)
+                logger.info(f"  {filepath} → {matched_teams}")
+
+        logger.info(f"Required teams from CODEOWNERS: {required_teams}")
+        return required_teams
 
     def get_stage(self, pr):
         """Get current review stage."""
         labels = {l.name for l in pr.labels}
-        return self.FINAL_REVIEW if self.FINAL_REVIEW in labels else self.EXPERT_REVIEW
+        if self.APPROVED in labels:
+            return self.APPROVED
+        if self.FINAL_REVIEW in labels:
+            return self.FINAL_REVIEW
+        return self.EXPERT_REVIEW
+
+    def _get_team_members(self, slug):
+        """Get all members of a team, with caching."""
+        if slug not in self._team_cache:
+            try:
+                self._team_cache[slug] = {
+                    m.login for m in self.org.get_team_by_slug(slug).get_members()
+                }
+            except Exception as e:
+                logger.warning(f"Could not get members for team {slug}: {e}")
+                self._team_cache[slug] = set()
+        return self._team_cache[slug]
+
+    def _get_teams_members(self, slugs):
+        """Get all members of multiple teams."""
+        members = set()
+        for slug in slugs:
+            members.update(self._get_team_members(slug))
+        return members
 
     def swap_labels(self):
-        """Get filtered reviewer emails who haven't approved yet."""
+        """Evaluate review state and update labels accordingly."""
         pr = self.pr
-        if self.stage == self.FINAL_REVIEW:
-            logger.info(f"PR #{self.pr.number} is in the {self.stage} stage. No reviewers needed.")
+        if pr.draft:
+            logger.info(f"PR #{pr.number} is a draft. Skipping label swap.")
             return
 
         # 1. Get the latest review state for everyone who has submitted a review
         latest_reviews = {}
         try:
             for review in pr.get_reviews():
-                if not review.user:  # Handle rare cases of deleted users
+                if not review.user:
                     continue
-                # Only track 'APPROVED' or 'CHANGES_REQUESTED' as definitive states
                 if review.state in ("APPROVED", "CHANGES_REQUESTED"):
                     if (
                         review.user.login not in latest_reviews
@@ -72,61 +158,138 @@ def swap_labels(self):
         except Exception as e:
             logger.warning(f"Could not get reviews for PR #{pr.number}: {e}")
 
-        # 2. Separate reviewers into approvers (List B) and non-approvers
         approvers = {user for user, review in latest_reviews.items() if review.state == "APPROVED"}
-        non_approving_reviewers = {
+        non_approvers = {
             user for user, review in latest_reviews.items() if review.state == "CHANGES_REQUESTED"
         }
 
-        # 3. Get all *currently pending* review requests
-        try:
-            pending_users_req, pending_teams_req = pr.get_review_requests()
-            pending_individuals = {r.login for r in pending_users_req}
-            pending_teams_slugs = {t.slug for t in pending_teams_req}
-        except Exception as e:
-            logger.warning(f"Could not get review requests for PR #{pr.number}: {e}")
-            pending_individuals = set()
-            pending_teams_slugs = set()
-
-        # 4. Filter pending teams based on the current stage
-        teams_to_query = (
-            pending_teams_slugs - self.EXCLUDED_TEAMS
-            if self.stage == self.EXPERT_REVIEW
-            else pending_teams_slugs & self.EXCLUDED_TEAMS
-        )
-
-        # 5. Get members from the required pending teams
-        pending_team_members = set()
-        for slug in teams_to_query:
+        # 2. Determine required teams from CODEOWNERS + changed files
+        required_teams = self._get_required_teams(pr)
+        if not required_teams:
+            logger.info(f"PR #{pr.number}: no CODEOWNERS teams matched changed files. Skipping.")
+            return
+
+        expert_required = required_teams - self.EXCLUDED_TEAMS
+        final_required = required_teams & self.EXCLUDED_TEAMS
+        logger.info(f"Expert teams required: {expert_required}")
+        logger.info(f"Final teams required: {final_required}")
+
+        # 3. Check which required teams still need approval (at least one member must approve)
+        #    If _get_team_members fails (returns {}), the team stays pending — conservative.
+        pending_expert_teams = set()
+        for team in expert_required:
+            members = self._get_team_members(team)
+            if not (members & approvers):
+                pending_expert_teams.add(team)
+
+        pending_final_teams = set()
+        for team in final_required:
+            members = self._get_team_members(team)
+            if not (members & approvers):
+                pending_final_teams.add(team)
+
+        # 4. Compute pending reviewers: unsatisfied team members + individual blockers
+        all_excluded_members = self._get_teams_members(self.EXCLUDED_TEAMS)
+        expert_non_approvers = non_approvers - all_excluded_members
+        final_non_approvers = non_approvers & all_excluded_members
+
+        pending_expert = (
+            self._get_teams_members(pending_expert_teams) | expert_non_approvers
+        ) - approvers
+        pending_final = (
+            self._get_teams_members(pending_final_teams) | final_non_approvers
+        ) - approvers
+
+        logger.info(f"Pending expert teams: {pending_expert_teams}, reviewers: {pending_expert}")
+        logger.info(f"Pending final teams: {pending_final_teams}, reviewers: {pending_final}")
+
+        needs_final_review = bool(final_required)
+
+        # 5. State machine: update labels based on current stage and pending reviewers
+        if self.stage == self.APPROVED:
+            self._handle_approved_stage(pr, pending_expert, pending_final)
+        elif self.stage == self.FINAL_REVIEW:
+            self._handle_final_review_stage(pr, pending_expert, pending_final)
+        else:
+            self._handle_expert_review_stage(pr, pending_expert, pending_final, needs_final_review)
+
+    def _handle_approved_stage(self, pr, pending_expert, pending_final):
+        """Handle PRs that already have the Approved label."""
+        if len(pending_expert) > 0 or len(pending_final) > 0:
+            # New reviewers appeared — revert
             try:
-                pending_team_members.update(
-                    m.login for m in self.org.get_team_by_slug(slug).get_members()
-                )
+                pr.remove_from_labels(self.APPROVED)
+                logger.info(f'Removed "{self.APPROVED}" from PR #{pr.number}')
             except Exception as e:
-                logger.warning(f"Could not get members for team {slug} on PR #{pr.number}: {e}")
+                logger.warning(f'Failed to remove "{self.APPROVED}" from PR #{pr.number}: {e}')
 
-        # 6. "List A": Combine all users who *still need to review*
-        all_required_reviewers = (
-            pending_individuals | pending_team_members | non_approving_reviewers
-        )
+            if len(pending_expert) > 0:
+                # Back to expert review — also remove Final Review if present
+                try:
+                    pr.remove_from_labels(self.FINAL_REVIEW)
+                except Exception:
+                    pass
+                logger.info(
+                    f'Reverted PR #{pr.number} to expert review — pending: {pending_expert}'
+                )
+            else:
+                # Expert review done but final review needed again
+                try:
+                    pr.add_to_labels(self.FINAL_REVIEW)
+                except Exception:
+                    pass
+                logger.info(f'Reverted PR #{pr.number} to final review — pending: {pending_final}')
+        else:
+            logger.info(f"PR #{pr.number} is approved. No changes needed.")
 
-        # 7. Final list (List A - List B):
-        pending_reviewers = all_required_reviewers - approvers
-        logger.info(f"Pending reviewers: {pending_reviewers}")
-        if len(pending_reviewers) == 0:
+    def _handle_final_review_stage(self, pr, pending_expert, pending_final):
+        """Handle PRs in the Final Review stage."""
+        if len(pending_expert) > 0:
+            # New expert reviewers appeared — revert to expert review
             try:
-                pr.remove_from_labels(self.EXPERT_REVIEW)
-                logger.info(f'Removed "{self.EXPERT_REVIEW}" label from PR #{pr.number}')
-            except Exception as e:
-                logger.warning(
-                    f'Failed to remove "{self.EXPERT_REVIEW}" label from PR #{pr.number}: {e}'
+                pr.remove_from_labels(self.FINAL_REVIEW)
+                logger.info(
+                    f'Removed "{self.FINAL_REVIEW}" from PR #{pr.number} — '
+                    f'new expert reviewers pending: {pending_expert}'
                 )
+            except Exception as e:
+                logger.warning(f'Failed to remove "{self.FINAL_REVIEW}" from PR #{pr.number}: {e}')
+        elif len(pending_final) == 0:
+            # All final reviewers approved — move to Approved, remove Final Review
+            try:
+                pr.remove_from_labels(self.FINAL_REVIEW)
+                logger.info(f'Removed "{self.FINAL_REVIEW}" from PR #{pr.number}')
+            except Exception as e:
+                logger.warning(f'Failed to remove "{self.FINAL_REVIEW}" from PR #{pr.number}: {e}')
+            try:
+                pr.add_to_labels(self.APPROVED)
+                logger.info(f'Added "{self.APPROVED}" to PR #{pr.number}')
+            except Exception as e:
+                logger.warning(f'Failed to add "{self.APPROVED}" to PR #{pr.number}: {e}')
+        else:
+            logger.info(f"PR #{pr.number} is in final review. Pending: {pending_final}")
+
+    def _handle_expert_review_stage(self, pr, pending_expert, pending_final, needs_final_review):
+        """Handle PRs in the Expert Review stage (no review labels yet)."""
+        if len(pending_expert) > 0:
+            logger.info(f"PR #{pr.number} is in expert review. Pending: {pending_expert}")
+            return
 
+        # All expert reviewers approved
+        if needs_final_review and len(pending_final) > 0:
+            # Final review teams are assigned and still pending
             try:
                 pr.add_to_labels(self.FINAL_REVIEW)
-                logger.info(f'Added "{self.FINAL_REVIEW}" label to PR #{pr.number}')
+                logger.info(f'Added "{self.FINAL_REVIEW}" to PR #{pr.number}')
+            except Exception as e:
+                logger.warning(f'Failed to add "{self.FINAL_REVIEW}" to PR #{pr.number}: {e}')
+        else:
+            # No final review needed, or final reviewers already approved
+            try:
+                pr.add_to_labels(self.APPROVED)
+                logger.info(f'Added "{self.APPROVED}" to PR #{pr.number}')
             except Exception as e:
-                logger.warning(f'Failed to add "{self.FINAL_REVIEW}" label to PR #{pr.number}: {e}')
+                logger.warning(f'Failed to add "{self.APPROVED}" to PR #{pr.number}: {e}')
 
 
 def main():
diff --git a/tests/test_utils/recipes/h100/gpt-dynamic-inference-with-coordinator.yaml b/tests/test_utils/recipes/h100/gpt-dynamic-inference-with-coordinator.yaml
index 19d523eea8d..0349896345b 100644
--- a/tests/test_utils/recipes/h100/gpt-dynamic-inference-with-coordinator.yaml
+++ b/tests/test_utils/recipes/h100/gpt-dynamic-inference-with-coordinator.yaml
@@ -58,24 +58,25 @@ products:
   - test_case: [gpt_dynamic_inference_tp8_pp1_dp1_583m_logitsmatch_zmq]
     products:
       - environment: [dev]
-        scope: [flaky]
+        scope: [mr]
         platforms: [dgx_h100]
   - test_case: [gpt_dynamic_inference_tp1_pp8_dp1_583m_logitsmatch_zmq]
     products:
       - environment: [dev]
-        scope: [flaky]
+        scope: [mr]
         platforms: [dgx_h100]
   - test_case: [gpt_dynamic_inference_tp1_pp1_dp8_583m_logitsmatch_zmq]
     products:
       - environment: [dev]
         scope: [mr]
         platforms: [dgx_h100]
+  - test_case: [gpt_dynamic_inference_tp2_pp2_dp2_583m_logitsmatch_zmq]
+    products:
+      - environment: [dev]
+        scope: [mr, mr-github]
+        platforms: [dgx_h100]
   # - test_case: [gpt_dynamic_inference_tp1_pp1_dp8_583m_throughputtest_zmq]
   #   products:
   #     - environment: [dev]
   #       scope: [mr]
   #       platforms: [dgx_h100]
-  - test_case: [gpt_dynamic_inference_tp2_pp2_dp2_583m_logitsmatch_zmq]
-    products:
-      - environment: [dev]
-        scope: [flaky]
diff --git a/tests/test_utils/recipes/h100/mamba.yaml b/tests/test_utils/recipes/h100/mamba.yaml
index 456a6cbccf7..703fb53160f 100644
--- a/tests/test_utils/recipes/h100/mamba.yaml
+++ b/tests/test_utils/recipes/h100/mamba.yaml
@@ -63,13 +63,21 @@ products:
       # - environment: [lts] # disabled until triton is bumped
       #   scope: [nightly]
 
-  # PP functional testing deferred
-  # - test_case: [hybrid_mr_mcore_te_tp1_pp4_cp1_dgx_a100_1N8G]
-  #   products:
-  #     - environment: [dev]
-  #       scope: [mr]
-  #     - environment: [lts] # disabled until triton is bumped
-  #       scope: [nightly]
+  - test_case: [hybrid_mr_mcore_te_tp1_pp4_cp1_dgx_a100_1N8G]
+    products:
+      - environment: [dev]
+        scope: [mr, mr-github]
+        platforms: [dgx_h100]
+      # - environment: [lts] # disabled until triton is bumped
+      #   scope: [nightly]
+
+  - test_case: [hybrid_mr_mcore_te_tp1_pp2_vpp2_cp1_dgx_a100_1N8G]
+    products:
+      - environment: [dev]
+        scope: [mr, mr-github]
+        platforms: [dgx_h100]
+      # - environment: [lts] # disabled until triton is bumped
+      #   scope: [nightly]
 
   - test_case: [hybrid_mr_mcore_te_tp2_pp1_cp1_dgx_a100_1N8G]
     products:
diff --git a/tests/test_utils/recipes/h100/mimo.yaml b/tests/test_utils/recipes/h100/mimo.yaml
index 88b17815ede..683a22feb2a 100644
--- a/tests/test_utils/recipes/h100/mimo.yaml
+++ b/tests/test_utils/recipes/h100/mimo.yaml
@@ -63,3 +63,11 @@ products:
     products:
       - environment: [dev]
         scope: [flaky]
+  - test_case: [mimo_vlm_pretrain_convergence_tp1_pp1_cp1_dp8_seq_packing]
+    products:
+      - environment: [dev]
+        scope: [mr-github]
+  - test_case: [mimo_vlm_pretrain_convergence_tp1_pp1_cp2_dp8]
+    products:
+      - environment: [dev]
+        scope: [mr-github]
diff --git a/tests/test_utils/recipes/h100/moe-dynamic-inference-with-coordinator.yaml b/tests/test_utils/recipes/h100/moe-dynamic-inference-with-coordinator.yaml
index 4ce11808bdc..bb6d056dfd4 100644
--- a/tests/test_utils/recipes/h100/moe-dynamic-inference-with-coordinator.yaml
+++ b/tests/test_utils/recipes/h100/moe-dynamic-inference-with-coordinator.yaml
@@ -3,7 +3,7 @@ format_version: 1
 maintainers: [mcore]
 loggers: [stdout]
 spec:
-  name: '{test_case}_{environment}_{platforms}'
+  name: "{test_case}_{environment}_{platforms}"
   model: moe
   build: mcore-pyt-{environment}
   nodes: 1
@@ -58,11 +58,15 @@ products:
   - test_case: [gpt_dynamic_inference_tp4_etp1_pp1_ep8_16B_logitsmatch_zmq]
     products:
       - environment: [dev]
-        scope: [flaky]
+        scope: [mr] #[mr, mr-github] - broken on github after NGC PyTorch26.02
         platforms: [dgx_h100]
   - test_case: [gpt_dynamic_inference_tp4_etp1_pp1_ep8_16B_logitsmatch_cudagraph_zmq]
     products:
       - environment: [dev]
-        scope: [mr-github]
+        scope: [mr] #[mr, mr-github] - broken on github after NGC PyTorch26.02
+        platforms: [dgx_h100]
+  - test_case: [gpt_dynamic_inference_tp4_etp1_pp1_ep8_16B_logitsmatch_zmq_suspend_resume]
+    products:
+      - environment: [dev]
+        scope: [mr, mr-github]
         platforms: [dgx_h100]
-  
diff --git a/tests/unit_tests/conftest.py b/tests/unit_tests/conftest.py
index e251a3c1e7e..62413313fe0 100644
--- a/tests/unit_tests/conftest.py
+++ b/tests/unit_tests/conftest.py
@@ -82,7 +82,7 @@ def ensure_test_data():
 
         try:
             # Download assets to /opt/data
-            download_and_extract_asset(assets_dir=str(data_path))
+            download_and_extract_asset(assets_dir=data_path)
 
             print("Test data downloaded successfully.")
 
diff --git a/tests/unit_tests/data/test_preprocess_data.py b/tests/unit_tests/data/test_preprocess_data.py
index e6922ec3748..8df8c63e052 100644
--- a/tests/unit_tests/data/test_preprocess_data.py
+++ b/tests/unit_tests/data/test_preprocess_data.py
@@ -2,6 +2,7 @@
 
 import json
 import os
+import runpy
 import sys
 import tempfile
 
@@ -201,6 +202,38 @@ def test_preprocess_data_gpt():
         do_test_preprocess_data(temp_dir, extra_args=gpt_args)
 
 
+def test_preprocess_data_gpt_optimal_workers():
+    with tempfile.TemporaryDirectory() as temp_dir:
+
+        # gpt specific args
+        gpt_args = [
+            "--input",
+            "/opt/data/datasets/dclm/dclm.jsonl",
+            "--output-prefix",
+            f"{temp_dir}/optimal_workers",
+            "--tokenizer-type",
+            "GPT2BPETokenizer",
+            "--vocab-file",
+            "/opt/data/tokenizers/megatron/gpt2-vocab.json",
+            "--merge-file",
+            "/opt/data/tokenizers/megatron/gpt2-merges.txt",
+            "--append-eod",
+            "--workers",
+            "2",
+            "--log-interval",
+            "1",
+            "--find-optimal-num-workers",
+            "--workers-to-check",
+            "2",
+            "4",
+            "8",
+            "--max-documents",
+            "1002",
+        ]
+        sys.argv = ["/opt/megatron-lm/tools/preprocess_data.py"] + gpt_args
+        runpy.run_path("/opt/megatron-lm/tools/preprocess_data.py", run_name="__main__")
+
+
 def bert_vocab(odir):
     if os.path.exists(__LOCAL_BERT_VOCAB):
         return __LOCAL_BERT_VOCAB
@@ -237,3 +270,4 @@ def test_preprocess_data_bert():
 if __name__ == "__main__":
     test_preprocess_data_gpt()
     test_preprocess_data_bert()
+    test_preprocess_data_gpt_optimal_workers()
diff --git a/tests/unit_tests/dist_checkpointing/models/test_moe_experts.py b/tests/unit_tests/dist_checkpointing/models/test_moe_experts.py
index 5d8153f49d7..f341af44754 100644
--- a/tests/unit_tests/dist_checkpointing/models/test_moe_experts.py
+++ b/tests/unit_tests/dist_checkpointing/models/test_moe_experts.py
@@ -21,7 +21,6 @@
 from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed
 from megatron.core.transformer.mlp import MLPSubmodules
 from megatron.core.transformer.moe.experts import (
-    GroupedMLP,
     SequentialMLP,
     TEGroupedMLP,
     TEGroupedMLPSubmodules,
@@ -56,9 +55,7 @@ def initialize_expert_layer(seed, glu=True, expert_type='sequential', fp8=False,
     )
     default_config_kwargs.update(**config_kwargs)
     transformer_config = TransformerConfig(**default_config_kwargs)
-    if expert_type == 'grouped':
-        model = GroupedMLP(num_local_experts, transformer_config, pg_collection)
-    elif expert_type == 'te_grouped':
+    if expert_type == 'te_grouped':
         layer_submodules = get_gpt_layer_with_transformer_engine_submodules(
             num_experts=num_moe_experts, moe_grouped_gemm=True
         )
@@ -98,14 +95,13 @@ def initialize_expert_layer(seed, glu=True, expert_type='sequential', fp8=False,
         )
     else:
         raise ValueError(
-            'expert_type can only be one of ["sequential", "te_sequential", "grouped",'
-            ' "te_grouped"]'
+            'expert_type can only be one of ["sequential", "te_sequential", "te_grouped"]'
         )
     return model
 
 
-expert_type = ['sequential', 'grouped']
-src_dest_expert_type = [('sequential', 'grouped'), ('grouped', 'sequential')]
+expert_type = ['sequential']
+src_dest_expert_type = []
 if is_te_min_version("1.7.0.dev0"):
     expert_type.append('te_sequential')
     src_dest_expert_type.append(('sequential', 'te_sequential'))
diff --git a/tests/unit_tests/dist_checkpointing/utils.py b/tests/unit_tests/dist_checkpointing/utils.py
index 5d7d42d9152..0aadaee3b29 100644
--- a/tests/unit_tests/dist_checkpointing/utils.py
+++ b/tests/unit_tests/dist_checkpointing/utils.py
@@ -6,6 +6,9 @@
 
 import torch
 
+from megatron.core.dist_checkpointing.strategies.cached_metadata_filesystem_reader import (
+    CachedMetadataFileSystemReader,
+)
 from megatron.core.models.gpt import GPTModel
 from megatron.core.models.gpt.gpt_layer_specs import (
     get_gpt_layer_local_spec,
@@ -167,6 +170,9 @@ def init_checkpointing_mock_args(args, ckpt_dir, fully_parallel=False):
     args.dist_ckpt_optim_fully_reshardable = False
     args.distrib_optim_fully_reshardable_mem_efficient = False
     args.phase_transition_iterations = None
+    # Clear the metadata cache to avoid contamination between tests
+
+    CachedMetadataFileSystemReader.clear_metadata_cache()
 
 
 def setup_model_and_optimizer(
@@ -224,7 +230,7 @@ def setup_model_and_optimizer(
                     optimizer.optimizer.state[p]['exp_avg_sq'] = torch.rand_like(p.data)
 
     optimizer.reload_model_params()
-
+    CachedMetadataFileSystemReader.clear_metadata_cache()
     return unwrap_model(model), optimizer
 
 
@@ -324,5 +330,5 @@ def setup_moe_model_and_optimizer(
                         opt.state[p]['exp_avg_sq'] = torch.rand_like(p.data)
 
     optimizer.reload_model_params()
-
+    CachedMetadataFileSystemReader.clear_metadata_cache()
     return unwrap_model(model), optimizer
diff --git a/tests/unit_tests/distributed/megatron_fsdp/test_mfsdp_fully_shard.py b/tests/unit_tests/distributed/megatron_fsdp/test_mfsdp_fully_shard.py
index d3ec9d3342a..a09f8f703c5 100644
--- a/tests/unit_tests/distributed/megatron_fsdp/test_mfsdp_fully_shard.py
+++ b/tests/unit_tests/distributed/megatron_fsdp/test_mfsdp_fully_shard.py
@@ -4,6 +4,7 @@
 import shutil
 from contextlib import nullcontext
 from copy import deepcopy
+from itertools import product
 from pathlib import Path
 
 import pytest
@@ -243,8 +244,11 @@ def teardown_class(cls):
     )
     @pytest.mark.parametrize("model_type", [CNN, TRANSFORMER, TE_TRANSFORMER])
     @pytest.mark.parametrize(
-        "dp_shard_strategy", [NO_SHARD, OPTIM, OPTIM_GRADS, OPTIM_GRADS_PARAMS]
+        # Sharding strategy for optimizer state, gradients, and parameters.
+        "dp_shard_strategy",
+        [NO_SHARD, OPTIM, OPTIM_GRADS, OPTIM_GRADS_PARAMS],
     )
+    # Test FSDP, HSDP, and HFSDP.
     @pytest.mark.parametrize("dp_outer_strategy", [None, NO_SHARD, OPTIM])
     @pytest.mark.parametrize(
         "mesh_dim_config",
@@ -283,7 +287,10 @@ def test_fully_shard(
         don't add any new parameters unless absolutely necessary,
         or if some combinations can be flattened or simplified.
         """
-        from megatron.core.distributed.fsdp.src.megatron_fsdp.fully_shard import fully_shard
+        from megatron.core.distributed.fsdp.src.megatron_fsdp import (
+            MixedPrecisionPolicy,
+            fully_shard,
+        )
 
         preserve_fp32_weights = common_args["preserve_fp32_weights"]
         init_model_with_meta_device = common_args["init_model_with_meta_device"]
@@ -295,16 +302,12 @@ def test_fully_shard(
                 "Meta device initialization (init_model_with_meta_device=True) is not "
                 "supported or necessary for the 'no_shard' / 0 sharding strategy."
             )
-        elif dp_outer_strategy == OPTIM:
-            if dp_shard_strategy != OPTIM_GRADS_PARAMS:
-                # TODO(@shjwudp, @cspades): Requires various modifications to support.
-                # [default0]:FAILED tests/unit_tests/distributed/test_mfsdp_fully_shard.py
-                # [False-True-True-True-mesh_dim_config0-optim-optim-cnn]
-                # [False-True-True-True-mesh_dim_config0-optim-optim_grads-cnn]
-                pytest.skip(
-                    f"dp_outer sharding strategy {dp_outer_strategy} requires "
-                    "zero_dp_strategy to be full-sharded ('optim_grads_params', 3)."
-                )
+        elif dp_outer_strategy == OPTIM and dp_shard_strategy != OPTIM_GRADS_PARAMS:
+            # TODO(@shjwudp, @cspades): Requires various modifications to support.
+            pytest.skip(
+                f"dp_outer sharding strategy {dp_outer_strategy} requires "
+                "zero_dp_strategy to be full-sharded ('optim_grads_params', 3)."
+            )
 
         # Construct device mesh.
         device_mesh = build_distributed_environment(mesh_dim_config)
@@ -318,7 +321,7 @@ def test_fully_shard(
             module=toy_model,
             optimizer=toy_adam,
             device_mesh=device_mesh,
-            dp_shard_dim=DP_SHARD_CP,
+            dp_shard_dim=DP_SHARD_CP if mesh_dim_config[2] > 1 else DP_SHARD,
             dp_outer_dim=DP_OUTER if dp_outer_strategy is not None else None,
             tp_dim=TP,
             hybrid_fsdp_group=(
@@ -327,11 +330,14 @@ def test_fully_shard(
             fsdp_unit_modules=fsdp_unit_modules,
             zero_dp_strategy=dp_shard_strategy,
             outer_dp_sharding_strategy=(
-                dp_outer_strategy if dp_outer_strategy is not None else "no_shard"
+                dp_outer_strategy if dp_outer_strategy is not None else NO_SHARD
+            ),
+            mixed_precision_policy=MixedPrecisionPolicy(
+                main_params_dtype=torch.float32 if preserve_fp32_weights else None,
+                main_grads_dtype=None,
             ),
-            preserve_fp32_weights=preserve_fp32_weights,
-            grad_reduce_in_fp32=False,
             init_model_with_meta_device=init_model_with_meta_device,
+            report_nan_in_param_grad=True,
         )
         model = torch.compile(model) if torch_compile else model
 
@@ -342,6 +348,8 @@ def test_fully_shard(
         for step in range(NUM_STEPS):
             # Synchronize model parameters and gradients on the final training step only.
             if step == NUM_STEPS - 1:
+                # Triggers all-reduce / reduce-scatter across DP-Outer, and
+                # synchronizes / concludes the gradient accumulation cycle.
                 model.set_model_auto_sync(True)
             else:
                 model.set_model_auto_sync(False)
@@ -363,11 +371,12 @@ def test_fully_shard(
                 isinstance(p.grad, torch.Tensor) and p.grad.to_local().count_nonzero().item() > 0
                 for p in model.parameters()
             )
-            sharding_group = (
-                device_mesh[HSDP].get_group()
-                if dp_outer_strategy == OPTIM
-                else device_mesh[DP_SHARD_CP].get_group()
-            )
+            sharding_dim = DP_SHARD
+            if dp_outer_strategy == OPTIM:
+                sharding_dim = HSDP
+            elif mesh_dim_config[2] > 1:
+                sharding_dim = DP_SHARD_CP
+            sharding_group = device_mesh[sharding_dim].get_group()
             if dp_shard_strategy != NO_SHARD:
                 # Because of uneven sharding, we need to gather the result from all ranks
                 # to verify if any gradients exist or not at this step of training.
@@ -411,14 +420,14 @@ def test_dcp_checkpoint_save_and_load(
         """
         from torch.distributed.tensor import DTensor
 
-        from megatron.core.distributed.fsdp.src.megatron_fsdp.fully_shard import fully_shard
+        from megatron.core.distributed.fsdp.src.megatron_fsdp import (
+            MixedPrecisionPolicy,
+            fully_shard,
+        )
 
         # Skip tests.
         if outer_shard_strategy == OPTIM and shard_strategy != OPTIM_GRADS_PARAMS:
-            # FIXME(@shjwudp, @cspades): This is an unexpected lack of support.
-            # [default0]:FAILED tests/unit_tests/distributed/test_mfsdp_fully_shard.py
-            # [False-True-True-True-mesh_dim_config0-optim-optim-cnn]
-            # [False-True-True-True-mesh_dim_config0-optim-optim_grads-cnn]
+            # TODO(@shjwudp, @cspades): Requires various modifications to support.
             pytest.skip(
                 f"dp_outer sharding strategy {outer_shard_strategy} requires "
                 "zero_dp_strategy to be full-sharded ('optim_grads_params', 3)."
@@ -449,15 +458,16 @@ def test_dcp_checkpoint_save_and_load(
             module=toy_model,
             optimizer=toy_adam,
             device_mesh=device_mesh,
-            dp_shard_dim=DP_SHARD_CP,
+            dp_shard_dim=DP_SHARD_CP if mesh_dim_config[2] > 1 else DP_SHARD,
             dp_outer_dim=DP_OUTER,
             tp_dim=TP,
             hybrid_fsdp_group=device_mesh[HSDP].get_group(),
             fsdp_unit_modules=fsdp_unit_modules,
             zero_dp_strategy=shard_strategy,
             outer_dp_sharding_strategy=outer_shard_strategy,
-            preserve_fp32_weights=True,
-            grad_reduce_in_fp32=True,
+            mixed_precision_policy=MixedPrecisionPolicy(
+                main_params_dtype=torch.float32, main_grads_dtype=torch.float32
+            ),
             init_model_with_meta_device=False,
             sync_model_each_microbatch=True,
         )
@@ -536,15 +546,16 @@ def test_dcp_checkpoint_save_and_load(
             module=toy_model,
             optimizer=toy_adam,
             device_mesh=device_mesh,
-            dp_shard_dim=DP_SHARD_CP,
+            dp_shard_dim=DP_SHARD_CP if mesh_dim_config[2] > 1 else DP_SHARD,
             dp_outer_dim=DP_OUTER,
             tp_dim=TP,
             hybrid_fsdp_group=device_mesh[HSDP].get_group(),
             fsdp_unit_modules=fsdp_unit_modules,
             zero_dp_strategy=shard_strategy,
             outer_dp_sharding_strategy=outer_shard_strategy,
-            preserve_fp32_weights=True,
-            grad_reduce_in_fp32=True,
+            mixed_precision_policy=MixedPrecisionPolicy(
+                main_params_dtype=torch.float32, main_grads_dtype=torch.float32
+            ),
             init_model_with_meta_device=False,
             sync_model_each_microbatch=True,
         )
@@ -658,7 +669,7 @@ def test_fully_shard_ez(self, shard_strategy):
         """
         Test fully_shard(device_mesh=None). Represents the easiest entrypoint to Megatron-FSDP.
         """
-        from megatron.core.distributed.fsdp.src.megatron_fsdp.fully_shard import (
+        from megatron.core.distributed.fsdp.src.megatron_fsdp import (
             fully_shard_model,
             fully_shard_optimizer,
         )
@@ -679,7 +690,7 @@ def test_fully_shard_ez(self, shard_strategy):
         toy_input = torch.randn(1, DIM_SIZE, DIM_SIZE).to("cuda")
         toy_target = torch.randn(1, DIM_SIZE, DIM_SIZE).to("cuda")
 
-        for step in range(NUM_STEPS):
+        for _ in range(NUM_STEPS):
 
             # Forward pass.
             output = mfsdp_model(toy_input, toy_input)
@@ -707,7 +718,8 @@ def test_fully_shard_te_quantized(self, init_model_with_meta_device, te_recipe):
             # TODO(@cspades, @ko3n1g): Add this test case in.
             pytest.skip(f"[Megatron CI/CD] MXFP8 requires Blackwell nodes to test.")
 
-        from megatron.core.distributed.fsdp.src.megatron_fsdp.fully_shard import (
+        from megatron.core.distributed.fsdp.src.megatron_fsdp import (
+            MixedPrecisionPolicy,
             fully_shard_model,
             fully_shard_optimizer,
         )
@@ -759,10 +771,13 @@ def test_fully_shard_te_quantized(self, init_model_with_meta_device, te_recipe):
             # its own row-wise and col-wise (transpose) buffer management
             # schedule that is natively managed by Megatron-FSDP.
             keep_fp8_transpose_cache=True,
-            # Required for FP8 parameters. The optimizer state (and gradients)
-            # are never quantized, as TE produces high-precision wgrad and
-            # dgrad from FP8 weights and activations. Already defaults to True.
-            preserve_fp32_weights=True,
+            mixed_precision_policy=MixedPrecisionPolicy(
+                # Required for FP8 parameters. The optimizer state (and gradients)
+                # are never quantized, as TE produces high-precision wgrad and
+                # dgrad from FP8 weights and activations. Defaults to FP32.
+                main_params_dtype=torch.float32
+            ),
+            report_nan_in_param_grad=True,
         )
 
         # Initialize the distributed optimizer on the MegatronFSDP model.
@@ -792,3 +807,136 @@ def test_fully_shard_te_quantized(self, init_model_with_meta_device, te_recipe):
             # Optimizer step.
             optimizer.step()
             optimizer.zero_grad()
+
+    @pytest.mark.skipif(
+        version.parse(torch.__version__) < version.parse('2.4.0'),
+        reason="Requires DTensor and DeviceMesh support in (approximately) PyTorch 2.4.0 or later.",
+    )
+    # Test non-FP8 and FP8 parameters.
+    @pytest.mark.parametrize("model_type", [TRANSFORMER, TE_TRANSFORMER])
+    @pytest.mark.parametrize(
+        # Test gradient all-reduce, reduce-scatter, and param all-gather.
+        "dp_shard_strategy",
+        [OPTIM, OPTIM_GRADS, OPTIM_GRADS_PARAMS],
+    )
+    # Test HSDP and HFSDP only. (FSDP collectives are a subset of HSDP.)
+    @pytest.mark.parametrize("dp_outer_strategy", [NO_SHARD, OPTIM])
+    @pytest.mark.parametrize("custom_main_params_dtype", [None, torch.float64])
+    @pytest.mark.parametrize("custom_main_grads_dtype", [None, torch.float32])
+    def test_fully_shard_custom_dtype(
+        self,
+        model_type,
+        dp_shard_strategy,
+        dp_outer_strategy,
+        custom_main_params_dtype,
+        custom_main_grads_dtype,
+    ):
+        """
+        Test custom data-types for gather and reduce communications.
+        """
+        from megatron.core.distributed.fsdp.src.megatron_fsdp import (
+            MixedPrecisionPolicy,
+            fully_shard_model,
+            fully_shard_optimizer,
+        )
+
+        if dp_outer_strategy == OPTIM and dp_shard_strategy != OPTIM_GRADS_PARAMS:
+            pytest.skip(
+                f"dp_outer sharding strategy {dp_outer_strategy} requires "
+                "zero_dp_strategy to be full-sharded ('optim_grads_params', 3)."
+            )
+        if model_type == TE_TRANSFORMER and custom_main_params_dtype is None:
+            pytest.skip(
+                f"TransformerEngine FP8 all-gather requires a main parameter buffer for FSDP."
+            )
+
+        # Construct device mesh with DP-Outer=2 and DP-Shard=4.
+        device_mesh = build_distributed_environment((2, 4, 1, 1))
+
+        # Construct toy model.
+        if model_type == TE_TRANSFORMER:
+            # Use FP8 model parameters to test data-type customization.
+            te_quant_recipe = te.common.recipe.DelayedScaling()
+            with te.pytorch.quantized_model_init(
+                recipe=te_quant_recipe,
+                # Needed for FP8 parameters with Megatron-FSDP.
+                preserve_high_precision_init_val=True,
+            ):
+                toy_model = ToyTETransformer(
+                    model_dim=64,
+                    num_heads=2,
+                    num_layers=2,
+                    output_dim=64,
+                    fuse_qkv_params=True,
+                    params_dtype=torch.bfloat16,
+                    device="meta",
+                )
+                fsdp_unit_modules = [te.pytorch.TransformerLayer, te.pytorch.Linear]
+        else:
+            toy_model, fsdp_unit_modules = build_toy_model(model_type, True)
+
+        # Fully-shard the model.
+        mfsdp_model = fully_shard_model(
+            module=toy_model,
+            device_mesh=device_mesh,
+            dp_shard_dim=DP_SHARD,
+            dp_outer_dim=DP_OUTER,
+            tp_dim=TP,
+            hybrid_fsdp_group=device_mesh[HSDP].get_group(),
+            fsdp_unit_modules=fsdp_unit_modules,
+            zero_dp_strategy=dp_shard_strategy,
+            outer_dp_sharding_strategy=dp_outer_strategy,
+            mixed_precision_policy=MixedPrecisionPolicy(
+                main_params_dtype=custom_main_params_dtype,
+                main_grads_dtype=custom_main_grads_dtype,
+                grad_comm_dtype=None,
+            ),
+            init_model_with_meta_device=True,
+            report_nan_in_param_grad=True,
+        )
+        # Verify that the main weight and main gradient buffers have the correct dtype.
+        main_weight_buffer = getattr(
+            mfsdp_model.param_and_grad_buffer.parameter_groups[0], "main_weight_buffer", None
+        )
+        if main_weight_buffer is not None:
+            assert main_weight_buffer.data.dtype == custom_main_params_dtype
+        if custom_main_grads_dtype is not None:
+            assert (
+                mfsdp_model.param_and_grad_buffer.parameter_groups[0].main_grad_buffer.data.dtype
+                == custom_main_grads_dtype
+            )
+
+        # Initialize the distributed optimizer on the MegatronFSDP model.
+        toy_adam = Adam(params=mfsdp_model.parameters(), lr=0.001)
+        optimizer = fully_shard_optimizer(optimizer=toy_adam)
+
+        # Mock input and target.
+        if model_type == TE_TRANSFORMER:
+            toy_input = torch.randn(16, 64, 64, dtype=torch.bfloat16).to("cuda")
+            toy_target = torch.randn(16, 64, 64, dtype=torch.bfloat16).to("cuda")
+        else:
+            toy_input = torch.randn(1, DIM_SIZE, DIM_SIZE).to("cuda")
+            toy_target = torch.randn(1, DIM_SIZE, DIM_SIZE).to("cuda")
+
+        # Test a different mixed-precision policy every step.
+        for grad_comm_dtype in [None, torch.float16]:
+            # Set up mixed-precision context manager to change policy every step.
+            with mfsdp_model.mixed_precision_context(
+                MixedPrecisionPolicy(grad_comm_dtype=grad_comm_dtype)
+            ):
+                # Forward pass.
+                if model_type == TE_TRANSFORMER:
+                    with te.pytorch.autocast(recipe=te_quant_recipe):
+                        output = mfsdp_model(toy_input)
+                elif model_type == TRANSFORMER:
+                    output = mfsdp_model(toy_input, toy_input)
+
+                # Loss.
+                loss = mse_loss(output, toy_target)
+
+                # Backward pass.
+                loss.backward()
+
+                # Optimizer step syncs gradient communication.
+                optimizer.step()
+                optimizer.zero_grad()
diff --git a/tests/unit_tests/distributed/test_param_and_grad_buffer.py b/tests/unit_tests/distributed/test_param_and_grad_buffer.py
index b60dfb1791b..48f815531c5 100644
--- a/tests/unit_tests/distributed/test_param_and_grad_buffer.py
+++ b/tests/unit_tests/distributed/test_param_and_grad_buffer.py
@@ -373,3 +373,94 @@ def test_force_all_reduce_uses_correct_collective(force_all_reduce: bool):
             ), "Expected all_reduce NOT to be called when force_all_reduce=False"
 
     Utils.destroy_model_parallel()
+
+
+class TestFreeOverlapBuffers:
+    """Tests for free_overlap_buffers() which releases GPU memory before async checkpoint saves."""
+
+    @staticmethod
+    def _make_model():
+        """Create a DDP-wrapped model with overlap_param_gather enabled."""
+        Utils.initialize_model_parallel()
+        ddp_config = DistributedDataParallelConfig(
+            grad_reduce_in_fp32=True,
+            use_distributed_optimizer=False,
+            overlap_grad_reduce=True,
+            overlap_param_gather=True,
+            bucket_size=None,
+        )
+        module = TestModel(
+            input_dim=32, output_dim=32, num_layers=2, bias=False, shared_embedding=False
+        ).bfloat16()
+        model = DistributedDataParallel(
+            TransformerConfig(num_attention_heads=1, num_layers=1),
+            ddp_config=ddp_config,
+            module=module,
+        )
+        return model
+
+    def test_bucket_group_clears_buffers(self):
+        """free_overlap_buffers on a bucket group should None-out per-bucket layerwise buffers."""
+        model = self._make_model()
+
+        for bg in model.bucket_groups:
+            # Simulate buffers that would be allocated by start_param_sync.
+            for bucket in bg.buckets:
+                bucket.layerwise_gather_list = [torch.empty(8), torch.empty(8)]
+                bucket._layerwise_src_buffer = torch.empty(16)
+
+            bg.free_overlap_buffers()
+
+            for bucket in bg.buckets:
+                assert (
+                    bucket.layerwise_gather_list is None
+                ), "layerwise_gather_list should be None after free_overlap_buffers"
+                assert (
+                    bucket._layerwise_src_buffer is None
+                ), "_layerwise_src_buffer should be None after free_overlap_buffers"
+
+        Utils.destroy_model_parallel()
+
+    def test_bucket_group_waits_on_pending_handle(self):
+        """free_overlap_buffers should wait() on any pending param_gather_handle."""
+        model = self._make_model()
+
+        for bg in model.bucket_groups:
+            mock_handle = mock.MagicMock()
+            bg.param_gather_handle = mock_handle
+
+            bg.free_overlap_buffers()
+
+            mock_handle.wait.assert_called_once()
+            assert (
+                bg.param_gather_handle is None
+            ), "param_gather_handle should be None after free_overlap_buffers"
+
+        Utils.destroy_model_parallel()
+
+    def test_bucket_group_noop_when_no_buffers(self):
+        """free_overlap_buffers should be safe to call when no buffers are allocated."""
+        model = self._make_model()
+
+        for bg in model.bucket_groups:
+            assert bg.param_gather_handle is None
+            for bucket in bg.buckets:
+                assert bucket.layerwise_gather_list is None
+                assert bucket._layerwise_src_buffer is None
+
+            # Should not raise.
+            bg.free_overlap_buffers()
+
+        Utils.destroy_model_parallel()
+
+    def test_ddp_free_overlap_buffers_delegates(self):
+        """DDP.free_overlap_buffers should call free_overlap_buffers on all bucket groups."""
+        model = self._make_model()
+
+        with mock.patch.object(type(model.bucket_groups[0]), 'free_overlap_buffers') as mock_free:
+            model.free_overlap_buffers()
+            assert mock_free.call_count == len(
+                model.bucket_groups + model.expert_parallel_bucket_groups
+            ), "free_overlap_buffers should be called on every bucket group"
+
+        Utils.destroy_model_parallel()
diff --git a/tests/unit_tests/fusions/test_rmsnorm_residual_fusion.py b/tests/unit_tests/fusions/test_rmsnorm_residual_fusion.py
new file mode 100644
index 00000000000..6c03e0fa801
--- /dev/null
+++ b/tests/unit_tests/fusions/test_rmsnorm_residual_fusion.py
@@ -0,0 +1,46 @@
+# Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+
+import pytest
+import torch
+from transformer_engine.pytorch import RMSNorm
+
+from megatron.core.extensions.transformer_engine import TEFusedResidualRMSNorm
+
+
+def baseline_rmsnorm_residual(x, rmsnorm: RMSNorm):
+    return rmsnorm(x), x
+
+
+@pytest.mark.parametrize("input_dtype", [torch.bfloat16, torch.float32])
+@pytest.mark.parametrize("normalized_shape", [256, 256 * 2, 256 * 4])
+def test_rmsnorm_residual_fusion(input_dtype, normalized_shape):
+    x_baseline = torch.randn(16, 32, normalized_shape, dtype=input_dtype, device="cuda")
+    x_baseline.requires_grad = True
+    x_fused = x_baseline.detach()
+    x_fused.requires_grad = True
+    baseline_rmsnorm = RMSNorm(normalized_shape=normalized_shape, dtype=input_dtype).cuda()
+    fused_rmsnorm = TEFusedResidualRMSNorm(
+        normalized_shape=normalized_shape, dtype=input_dtype
+    ).cuda()
+
+    # baseline
+    baseline_y, baseline_residual = baseline_rmsnorm_residual(x_baseline, baseline_rmsnorm)
+    baseline_loss = baseline_y.sum() + baseline_residual.sum()
+    baseline_loss.backward()
+
+    # fused
+    fused_y, fused_residual = fused_rmsnorm(x_fused)
+    fused_loss = fused_y.sum() + fused_residual.sum()
+    fused_loss.backward()
+
+    # Use tolerances appropriate for dtype (pattern from other tests)
+    tols = (
+        dict(rtol=1e-6, atol=1e-6) if input_dtype is torch.float32 else dict(rtol=2e-2, atol=1e-2)
+    )
+
+    assert fused_y.dtype == baseline_y.dtype
+    assert torch.allclose(fused_y, baseline_y, **tols)
+    assert fused_residual.dtype == baseline_residual.dtype
+    assert torch.allclose(fused_residual, baseline_residual, **tols)
+    assert x_fused.grad.dtype == x_baseline.grad.dtype
+    assert torch.allclose(x_baseline.grad, x_fused.grad, **tols)
diff --git a/tests/unit_tests/inference/contexts/attention_metadata/test_mamba_metadata.py b/tests/unit_tests/inference/contexts/attention_metadata/test_mamba_metadata.py
index dd34061888e..7e76ce4b7b0 100644
--- a/tests/unit_tests/inference/contexts/attention_metadata/test_mamba_metadata.py
+++ b/tests/unit_tests/inference/contexts/attention_metadata/test_mamba_metadata.py
@@ -100,6 +100,7 @@ def test_update_decode_only_exact_match(self, metadata_context):
 
         expected_decode = torch.arange(4, dtype=torch.int32, device=metadata_context.device)
         assert torch.equal(metadata_context.batch_indices_decode, expected_decode)
+
         assert metadata_context.batch_indices_prefill is None
         assert metadata_context.batch_indices_chunked_prefill is None
         assert metadata_context.device_decode_prefill is None
@@ -124,6 +125,7 @@ def test_update_decode_only_padded(self, metadata_context):
             [0, 1, -1, -1], dtype=torch.int32, device=metadata_context.device
         )
         assert torch.equal(metadata_context.batch_indices_decode, expected_decode)
+
         assert metadata_context.batch_indices_prefill is None
         assert metadata_context.batch_indices_chunked_prefill is None
         assert metadata_context.device_decode_prefill is None
@@ -144,6 +146,7 @@ def test_update_chunked_enabled_no_prefill_reqs(self, metadata_context):
         # Should behave exactly like decode-only (chunked logic skipped if real_prefill == 0)
         expected_decode = torch.tensor([0, 1], dtype=torch.int32, device=metadata_context.device)
         assert torch.equal(metadata_context.batch_indices_decode, expected_decode)
+
         assert metadata_context.batch_indices_chunked_prefill is None
         assert metadata_context.batch_indices_prefill is None
         assert metadata_context.cu_seqlens is None
@@ -242,7 +245,7 @@ def test_update_mixed_batch_exact(self, metadata_context):
         assert torch.equal(metadata_context.batch_indices_prefill, expected_prefill)
 
         expected_device_counts = torch.tensor(
-            [2, 2], dtype=torch.int32, device=metadata_context.device
+            [2, 30], dtype=torch.int32, device=metadata_context.device
         )
         assert torch.equal(metadata_context.device_decode_prefill, expected_device_counts)
 
@@ -288,7 +291,7 @@ def test_update_padded_prefill_and_decode(self, metadata_context):
         assert torch.equal(metadata_context.batch_indices_prefill, expected_prefill)
 
         expected_device_counts = torch.tensor(
-            [1, 1], dtype=torch.int32, device=metadata_context.device
+            [1, 10], dtype=torch.int32, device=metadata_context.device
         )
         assert torch.equal(metadata_context.device_decode_prefill, expected_device_counts)
 
@@ -334,7 +337,7 @@ def test_update_chunked_prefill_mixed_exact(self, metadata_context):
         assert torch.equal(metadata_context.batch_indices_prefill, expected_prefill)
 
         expected_device_counts = torch.tensor(
-            [1, 2], dtype=torch.int32, device=metadata_context.device
+            [1, 60], dtype=torch.int32, device=metadata_context.device
         )
         assert torch.equal(metadata_context.device_decode_prefill, expected_device_counts)
 
@@ -375,7 +378,7 @@ def test_update_chunked_prefill_mixed_padded(self, metadata_context):
         assert torch.equal(metadata_context.batch_indices_prefill, expected_prefill)
 
         expected_device_counts = torch.tensor(
-            [2, 2], dtype=torch.int32, device=metadata_context.device
+            [2, 60], dtype=torch.int32, device=metadata_context.device
         )
         assert torch.equal(metadata_context.device_decode_prefill, expected_device_counts)
 
diff --git a/tests/unit_tests/inference/contexts/test_dynamic_context.py b/tests/unit_tests/inference/contexts/test_dynamic_context.py
index 67d62c7e4fa..4425a37347e 100644
--- a/tests/unit_tests/inference/contexts/test_dynamic_context.py
+++ b/tests/unit_tests/inference/contexts/test_dynamic_context.py
@@ -2,6 +2,7 @@
 
 import contextlib
 import math
+from unittest import mock
 
 import pytest
 import torch
@@ -59,6 +60,8 @@ def _get_dynamic_context(
         is_hybrid_model=False,
         layer_type_list=None,
         paused_buffer_size_gb=None,
+        num_cuda_graphs=None,
+        num_speculative_tokens=0,
     ):
         if is_hybrid_model:
             if layer_type_list is None:
@@ -66,7 +69,11 @@ def _get_dynamic_context(
             mamba_conv_states_shape = (544, 4)
             mamba_ssm_states_shape = (8, 64, 16)
             mamba_inference_state_config = MambaInferenceStateConfig(
-                layer_type_list, mamba_conv_states_shape, mamba_ssm_states_shape
+                layer_type_list,
+                mamba_conv_states_shape,
+                mamba_ssm_states_shape,
+                params_dtype,
+                params_dtype,
             )
         else:
             mamba_inference_state_config = None
@@ -80,7 +87,7 @@ def _get_dynamic_context(
             ),
             inference_config=InferenceConfig(
                 max_sequence_length=max_sequence_length,
-                num_cuda_graphs=None,
+                num_cuda_graphs=num_cuda_graphs,
                 use_cuda_graphs_for_non_decode_steps=True,
                 buffer_size_gb=buffer_size_gb,
                 paused_buffer_size_gb=(
@@ -88,6 +95,7 @@ def _get_dynamic_context(
                 ),
                 block_size_tokens=block_size_tokens,
                 max_tokens=max_tokens,
+                num_speculative_tokens=num_speculative_tokens,
                 mamba_inference_state_config=mamba_inference_state_config,
                 use_flashinfer_fused_rope=None,  # default to using flash-infer if available
                 # this is for compatibility with the LTS environment
@@ -821,6 +829,10 @@ def test_release_memory_blocks_for_finished_requests(self, is_hybrid_model):
             dynamic_context.request_to_kv_block_ids[i, 0] = initial_blocks[i]
             dynamic_context.request_query_lengths[i] = 1
             dynamic_context.request_ids[i] = i
+            dynamic_context.request_last_kv_block_id[i] = initial_blocks[i]
+            dynamic_context.request_last_kv_block_offset[i] = 0
+            dynamic_context.request_kv_block_counts[i] = 1
+            dynamic_context.request_in_prefill_status_tensor[i] = 0
             if is_hybrid_model:
                 dynamic_context.mamba_conv_states[:, i, :, :].fill_(
                     float(i + 1)
@@ -909,9 +921,16 @@ def test_finished_requests_with_multiple_blocks(self, is_hybrid_model):
         for i in range(3):
             dynamic_context.request_query_lengths[i] = 1
             dynamic_context.request_ids[i] = i
+            dynamic_context.request_last_kv_block_id[i] = dynamic_context.request_to_kv_block_ids[
+                i, dynamic_context.request_kv_block_counts[i] - 1
+            ]
+            dynamic_context.request_last_kv_block_offset[i] = 0
+            dynamic_context.request_in_prefill_status_tensor[i] = 0
             if is_hybrid_model:
                 dynamic_context.mamba_conv_states[:, i, :, :].fill_(float(i + 1))
                 dynamic_context.mamba_ssm_states[:, i, :, :, :].fill_(float(i + 1))
+                dynamic_context.mamba_metadata.request_to_mamba_state_idx[i] = i
+                dynamic_context.mamba_metadata.mamba_state_free_slot_count -= 1
 
         # Create an active_requests_mask where all requests are finished
         active_requests_mask = torch.tensor([0, 0, 0], device=torch.cuda.current_device())
@@ -1237,29 +1256,34 @@ def test_pipeline_parallel_uneven_layers(self):
 
         mamba_conv_states_shape = (544, 4)
         mamba_ssm_states_shape = (8, 64, 16)
+        params_dtype = torch.float32
 
         if rank == 0:
             mamba_inference_state_config = MambaInferenceStateConfig(
                 [Symbols.MAMBA] + [Symbols.ATTENTION] * 4,
                 mamba_conv_states_shape,
                 mamba_ssm_states_shape,
+                params_dtype,
+                params_dtype,
             )
         else:
             mamba_inference_state_config = MambaInferenceStateConfig(
                 [Symbols.MAMBA] * 4 + [Symbols.ATTENTION],
                 mamba_conv_states_shape,
                 mamba_ssm_states_shape,
+                params_dtype,
+                params_dtype,
             )
 
         context = DynamicInferenceContext(
             model_config=TransformerConfig(
-                params_dtype=torch.float32,
+                params_dtype=params_dtype,
                 num_layers=10,
                 kv_channels=64,
                 num_attention_heads=8,
                 pipeline_model_parallel_size=pp_size,
                 tensor_model_parallel_size=1,
-                pipeline_dtype=torch.float32,
+                pipeline_dtype=params_dtype,
             ),
             inference_config=InferenceConfig(
                 max_sequence_length=128,
@@ -1308,7 +1332,11 @@ def test_mamba_memory_ratio_allocation(self, ratio):
         mamba_conv_states_shape = (544, 4)
         mamba_ssm_states_shape = (8, 64, 16)
         mamba_config = MambaInferenceStateConfig(
-            layer_type_list, mamba_conv_states_shape, mamba_ssm_states_shape
+            layer_type_list,
+            mamba_conv_states_shape,
+            mamba_ssm_states_shape,
+            params_dtype,
+            params_dtype,
         )
 
         context = DynamicInferenceContext(
@@ -1385,3 +1413,113 @@ def test_max_requests_less_than_tp_size(self):
 
         with pytest.raises(AssertionError):
             DynamicInferenceContext(model_config=model_config, inference_config=inference_config)
+
+    @pytest.mark.internal
+    @rounder_override(64)
+    @pytest.mark.parametrize("is_hybrid_model", [False, True])
+    @pytest.mark.parametrize("num_cuda_graphs", [-1, 16, 32])
+    @pytest.mark.parametrize("num_speculative_tokens", [0, 3])
+    def test_add_dummy_requests_for_expert_parallel_step_matches_slow_path(
+        self, is_hybrid_model: bool, num_cuda_graphs: int, num_speculative_tokens: int
+    ):
+        """The fast path (add_dummy_requests_for_expert_parallel_step) must leave
+        the same observable state as the slow path
+        (add_dummy_requests_for_cudagraph_capture(min(cuda_graph_dims))).
+        """
+        self._setup_model_parallel_group(1, 1)
+
+        ctx = self._get_dynamic_context(
+            params_dtype=torch.float32,
+            num_layers=4,
+            kv_channels=8,
+            num_attention_heads=2,
+            max_sequence_length=512,
+            buffer_size_gb=0.03,
+            block_size_tokens=128,
+            max_tokens=None,
+            is_hybrid_model=is_hybrid_model,
+            layer_type_list=(
+                [Symbols.MAMBA, Symbols.ATTENTION, Symbols.MLP, Symbols.ATTENTION]
+                if is_hybrid_model
+                else None
+            ),
+            num_cuda_graphs=num_cuda_graphs,
+            num_speculative_tokens=num_speculative_tokens,
+        )
+
+        smallest = min(ctx.cuda_graph_batch_dimensions_list)
+        N = smallest.decode_req_count
+        T = smallest.token_count  # N * (num_speculative_tokens + 1)
+        assert smallest.prefill_req_count == 0, "smallest graph must be decode-only"
+
+        # --- slow path (reference) ---
+        ctx.add_dummy_requests_for_cudagraph_capture(smallest)
+
+        slow_total_request_count = ctx.total_request_count
+        slow_active_token_count = ctx.active_token_count
+        slow_num_prefill_requests = ctx.num_prefill_requests
+        slow_request_query_lengths = ctx.request_query_lengths[:N].clone()
+        slow_request_kv_length_offsets = ctx.request_kv_length_offsets[:N].clone()
+        slow_request_to_kv_block_ids_col0 = ctx.request_to_kv_block_ids[:N, 0].clone()
+        slow_token_to_block_idx = ctx.token_to_block_idx[:T].clone()
+        slow_token_to_local_pos = ctx.token_to_local_position_within_kv_block[:T].clone()
+        if is_hybrid_model:
+            slow_token_to_request_idx = ctx.token_to_request_idx[:T].clone()
+            slow_mamba = ctx.mamba_metadata.request_to_mamba_state_idx[:N].clone()
+
+        # --- reset and run fast path ---
+        ctx.reset()
+        ctx.add_dummy_requests_for_expert_parallel_step()
+
+        # 1. Scalar counts
+        assert ctx.total_request_count == slow_total_request_count
+        assert ctx.active_token_count == slow_active_token_count
+        assert ctx.num_prefill_requests == slow_num_prefill_requests
+
+        # 2. Per-request MHA state
+        assert torch.equal(ctx.request_query_lengths[:N], slow_request_query_lengths)
+        assert torch.equal(ctx.request_kv_length_offsets[:N], slow_request_kv_length_offsets)
+        assert torch.equal(ctx.request_to_kv_block_ids[:N, 0], slow_request_to_kv_block_ids_col0)
+
+        # 3. Token-level state
+        dummy_block_idx = ctx.block_allocator.dummy_block_idx
+        assert torch.all(ctx.token_to_block_idx[:T] == dummy_block_idx)
+        assert torch.equal(ctx.token_to_block_idx[:T], slow_token_to_block_idx)
+        assert torch.equal(ctx.token_to_local_position_within_kv_block[:T], slow_token_to_local_pos)
+
+        if is_hybrid_model:
+            # 4. token_to_request_idx
+            assert torch.equal(ctx.token_to_request_idx[:T], slow_token_to_request_idx)
+
+            # 5. Mamba state slots allocated (indices may differ, but must be valid and unique)
+            fast_mamba = ctx.mamba_metadata.request_to_mamba_state_idx[:N]
+            assert (fast_mamba >= 0).all(), "fast path should allocate valid mamba slots"
+            assert (slow_mamba >= 0).all(), "slow path should allocate valid mamba slots"
+            assert fast_mamba.unique().numel() == N, "fast path mamba slots must be unique"
+
+    @pytest.mark.internal
+    def test_gqa_high_tp_partition_heads(self):
+        """Tests that TP > GQA results in 1 attention head per partition."""
+        tp_size = 8
+        self._setup_model_parallel_group(tensor_parallel_size=tp_size, pipeline_parallel_size=1)
+
+        model_config = TransformerConfig(
+            params_dtype=torch.float32,
+            num_layers=2,
+            kv_channels=64,
+            num_attention_heads=16,
+            num_query_groups=2,  # GQA = 2
+            tensor_model_parallel_size=tp_size,
+        )
+
+        # max_requests must be divisible by TP size (8) and REQUEST_ROUNDER
+        inference_config = InferenceConfig(
+            max_sequence_length=512, buffer_size_gb=0.1, block_size_tokens=128, max_requests=8
+        )
+
+        dynamic_context = DynamicInferenceContext(
+            model_config=model_config, inference_config=inference_config
+        )
+
+        # With TP=8 and GQA=2, num_attention_heads_per_partition should be clamped to 1
+        assert dynamic_context.num_attention_heads_per_partition == 1
diff --git a/tests/unit_tests/inference/contexts/test_dynamic_prefix_caching.py b/tests/unit_tests/inference/contexts/test_dynamic_prefix_caching.py
new file mode 100644
index 00000000000..9122f5e8202
--- /dev/null
+++ b/tests/unit_tests/inference/contexts/test_dynamic_prefix_caching.py
@@ -0,0 +1,900 @@
+# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
+
+import asyncio
+from collections import deque
+
+import pytest
+import torch
+
+from megatron.core.inference.config import InferenceConfig, PrefixCachingEvictionPolicy
+from megatron.core.inference.contexts.dynamic_context import DynamicInferenceContext
+from megatron.core.inference.engines.dynamic_engine import DynamicInferenceEngine
+from megatron.core.inference.inference_request import (
+    HASH_PRIME,
+    DynamicInferenceRequest,
+    DynamicInferenceRequestRecord,
+    Status,
+    compute_block_hashes_batched,
+)
+from megatron.core.inference.sampling_params import SamplingParams
+from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed
+from megatron.core.transformer.transformer_config import TransformerConfig
+from tests.unit_tests.test_utilities import Utils
+
+# =========================================================================
+# Base class + helpers
+# =========================================================================
+
+
+class PrefixCachingTestBase:
+    """Base class with shared setup/teardown and helper methods."""
+
+    def setup_method(self, method):
+        Utils.initialize_model_parallel(
+            tensor_model_parallel_size=1, pipeline_model_parallel_size=1
+        )
+        model_parallel_cuda_manual_seed(123)
+
+    def teardown_method(self, method):
+        Utils.destroy_model_parallel()
+
+    def _ctx(
+        self,
+        *,
+        buffer_size_gb=0.1,
+        block_size_tokens=32,
+        max_sequence_length=512,
+        rounder=64,
+        enable_prefix_caching=True,
+        max_tokens=None,
+        prefix_caching_eviction_policy=PrefixCachingEvictionPolicy.LRU,
+    ):
+        """Create a DynamicInferenceContext with sensible test defaults."""
+        DynamicInferenceContext.ROUNDER = rounder
+        DynamicInferenceContext.TOKEN_ROUNDER = rounder
+        DynamicInferenceContext.REQUEST_ROUNDER = rounder
+
+        transformer_config = TransformerConfig(
+            params_dtype=torch.float32,
+            num_layers=4,
+            kv_channels=8,
+            num_attention_heads=2,
+            hidden_size=16,
+            tensor_model_parallel_size=1,
+            pipeline_model_parallel_size=1,
+            use_cpu_initialization=True,
+        )
+        inference_config = InferenceConfig(
+            max_sequence_length=max_sequence_length,
+            buffer_size_gb=buffer_size_gb,
+            paused_buffer_size_gb=0.2 * buffer_size_gb,
+            block_size_tokens=block_size_tokens,
+            max_tokens=max_tokens,
+            mamba_inference_state_config=None,
+            use_flashinfer_fused_rope=None,
+            unified_memory_level=0,
+            enable_prefix_caching=enable_prefix_caching,
+            prefix_caching_eviction_policy=prefix_caching_eviction_policy,
+        )
+        return DynamicInferenceContext(
+            model_config=transformer_config, inference_config=inference_config
+        )
+
+    @staticmethod
+    def _req(ctx, prompt_tokens, request_id=1, *, enable_prefix_caching=True):
+        """Create a DynamicInferenceRequest with sensible defaults."""
+        return DynamicInferenceRequest(
+            request_id=request_id,
+            prompt_tokens=prompt_tokens,
+            sampling_params=SamplingParams(num_tokens_to_generate=10),
+            block_size_tokens=ctx.block_size_tokens,
+            enable_prefix_caching=enable_prefix_caching,
+        )
+
+    @staticmethod
+    def _prompt(num_tokens, offset=0):
+        """Create a prompt tensor on CUDA."""
+        return torch.arange(offset, offset + num_tokens, device=torch.cuda.current_device())
+
+    @staticmethod
+    def _block_ids(ctx, req_idx, n):
+        """Extract first n block IDs for a request as a list of ints."""
+        return [ctx.request_to_kv_block_ids[req_idx][i].item() for i in range(n)]
+
+
+class _StubEngine(DynamicInferenceEngine):
+    """Lightweight engine subclass that skips full __init__ for unit testing."""
+
+    def __init__(self, context: DynamicInferenceContext, *, enable_chunked_prefill=False):
+        self.context = context
+        self.enable_chunked_prefill = enable_chunked_prefill
+        self._prefix_coordination_waits = 0
+        self._loop = asyncio.new_event_loop()
+        self.waiting_request_ids: deque = deque()
+        self.requests = {}
+
+
+# =========================================================================
+# Class 1: TestBlockSharingAndRefCounts (5 tests)
+# =========================================================================
+
+
+class TestBlockSharingAndRefCounts(PrefixCachingTestBase):
+
+    @pytest.mark.internal
+    def test_identical_prompts_share_all_blocks(self):
+        """N=10 identical requests use only K blocks, not N*K."""
+        ctx = self._ctx()
+        bs = ctx.block_size_tokens
+        alloc = ctx.block_allocator
+        prompt = self._prompt(bs * 3)
+
+        ctx.add_request(self._req(ctx, prompt.clone()))
+        first_blocks = self._block_ids(ctx, 0, 3)
+        avail_after_first = alloc.total_avail
+
+        for i in range(2, 11):
+            ctx.add_request(self._req(ctx, prompt.clone(), request_id=i))
+
+        # Pool unchanged after 9 more requests
+        assert alloc.total_avail == avail_after_first
+
+        # All share the same block IDs
+        for req_idx in range(1, 10):
+            assert self._block_ids(ctx, req_idx, 3) == first_blocks
+
+        # Ref counts == 10
+        for bid in first_blocks:
+            assert alloc.block_ref_counts[bid].item() == 10
+
+    @pytest.mark.internal
+    def test_divergent_suffix_shares_common_prefix(self):
+        """[A,B,C] + [A,B,D]: blocks 0-1 shared (ref=2), blocks 2,3 separate (ref=1)."""
+        ctx = self._ctx()
+        bs = ctx.block_size_tokens
+        alloc = ctx.block_allocator
+
+        prompt1 = self._prompt(bs * 3)
+        ctx.add_request(self._req(ctx, prompt1))
+        r1 = self._block_ids(ctx, 0, 3)
+
+        prompt2 = prompt1.clone()
+        prompt2[bs * 2 :] += 1000
+        ctx.add_request(self._req(ctx, prompt2, request_id=2))
+        r2 = self._block_ids(ctx, 1, 3)
+
+        assert r2[0] == r1[0] and r2[1] == r1[1], "Blocks 0,1 shared"
+        assert r2[2] != r1[2], "Block 2 separate"
+        assert alloc.block_ref_counts[r1[0]].item() == 2
+        assert alloc.block_ref_counts[r1[1]].item() == 2
+        assert alloc.block_ref_counts[r1[2]].item() == 1
+        assert alloc.block_ref_counts[r2[2]].item() == 1
+
+    @pytest.mark.internal
+    def test_broken_chain_stops_sharing(self):
+        """[X,W,Z] vs [X,Y,Z]: only block 0 shared due to parent chain break."""
+        ctx = self._ctx()
+        bs = ctx.block_size_tokens
+        alloc = ctx.block_allocator
+
+        prompt1 = self._prompt(bs * 3)
+        ctx.add_request(self._req(ctx, prompt1))
+        r1 = self._block_ids(ctx, 0, 3)
+
+        prompt2 = prompt1.clone()
+        prompt2[bs : bs * 2] += 5000
+        ctx.add_request(self._req(ctx, prompt2, request_id=2))
+        r2 = self._block_ids(ctx, 1, 3)
+
+        assert r2[0] == r1[0], "Block 0 shared"
+        assert r2[1] != r1[1], "Block 1 NOT shared"
+        assert r2[2] != r1[2], "Block 2 NOT shared (hash chain broken)"
+        assert alloc.block_ref_counts[r1[0]].item() == 2
+        assert alloc.block_ref_counts[r1[1]].item() == 1
+
+    @pytest.mark.internal
+    def test_disabled_means_no_sharing(self):
+        """enable_prefix_caching=False: identical prompts allocate separate blocks."""
+        ctx = self._ctx(enable_prefix_caching=False)
+        bs = ctx.block_size_tokens
+        prompt = self._prompt(bs * 2)
+
+        ctx.add_request(self._req(ctx, prompt.clone(), enable_prefix_caching=False))
+        r1 = set(self._block_ids(ctx, 0, 2))
+
+        ctx.add_request(self._req(ctx, prompt.clone(), request_id=2, enable_prefix_caching=False))
+        r2 = set(self._block_ids(ctx, 1, 2))
+
+        assert r1.isdisjoint(r2)
+
+    @pytest.mark.internal
+    def test_disabled_allocator_has_no_caching_state(self):
+        """Disabled allocator lacks caching attrs; REF_ZERO lacks timestamps."""
+        ctx_disabled = self._ctx(enable_prefix_caching=False)
+        alloc_d = ctx_disabled.block_allocator
+        assert not hasattr(alloc_d, 'block_hashes')
+        assert not hasattr(alloc_d, 'hash_to_block_id')
+        assert not hasattr(alloc_d, 'block_ref_counts')
+
+        ctx_rz = self._ctx(prefix_caching_eviction_policy=PrefixCachingEvictionPolicy.REF_ZERO)
+        alloc_rz = ctx_rz.block_allocator
+        assert not hasattr(alloc_rz, 'block_timestamps')
+
+
+# =========================================================================
+# Class 2: TestPrefillTokenSavings (4 tests)
+# =========================================================================
+
+
+class TestPrefillTokenSavings(PrefixCachingTestBase):
+
+    @pytest.mark.internal
+    def test_lifetime_prefill_count_with_vs_without(self):
+        """Enabled saves tokens vs disabled for two identical prompts."""
+        bs = 32
+
+        # Enabled
+        ctx_on = self._ctx()
+        prompt = self._prompt(bs * 4)
+        ctx_on.add_request(self._req(ctx_on, prompt.clone()))
+        ctx_on.add_request(self._req(ctx_on, prompt.clone(), request_id=2))
+        enabled_total = ctx_on.lifetime_prefill_token_count
+
+        # Disabled
+        ctx_off = self._ctx(enable_prefix_caching=False)
+        ctx_off.add_request(self._req(ctx_off, prompt.clone(), enable_prefix_caching=False))
+        ctx_off.add_request(
+            self._req(ctx_off, prompt.clone(), request_id=2, enable_prefix_caching=False)
+        )
+        disabled_total = ctx_off.lifetime_prefill_token_count
+
+        # Enabled: 4*bs + 1 (min guard). Disabled: 4*bs + 4*bs.
+        assert enabled_total == bs * 4 + 1
+        assert disabled_total == bs * 4 * 2
+        assert enabled_total < disabled_total
+
+    @pytest.mark.internal
+    def test_partial_match_reduces_prefill_proportionally(self):
+        """First req fills 3 blocks, second matches 2 of 3, adds bs tokens."""
+        ctx = self._ctx()
+        bs = ctx.block_size_tokens
+
+        prompt1 = self._prompt(bs * 3)
+        ctx.add_request(self._req(ctx, prompt1.clone()))
+
+        prompt2 = prompt1.clone()
+        prompt2[bs * 2 :] += 1000
+        ctx.add_request(self._req(ctx, prompt2, request_id=2))
+
+        assert ctx.lifetime_prefill_token_count == bs * 3 + bs
+
+    @pytest.mark.internal
+    def test_full_match_adds_single_token(self):
+        """Prompt exactly N*bs, fully matched: second request query length == 1."""
+        ctx = self._ctx()
+        bs = ctx.block_size_tokens
+        prompt = self._prompt(bs * 3)
+
+        ctx.add_request(self._req(ctx, prompt.clone()))
+        tokens_after_a = ctx.active_token_count
+
+        ctx.add_request(self._req(ctx, prompt.clone(), request_id=2))
+        assert ctx.active_token_count - tokens_after_a == 1
+        assert ctx.request_query_lengths[1].item() == 1
+
+    @pytest.mark.internal
+    def test_no_match_adds_full_prompt(self):
+        """Different prefix: full chunk_length added to lifetime count."""
+        ctx = self._ctx()
+        bs = ctx.block_size_tokens
+
+        ctx.add_request(self._req(ctx, self._prompt(bs * 2)))
+        ctx.add_request(self._req(ctx, self._prompt(bs * 2, offset=9000), request_id=2))
+
+        assert ctx.lifetime_prefill_token_count == bs * 2 + bs * 2
+
+
+# =========================================================================
+# Class 3: TestRefCountLifecycle (5 tests)
+# =========================================================================
+
+
+class TestRefCountLifecycle(PrefixCachingTestBase):
+
+    @pytest.mark.internal
+    def test_lru_ref_decrement_preserves_cached_blocks(self):
+        """LRU mode: add 2, release both. Ref 2 -> 1 -> 0. Hash still in dict."""
+        ctx = self._ctx()
+        bs = ctx.block_size_tokens
+        alloc = ctx.block_allocator
+        prompt = self._prompt(bs * 2)
+
+        ctx.add_request(self._req(ctx, prompt.clone()))
+        ctx.add_request(self._req(ctx, prompt.clone(), request_id=2))
+        b0, b1 = self._block_ids(ctx, 0, 2)
+        b0_hash = alloc.block_hashes[b0].item()
+
+        assert alloc.block_ref_counts[b0].item() == 2
+
+        ctx.release_memory_blocks_from_request_indexes(torch.tensor([0]))
+        assert alloc.block_ref_counts[b0].item() == 1
+        assert b0_hash in alloc.hash_to_block_id
+
+        ctx.release_memory_blocks_from_request_indexes(torch.tensor([1]))
+        assert alloc.block_ref_counts[b0].item() == 0
+        assert alloc.block_ref_counts[b1].item() == 0
+        assert b0_hash in alloc.hash_to_block_id, "LRU keeps cached blocks"
+
+    @pytest.mark.internal
+    def test_lru_cached_blocks_reused_by_new_request(self):
+        """LRU mode: released blocks (ref=0, cached) reused by same-prefix request."""
+        ctx = self._ctx()
+        bs = ctx.block_size_tokens
+        alloc = ctx.block_allocator
+        prompt = self._prompt(bs * 2)
+
+        ctx.add_request(self._req(ctx, prompt.clone()))
+        b0, b1 = self._block_ids(ctx, 0, 2)
+
+        ctx.release_memory_blocks_from_request_indexes(torch.tensor([0]))
+        ctx.total_request_count = 0
+        assert alloc.block_ref_counts[b0].item() == 0
+        assert alloc.block_hashes[b0].item() in alloc.hash_to_block_id
+
+        ctx.add_request(self._req(ctx, prompt.clone(), request_id=2))
+        assert self._block_ids(ctx, 0, 2) == [b0, b1]
+        assert alloc.block_ref_counts[b0].item() == 1
+
+    @pytest.mark.internal
+    def test_lru_eviction_frees_oldest_cached_first(self):
+        """LRU mode, tiny buffer: create cached groups A, B. Fill memory. A evicted first."""
+        ctx = self._ctx(buffer_size_gb=0.01, rounder=1)
+        bs = ctx.block_size_tokens
+        alloc = ctx.block_allocator
+
+        # Active request (protected)
+        ctx.add_request(self._req(ctx, self._prompt(bs * 2)))
+        active_blocks = ctx.request_to_kv_block_ids[0][:2].clone()
+
+        # Cached request (evictable)
+        ctx.add_request(self._req(ctx, self._prompt(bs * 2, offset=5000), request_id=2))
+        cached_blocks = ctx.request_to_kv_block_ids[1][:2].clone()
+        cached_hash = alloc.block_hashes[cached_blocks[0].item()].item()
+        ctx.release_memory_blocks_from_request_indexes(torch.tensor([1]))
+        ctx.total_request_count = 1
+        assert alloc.block_ref_counts[cached_blocks[0].item()].item() == 0
+
+        # Fill remaining space to trigger eviction of cached blocks
+        for i in range(20):
+            try:
+                ctx.add_request(
+                    self._req(ctx, self._prompt(bs * 2, offset=(i + 10) * 1000), request_id=i + 100)
+                )
+            except Exception:
+                break
+
+        # Active blocks remain protected
+        for bid in active_blocks:
+            assert alloc.block_ref_counts[bid.item()].item() == 1
+
+    @pytest.mark.internal
+    def test_refzero_deregisters_on_last_release(self):
+        """REF_ZERO: two reqs sharing prefix. Release both: hash removed, blocks returned."""
+        ctx = self._ctx(prefix_caching_eviction_policy=PrefixCachingEvictionPolicy.REF_ZERO)
+        bs = ctx.block_size_tokens
+        alloc = ctx.block_allocator
+        prompt = self._prompt(bs * 2)
+
+        ctx.add_request(self._req(ctx, prompt.clone()))
+        ctx.add_request(self._req(ctx, prompt.clone(), request_id=2))
+
+        b0, b1 = self._block_ids(ctx, 0, 2)
+        b0_hash = alloc.block_hashes[b0].item()
+        avail_before = alloc.total_avail
+
+        # Release first: ref=1, hash persists
+        ctx.release_memory_blocks_from_request_indexes(torch.tensor([0]))
+        assert alloc.block_ref_counts[b0].item() == 1
+        assert b0_hash in alloc.hash_to_block_id
+
+        # Release second: ref=0, hash removed, blocks returned
+        ctx.release_memory_blocks_from_request_indexes(torch.tensor([1]))
+        assert alloc.block_ref_counts[b0].item() == 0
+        assert b0_hash not in alloc.hash_to_block_id
+        assert alloc.block_hashes[b0].item() == -1
+        assert alloc.block_hashes[b1].item() == -1
+        assert alloc.total_avail == avail_before + 2
+
+    @pytest.mark.internal
+    def test_refzero_released_blocks_not_discoverable(self):
+        """REF_ZERO: after full release, same-prefix request gets fresh blocks."""
+        ctx = self._ctx(prefix_caching_eviction_policy=PrefixCachingEvictionPolicy.REF_ZERO)
+        bs = ctx.block_size_tokens
+        alloc = ctx.block_allocator
+        prompt = self._prompt(bs * 2)
+
+        ctx.add_request(self._req(ctx, prompt.clone()))
+        first_blocks = self._block_ids(ctx, 0, 2)
+
+        ctx.release_memory_blocks_from_request_indexes(torch.tensor([0]))
+        ctx.total_request_count = 0
+
+        # Hash lookup returns None, so new blocks allocated
+        ctx.add_request(self._req(ctx, prompt.clone(), request_id=2))
+        new_blocks = self._block_ids(ctx, 0, 2)
+        assert alloc.block_ref_counts[new_blocks[0]].item() == 1
+        assert alloc.block_ref_counts[new_blocks[1]].item() == 1
+
+
+# =========================================================================
+# Class 4: TestHashComputation (3 tests)
+# =========================================================================
+
+
+class TestHashComputation(PrefixCachingTestBase):
+
+    @pytest.mark.internal
+    def test_determinism_and_range(self):
+        """Same tokens -> same hashes. All in [1, HASH_PRIME]. Different tokens differ."""
+        tokens = self._prompt(32)
+        h1 = compute_block_hashes_batched(tokens, 32)
+        h2 = compute_block_hashes_batched(tokens, 32)
+        assert h1 == h2
+        assert len(h1) == 1
+        assert 1 <= h1[0] <= HASH_PRIME
+
+        h_diff = compute_block_hashes_batched(self._prompt(32, offset=1), 32)
+        assert h_diff[0] != h1[0]
+
+    @pytest.mark.internal
+    def test_parent_chaining_differentiates_position(self):
+        """4 blocks of all-zero tokens: all hashes distinct due to parent chaining."""
+        ctx = self._ctx()
+        bs = ctx.block_size_tokens
+        zeros = torch.zeros(bs * 4, device=torch.cuda.current_device(), dtype=torch.long)
+        hashes = compute_block_hashes_batched(zeros, bs)
+        assert len(hashes) == 4
+        assert len(set(hashes)) == 4
+
+    @pytest.mark.internal
+    def test_edge_cases(self):
+        """Short, empty, and long prompts produce expected hash counts."""
+        ctx = self._ctx()
+        bs = ctx.block_size_tokens
+
+        # Shorter than block_size -> empty
+        assert compute_block_hashes_batched(self._prompt(bs // 2), bs) == []
+
+        # Empty prompt -> empty
+        empty = torch.tensor([], device=torch.cuda.current_device(), dtype=torch.long)
+        assert compute_block_hashes_batched(empty, bs) == []
+
+        # 120-block prompt -> 120 positive hashes
+        long_prompt = torch.arange(bs * 120, device=torch.cuda.current_device(), dtype=torch.long)
+        h = compute_block_hashes_batched(long_prompt, bs)
+        assert len(h) == 120
+        assert all(v > 0 for v in h)
+
+
+# =========================================================================
+# Class 5: TestRegistrationAndDiscovery (4 tests)
+# =========================================================================
+
+
+class TestRegistrationAndDiscovery(PrefixCachingTestBase):
+
+    @pytest.mark.internal
+    def test_blocks_discoverable_after_add_request(self):
+        """After add_request, each block's hash maps to its block ID."""
+        ctx = self._ctx()
+        bs = ctx.block_size_tokens
+        alloc = ctx.block_allocator
+        prompt = self._prompt(bs * 2)
+
+        req = self._req(ctx, prompt)
+        ctx.add_request(req)
+        b0, b1 = self._block_ids(ctx, 0, 2)
+        h0, h1 = req.precomputed_block_hashes
+
+        assert alloc.hash_to_block_id.get(h0) == b0
+        assert alloc.hash_to_block_id.get(h1) == b1
+        assert alloc.block_hashes[b0].item() == h0
+        assert alloc.block_hashes[b1].item() == h1
+
+    @pytest.mark.internal
+    def test_partial_block_not_registered(self):
+        """Prompt of bs + bs//2: first block registered, second block not."""
+        ctx = self._ctx()
+        bs = ctx.block_size_tokens
+        alloc = ctx.block_allocator
+
+        prompt = self._prompt(bs + bs // 2)
+        ctx.add_request(self._req(ctx, prompt))
+        b0, b1 = self._block_ids(ctx, 0, 2)
+
+        assert alloc.block_hashes[b0].item() != -1, "Complete block registered"
+        assert alloc.block_hashes[b1].item() == -1, "Partial block not registered"
+
+    @pytest.mark.internal
+    def test_decode_does_not_register_completed_blocks(self):
+        """Partial block stays unregistered after decode fills it (known limitation)."""
+        ctx = self._ctx()
+        bs = ctx.block_size_tokens
+        alloc = ctx.block_allocator
+
+        prompt = self._prompt(bs + (bs - 1))
+        ctx.add_request(self._req(ctx, prompt))
+        b0, b1 = self._block_ids(ctx, 0, 2)
+
+        assert alloc.block_hashes[b0].item() != -1
+        assert alloc.block_hashes[b1].item() == -1
+
+        # One decode step fills block 1
+        active_mask = torch.ones(1, device=torch.cuda.current_device(), dtype=torch.int32)
+        new_tokens = torch.tensor([100], device=torch.cuda.current_device())
+        ctx.update_requests(active_mask, new_tokens)
+
+        assert alloc.block_hashes[b1].item() == -1, "Decode does not register blocks"
+
+    @pytest.mark.internal
+    def test_second_request_finds_registered_blocks(self):
+        """After req1 registers 3 blocks, req2's hashes all resolve in hash_to_block_id."""
+        ctx = self._ctx()
+        bs = ctx.block_size_tokens
+        alloc = ctx.block_allocator
+        prompt = self._prompt(bs * 3)
+
+        req1 = self._req(ctx, prompt.clone())
+        ctx.add_request(req1)
+
+        req2 = self._req(ctx, prompt.clone(), request_id=2)
+        for h in req2.precomputed_block_hashes:
+            assert h in alloc.hash_to_block_id, f"Hash {h} should be discoverable"
+
+
+# =========================================================================
+# Class 6: TestBlockAllocation (3 tests)
+# =========================================================================
+
+
+class TestBlockAllocation(PrefixCachingTestBase):
+
+    @pytest.mark.internal
+    def test_matched_blocks_not_allocated_from_pool(self):
+        """Req1 allocates 4 blocks. Req2 same prefix: pool unchanged, same block IDs."""
+        ctx = self._ctx()
+        bs = ctx.block_size_tokens
+        alloc = ctx.block_allocator
+        prompt = self._prompt(bs * 4)
+
+        ctx.add_request(self._req(ctx, prompt.clone()))
+        first_blocks = self._block_ids(ctx, 0, 4)
+        avail_after_first = alloc.total_avail
+
+        ctx.add_request(self._req(ctx, prompt.clone(), request_id=2))
+        assert self._block_ids(ctx, 1, 4) == first_blocks
+        assert alloc.total_avail == avail_after_first
+
+    @pytest.mark.internal
+    def test_extended_prompt_allocates_only_new_blocks(self):
+        """Req1: 3 blocks. Req2: 5 blocks (same first 3). Pool drops by exactly 2."""
+        ctx = self._ctx()
+        bs = ctx.block_size_tokens
+        alloc = ctx.block_allocator
+
+        prompt1 = self._prompt(bs * 3)
+        ctx.add_request(self._req(ctx, prompt1))
+        avail_after_first = alloc.total_avail
+
+        prompt2 = torch.cat([prompt1, self._prompt(bs * 2, offset=1000)])
+        ctx.add_request(self._req(ctx, prompt2, request_id=2))
+        assert alloc.total_avail == avail_after_first - 2
+
+    @pytest.mark.internal
+    def test_check_availability_accounts_for_prefix_match(self):
+        """check_availability returns kv_cache_available=True when matched blocks cover need."""
+        ctx = self._ctx(buffer_size_gb=0.01, rounder=1)
+        bs = ctx.block_size_tokens
+        alloc = ctx.block_allocator
+        prompt = self._prompt(bs * 2)
+
+        ctx.add_request(self._req(ctx, prompt.clone()))
+
+        # Fill remaining pool
+        while alloc.total_avail > 0:
+            alloc.allocate_memory_blocks(1)
+
+        # New request with same prefix: all blocks matched, needs 0 from pool
+        req2 = self._req(ctx, prompt.clone(), request_id=2)
+        _, _, kv_available = ctx.check_availability(req2)
+        assert kv_available, "Matched blocks don't need pool allocation"
+
+
+# =========================================================================
+# Class 7: TestEngineScheduling (6 tests)
+# =========================================================================
+
+
+class TestEngineScheduling(PrefixCachingTestBase):
+
+    def _engine(self, ctx, **kwargs):
+        """Create a _StubEngine wrapping ctx."""
+        return _StubEngine(ctx, **kwargs)
+
+    def _add_to_waiting(self, engine, ctx, req):
+        """Register req with engine and put it in the waiting queue."""
+        request_id = req.request_id
+        engine.requests[request_id] = type(
+            "Entry",
+            (),
+            {
+                "record": DynamicInferenceRequestRecord.from_request(req),
+                "future": engine._loop.create_future(),
+            },
+        )()
+        req.status = Status.ACTIVE_AND_GENERATING_TOKENS
+        req.sampling_params.num_tokens_to_generate = 10
+        engine.waiting_request_ids.append(request_id)
+
+    @pytest.mark.internal
+    def test_shared_prefix_defers_second_request(self):
+        """Two requests with shared prefix: first scheduled, second deferred."""
+        ctx = self._ctx()
+        bs = ctx.block_size_tokens
+        engine = self._engine(ctx)
+        prompt = self._prompt(bs * 2)
+
+        req1 = self._req(ctx, prompt.clone())
+        self._add_to_waiting(engine, ctx, req1)
+        req2 = self._req(ctx, prompt.clone(), request_id=2)
+        self._add_to_waiting(engine, ctx, req2)
+
+        engine.schedule_non_chunked_prefill()
+        assert ctx.total_request_count == 1
+        assert len(engine.waiting_request_ids) == 1
+        assert engine.waiting_request_ids[0] == 2
+        assert engine._prefix_coordination_waits == 1
+
+    @pytest.mark.internal
+    def test_scheduler_skips_deferred_to_schedule_non_conflicting(self):
+        """req1+req2 share prefix A, req3 has prefix B. req1 and req3 scheduled, req2 deferred."""
+        ctx = self._ctx()
+        bs = ctx.block_size_tokens
+        engine = self._engine(ctx)
+
+        prompt_a = self._prompt(bs * 2)
+        prompt_b = self._prompt(bs * 2, offset=5000)
+
+        req1 = self._req(ctx, prompt_a.clone())
+        self._add_to_waiting(engine, ctx, req1)
+        req2 = self._req(ctx, prompt_a.clone(), request_id=2)
+        self._add_to_waiting(engine, ctx, req2)
+        req3 = self._req(ctx, prompt_b.clone(), request_id=3)
+        self._add_to_waiting(engine, ctx, req3)
+
+        engine.schedule_non_chunked_prefill()
+        assert ctx.total_request_count == 2, "req1 and req3 scheduled"
+        assert len(engine.waiting_request_ids) == 1
+        assert engine.waiting_request_ids[0] == 2, "req2 deferred"
+
+    @pytest.mark.internal
+    def test_registered_prefix_allows_immediate_scheduling(self):
+        """Prefix already in context: second request scheduled without deferral."""
+        ctx = self._ctx()
+        bs = ctx.block_size_tokens
+        engine = self._engine(ctx)
+        prompt = self._prompt(bs * 2)
+
+        # Add first request directly to context (hashes registered)
+        ctx.add_request(self._req(ctx, prompt.clone()))
+
+        req2 = self._req(ctx, prompt.clone(), request_id=2)
+        self._add_to_waiting(engine, ctx, req2)
+
+        engine.schedule_non_chunked_prefill()
+        assert len(engine.waiting_request_ids) == 0
+        assert ctx.total_request_count == 2
+        assert engine._prefix_coordination_waits == 0
+
+    @pytest.mark.internal
+    def test_deferred_request_schedulable_after_registration(self):
+        """Round 1: req1 scheduled, req2 deferred. Round 2: req2 scheduled."""
+        ctx = self._ctx()
+        bs = ctx.block_size_tokens
+        engine = self._engine(ctx)
+        prompt = self._prompt(bs * 2)
+
+        req1 = self._req(ctx, prompt.clone())
+        self._add_to_waiting(engine, ctx, req1)
+        req2 = self._req(ctx, prompt.clone(), request_id=2)
+        self._add_to_waiting(engine, ctx, req2)
+
+        engine.schedule_non_chunked_prefill()
+        assert ctx.total_request_count == 1
+        assert len(engine.waiting_request_ids) == 1
+
+        # Round 2: hashes now registered from req1
+        engine.schedule_non_chunked_prefill()
+        assert ctx.total_request_count == 2
+        assert len(engine.waiting_request_ids) == 0
+
+    @pytest.mark.internal
+    def test_coordination_metrics_track_deferrals(self):
+        """get_prefix_coordination_metrics tracks waits correctly."""
+        ctx = self._ctx()
+        bs = ctx.block_size_tokens
+        engine = self._engine(ctx)
+        prompt = self._prompt(bs * 2)
+
+        assert engine.get_prefix_coordination_metrics() == {"waits": 0}
+
+        req1 = self._req(ctx, prompt.clone())
+        self._add_to_waiting(engine, ctx, req1)
+        req2 = self._req(ctx, prompt.clone(), request_id=2)
+        self._add_to_waiting(engine, ctx, req2)
+
+        engine.schedule_non_chunked_prefill()
+        assert engine.get_prefix_coordination_metrics() == {"waits": 1}
+
+        # Second round: no new deferral
+        engine.schedule_non_chunked_prefill()
+        assert engine.get_prefix_coordination_metrics() == {"waits": 1}
+        assert len(engine.waiting_request_ids) == 0
+
+    @pytest.mark.internal
+    def test_chunked_prefill_defers_conflicting_request(self):
+        """Chunked prefill path has the same deferral logic."""
+        ctx = self._ctx()
+        bs = ctx.block_size_tokens
+        engine = self._engine(ctx, enable_chunked_prefill=True)
+        prompt = self._prompt(bs * 2)
+
+        req1 = self._req(ctx, prompt.clone())
+        self._add_to_waiting(engine, ctx, req1)
+        req2 = self._req(ctx, prompt.clone(), request_id=2)
+        self._add_to_waiting(engine, ctx, req2)
+
+        engine.schedule_chunked_prefill()
+        assert ctx.total_request_count == 1
+        assert len(engine.waiting_request_ids) == 1
+        assert engine._prefix_coordination_waits == 1
+
+
+# =========================================================================
+# Class 9: TestHybridModelMemoryOnly (4 tests)
+# =========================================================================
+
+
+class TestHybridModelMemoryOnly(PrefixCachingTestBase):
+    """Hybrid models disable prefill skipping but still share blocks for memory savings."""
+
+    def _hybrid_ctx(self, **kwargs):
+        """Create a context with a hybrid (Transformer + Mamba) model config."""
+        from megatron.core.inference.config import MambaInferenceStateConfig
+
+        params_dtype = torch.float32
+
+        mamba_config = MambaInferenceStateConfig(
+            layer_type_list=["*", "M", "*", "M"],
+            conv_states_shape=(4, 8),
+            ssm_states_shape=(4, 16),
+            conv_states_dtype=params_dtype,
+            ssm_states_dtype=params_dtype,
+        )
+
+        defaults = dict(
+            buffer_size_gb=0.1,
+            block_size_tokens=32,
+            max_sequence_length=512,
+            rounder=64,
+            enable_prefix_caching=True,
+            prefix_caching_eviction_policy=PrefixCachingEvictionPolicy.LRU,
+        )
+        defaults.update(kwargs)
+
+        DynamicInferenceContext.ROUNDER = defaults["rounder"]
+        DynamicInferenceContext.TOKEN_ROUNDER = defaults["rounder"]
+        DynamicInferenceContext.REQUEST_ROUNDER = defaults["rounder"]
+
+        transformer_config = TransformerConfig(
+            params_dtype=params_dtype,
+            num_layers=4,
+            kv_channels=8,
+            num_attention_heads=2,
+            hidden_size=16,
+            tensor_model_parallel_size=1,
+            pipeline_model_parallel_size=1,
+            use_cpu_initialization=True,
+        )
+        inference_config = InferenceConfig(
+            max_sequence_length=defaults["max_sequence_length"],
+            buffer_size_gb=defaults["buffer_size_gb"],
+            paused_buffer_size_gb=0.2 * defaults["buffer_size_gb"],
+            block_size_tokens=defaults["block_size_tokens"],
+            max_tokens=defaults.get("max_tokens"),
+            mamba_inference_state_config=mamba_config,
+            use_flashinfer_fused_rope=None,
+            unified_memory_level=0,
+            enable_prefix_caching=defaults["enable_prefix_caching"],
+            prefix_caching_eviction_policy=defaults["prefix_caching_eviction_policy"],
+        )
+        return DynamicInferenceContext(
+            model_config=transformer_config, inference_config=inference_config
+        )
+
+    @pytest.mark.internal
+    def test_no_prefill_skipping_for_hybrid_model(self):
+        """prefix_skip_tokens == 0 and effective_chunk_length == chunk_length even when blocks match."""
+        ctx = self._hybrid_ctx()
+        bs = ctx.block_size_tokens
+        prompt = self._prompt(bs * 3)
+
+        assert ctx.is_hybrid_model
+
+        # First request: registers blocks.
+        req1 = self._req(ctx, prompt.clone())
+        ctx.add_request(req1)
+
+        # Second request: blocks match, but hybrid model forces no skipping.
+        req2 = self._req(ctx, prompt.clone(), request_id=2)
+        (matched, num_from_pool, _, _, prefix_skip, eff_chunk) = ctx._compute_prefix_match(
+            req2, len(prompt)
+        )
+        assert len(matched) == 3, "should find 3 matching blocks"
+        assert prefix_skip == 0, "hybrid model must not skip prefill tokens"
+        assert eff_chunk == len(prompt), "effective chunk must equal full prompt length"
+
+    @pytest.mark.internal
+    def test_matched_blocks_reused_saving_memory(self):
+        """Second hybrid request reuses matched blocks (fewer allocated from pool)."""
+        ctx = self._hybrid_ctx()
+        bs = ctx.block_size_tokens
+        alloc = ctx.block_allocator
+        prompt = self._prompt(bs * 3)
+
+        ctx.add_request(self._req(ctx, prompt.clone()))
+        avail_after_first = alloc.total_avail
+
+        ctx.add_request(self._req(ctx, prompt.clone(), request_id=2))
+        avail_after_second = alloc.total_avail
+
+        # Second request should not consume any additional blocks from pool.
+        assert avail_after_second == avail_after_first
+
+    @pytest.mark.internal
+    def test_ref_counts_incremented_for_matched_blocks(self):
+        """Matched blocks have ref_count == 2 after two hybrid requests share them."""
+        ctx = self._hybrid_ctx()
+        bs = ctx.block_size_tokens
+        alloc = ctx.block_allocator
+        prompt = self._prompt(bs * 3)
+
+        ctx.add_request(self._req(ctx, prompt.clone()))
+        first_blocks = self._block_ids(ctx, 0, 3)
+
+        ctx.add_request(self._req(ctx, prompt.clone(), request_id=2))
+
+        for bid in first_blocks:
+            assert alloc.block_ref_counts[bid].item() == 2
+
+    @pytest.mark.internal
+    def test_all_prompt_tokens_in_context(self):
+        """All prompt tokens are processed (not skipped) for hybrid model requests."""
+        ctx = self._hybrid_ctx()
+        bs = ctx.block_size_tokens
+        prompt = self._prompt(bs * 2)
+
+        # First request registers blocks.
+        req1 = self._req(ctx, prompt.clone())
+        ctx.add_request(req1)
+        tokens_after_first = ctx.active_token_count
+
+        # Second request: even with matching blocks, all tokens must be processed.
+        req2 = self._req(ctx, prompt.clone(), request_id=2)
+        ctx.add_request(req2)
+
+        # All prompt tokens from req2 should be active (none skipped).
+        assert ctx.active_token_count - tokens_after_first == len(prompt)
+
+        # KV offset should be 0 (no prefix skip).
+        assert ctx.request_kv_length_offsets[1].item() == 0
diff --git a/tests/unit_tests/inference/engines/test_dynamic_engine.py b/tests/unit_tests/inference/engines/test_dynamic_engine.py
index e679b5d7c64..001071fccb6 100644
--- a/tests/unit_tests/inference/engines/test_dynamic_engine.py
+++ b/tests/unit_tests/inference/engines/test_dynamic_engine.py
@@ -8,6 +8,7 @@
 from dataclasses import dataclass, field
 from functools import partial
 from typing import Dict, List, Optional, Tuple
+from unittest import mock
 
 import pytest
 import torch
@@ -28,6 +29,7 @@
     TokenOverflowError,
 )
 from megatron.core.inference.engines import DynamicInferenceEngine
+from megatron.core.inference.engines.dynamic_engine import EngineState
 from megatron.core.inference.inference_request import DynamicInferenceRequest, Status
 from megatron.core.inference.model_inference_wrappers.gpt.gpt_inference_wrapper import (
     GPTInferenceWrapper,
@@ -40,6 +42,7 @@
     get_gpt_layer_local_spec,
     get_gpt_layer_with_inference_spec,
     get_gpt_layer_with_transformer_engine_spec,
+    get_gpt_mtp_block_spec,
 )
 from megatron.core.models.gpt.gpt_model import GPTModel
 from megatron.core.models.mamba.mamba_layer_specs import mamba_stack_spec
@@ -123,6 +126,7 @@ class DynamicEngineTestConfig:
     materialize_only_last_token_logits: bool = True
     skip_prompt_log_probs: bool = False
     enable_chunked_prefill: bool = False
+    enable_prefix_caching: bool = False
     cuda_graph_scope: List[CudaGraphScope] = field(
         default_factory=lambda: [CudaGraphScope.full_iteration_inference]
     )
@@ -138,19 +142,22 @@ class DynamicEngineTestConfig:
     kv_cache_management_mode: str = "persist"
     static_kv_memory_pointers: bool = True
     track_generated_token_events: bool = False
-
-    fp8: bool = False
+    num_speculative_tokens: int = 0
 
     def __post_init__(self):
 
         # Compute max_sequence_length.
-        assert self.max_sequence_length is None
-        assert self.num_tokens_to_generate is None or self.num_tokens_total is None
-        if self.num_tokens_to_generate is not None:
-            self.max_sequence_length = self.max_prompt_length + self.num_tokens_to_generate
-        else:
-            assert self.num_tokens_total is not None
-            self.max_sequence_length = self.num_tokens_total
+        if self.max_sequence_length is None:
+            assert self.num_tokens_to_generate is None or self.num_tokens_total is None
+            if self.num_tokens_to_generate is not None:
+                self.max_sequence_length = (
+                    self.max_prompt_length
+                    + self.num_tokens_to_generate
+                    + self.num_speculative_tokens
+                )
+            else:
+                assert self.num_tokens_total is not None
+                self.max_sequence_length = self.num_tokens_total + self.num_speculative_tokens
 
         # Default paused buffer size.
         if self.context_paused_buffer_size_gb is None:
@@ -258,10 +265,12 @@ def _build_inference_context(
                 ),
                 static_kv_memory_pointers=test_config.static_kv_memory_pointers,
                 enable_chunked_prefill=test_config.enable_chunked_prefill,
+                enable_prefix_caching=test_config.enable_prefix_caching,
                 use_flashinfer_fused_rope=None,  # default to using flash-infer if available
                 # this is for compatibility with the LTS environment
                 unified_memory_level=0,  # unit tests currently broken with UVM
                 track_generated_token_events=test_config.track_generated_token_events,
+                num_speculative_tokens=test_config.num_speculative_tokens,
             ),
         )
 
@@ -295,6 +304,7 @@ def _build_test_env(cls, test_config):
             transformer_config = TransformerConfig(
                 params_dtype=torch.bfloat16,
                 num_layers=4,
+                mtp_num_layers=test_config.num_speculative_tokens,
                 hidden_size=128 if test_config.fp8 else 32,
                 num_attention_heads=4,
                 use_cpu_initialization=True,
@@ -336,6 +346,14 @@ def _build_test_env(cls, test_config):
             elif test_config.transformer_impl == "inference_optimized":
                 layer_spec = get_gpt_layer_with_inference_spec()
 
+            # MTP block spec (needed for speculative decoding).
+            mtp_block_spec = None
+            if test_config.num_speculative_tokens > 0:
+                use_te = test_config.fp8 or test_config.transformer_impl == "transformer_engine"
+                mtp_block_spec = get_gpt_mtp_block_spec(
+                    config=transformer_config, spec=layer_spec, use_transformer_engine=use_te
+                )
+
             # GPT model.
             model = GPTModel(
                 config=transformer_config,
@@ -345,6 +363,7 @@ def _build_test_env(cls, test_config):
                 parallel_output=True,
                 pre_process=parallel_state.is_pipeline_first_stage(),
                 post_process=parallel_state.is_pipeline_last_stage(),
+                mtp_block_spec=mtp_block_spec,
             ).cuda()
         elif test_config.model_provider == "mamba":
             pp_size = test_config.pipeline_model_parallel_size
@@ -354,6 +373,7 @@ def _build_test_env(cls, test_config):
                 num_layers=(
                     3 if pp_size == 1 else 6
                 ),  # 1 Mamba layer, 1 attention layer, 1 MLP layer
+                mtp_num_layers=test_config.num_speculative_tokens,
                 hidden_size=256,  # The Mamba layer places several constraints on this
                 mamba_num_heads=16,
                 num_attention_heads=16,
@@ -389,8 +409,9 @@ def _build_test_env(cls, test_config):
                 vocab_size=test_config.vocab_size,
                 max_sequence_length=test_config.max_sequence_length,
                 parallel_output=True,
-                hybrid_attention_ratio=0.3,
-                hybrid_mlp_ratio=0.3,
+                hybrid_layer_pattern=(
+                    "M*-" if pp_size == 1 else "M*-|M*-"
+                ),  # 3 or 6 layers (2 PP stages)
                 pre_process=parallel_state.is_pipeline_first_stage(),
                 post_process=parallel_state.is_pipeline_last_stage(),
             ).cuda()
@@ -455,7 +476,7 @@ def _run_step(cls, env):
         # Suspend + resume.
         if (
             env.config.suspend_resume_interval is not None
-            and env.engine.step_count % env.config.suspend_resume_interval == 0
+            and env.engine.context.step_count % env.config.suspend_resume_interval == 0
         ):
             suspend_resume_mems = {}
             suspend_resume_mems["start"] = torch.cuda.memory_stats()
@@ -463,7 +484,7 @@ def _run_step(cls, env):
             suspend_resume_mems["mid"] = torch.cuda.memory_stats()
             env.engine.resume()  # resume.
             suspend_resume_mems["end"] = torch.cuda.memory_stats()
-            env.mem_usage["suspend_resume"][env.engine.step_count] = suspend_resume_mems
+            env.mem_usage["suspend_resume"][env.engine.context.step_count] = suspend_resume_mems
 
         # Nothing done?
         finished_request_records = result["finished_request_records"]
@@ -1532,26 +1553,45 @@ def test_chunked_prefill_avoid_single_token_chunk(self):
     @pytest.mark.skipif(
         not is_fa_min_version("2.7.3"), reason="need latest flash attn for dynamic batching"
     )
+    @pytest.mark.parametrize("materialize_only_last_token_logits", [True, False])
+    @pytest.mark.parametrize("skip_prompt_log_probs", [True, False])
     @torch.inference_mode()
-    def test_chunked_prefill_with_log_probs(self):
+    def test_chunked_prefill_with_log_probs(
+        self, materialize_only_last_token_logits: bool, skip_prompt_log_probs: bool
+    ):
         """
-        Test that chunked prefill correctly handles log probs with materialize_only_last_token_logits.
-        This verifies that intermediate log probs are properly discarded during chunked prefill.
+        Test that chunked prefill correctly handles log probs across all branches
+        of the log-prob accumulation logic.
         When materialize_only_last_token_logits=True, skip_prompt_log_probs must be True.
         """
+        if materialize_only_last_token_logits and not skip_prompt_log_probs:
+            with pytest.raises(AssertionError, match="only last token logits are materialized"):
+                self._run_test(
+                    num_requests=1,
+                    min_prompt_length=1200,
+                    max_prompt_length=1200,
+                    num_tokens_to_generate=8,
+                    materialize_only_last_token_logits=True,
+                    return_log_probs=True,
+                    skip_prompt_log_probs=False,
+                    model_provider="gpt",
+                    context_block_size_tokens=256,
+                    context_max_tokens=1000,
+                    enable_chunked_prefill=True,
+                )
+            return
+
         prompt_length = 1200
         num_tokens_to_generate = 8
 
-        # Run with chunked prefill, materialize_only_last_token_logits=True, and skip_prompt_log_probs=True
-        # This is the only valid combination for chunked prefill with last-token-only logits
         env = self._run_test(
             num_requests=1,
             min_prompt_length=prompt_length,
             max_prompt_length=prompt_length,
             num_tokens_to_generate=num_tokens_to_generate,
-            materialize_only_last_token_logits=True,
+            materialize_only_last_token_logits=materialize_only_last_token_logits,
             return_log_probs=True,
-            skip_prompt_log_probs=True,
+            skip_prompt_log_probs=skip_prompt_log_probs,
             model_provider="gpt",
             context_block_size_tokens=256,
             context_max_tokens=1000,
@@ -1572,11 +1612,17 @@ def test_chunked_prefill_with_log_probs(self):
                 f"generated log probs, got {len(request.generated_log_probs)}"
             )
 
-            # When skip_prompt_log_probs is True, prompt_log_probs should be empty
-            assert request.prompt_log_probs is None or len(request.prompt_log_probs) == 0, (
-                f"Request {request.request_id}: prompt_log_probs should be empty when "
-                f"skip_prompt_log_probs=True, but got {len(request.prompt_log_probs) if request.prompt_log_probs else 0} items"
-            )
+            if skip_prompt_log_probs:
+                assert request.prompt_log_probs is None or len(request.prompt_log_probs) == 0, (
+                    f"Request {request.request_id}: prompt_log_probs should be empty when "
+                    f"skip_prompt_log_probs=True, but got "
+                    f"{len(request.prompt_log_probs) if request.prompt_log_probs else 0} items"
+                )
+            else:
+                assert len(request.prompt_log_probs) == prompt_length - 1, (
+                    f"Request {request.request_id}: Expected {prompt_length - 1} "
+                    f"prompt log probs, got {len(request.prompt_log_probs)}"
+                )
 
             # Validate each generated log prob
             for i, log_prob in enumerate(request.generated_log_probs):
@@ -1733,7 +1779,7 @@ def test_max_requests(self, max_requests: int | None):
         env = self._run_test(
             context_max_requests=max_requests, num_tokens_to_generate=16, num_gap_steps=1
         )
-        step_count = env.engine.step_count
+        step_count = env.engine.context.step_count
         context = env.engine.context
         if max_requests is None:
             assert context.max_requests == 816
@@ -1773,7 +1819,7 @@ def test_suspend_resume_cycle(self, kv_cache_management_mode, static_kv_memory_p
         engine = env.engine
         context = engine.context
 
-        assert not engine.is_suspended
+        assert engine.state != EngineState.SUSPENDED
         assert context.is_tensor_state_allocated
 
         deallocates = kv_cache_management_mode != "persist"
@@ -1796,7 +1842,7 @@ def test_suspend_resume_cycle(self, kv_cache_management_mode, static_kv_memory_p
 
         # Suspend.
         engine.suspend()
-        assert engine.is_suspended
+        assert engine.state == EngineState.SUSPENDED
         assert not context.is_tensor_state_allocated
 
         gc.collect()
@@ -1826,7 +1872,7 @@ def test_suspend_resume_cycle(self, kv_cache_management_mode, static_kv_memory_p
 
         # Resume.
         engine.resume()
-        assert not engine.is_suspended
+        assert engine.state != EngineState.SUSPENDED
         assert context.is_tensor_state_allocated
 
         if deallocates and not uses_tms:
diff --git a/tests/unit_tests/inference/test_batch_dimension_utils.py b/tests/unit_tests/inference/test_batch_dimension_utils.py
new file mode 100644
index 00000000000..81d538ec60e
--- /dev/null
+++ b/tests/unit_tests/inference/test_batch_dimension_utils.py
@@ -0,0 +1,372 @@
+# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
+
+"""
+Unit tests for CUDAGraphBatchDimensionBuilder.match_graph_config with expert parallelism.
+"""
+
+import pytest
+import torch
+import torch.distributed as dist
+
+from megatron.core import parallel_state as ps
+from megatron.core.inference.batch_dimensions_utils import (
+    CUDAGraphBatchDimensionBuilder,
+    InferenceBatchDimensions,
+)
+from tests.unit_tests.test_utilities import Utils
+
+BD = InferenceBatchDimensions
+
+# Common config shared across tests
+MAX_REQUESTS = 256
+MAX_TOKENS = 2048
+MAX_SEQ_LEN = 4096
+TP_SIZE = 1
+MIXED_PREFILL_COUNT = 16
+
+
+def _generate_graphs(num_cuda_graphs, use_non_decode=True):
+    """Generate cuda graph batch dimensions using the builder."""
+    graph_list, _ = CUDAGraphBatchDimensionBuilder.generate_cuda_graph_batch_dimensions_list(
+        tp_size=TP_SIZE,
+        num_cuda_graphs=num_cuda_graphs,
+        cuda_graph_max_tokens=MAX_REQUESTS,
+        cuda_graph_mixed_prefill_request_count=MIXED_PREFILL_COUNT,
+        max_requests=MAX_REQUESTS,
+        max_tokens=MAX_TOKENS,
+        max_sequence_length=MAX_SEQ_LEN,
+        use_cuda_graphs_for_non_decode_steps=use_non_decode,
+    )
+    return graph_list
+
+
+def _match(
+    real, graph_list, ep_group, strict=False, decode_only=False, explicit_chunked_prefill=False
+):
+    return CUDAGraphBatchDimensionBuilder.match_graph_config(
+        real_batch_dim=real,
+        cuda_graph_batch_dimensions_list=graph_list,
+        strict=strict,
+        decode_only_cuda_graphs=decode_only,
+        explicit_chunked_prefill=explicit_chunked_prefill,
+        ep_group=ep_group,
+        smallest_non_decode_cuda_graph_size=min(MIXED_PREFILL_COUNT, MAX_REQUESTS),
+    )
+
+
+def _assert_consistent_across_ranks(result, ep_group):
+    """Assert that the match result is the same on every EP rank.
+
+    Either all ranks return None, or all ranks return a config with the
+    same token_count (which is what the all-reduce synchronises).
+    """
+    if result is None:
+        flag = torch.zeros(1, dtype=torch.int32, device="cuda")
+    else:
+        flag = torch.ones(1, dtype=torch.int32, device="cuda")
+
+    # If any rank got None, all must get None; if any rank got a match, all must.
+    flag_sum = flag.clone()
+    dist.all_reduce(flag_sum, op=dist.ReduceOp.SUM, group=ep_group)
+    ep_size = dist.get_world_size(ep_group)
+    assert (
+        flag_sum.item() == 0 or flag_sum.item() == ep_size
+    ), f"Inconsistent match: {flag_sum.item()}/{ep_size} ranks got a match"
+
+    if result is not None:
+        tc = torch.tensor([result.token_count], dtype=torch.int32, device="cuda")
+        tc_max = tc.clone()
+        tc_min = tc.clone()
+        dist.all_reduce(tc_max, op=dist.ReduceOp.MAX, group=ep_group)
+        dist.all_reduce(tc_min, op=dist.ReduceOp.MIN, group=ep_group)
+        assert (
+            tc_max.item() == tc_min.item()
+        ), f"Token count mismatch across EP ranks: min={tc_min.item()}, max={tc_max.item()}"
+
+
+class TestCUDAGraphTokenCountAlignment:
+    """Verify that mixed/prefill graph token counts are a subset of decode graph token counts."""
+
+    @pytest.mark.parametrize("num_cuda_graphs", [1, 16, 32, -1])
+    def test_mixed_token_counts_subset_of_decode(self, num_cuda_graphs):
+        """Every token count in the mixed/prefill graph pool must also appear
+        in the decode-only pool. Otherwise, when EP syncs token counts across
+        ranks, decode-only ranks cannot find a graph at the same token count
+        as prefill ranks, causing inconsistent matching."""
+        graph_list = _generate_graphs(num_cuda_graphs)
+
+        decode_token_counts = {bd.token_count for bd in graph_list if bd.prefill_req_count == 0}
+        mixed_token_counts = {bd.token_count for bd in graph_list if bd.prefill_req_count > 0}
+
+        mixed_only = mixed_token_counts - decode_token_counts
+        assert not mixed_only, (
+            f"Mixed/prefill token counts with no decode graph: {sorted(mixed_only)}. "
+            f"This will cause EP rank mismatch when some ranks are decode-only "
+            f"and others have prefill."
+        )
+
+        # Decode-only token counts not in the mixed pool are allowed, but only
+        # below MIXED_PREFILL_COUNT. The EP adjustment elevates token counts to
+        # at least MIXED_PREFILL_COUNT when any rank has prefill, so any decode
+        # token count >= MIXED_PREFILL_COUNT must have a mixed counterpart.
+        decode_only = decode_token_counts - mixed_token_counts
+        large_decode_only = {tc for tc in decode_only if tc >= MIXED_PREFILL_COUNT}
+        assert not large_decode_only, (
+            f"Decode-only token counts >= MIXED_PREFILL_COUNT ({MIXED_PREFILL_COUNT}) "
+            f"with no mixed/prefill graph: {sorted(large_decode_only)}. "
+            f"The EP token count elevation cannot guarantee alignment for these."
+        )
+
+
+class TestMatchGraphConfigWithEP:
+    """Tests for match_graph_config with expert parallelism.
+
+    Uses the world group as the EP group (all 8 GPUs form one EP group).
+    """
+
+    def setup_method(self, method):
+        Utils.initialize_model_parallel(
+            tensor_model_parallel_size=1,
+            pipeline_model_parallel_size=1,
+            expert_model_parallel_size=Utils.world_size,
+        )
+
+    def teardown_method(self, method):
+        Utils.destroy_model_parallel()
+
+    @staticmethod
+    def _get_ep_group():
+        """Return the EP group created by initialize_model_parallel."""
+        return ps.get_expert_model_parallel_group()
+
+    # ------------------------------------------------------------------ #
+    # 1. All ranks same decode batch → consistent match
+    # ------------------------------------------------------------------ #
+    @pytest.mark.internal
+    @pytest.mark.parametrize("num_cuda_graphs", [1, 16, 32, -1])
+    def test_uniform_decode_batch(self, num_cuda_graphs):
+        """All EP ranks have the same decode-only batch → should all match the same graph."""
+        ep_group = self._get_ep_group()
+        graph_list = _generate_graphs(num_cuda_graphs)
+        real = BD(token_count=32, prefill_req_count=0, decode_req_count=32)
+
+        result = _match(real, graph_list, ep_group=ep_group)
+        _assert_consistent_across_ranks(result, ep_group)
+        assert result is not None, "Should find a matching graph for uniform decode batch"
+
+    # ------------------------------------------------------------------ #
+    # 2. Different token counts across EP ranks → all-reduce takes max
+    # ------------------------------------------------------------------ #
+    @pytest.mark.internal
+    @pytest.mark.parametrize("num_cuda_graphs", [1, 16, 32, -1])
+    def test_varying_decode_token_counts(self, num_cuda_graphs):
+        """EP ranks have different decode token counts. The all-reduce
+        should take the max, and all ranks should match the same graph."""
+        ep_group = self._get_ep_group()
+        graph_list = _generate_graphs(num_cuda_graphs)
+        rank = dist.get_rank()
+
+        # Each rank gets a different token count: 8, 16, 24, ...
+        token_count = (rank + 1) * 8
+        real = BD(token_count=token_count, prefill_req_count=0, decode_req_count=token_count)
+
+        result = _match(real, graph_list, ep_group=ep_group)
+        _assert_consistent_across_ranks(result, ep_group)
+        assert result is not None
+
+    # ------------------------------------------------------------------ #
+    # 3. decode_only_cuda_graphs=True, some ranks have prefill → all None
+    # ------------------------------------------------------------------ #
+    @pytest.mark.internal
+    @pytest.mark.parametrize("num_cuda_graphs", [1, 16, 32, -1])
+    def test_decode_only_graphs_with_mixed_ranks(self, num_cuda_graphs):
+        """When decode_only_cuda_graphs=True and at least one EP rank has a
+        prefill request, ALL ranks should get None (eager mode)."""
+        ep_group = self._get_ep_group()
+        graph_list = _generate_graphs(num_cuda_graphs)
+        rank = dist.get_rank()
+
+        # Rank 0 has a mixed batch (prefill + decode), all others decode-only
+        if rank == 0:
+            real = BD(token_count=64, prefill_req_count=2, decode_req_count=10)
+        else:
+            real = BD(token_count=32, prefill_req_count=0, decode_req_count=32)
+
+        result = _match(real, graph_list, ep_group=ep_group, decode_only=True)
+        _assert_consistent_across_ranks(result, ep_group)
+        assert (
+            result is None
+        ), "All ranks should run eager when decode_only=True and some rank has prefill"
+
+    # ------------------------------------------------------------------ #
+    # 4. explicit_chunked_prefill=True, some ranks prefill → all None
+    # ------------------------------------------------------------------ #
+    @pytest.mark.internal
+    @pytest.mark.parametrize("num_cuda_graphs", [1, 16, 32, -1])
+    def test_explicit_chunked_prefill_with_mixed_ranks(self, num_cuda_graphs):
+        """When explicit_chunked_prefill=True and some EP rank has prefill,
+        ALL ranks should get None (eager mode)."""
+        ep_group = self._get_ep_group()
+        graph_list = _generate_graphs(num_cuda_graphs)
+        rank = dist.get_rank()
+
+        if rank == 0:
+            real = BD(token_count=64, prefill_req_count=2, decode_req_count=10)
+        else:
+            real = BD(token_count=32, prefill_req_count=0, decode_req_count=32)
+
+        result = _match(real, graph_list, ep_group=ep_group, explicit_chunked_prefill=True)
+        _assert_consistent_across_ranks(result, ep_group)
+        assert result is None, "All ranks should run eager with explicit_chunked_prefill"
+
+    # ------------------------------------------------------------------ #
+    # 5. Mixed prefill graphs with strict matching
+    # ------------------------------------------------------------------ #
+    @pytest.mark.internal
+    @pytest.mark.parametrize("num_cuda_graphs", [1, 16, 32, -1])
+    def test_strict_matching_with_mixed_prefill(self, num_cuda_graphs):
+        """With strict matching, request counts are synced across EP ranks
+        via all-reduce. All ranks should still get a consistent result."""
+        ep_group = self._get_ep_group()
+        graph_list = _generate_graphs(num_cuda_graphs)
+        rank = dist.get_rank()
+
+        # Varying prefill/decode split across ranks
+        prefill = min(rank + 1, MIXED_PREFILL_COUNT)
+        decode = 16 - prefill
+        real = BD(token_count=64, prefill_req_count=prefill, decode_req_count=decode)
+
+        result = _match(real, graph_list, ep_group=ep_group, strict=True)
+        _assert_consistent_across_ranks(result, ep_group)
+
+    # ------------------------------------------------------------------ #
+    # 6. Non-strict matching with mixed prefill
+    # ------------------------------------------------------------------ #
+    @pytest.mark.internal
+    @pytest.mark.parametrize("num_cuda_graphs", [1, 16, 32, -1])
+    def test_non_strict_matching_with_mixed_prefill(self, num_cuda_graphs):
+        """Non-strict matching: prefill slots can serve decode. Token count
+        is synced across EP ranks; result must be consistent."""
+        ep_group = self._get_ep_group()
+        graph_list = _generate_graphs(num_cuda_graphs)
+        rank = dist.get_rank()
+
+        prefill = min(rank + 1, MIXED_PREFILL_COUNT)
+        decode = 16 - prefill
+        real = BD(token_count=64, prefill_req_count=prefill, decode_req_count=decode)
+
+        result = _match(real, graph_list, ep_group=ep_group)
+        _assert_consistent_across_ranks(result, ep_group)
+
+    # ------------------------------------------------------------------ #
+    # 7. Mixed decode/prefill across ranks — strict matching
+    # ------------------------------------------------------------------ #
+    @pytest.mark.internal
+    @pytest.mark.parametrize("num_cuda_graphs", [1, 16, 32, -1])
+    def test_mixed_decode_and_prefill_ranks_strict(self, num_cuda_graphs):
+        """Some EP ranks are pure decode, others have prefill requests.
+        With strict matching the all-reduce syncs request counts to the
+        max across ranks. Result must be consistent."""
+        ep_group = self._get_ep_group()
+        graph_list = _generate_graphs(num_cuda_graphs)
+        rank = dist.get_rank()
+
+        # Even ranks: pure decode (32 tokens)
+        # Odd ranks: mixed prefill (64 tokens, 2 prefill + 14 decode)
+        if rank % 2 == 0:
+            real = BD(token_count=32, prefill_req_count=0, decode_req_count=32)
+        else:
+            real = BD(token_count=64, prefill_req_count=2, decode_req_count=14)
+
+        result = _match(real, graph_list, ep_group=ep_group, strict=True)
+        _assert_consistent_across_ranks(result, ep_group)
+
+    # ------------------------------------------------------------------ #
+    # 8. Mixed decode/prefill across ranks — non-strict matching
+    # ------------------------------------------------------------------ #
+    @pytest.mark.internal
+    @pytest.mark.parametrize("num_cuda_graphs", [1, 16, 32, -1])
+    def test_mixed_decode_and_prefill_ranks_non_strict(self, num_cuda_graphs):
+        """Some EP ranks are pure decode, others have prefill requests.
+        Non-strict matching only syncs token counts (not request counts).
+        Result must be consistent."""
+        ep_group = self._get_ep_group()
+        graph_list = _generate_graphs(num_cuda_graphs)
+        rank = dist.get_rank()
+
+        # Even ranks: pure decode (32 tokens)
+        # Odd ranks: mixed prefill (64 tokens, 2 prefill + 14 decode)
+        if rank % 2 == 0:
+            real = BD(token_count=32, prefill_req_count=0, decode_req_count=32)
+        else:
+            real = BD(token_count=64, prefill_req_count=2, decode_req_count=14)
+
+        result = _match(real, graph_list, ep_group=ep_group)
+        _assert_consistent_across_ranks(result, ep_group)
+
+    # ------------------------------------------------------------------ #
+    # 9. All ranks decode-only with decode_only_cuda_graphs → should match
+    # ------------------------------------------------------------------ #
+    @pytest.mark.internal
+    @pytest.mark.parametrize("num_cuda_graphs", [1, 16, 32, -1])
+    def test_decode_only_graphs_all_decode(self, num_cuda_graphs):
+        """When all EP ranks are decode-only and decode_only_cuda_graphs=True,
+        a match should be found."""
+        ep_group = self._get_ep_group()
+        graph_list = _generate_graphs(num_cuda_graphs)
+        rank = dist.get_rank()
+
+        token_count = (rank + 1) * 4
+        real = BD(token_count=token_count, prefill_req_count=0, decode_req_count=token_count)
+
+        result = _match(real, graph_list, ep_group=ep_group, decode_only=True)
+        _assert_consistent_across_ranks(result, ep_group)
+        assert result is not None, "All-decode batch with decode_only_cuda_graphs should match"
+
+    # ------------------------------------------------------------------ #
+    # 10. Real batch exceeds all graphs → None on all ranks
+    # ------------------------------------------------------------------ #
+    @pytest.mark.internal
+    @pytest.mark.parametrize("num_cuda_graphs", [1, 16, 32, -1])
+    def test_oversized_batch_returns_none(self, num_cuda_graphs):
+        """When the real batch is larger than any available graph, all ranks
+        should get None."""
+        ep_group = self._get_ep_group()
+        graph_list = _generate_graphs(num_cuda_graphs)
+
+        # Token count exceeds MAX_TOKENS on all ranks
+        real = BD(
+            token_count=MAX_TOKENS + 100,
+            prefill_req_count=0,
+            decode_req_count=min(MAX_TOKENS + 100, MAX_REQUESTS),
+        )
+
+        result = _match(real, graph_list, ep_group=ep_group)
+        _assert_consistent_across_ranks(result, ep_group)
+        assert result is None, "Oversized batch should not match any graph"
+
+    # ------------------------------------------------------------------ #
+    # 11. One EP rank has huge batch → all-reduce lifts to max → no match
+    # ------------------------------------------------------------------ #
+    @pytest.mark.internal
+    @pytest.mark.parametrize("num_cuda_graphs", [1, 16, 32, -1])
+    def test_one_rank_oversized_forces_no_match(self, num_cuda_graphs):
+        """If one EP rank has a batch exceeding all graph capacities, the
+        all-reduce max lifts everyone → no match on any rank."""
+        ep_group = self._get_ep_group()
+        graph_list = _generate_graphs(num_cuda_graphs)
+        rank = dist.get_rank()
+
+        if rank == 0:
+            # This rank has a batch that exceeds all graphs
+            real = BD(
+                token_count=MAX_TOKENS + 100,
+                prefill_req_count=0,
+                decode_req_count=min(MAX_TOKENS + 100, MAX_REQUESTS),
+            )
+        else:
+            real = BD(token_count=8, prefill_req_count=0, decode_req_count=8)
+
+        result = _match(real, graph_list, ep_group=ep_group)
+        _assert_consistent_across_ranks(result, ep_group)
+        assert result is None, "All-reduce max from oversized rank should cause no match"
diff --git a/tests/unit_tests/inference/test_data_parallel_inference_coordinator.py b/tests/unit_tests/inference/test_data_parallel_inference_coordinator.py
index 57326291a73..be310c62f29 100644
--- a/tests/unit_tests/inference/test_data_parallel_inference_coordinator.py
+++ b/tests/unit_tests/inference/test_data_parallel_inference_coordinator.py
@@ -2,16 +2,25 @@
 
 import asyncio
 import itertools
+import multiprocessing
+import os
 import time
+import unittest.mock
 from collections import deque
 from typing import Dict, Optional
 
 import msgpack
 import pytest
 import torch
-from tqdm import tqdm
 
-from megatron.core.inference.engines.dynamic_engine import DynamicInferenceEngine, RequestEntry
+from megatron.core.inference.data_parallel_inference_coordinator import (
+    DataParallelInferenceCoordinator,
+)
+from megatron.core.inference.engines.dynamic_engine import (
+    DynamicInferenceEngine,
+    EngineState,
+    RequestEntry,
+)
 from megatron.core.inference.headers import Headers
 from megatron.core.inference.inference_client import InferenceClient
 from megatron.core.inference.inference_request import (
@@ -34,7 +43,6 @@
 NUM_REQUESTS = 10
 NUM_TOKENS = 2
 DEFAULT_PORT = 46581
-ZMQ_FLAKY_SHUTDOWN = True
 
 
 class DummyTokenizer:
@@ -64,6 +72,10 @@ class DummyContext:
 
     def __init__(self):
         self.active_cnt = 0
+        self.step_count = 0
+        self.block_size_tokens = 64
+        self.enable_prefix_caching = False
+        self.prefix_caching_coordinator_policy = None
 
     def get_active_request_count(self) -> int:
         return self.active_cnt
@@ -86,29 +98,55 @@ def __init__(self):
         """We cannot call super().__init__() because it requires complex setup."""
         self.waiting_request_ids = deque()
         self.requests: Dict[int, RequestEntry] = {}
-        self.suspend_signal = False
-        self.is_suspended = False
         self._loop = get_asyncio_loop()
         self.context = DummyContext()
         self.controller = DummyController()
-        self.running = asyncio.Event()
-        self.paused = asyncio.Event()
-        self.stopped = asyncio.Event()
         self.pending_microbatch = deque()
-        self.received_pause: bool = False
-        self.received_stop: bool = False
         self.pg_collection = ProcessGroupCollection.use_mpu_process_groups()
         self.rank = torch.distributed.get_rank()
 
+        # State machine (mirrors dynamic_engine.py reset()).
+        self.state = EngineState.RUNNING
+        self._state_events = {k: asyncio.Event() for k in self._STATE_EVENTS}
+        self._state_events[EngineState.RUNNING].set()
+        self._pending_signals = deque()
+        self.resume_request_ids = None
+        self.use_coordinator = False
+
+        self.ep_world_size = 1
+
+        self.step_start_event = unittest.mock.MagicMock()
+        self.step_end_event = unittest.mock.MagicMock()
+
+    async def run_engine_with_coordinator(self, *, loop=None):
+        """Override to bypass @trace_async_exceptions for testability.
+
+        In production, @trace_async_exceptions converts AssertionError to sys.exit(1) -> SystemExit.
+        In Python 3.12+, asyncio re-raises SystemExit from tasks in the main thread.
+        For tests, we let AssertionErrors propagate directly so pytest.raises can catch them.
+        """
+        return await DynamicInferenceEngine.run_engine_with_coordinator.__wrapped__(self, loop=loop)
+
+    def suspend(self):
+        pass
+
+    def resume(self):
+        pass
+
     def add_request(
         self, request_id: int, prompt: str, sampling_params: Optional[SamplingParams] = None
     ) -> asyncio.Future[DynamicInferenceRequestRecord]:
         """Dummy add_request."""
 
+        # Mock tokenization to prevent `prompt_tokens == None`.
+        prompt_tokens = (
+            torch.arange(len(prompt.split())) if isinstance(prompt, str) else torch.tensor(prompt)
+        )
         self.requests[request_id] = RequestEntry(
             record=DynamicInferenceRequestRecord.from_request(
                 DynamicInferenceRequest(
                     prompt=prompt,
+                    prompt_tokens=prompt_tokens,
                     request_id=request_id,
                     sampling_params=sampling_params,
                     status=Status.WAITING_IN_QUEUE,
@@ -122,6 +160,8 @@ def add_request(
 
     async def async_step(self, *, verbose: Optional[bool] = False) -> Dict:
         """Dummy async_step."""
+        await asyncio.sleep(0)
+
         # Finish "active" requests.
         finished_request_records = []
         to_remove = []
@@ -139,7 +179,8 @@ async def async_step(self, *, verbose: Optional[bool] = False) -> Dict:
                 # Send signal to coordinator.
                 if self.is_mp_coordinator:
                     payload = msgpack.packb(
-                        [Headers.ENGINE_REPLY.value, [entry.record.serialize()]], use_bin_type=True
+                        [Headers.ENGINE_REPLY.value, [entry.record.merge().serialize()]],
+                        use_bin_type=True,
                     )
                     self.socket_for_receiving_requests.send(payload)
 
@@ -163,6 +204,42 @@ async def async_step(self, *, verbose: Optional[bool] = False) -> Dict:
         }
 
 
+async def cleanup_engine(engine, client=None, timeout=30.0):
+    """Disconnect an engine between tests. The coordinator stays alive."""
+    task = getattr(engine, 'engine_loop_task', None)
+    if task is not None and not task.done():
+        if client is not None:
+            client.pause_engines()
+        try:
+            await asyncio.wait_for(engine.wait_until(EngineState.PAUSED), timeout=timeout)
+        except (asyncio.TimeoutError, Exception):
+            pass
+
+        sub = getattr(engine, 'model_parallel_num_msgs_subscriber_socket', None)
+        if sub is not None:
+            sub.setsockopt(zmq.RCVTIMEO, 1000)
+
+        # Close ZMQ communicator sockets to unblock any stuck ranks.
+        for attr in ('expert_parallel_zmq_communicator', 'world_zmq_communicator'):
+            comm = getattr(engine, attr, None)
+            if comm is not None:
+                comm.close()
+
+        task.cancel()
+        try:
+            await asyncio.wait_for(asyncio.shield(task), timeout=5.0)
+        except (asyncio.TimeoutError, asyncio.CancelledError, Exception):
+            pass
+
+    if client is not None:
+        # Walk the coordinator back to RUNNING regardless of its current state
+        # so the next test starts cleanly.  Each call is a no-op when the
+        # coordinator is already in the target state (just logs a warning).
+        client.resume_engines()  # SUSPENDED → PAUSED (no-op otherwise)
+        client.unpause_engines()  # PAUSED    → RUNNING (no-op otherwise)
+        client.stop()
+
+
 @pytest.fixture
 def initialize_model_parallel(request, monkeypatch):
     """Fixture to initialize and destroy model parallel.
@@ -179,12 +256,65 @@ def initialize_model_parallel(request, monkeypatch):
         pipeline_model_parallel_size=pp,
         expert_model_parallel_size=ep,
     )
-    dp = world_size // (tp * pp * ep)
+    dp = world_size // (tp * pp)
     yield world_size, dp, tp, pp, ep
     Utils.destroy_model_parallel()
 
 
-@pytest.mark.skipif(ZMQ_FLAKY_SHUTDOWN, reason="ZMQ shutdown is flaky")
+@pytest.fixture(scope="class")
+def coordinator():
+    """Launch a single coordinator process for the entire test class.
+
+    Only rank 0 spawns the coordinator process.  Non-rank-0 processes use a
+    placeholder address; the real address is broadcast inside each test's call
+    to start_listening_to_data_parallel_coordinator (which broadcasts dp_addr
+    from dp_src within the DP process group).
+
+    The coordinator is spawned with data_parallel_size=0 so it doesn't block
+    waiting for engines; engines register dynamically via the empty-payload
+    re-registration path.
+    """
+    rank = int(os.environ.get("RANK", "0"))
+
+    if rank == 0:
+        spawn_context = multiprocessing.get_context('spawn')
+        pipe_parent, pipe_child = spawn_context.Pipe()
+        ready_event = spawn_context.Event()
+        proc = spawn_context.Process(
+            target=DataParallelInferenceCoordinator.entrypoint,
+            args=(pipe_child, ready_event, 0, DummyTokenizer(), DEFAULT_PORT, False),
+        )
+        proc.start()
+
+        # Wait for the coordinator to bind its socket and send the address.
+        while not pipe_parent.poll(timeout=0.1):
+            assert proc.is_alive(), "Coordinator process died during init"
+        dp_addr = pipe_parent.recv()
+        pipe_parent.close()
+        ready_event.wait(timeout=10.0)
+    else:
+        proc = None
+        # Placeholder: the engine setup broadcasts rank 0's actual address.
+        dp_addr = f"tcp://localhost:{DEFAULT_PORT}"
+
+    yield dp_addr
+
+    # Only rank 0 tears down the coordinator process.
+    if rank == 0 and proc is not None and proc.is_alive():
+        ctx = zmq.Context()
+        sock = ctx.socket(zmq.DEALER)
+        sock.connect(dp_addr)
+        sock.send(msgpack.packb([Headers.CONNECT.value], use_bin_type=True))
+        sock.recv()  # CONNECT_ACK
+        sock.send(msgpack.packb([Headers.SHUTDOWN.value], use_bin_type=True))
+        sock.close(linger=1000)
+        ctx.term()
+        proc.join(timeout=10.0)
+        if proc.is_alive():
+            proc.terminate()
+            proc.join(timeout=5.0)
+
+
 class TestCoordinator:
     """Test class for Data Parallel Inference Coordinator."""
 
@@ -195,26 +325,43 @@ def build_requests(self, num_requests=NUM_REQUESTS, num_tokens=NUM_TOKENS):
             for _ in range(num_requests)
         ]
 
-    async def run_coordinator_test(
-        self,
-        *,
-        launch_coordinator=True,
-        stop_engines=True,
-        num_requests=NUM_REQUESTS,
-        num_tokens=NUM_TOKENS,
-    ):
-        """Run a coordinator test. Model parallel must already be initialized."""
+    @pytest.mark.internal
+    @pytest.mark.skipif(not HAVE_ZMQ, reason="pyzmq is required for this test")
+    @pytest.mark.asyncio
+    @pytest.mark.parametrize(
+        "initialize_model_parallel",
+        [
+            pytest.param((tp, pp, ep), id=f"tp{tp}-pp{pp}-ep{ep}")
+            for tp, pp, ep in itertools.product([1, 2], [1, 2], [1, 2])
+            if tp * pp * ep <= Utils.world_size
+        ],
+        indirect=["initialize_model_parallel"],
+    )
+    async def test_parallel_configs(self, initialize_model_parallel, coordinator):
+        """Test coordinator with various TP, PP, and EP configurations."""
+        dp_addr = coordinator
+        port = int(dp_addr.rsplit(":", 1)[-1])
+        requests = self.build_requests()
         engine = DummyEngine()
-        requests = self.build_requests(num_requests, num_tokens)
+        rank = torch.distributed.get_rank()
 
-        dp_addr = await engine.start_listening_to_data_parallel_coordinator(
-            inference_coordinator_port=DEFAULT_PORT, launch_inference_coordinator=launch_coordinator
+        await engine.start_listening_to_data_parallel_coordinator(
+            inference_coordinator_port=port, launch_inference_coordinator=False
         )
 
+        # Ensure all engines are registered before submitting requests.
+        await asyncio.wait_for(
+            asyncio.get_event_loop().run_in_executor(None, torch.distributed.barrier), timeout=30.0
+        )
+
+        client = None
         try:
-            if torch.distributed.get_rank() == 0:
+            if rank == 0:
+                # Yield so engine loop can run before we block the event loop
+                # with the client's synchronous connect handshake.
+                await asyncio.sleep(0)
                 client = InferenceClient(dp_addr)
-                await client.start()
+                client.start()
 
                 futures = [
                     client.add_request(prompt=prompt, sampling_params=params)
@@ -222,268 +369,303 @@ async def run_coordinator_test(
                 ]
                 results = await asyncio.wait_for(asyncio.gather(*futures), timeout=10.0)
 
-                for record in results:
-                    assert record[-1].status == Status.COMPLETED
+                for result in results:
+                    assert result["status"] == Status.COMPLETED.name
+
+            await asyncio.wait_for(
+                asyncio.get_event_loop().run_in_executor(None, torch.distributed.barrier),
+                timeout=30.0,
+            )
         finally:
+            await cleanup_engine(engine, client)
+
+    @pytest.mark.internal
+    @pytest.mark.skipif(not HAVE_ZMQ, reason="pyzmq is required for this test")
+    @pytest.mark.asyncio
+    async def test_deserialize_flag(self, initialize_model_parallel, coordinator):
+        """Test that the correct response type is returned based on the deserialize flag."""
+        dp_addr = coordinator
+        port = int(dp_addr.rsplit(":", 1)[-1])
+        engine = DummyEngine()
+        requests = self.build_requests(num_requests=2)
+
+        await engine.start_listening_to_data_parallel_coordinator(
+            inference_coordinator_port=port, launch_inference_coordinator=False
+        )
+
+        await asyncio.wait_for(
+            asyncio.get_event_loop().run_in_executor(None, torch.distributed.barrier), timeout=30.0
+        )
+
+        client = None
+        try:
             if torch.distributed.get_rank() == 0:
-                if stop_engines:
-                    await asyncio.wait_for(client.stop_engines(), timeout=10.0)
+                await asyncio.sleep(0)
+                # Test deserialize=True
+                client = InferenceClient(dp_addr, deserialize=True)
+                client.start()
+                futures = [
+                    client.add_request(prompt=prompt, sampling_params=params)
+                    for prompt, params in requests
+                ]
+                results = await asyncio.wait_for(asyncio.gather(*futures), timeout=10.0)
+                for result in results:
+                    assert isinstance(result, DynamicInferenceRequest)
                 client.stop()
-            if stop_engines:
-                try:
-                    await asyncio.wait_for(engine.engine_loop_task, timeout=30.0)
-                except asyncio.TimeoutError:
-                    engine.engine_loop_task.cancel()
 
-        return dp_addr
+                # Test deserialize=False (default)
+                client = InferenceClient(dp_addr)
+                client.start()
+                futures = [
+                    client.add_request(prompt=prompt, sampling_params=params)
+                    for prompt, params in requests
+                ]
+                results = await asyncio.wait_for(asyncio.gather(*futures), timeout=10.0)
+                for result in results:
+                    assert isinstance(result, dict)
+
+            await asyncio.wait_for(
+                asyncio.get_event_loop().run_in_executor(None, torch.distributed.barrier),
+                timeout=30.0,
+            )
+        finally:
+            await cleanup_engine(engine, client)
 
     @pytest.mark.internal
     @pytest.mark.skipif(not HAVE_ZMQ, reason="pyzmq is required for this test")
     @pytest.mark.asyncio
     @pytest.mark.parametrize(
         "initialize_model_parallel",
-        [
-            pytest.param((tp, pp, ep), id=f"tp{tp}-pp{pp}-ep{ep}")
-            for tp, pp, ep in itertools.product([1, 2], [1, 2], [1, 2])
-            if tp * pp * ep <= Utils.world_size
-        ],
+        [pytest.param((2, 2, 2), id="tp2-pp2-ep2")],
         indirect=["initialize_model_parallel"],
     )
-    async def test_parallel_configs(self, initialize_model_parallel):
-        """Test coordinator with various TP, PP, and EP configurations."""
-        await self.run_coordinator_test()
+    async def test_control_logic_lifecycle(self, initialize_model_parallel, coordinator):
+        """Comprehensive lifecycle test for the engine state machine."""
+        # States where paused stays set: once set during PAUSE, it's only cleared by UNPAUSE.
+        PAUSED_FAMILY = {
+            EngineState.PAUSED,
+            EngineState.UNPAUSING,
+            EngineState.SUSPENDING,
+            EngineState.SUSPENDED,
+            EngineState.RESUMING,
+            EngineState.STOPPING,
+            EngineState.STOPPED,
+        }
 
-    @pytest.mark.internal
-    @pytest.mark.skipif(not HAVE_ZMQ, reason="pyzmq is required for this test")
-    @pytest.mark.asyncio
-    async def test_coordinator_lifecycle(self, initialize_model_parallel):
-        """Test coordinator connection and port conflict behavior."""
-        engine1 = DummyEngine()
-        engine2 = None
-        engine3 = None
-        third_addr = None
-
-        # Launch first coordinator - binds to DEFAULT_PORT
-        first_addr = await engine1.start_listening_to_data_parallel_coordinator(
-            inference_coordinator_port=DEFAULT_PORT, launch_inference_coordinator=True
-        )
+        def assert_state(eng, expected):
+            """Assert engine state and all four event flags are consistent."""
+            assert eng.state == expected, f"Expected state {expected}, got {eng.state}"
+            assert eng._state_events[EngineState.RUNNING].is_set() == (
+                expected == EngineState.RUNNING
+            ), f"RUNNING.is_set()={eng._state_events[EngineState.RUNNING].is_set()} for state={expected}"
+            assert eng._state_events[EngineState.PAUSED].is_set() == (
+                expected in PAUSED_FAMILY
+            ), f"PAUSED.is_set()={eng._state_events[EngineState.PAUSED].is_set()} for state={expected}"
+            assert eng._state_events[EngineState.SUSPENDED].is_set() == (
+                expected == EngineState.SUSPENDED
+            ), f"SUSPENDED.is_set()={eng._state_events[EngineState.SUSPENDED].is_set()} for state={expected}"
+            assert eng._state_events[EngineState.STOPPED].is_set() == (
+                expected == EngineState.STOPPED
+            ), f"STOPPED.is_set()={eng._state_events[EngineState.STOPPED].is_set()} for state={expected}"
+
+        dp_addr = coordinator
+        port = int(dp_addr.rsplit(":", 1)[-1])
+        requests = self.build_requests(num_requests=16)
+        engine = DummyEngine()
+        client = None
+        doomed_futures = []
+        rank = torch.distributed.get_rank()
 
         try:
-            # Cancel engine1 loop without sending stop to coordinator
-            # This keeps coordinator process alive and holding the port
-            engine1.engine_loop_task.cancel()
-            try:
-                await engine1.engine_loop_task
-            except asyncio.CancelledError:
-                pass
-
-            # Connect engine2 to existing coordinator (don't launch new one)
-            engine2 = DummyEngine()
-            second_addr = await engine2.start_listening_to_data_parallel_coordinator(
-                inference_coordinator_port=DEFAULT_PORT, launch_inference_coordinator=False
+            await engine.start_listening_to_data_parallel_coordinator(
+                inference_coordinator_port=port, launch_inference_coordinator=False
             )
 
-            # Should connect to same port, but will not always in CI due to port conflicts.
-            first_port = int(first_addr.rsplit(":", 1)[-1])
-            second_port = int(second_addr.rsplit(":", 1)[-1])
-            # assert second_port == first_port
-
-            # Cancel engine2
-            engine2.engine_loop_task.cancel()
-            try:
-                await engine2.engine_loop_task
-            except asyncio.CancelledError:
-                pass
-
-            # Launch new coordinator - should get different port since first is holding it
-            engine3 = DummyEngine()
-            third_addr = await engine3.start_listening_to_data_parallel_coordinator(
-                inference_coordinator_port=DEFAULT_PORT, launch_inference_coordinator=True
+            # Synchronize all ranks so every engine has registered.
+            await asyncio.wait_for(
+                asyncio.get_event_loop().run_in_executor(None, torch.distributed.barrier),
+                timeout=30.0,
             )
 
-            # Verify we got a different port due to conflict
-            third_port = int(third_addr.rsplit(":", 1)[-1])
-            assert (
-                third_port != first_port
-            ), f"Expected different port due to conflict, but got same: {third_port}"
+            if rank == 0:
+                client = InferenceClient(dp_addr)
+                client.start()
 
-        finally:
-            # Clean up engine3's coordinator
-            if engine3 is not None and third_addr is not None:
-                client3 = InferenceClient(third_addr)
-                await client3.start()
-                await asyncio.wait_for(client3.stop_engines(), timeout=10.0)
-                client3.stop()
-                try:
-                    await asyncio.wait_for(engine3.engine_loop_task, timeout=30.0)
-                except asyncio.TimeoutError:
-                    engine3.engine_loop_task.cancel()
-
-            # Rebuild engine and reconnect to engine1's coordinator
-            first_port = int(first_addr.rsplit(":", 1)[-1])
-            engine1 = DummyEngine()
-            await engine1.start_listening_to_data_parallel_coordinator(
-                inference_coordinator_port=first_port, launch_inference_coordinator=False
-            )
-            client1 = InferenceClient(first_addr)
-            await client1.start()
-            await asyncio.wait_for(client1.stop_engines(), timeout=10.0)
-            client1.stop()
-            try:
-                await asyncio.wait_for(engine1.engine_loop_task, timeout=30.0)
-            except asyncio.TimeoutError:
-                engine1.engine_loop_task.cancel()
+                await asyncio.wait_for(engine.wait_until(EngineState.RUNNING), timeout=5.0)
+                assert_state(engine, EngineState.RUNNING)
 
-    @pytest.mark.internal
-    @pytest.mark.skipif(not HAVE_ZMQ, reason="pyzmq is required for this test")
-    @pytest.mark.asyncio
-    async def test_pause(self, initialize_model_parallel):
-        """Test pause and resume functionality."""
-        engine = DummyEngine()
-        requests = self.build_requests(num_requests=32)
+                # Try to submit signals out of FSM order.
+                # The coordinator's state machine filters these out.
+                client.suspend_engines()
+                await asyncio.sleep(0.1)
+                assert_state(engine, EngineState.RUNNING)
+                client.resume_engines()
+                await asyncio.sleep(0.1)
+                assert_state(engine, EngineState.RUNNING)
+                client.stop_engines()
+                await asyncio.sleep(0.1)
+                assert_state(engine, EngineState.RUNNING)
 
-        dp_addr = await engine.start_listening_to_data_parallel_coordinator(
-            inference_coordinator_port=DEFAULT_PORT, launch_inference_coordinator=True
-        )
+                # Submit and complete requests while running.
+                futures = [client.add_request(prompt=p, sampling_params=s) for p, s in requests[:2]]
+                results = await asyncio.wait_for(asyncio.gather(*futures), timeout=5.0)
+                for result in results:
+                    assert result["status"] == Status.COMPLETED.name
+
+                # Submit requests while RUNNING, then PAUSE before they drain.
+                # These must survive the PAUSE (not be drained during PAUSING).
+                pre_pause_futures = [
+                    client.add_request(prompt=p, sampling_params=s) for p, s in requests[2:3]
+                ]
+                client.pause_engines()
+                await asyncio.wait_for(engine.wait_until(EngineState.PAUSED), timeout=5.0)
+                assert_state(engine, EngineState.PAUSED)
 
-        success = True
-        try:
-            if torch.distributed.get_rank() == 0:
-                client = InferenceClient(dp_addr)
-                await client.start()
+                # Pre-pause requests must NOT have been drained.
+                done, pending = await asyncio.wait(pre_pause_futures, timeout=0.5)
+                assert len(pending) > 0, "Pre-pause requests should not drain during PAUSING"
 
-                # Submit requests and pause after completion.
-                futures = [client.add_request(prompt=p, sampling_params=s) for p, s in requests[:2]]
+                # Try pausing again and see if it breaks.
+                client.pause_engines()
                 await asyncio.sleep(0.1)
-                awaitables = futures + [client.pause_engines()]
-                try:
-                    await asyncio.wait_for(asyncio.gather(*awaitables), timeout=0.5)
-                except asyncio.TimeoutError:
-                    pytest.fail("Pause operation timed out.")
-
-                # Ensure that requests can be added while paused.
-                prompt, params = requests[2]
-                future = client.add_request(prompt=prompt, sampling_params=params)
-                with pytest.raises(asyncio.TimeoutError):
-                    await asyncio.wait_for(future, timeout=0.1)
-
-                # Resume and verify new requests complete.
-                client.unpause_engines()
-                # TODO: The system should not be incorrectly raising a cancelled error here.
-                with pytest.raises(asyncio.CancelledError):
-                    await future
+                assert_state(engine, EngineState.PAUSED)
+
+                # Requests submitted while PAUSED should queue, not complete.
+                paused_futures = [
+                    client.add_request(prompt=p, sampling_params=s) for p, s in requests[3:5]
+                ]
+                # Use asyncio.wait (not wait_for) so futures aren't cancelled.
+                done, pending = await asyncio.wait(paused_futures, timeout=0.5)
+                assert len(done) == 0, "No requests should complete while paused"
+                assert len(pending) == 2
 
+                # UNPAUSE and verify all in-flight requests (pre-pause + paused) complete.
+                client.unpause_engines()
+                await asyncio.wait_for(engine.wait_until(EngineState.RUNNING), timeout=5.0)
+                all_queued = pre_pause_futures + paused_futures
+                results = await asyncio.wait_for(asyncio.gather(*all_queued), timeout=10.0)
+                for result in results:
+                    assert result["status"] == Status.COMPLETED.name
+                assert_state(engine, EngineState.RUNNING)
+
+                # Engine processes new requests normally after unpause.
                 futures = [
-                    client.add_request(prompt=p, sampling_params=s) for p, s in requests[3:4]
+                    client.add_request(prompt=p, sampling_params=s) for p, s in requests[5:7]
                 ]
+                results = await asyncio.wait_for(asyncio.gather(*futures), timeout=5.0)
+                for result in results:
+                    assert result["status"] == Status.COMPLETED.name
+
+                # Suspend.
+                client.pause_engines()
+                await asyncio.wait_for(engine.wait_until(EngineState.PAUSED), timeout=5.0)
+                assert_state(engine, EngineState.PAUSED)
+
+                client.suspend_engines()
+                await asyncio.wait_for(engine.wait_until(EngineState.SUSPENDED), timeout=5.0)
+                assert_state(engine, EngineState.SUSPENDED)
+
+                # Try pausing again and see if it breaks.
+                client.pause_engines()
                 await asyncio.sleep(0.1)
-                try:
-                    await asyncio.wait_for(asyncio.gather(*futures), timeout=0.5)
-                except asyncio.TimeoutError:
-                    pytest.fail("Resumed requests did not complete in time.")
-        except:
-            success = False
+                assert_state(engine, EngineState.SUSPENDED)
+
+                # Try suspending again and see if it breaks.
+                client.pause_engines()
+                await asyncio.sleep(0.1)
+                assert_state(engine, EngineState.SUSPENDED)
+
+                # Resume.
+                client.resume_engines()
+                await asyncio.wait_for(engine.wait_until(EngineState.RESUMED), timeout=5.0)
+                assert_state(engine, EngineState.PAUSED)
+                assert not engine._state_events[EngineState.SUSPENDED].is_set()
+
+                # Engine processes requests after suspend/resume cycle.
+                client.unpause_engines()
+                await asyncio.wait_for(engine.wait_until(EngineState.RUNNING), timeout=5.0)
+
+                futures = [
+                    client.add_request(prompt=p, sampling_params=s) for p, s in requests[7:10]
+                ]
+                results = await asyncio.wait_for(asyncio.gather(*futures), timeout=5.0)
+                for result in results:
+                    assert result["status"] == Status.COMPLETED.name
+
+                # Submit requests that will be cancelled on STOP.
+                client.pause_engines()
+                await asyncio.wait_for(engine.wait_until(EngineState.PAUSED), timeout=5.0)
+                assert_state(engine, EngineState.PAUSED)
+
+                doomed_futures = [
+                    client.add_request(prompt=p, sampling_params=s) for p, s in requests[10:13]
+                ]
+
+            # Synchronize all ranks before STOP.
+            await asyncio.wait_for(
+                asyncio.get_event_loop().run_in_executor(None, torch.distributed.barrier),
+                timeout=30.0,
+            )
+
+            if rank == 0:
+                # Verify doomed futures are still pending.
+                for f in doomed_futures:
+                    assert not f.done(), "Client futures should still be pending"
+                client.stop_engines()
+
+            await asyncio.wait_for(engine.wait_until(EngineState.STOPPED), timeout=60.0)
+            assert_state(engine, EngineState.STOPPED)
+
         finally:
-            try:
-                if torch.distributed.get_rank() == 0:
-                    await asyncio.wait_for(client.stop_engines(), timeout=5.0)
-                    client.stop()
-                await asyncio.wait_for(engine.engine_loop_task, timeout=30.0)
-            except asyncio.TimeoutError:
-                engine.engine_loop_task.cancel()
-        assert success, "Pause/resume test failed."
+            await cleanup_engine(engine, client)
+
+        # cleanup_engine called client.stop() which cancels pending futures.
+        if torch.distributed.get_rank() == 0:
+            for f in doomed_futures:
+                assert f.cancelled(), "Client futures should be cancelled after client.stop()"
 
+    @pytest.mark.flaky
+    @pytest.mark.flaky_in_dev
     @pytest.mark.internal
     @pytest.mark.skipif(not HAVE_ZMQ, reason="pyzmq is required for this test")
     @pytest.mark.asyncio
-    async def test_throughput(self, initialize_model_parallel):
-        """Throughput test with no TP or PP."""
+    async def test_throughput(self, initialize_model_parallel, coordinator):
+        """Throughput benchmark: measures ZMQ packet rate."""
+        _, dp, _, _, _ = initialize_model_parallel
         num_requests = 10**4
         num_iterations = 10
 
+        dp_addr = coordinator
+        port = int(dp_addr.rsplit(":", 1)[-1])
         engine = DummyEngine()
         requests = self.build_requests(num_requests=num_requests)
 
-        start_time = time.time()
-        dp_addr = await engine.start_listening_to_data_parallel_coordinator(
-            inference_coordinator_port=DEFAULT_PORT, launch_inference_coordinator=True
+        await engine.start_listening_to_data_parallel_coordinator(
+            inference_coordinator_port=port, launch_inference_coordinator=False
         )
 
+        # Ensure all engines are registered before submitting requests.
+        await asyncio.get_event_loop().run_in_executor(None, torch.distributed.barrier)
+
+        client = None
         try:
             if torch.distributed.get_rank() == 0:
                 client = InferenceClient(dp_addr)
-                await client.start()
-                init_time = time.time()
+                client.start()
 
+                start_time = time.time()
                 for _ in range(num_iterations):
                     futures = []
-                    for prompt, sampling_params in tqdm(requests, "add_requests"):
+                    for prompt, sampling_params in requests:
                         fut = client.add_request(prompt=prompt, sampling_params=sampling_params)
                         futures.append(fut)
-                    await asyncio.wait_for(asyncio.gather(*futures), timeout=10.0)
-                done_time = time.time()
+                    await asyncio.wait_for(asyncio.gather(*futures), timeout=30.0)
+                elapsed_ms = (time.time() - start_time) * 1e3
+                total = num_requests * num_iterations // dp
+                print(
+                    f"ZMQ throughput: {total / elapsed_ms:.2f} requests/ms "
+                    f"({total} reqs in {elapsed_ms:.0f} ms)"
+                )
+            await asyncio.get_event_loop().run_in_executor(None, torch.distributed.barrier)
         finally:
-            if torch.distributed.get_rank() == 0:
-                await asyncio.wait_for(client.stop_engines(), timeout=10.0)
-                client.stop()
-            try:
-                await asyncio.wait_for(engine.engine_loop_task, timeout=30.0)
-            except asyncio.TimeoutError:
-                engine.engine_loop_task.cancel()
-
-        stop_time = time.time()
-
-        flags = torch.tensor([1, 1, 1], dtype=torch.int, device=torch.cuda.current_device())
-
-        init_duration = golden_init_duration = None
-        run_duration = golden_run_duration = None
-        stop_duration = golden_stop_duration = None
-
-        if torch.distributed.get_rank() == 0:
-            init_duration = (init_time - start_time) * 10**3
-            golden_init_duration = 6974.43  # ms
-            run_duration = (done_time - init_time) * 10**3
-            golden_run_duration = 4392.63  # ms
-            stop_duration = (stop_time - done_time) * 10**3
-            golden_stop_duration = 931.49  # ms
-
-            def clamp_to_golden_value(value, golden_value, delta=0.1):
-                return value > golden_value * (1 - delta) and value < golden_value * (1 + delta)
-
-            if not clamp_to_golden_value(init_duration, golden_init_duration, delta=0.5):
-                flags[0] = 0
-            if not clamp_to_golden_value(run_duration, golden_run_duration, delta=0.2):
-                flags[1] = 0
-            if not clamp_to_golden_value(stop_duration, golden_stop_duration, delta=1.0):
-                flags[2] = 0
-
-        # Synchronize results
-        torch.distributed.broadcast(flags, src=0)
-
-        if torch.distributed.get_rank() == 0:
-            print(f"Initialization time: {init_duration:.2f} ms")
-            print(f"Run time: {run_duration:.2f} ms")
-            print(f"Stop time: {stop_duration:.2f} ms")
-
-            assert flags[0].item() == 1, (
-                f"WARNING: Init duration {init_duration:.2f}s deviates from "
-                f"golden value {golden_init_duration:.2f}s"
-            )
-            assert flags[1].item() == 1, (
-                f"WARNING: Run duration {run_duration:.2f}s deviates from "
-                f"golden value {golden_run_duration:.2f}s"
-            )
-            assert flags[2].item() == 1, (
-                f"WARNING: Stop duration {stop_duration:.2f}s deviates from "
-                f"golden value {golden_stop_duration:.2f}s"
-            )
-
-            print(
-                f"ZMQ throughput is approximately "
-                f"{num_requests * num_iterations / run_duration:.2f} "
-                f"requests/ms"
-            )
-        else:
-            assert flags[0].item() == 1
-            assert flags[1].item() == 1
-            assert flags[2].item() == 1
+            await cleanup_engine(engine, client)
diff --git a/tests/unit_tests/inference/test_dynamic_prefix_caching_coordinator.py b/tests/unit_tests/inference/test_dynamic_prefix_caching_coordinator.py
new file mode 100644
index 00000000000..e8d2bc728ef
--- /dev/null
+++ b/tests/unit_tests/inference/test_dynamic_prefix_caching_coordinator.py
@@ -0,0 +1,571 @@
+# Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+
+"""Tests for prefix-cache-aware coordinator routing.
+
+Validates that the DataParallelInferenceCoordinator correctly computes block
+hashes from prompts, routes requests to the DP rank with the longest consecutive
+prefix match, and maintains per-rank shadow state (cached hashes and timestamps).
+"""
+
+import asyncio
+import itertools
+from collections import deque
+from typing import Dict, Optional
+from unittest.mock import MagicMock
+
+import msgpack
+import pytest
+import torch
+
+from megatron.core.inference.config import PrefixCachingCoordinatorPolicy
+from megatron.core.inference.data_parallel_inference_coordinator import (
+    DataParallelInferenceCoordinator,
+)
+from megatron.core.inference.engines.dynamic_engine import DynamicInferenceEngine, RequestEntry
+from megatron.core.inference.headers import Headers
+from megatron.core.inference.inference_client import InferenceClient
+from megatron.core.inference.inference_request import (
+    DynamicInferenceRequest,
+    DynamicInferenceRequestRecord,
+    Status,
+    compute_block_hashes_batched,
+)
+from megatron.core.inference.sampling_params import SamplingParams
+from megatron.core.process_groups_config import ProcessGroupCollection
+from megatron.core.utils import get_asyncio_loop
+from tests.unit_tests.test_utilities import Utils
+
+try:
+    import zmq
+
+    HAVE_ZMQ = True
+except ImportError:
+    HAVE_ZMQ = False
+
+
+# ============================================================================
+# Shared fixtures and helpers
+# ============================================================================
+
+DEFAULT_PORT = 46582
+ZMQ_FLAKY_SHUTDOWN = True
+
+BLOCK_SIZE = 4
+
+
+class DummyTokenizer:
+    """Dummy tokenizer that splits on whitespace and converts to ints."""
+
+    def __init__(self, vocab_size: int = 10, bos: int | None = None, eod: int = 0, pad: int = 0):
+        self.vocab_size = vocab_size
+        self.bos = bos
+        self.eod = eod
+        self.pad = pad
+
+    def tokenize(self, prompt):
+        if isinstance(prompt, str):
+            return [int(tok) % self.vocab_size for tok in prompt.strip().split()]
+        return list(prompt)
+
+    def detokenize(self, tokens, skip_special_tokens: bool = False):
+        if isinstance(tokens, torch.Tensor):
+            tokens = tokens.tolist()
+        if skip_special_tokens and self.eod in tokens:
+            tokens = [tok for tok in tokens if tok != self.eod]
+        return " ".join(str(tok) for tok in tokens)
+
+
+class DummyContext:
+    """Dummy inference context."""
+
+    def __init__(self):
+        self.active_cnt = 0
+
+    def get_active_request_count(self) -> int:
+        return self.active_cnt
+
+
+class DummyController:
+    """Dummy inference controller."""
+
+    def __init__(self):
+        self.tokenizer = DummyTokenizer()
+
+    def dummy_forward(self):
+        pass
+
+
+class DummyEngine(DynamicInferenceEngine):
+    """Dummy inference engine that only implements coordinator-related methods."""
+
+    def __init__(self):
+        self.waiting_request_ids = deque()
+        self.requests: Dict[int, RequestEntry] = {}
+        self.suspend_signal = False
+        self.is_suspended = False
+        self._loop = get_asyncio_loop()
+        self.context = DummyContext()
+        self.controller = DummyController()
+        self.running = asyncio.Event()
+        self.paused = asyncio.Event()
+        self.stopped = asyncio.Event()
+        self.pending_microbatch = deque()
+        self.received_pause: bool = False
+        self.received_stop: bool = False
+        self.pg_collection = ProcessGroupCollection.use_mpu_process_groups()
+        self.rank = torch.distributed.get_rank()
+
+    def add_request(
+        self, request_id: int, prompt: str, sampling_params: Optional[SamplingParams] = None
+    ) -> asyncio.Future[DynamicInferenceRequestRecord]:
+        self.requests[request_id] = RequestEntry(
+            record=DynamicInferenceRequestRecord.from_request(
+                DynamicInferenceRequest(
+                    prompt=prompt,
+                    request_id=request_id,
+                    sampling_params=sampling_params,
+                    status=Status.WAITING_IN_QUEUE,
+                )
+            ),
+            future=self._loop.create_future(),
+        )
+        self.waiting_request_ids.append(request_id)
+        return self.requests[request_id].future
+
+    async def async_step(self, *, verbose: Optional[bool] = False) -> Dict:
+        finished_request_records = []
+        to_remove = []
+        for request_id, entry in self.requests.items():
+            request = entry.record[-1]
+            if request.status == Status.ACTIVE_AND_GENERATING_TOKENS:
+                request.sampling_params.num_tokens_to_generate -= 1
+                if request.sampling_params.num_tokens_to_generate > 0:
+                    continue
+                request.status = Status.COMPLETED
+                self.context.active_cnt -= 1
+                finished_request_records.append(entry.record)
+                entry.future.set_result(entry.record)
+                to_remove.append(request_id)
+                if self.is_mp_coordinator:
+                    payload = msgpack.packb(
+                        [Headers.ENGINE_REPLY.value, [entry.record.serialize()]], use_bin_type=True
+                    )
+                    self.socket_for_receiving_requests.send(payload)
+
+        for request_id in to_remove:
+            del self.requests[request_id]
+
+        active_request_ids = []
+        while self.waiting_request_ids:
+            request_id = self.waiting_request_ids.popleft()
+            record = self.requests[request_id].record
+            record[-1].status = Status.ACTIVE_AND_GENERATING_TOKENS
+            self.context.active_cnt += 1
+            active_request_ids.append(request_id)
+
+        return {
+            "active_request_ids": active_request_ids,
+            "finished_request_records": finished_request_records,
+            "step_time": 0.01,
+            "cuda_graph_request_count": 1,
+        }
+
+
+@pytest.fixture
+def initialize_model_parallel(request, monkeypatch):
+    monkeypatch.setenv("CUDA_DEVICE_MAX_CONNECTIONS", "1")
+    tp, pp, ep = getattr(request, "param", (1, 1, 1))
+    world_size = Utils.world_size
+    Utils.initialize_model_parallel(
+        tensor_model_parallel_size=tp,
+        pipeline_model_parallel_size=pp,
+        expert_model_parallel_size=ep,
+    )
+    dp = world_size // (tp * pp * ep)
+    yield world_size, dp, tp, pp, ep
+    Utils.destroy_model_parallel()
+
+
+def make_coordinator_direct(
+    data_parallel_size=2,
+    block_size_tokens=BLOCK_SIZE,
+    enable_prefix_caching=True,
+    deterministic_mode=True,
+):
+    """Create a coordinator with mock ZMQ, for unit testing routing logic.
+
+    Returns the coordinator instance with fake rank identities.
+    """
+    coordinator = object.__new__(DataParallelInferenceCoordinator)
+    coordinator.tokenizer = DummyTokenizer()
+    coordinator.data_parallel_size = data_parallel_size
+    coordinator.block_size_tokens = block_size_tokens
+    coordinator.enable_prefix_caching = enable_prefix_caching
+    coordinator.prefix_caching_coordinator_policy = PrefixCachingCoordinatorPolicy.LONGEST_PREFIX
+
+    # Create fake rank identities.
+    coordinator.identities_of_data_parallel_ranks = deque(
+        [f"rank_{i}".encode() for i in range(data_parallel_size)]
+    )
+    if deterministic_mode:
+        coordinator.identities_of_data_parallel_ranks = deque(
+            sorted(coordinator.identities_of_data_parallel_ranks)
+        )
+    coordinator.data_parallel_rank_iterator = itertools.cycle(
+        coordinator.identities_of_data_parallel_ranks
+    )
+
+    coordinator.hash_to_rank_info = {}
+    coordinator._round_robin_idx = 0
+    coordinator._assignment_counter = 0
+
+    return coordinator
+
+
+# ============================================================================
+# Test classes
+# ============================================================================
+
+
+class TestCoordinatorHashComputation:
+    """Test that the coordinator computes correct block hashes from prompts."""
+
+    def test_hash_from_token_list(self):
+        """Hashes from a list of token IDs match compute_block_hashes_batched."""
+        coordinator = make_coordinator_direct()
+        tokens = [1, 2, 3, 4, 5, 6, 7, 8]
+        hashes = coordinator.compute_request_hashes(tokens)
+
+        expected = compute_block_hashes_batched(torch.tensor(tokens, dtype=torch.int64), BLOCK_SIZE)
+        assert hashes == expected
+        assert len(hashes) == 2  # 8 tokens / block_size 4 = 2 blocks
+
+    def test_hash_from_string_prompt(self):
+        """Hashes from a string prompt match hashes from tokenized form."""
+        coordinator = make_coordinator_direct()
+        prompt = "1 2 3 4 5 6 7 8"
+        hashes_from_str = coordinator.compute_request_hashes(prompt)
+
+        # DummyTokenizer tokenizes "1 2 3 4 5 6 7 8" -> [1, 2, 3, 4, 5, 6, 7, 8]
+        hashes_from_list = coordinator.compute_request_hashes([1, 2, 3, 4, 5, 6, 7, 8])
+        assert hashes_from_str == hashes_from_list
+
+    def test_hash_empty_when_disabled(self):
+        """Returns empty list when prefix caching is disabled."""
+        coordinator = make_coordinator_direct(enable_prefix_caching=False)
+        hashes = coordinator.compute_request_hashes([1, 2, 3, 4])
+        assert hashes == []
+
+    def test_hash_empty_when_no_block_size(self):
+        """Returns empty list when block_size_tokens is None."""
+        coordinator = make_coordinator_direct(block_size_tokens=None)
+        hashes = coordinator.compute_request_hashes([1, 2, 3, 4])
+        assert hashes == []
+
+    def test_hash_partial_block_ignored(self):
+        """Tokens that don't fill a complete block produce no hash."""
+        coordinator = make_coordinator_direct()
+        hashes = coordinator.compute_request_hashes([1, 2, 3])
+        assert hashes == []
+
+    def test_hash_deterministic(self):
+        """Same tokens always produce the same hashes."""
+        coordinator = make_coordinator_direct()
+        tokens = [10, 20, 30, 40, 50, 60, 70, 80]
+        h1 = coordinator.compute_request_hashes(tokens)
+        h2 = coordinator.compute_request_hashes(tokens)
+        assert h1 == h2
+
+    def test_hash_parent_chaining(self):
+        """Different prefixes produce different hashes even for same block tokens."""
+        coordinator = make_coordinator_direct()
+        # Two prompts share tokens [5,6,7,8] in block 2, but differ in block 1.
+        h1 = coordinator.compute_request_hashes([1, 2, 3, 4, 5, 6, 7, 8])
+        h2 = coordinator.compute_request_hashes([9, 8, 7, 6, 5, 6, 7, 8])
+
+        # Block 1 hashes differ.
+        assert h1[0] != h2[0]
+        # Block 2 hashes also differ due to parent chaining.
+        assert h1[1] != h2[1]
+
+
+class TestCoordinatorPrefixRouting:
+    """Test routing decisions based on prefix cache affinity."""
+
+    def test_no_match_uses_round_robin(self):
+        """When no rank has matching hashes, falls back to round-robin."""
+        coordinator = make_coordinator_direct()
+        hashes = coordinator.compute_request_hashes([1, 2, 3, 4])
+
+        rank1 = coordinator.get_best_data_parallel_rank(hashes)
+        rank2 = coordinator.get_best_data_parallel_rank(hashes)
+        # Round-robin cycles through ranks.
+        assert rank1 != rank2
+
+    def test_routes_to_rank_with_longest_match(self):
+        """Request is routed to the rank with the longest consecutive prefix match."""
+        coordinator = make_coordinator_direct()
+        tokens = [1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2]
+        hashes = coordinator.compute_request_hashes(tokens)
+        assert len(hashes) == 3  # 12 tokens / 4 = 3 blocks
+
+        rank_0 = coordinator.identities_of_data_parallel_ranks[0]
+        rank_1 = coordinator.identities_of_data_parallel_ranks[1]
+
+        # rank_0 has first block only.
+        coordinator.hash_to_rank_info.setdefault(hashes[0], {})[rank_0] = 1
+
+        # rank_1 has first two blocks.
+        coordinator.hash_to_rank_info.setdefault(hashes[0], {})[rank_1] = 1
+        coordinator.hash_to_rank_info.setdefault(hashes[1], {})[rank_1] = 1
+
+        selected = coordinator.get_best_data_parallel_rank(hashes)
+        assert selected == rank_1
+
+    def test_recency_tiebreaker(self):
+        """When two ranks tie on match length, prefer the more recent one."""
+        coordinator = make_coordinator_direct()
+        tokens = [1, 2, 3, 4, 5, 6, 7, 8]
+        hashes = coordinator.compute_request_hashes(tokens)
+
+        rank_0 = coordinator.identities_of_data_parallel_ranks[0]
+        rank_1 = coordinator.identities_of_data_parallel_ranks[1]
+
+        # Both ranks have both blocks, but rank_1 has higher timestamps.
+        for h in hashes:
+            coordinator.hash_to_rank_info.setdefault(h, {})[rank_0] = 1
+            coordinator.hash_to_rank_info.setdefault(h, {})[rank_1] = 5
+
+        selected = coordinator.get_best_data_parallel_rank(hashes)
+        assert selected == rank_1
+
+    def test_empty_hashes_uses_round_robin(self):
+        """Empty hash list falls back to round-robin."""
+        coordinator = make_coordinator_direct()
+        rank1 = coordinator.get_best_data_parallel_rank([])
+        rank2 = coordinator.get_best_data_parallel_rank([])
+        assert rank1 != rank2
+
+    def test_disabled_prefix_caching_uses_round_robin(self):
+        """With prefix caching disabled, always uses round-robin."""
+        coordinator = make_coordinator_direct(enable_prefix_caching=False)
+        rank1 = coordinator.get_best_data_parallel_rank([1, 2, 3])
+        rank2 = coordinator.get_best_data_parallel_rank([1, 2, 3])
+        assert rank1 != rank2
+
+
+class TestCoordinatorShadowState:
+    """Test that shadow state (rank_cached_hashes, timestamps) is updated correctly."""
+
+    def test_update_rank_hashes_adds_to_set(self):
+        """_update_rank_hashes adds hashes to the rank's set."""
+        coordinator = make_coordinator_direct()
+        rank_0 = coordinator.identities_of_data_parallel_ranks[0]
+
+        coordinator._update_rank_hashes(rank_0, [100, 200, 300])
+        assert all(rank_0 in coordinator.hash_to_rank_info[h] for h in [100, 200, 300])
+
+    def test_update_rank_hashes_increments_counter(self):
+        """Each call to _update_rank_hashes increments the assignment counter."""
+        coordinator = make_coordinator_direct()
+        rank_0 = coordinator.identities_of_data_parallel_ranks[0]
+
+        assert coordinator._assignment_counter == 0
+        coordinator._update_rank_hashes(rank_0, [100])
+        assert coordinator._assignment_counter == 1
+        coordinator._update_rank_hashes(rank_0, [200])
+        assert coordinator._assignment_counter == 2
+
+    def test_timestamps_updated_on_reassignment(self):
+        """Re-assigning a hash to the same rank updates its timestamp."""
+        coordinator = make_coordinator_direct()
+        rank_0 = coordinator.identities_of_data_parallel_ranks[0]
+
+        coordinator._update_rank_hashes(rank_0, [100])
+        ts1 = coordinator.hash_to_rank_info[100][rank_0]
+
+        coordinator._update_rank_hashes(rank_0, [100])
+        ts2 = coordinator.hash_to_rank_info[100][rank_0]
+
+        assert ts2 > ts1
+
+    def test_multiple_requests_accumulate_hashes(self):
+        """Multiple requests to the same rank accumulate their hashes."""
+        coordinator = make_coordinator_direct()
+        rank_0 = coordinator.identities_of_data_parallel_ranks[0]
+
+        coordinator._update_rank_hashes(rank_0, [10, 20])
+        coordinator._update_rank_hashes(rank_0, [30, 40])
+        assert all(rank_0 in coordinator.hash_to_rank_info[h] for h in [10, 20, 30, 40])
+
+    def test_hash_can_appear_in_multiple_ranks(self):
+        """The same hash can be owned by multiple ranks."""
+        coordinator = make_coordinator_direct()
+        rank_0 = coordinator.identities_of_data_parallel_ranks[0]
+        rank_1 = coordinator.identities_of_data_parallel_ranks[1]
+
+        coordinator._update_rank_hashes(rank_0, [100])
+        coordinator._update_rank_hashes(rank_1, [100])
+
+        assert rank_0 in coordinator.hash_to_rank_info[100]
+        assert rank_1 in coordinator.hash_to_rank_info[100]
+
+    def test_routing_then_state_update_flow(self):
+        """Full flow: compute hashes, route, update state, then re-route to same rank."""
+        coordinator = make_coordinator_direct()
+
+        tokens = [1, 2, 3, 4, 5, 6, 7, 8]
+        hashes = coordinator.compute_request_hashes(tokens)
+
+        # First request: no matches, round-robin.
+        rank = coordinator.get_best_data_parallel_rank(hashes)
+        coordinator._update_rank_hashes(rank, hashes)
+
+        # Second request with same tokens: should go to same rank.
+        rank2 = coordinator.get_best_data_parallel_rank(hashes)
+        assert rank2 == rank
+
+
+@pytest.mark.skipif(ZMQ_FLAKY_SHUTDOWN, reason="ZMQ shutdown is flaky")
+class TestCoordinatorEndToEnd:
+    """End-to-end test with real ZMQ sockets and DummyEngines."""
+
+    async def run_coordinator_test(
+        self, requests, block_size_tokens=BLOCK_SIZE, enable_prefix_caching=True
+    ):
+        """Submit requests through a real coordinator and return results."""
+        engine = DummyEngine()
+
+        dp_addr = await engine.start_listening_to_data_parallel_coordinator(
+            inference_coordinator_port=DEFAULT_PORT, launch_inference_coordinator=True
+        )
+
+        try:
+            if torch.distributed.get_rank() == 0:
+                client = InferenceClient(dp_addr)
+                client.start()
+
+                futures = [
+                    client.add_request(prompt=prompt, sampling_params=params)
+                    for prompt, params in requests
+                ]
+                results = await asyncio.wait_for(asyncio.gather(*futures), timeout=10.0)
+
+                for record in results:
+                    assert record[-1].status == Status.COMPLETED
+        finally:
+            if torch.distributed.get_rank() == 0:
+                await asyncio.wait_for(client.stop_engines(), timeout=10.0)
+                client.stop()
+            try:
+                await asyncio.wait_for(engine.engine_loop_task, timeout=30.0)
+            except asyncio.TimeoutError:
+                engine.engine_loop_task.cancel()
+
+    @pytest.mark.internal
+    @pytest.mark.skipif(not HAVE_ZMQ, reason="pyzmq is required")
+    @pytest.mark.asyncio
+    async def test_shared_prefix_requests(self, initialize_model_parallel):
+        """Requests with shared prefixes complete successfully through the coordinator."""
+        requests = [
+            ("1 2 3 4 5 6 7 8", SamplingParams(num_tokens_to_generate=2)),
+            ("1 2 3 4 9 8 7 6", SamplingParams(num_tokens_to_generate=2)),
+            ("1 2 3 4 5 6 7 8", SamplingParams(num_tokens_to_generate=2)),
+        ]
+        await self.run_coordinator_test(requests)
+
+
+def make_first_prefix_block_coordinator(**kwargs):
+    """Create a coordinator configured with FIRST_PREFIX_BLOCK policy."""
+    coordinator = make_coordinator_direct(**kwargs)
+    coordinator.prefix_caching_coordinator_policy = (
+        PrefixCachingCoordinatorPolicy.FIRST_PREFIX_BLOCK
+    )
+    return coordinator
+
+
+class TestFirstPrefixBlockRouting:
+    """Test routing decisions using the FIRST_PREFIX_BLOCK policy."""
+
+    def test_first_block_match_routes_to_rank(self):
+        """Request is routed to the rank that has the first block cached."""
+        coordinator = make_first_prefix_block_coordinator()
+        tokens = [1, 2, 3, 4, 5, 6, 7, 8]
+        hashes = coordinator.compute_request_hashes(tokens)
+
+        rank_0 = coordinator.identities_of_data_parallel_ranks[0]
+        rank_1 = coordinator.identities_of_data_parallel_ranks[1]
+
+        # Only rank_1 has the first block.
+        coordinator.hash_to_rank_info.setdefault(hashes[0], {})[rank_1] = 1
+
+        selected = coordinator.get_best_data_parallel_rank(hashes[:1])
+        assert selected == rank_1
+
+    def test_first_block_ignores_longer_match(self):
+        """Rank with more blocks cached is not preferred; only first block matters."""
+        coordinator = make_first_prefix_block_coordinator()
+        tokens = [1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2]
+        hashes = coordinator.compute_request_hashes(tokens)
+        assert len(hashes) == 3
+
+        rank_0 = coordinator.identities_of_data_parallel_ranks[0]
+        rank_1 = coordinator.identities_of_data_parallel_ranks[1]
+
+        # rank_0 has first block only, with higher timestamp.
+        coordinator.hash_to_rank_info.setdefault(hashes[0], {})[rank_0] = 10
+
+        # rank_1 has all three blocks, but lower timestamp on first block.
+        for h in hashes:
+            coordinator.hash_to_rank_info.setdefault(h, {})[rank_1] = 1
+
+        # rank_0 wins because it has higher recency on the first block.
+        # Caller truncates to [:1] before calling get_best_data_parallel_rank.
+        selected = coordinator.get_best_data_parallel_rank(hashes[:1])
+        assert selected == rank_0
+
+    def test_first_block_recency_tiebreaker(self):
+        """When multiple ranks have the first block, higher timestamp wins."""
+        coordinator = make_first_prefix_block_coordinator()
+        tokens = [1, 2, 3, 4, 5, 6, 7, 8]
+        hashes = coordinator.compute_request_hashes(tokens)
+
+        rank_0 = coordinator.identities_of_data_parallel_ranks[0]
+        rank_1 = coordinator.identities_of_data_parallel_ranks[1]
+
+        # Both ranks have the first block.
+        coordinator.hash_to_rank_info.setdefault(hashes[0], {})[rank_0] = 3
+        coordinator.hash_to_rank_info.setdefault(hashes[0], {})[rank_1] = 7
+
+        selected = coordinator.get_best_data_parallel_rank(hashes[:1])
+        assert selected == rank_1
+
+    def test_no_first_block_match_uses_round_robin(self):
+        """Falls back to round-robin when no rank has the first block."""
+        coordinator = make_first_prefix_block_coordinator()
+        tokens = [1, 2, 3, 4, 5, 6, 7, 8]
+        hashes = coordinator.compute_request_hashes(tokens)
+
+        rank_0 = coordinator.identities_of_data_parallel_ranks[0]
+
+        # rank_0 has block 1 (second block), but not block 0.
+        coordinator.hash_to_rank_info.setdefault(hashes[1], {})[rank_0] = 1
+
+        # No rank has the first block, so round-robin.
+        r1 = coordinator.get_best_data_parallel_rank(hashes[:1])
+        r2 = coordinator.get_best_data_parallel_rank(hashes[:1])
+        assert r1 != r2
+
+    def test_first_block_policy_with_single_block_prompt(self):
+        """Works correctly with a prompt that has only one block."""
+        coordinator = make_first_prefix_block_coordinator()
+        tokens = [1, 2, 3, 4]
+        hashes = coordinator.compute_request_hashes(tokens)
+        assert len(hashes) == 1
+
+        rank_1 = coordinator.identities_of_data_parallel_ranks[1]
+
+        coordinator.hash_to_rank_info.setdefault(hashes[0], {})[rank_1] = 1
+
+        selected = coordinator.get_best_data_parallel_rank(hashes[:1])
+        assert selected == rank_1
diff --git a/tests/unit_tests/inference/test_moe_inference.py b/tests/unit_tests/inference/test_moe_inference.py
new file mode 100644
index 00000000000..4d515db2d30
--- /dev/null
+++ b/tests/unit_tests/inference/test_moe_inference.py
@@ -0,0 +1,382 @@
+# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
+
+"""Unit tests for inference-optimized MoE components.
+
+Config is modeled after nanov3 (Nemotron-6 3B Hybrid MoE) with smaller
+dimensions for fast unit test execution:
+- squared_relu activation (not swiglu/gated)
+- sigmoid router score function with expert bias
+- topk=6, topk_scaling_factor=2.5
+- shared experts
+"""
+
+import pytest
+import torch
+
+from megatron.core.activations import squared_relu
+from megatron.core.inference.communication.torch_symm_triton import are_tensors_nvls_eligible
+from megatron.core.transformer.transformer_config import TransformerConfig
+from megatron.core.utils import is_te_min_version, is_torch_min_version
+from megatron.training.initialize import _set_random_seed
+from tests.unit_tests.test_utilities import Utils
+
+# Reusable skip decorators
+requires_te = pytest.mark.skipif(
+    not is_te_min_version("1.7.0.dev0"), reason="Requires transformer-engine >= 1.7.0"
+)
+requires_torch_grouped_mm = pytest.mark.skipif(
+    not is_torch_min_version("2.10") or not hasattr(torch, '_grouped_mm'),
+    reason="Requires PyTorch >= 2.10 with torch._grouped_mm",
+)
+
+# ──────────────────────────────────────────────────────────────────────
+# NanoV3-like config (scaled down from 2688→128 hidden, 128→8 experts)
+# ──────────────────────────────────────────────────────────────────────
+
+NANOV3_BASE = dict(
+    num_layers=1,
+    hidden_size=128,
+    ffn_hidden_size=128,
+    num_attention_heads=4,
+    num_query_groups=2,
+    num_moe_experts=8,
+    moe_ffn_hidden_size=128,
+    moe_router_topk=6,
+    moe_router_score_function="sigmoid",
+    moe_router_enable_expert_bias=True,
+    moe_router_topk_scaling_factor=2.5,
+    moe_shared_expert_intermediate_size=256,
+    moe_router_dtype='fp32',
+    moe_shared_expert_overlap=False,
+    moe_grouped_gemm=True,
+    moe_token_dispatcher_type="alltoall",
+    moe_aux_loss_coeff=0.01,
+    activation_func=squared_relu,
+    normalization="RMSNorm",
+    add_bias_linear=False,
+    bf16=True,
+    params_dtype=torch.bfloat16,
+    transformer_impl="inference_optimized",
+)
+
+
+def _make_base_config(**overrides):
+    """Create a TransformerConfig with nanov3-like defaults."""
+    params = {**NANOV3_BASE, **overrides}
+    return TransformerConfig(**params)
+
+
+# ──────────────────────────────────────────────────────────────────────
+# InferenceTopKRouter
+# ──────────────────────────────────────────────────────────────────────
+
+
+@pytest.mark.internal
+class TestInferenceTopKRouter:
+
+    @classmethod
+    def setup_class(cls):
+        Utils.initialize_model_parallel(1, 1)
+        _set_random_seed(seed_=123, data_parallel_random_init=False)
+
+    @classmethod
+    def teardown_class(cls):
+        Utils.destroy_model_parallel()
+
+    def _make_router(self, **config_overrides):
+        from megatron.core.transformer.moe.moe_utils import get_default_pg_collection
+        from megatron.core.transformer.moe.router import InferenceTopKRouter
+
+        config = _make_base_config(**config_overrides)
+        return (
+            InferenceTopKRouter(config=config, pg_collection=get_default_pg_collection())
+            .cuda()
+            .to(torch.bfloat16)
+        )
+
+    def test_init_rejects_num_groups(self):
+        """InferenceTopKRouter requires moe_router_num_groups=None."""
+        with pytest.raises(AssertionError, match="moe_router_num_groups"):
+            self._make_router(moe_router_num_groups=2)
+
+    def test_config_rejects_non_fp32_router_dtype(self):
+        """inference_optimized config requires moe_router_dtype='fp32'."""
+        with pytest.raises(ValueError, match="moe-router-dtype"):
+            _make_base_config(
+                transformer_impl="inference_optimized", add_qkv_bias=False, moe_router_dtype=None
+            )
+
+    @pytest.mark.parametrize("score_fn", ["none", "invalid"])
+    def test_init_rejects_unsupported_score_function(self, score_fn):
+        """InferenceTopKRouter requires sigmoid or softmax score function."""
+        with pytest.raises(AssertionError, match="moe_router_score_function"):
+            self._make_router(
+                moe_router_score_function=score_fn, moe_router_enable_expert_bias=False
+            )
+
+    @pytest.mark.parametrize("score_fn", ["sigmoid", "softmax"])
+    def test_init_accepts_valid_score_function(self, score_fn):
+        """InferenceTopKRouter accepts sigmoid and softmax."""
+        # Expert bias only valid with sigmoid; disable it for softmax
+        enable_bias = score_fn == "sigmoid"
+        router = self._make_router(
+            moe_router_score_function=score_fn, moe_router_enable_expert_bias=enable_bias
+        )
+        assert router is not None
+
+    def test_set_unset_inference_mode(self):
+        """Toggle is_inference_cuda_graphed_iteration flag."""
+        router = self._make_router()
+        assert not router.is_inference_cuda_graphed_iteration
+
+        router.set_inference_cuda_graphed_iteration()
+        assert router.is_inference_cuda_graphed_iteration
+
+        router.unset_inference_cuda_graphed_iteration()
+        assert not router.is_inference_cuda_graphed_iteration
+
+    def test_training_mode_forward_returns_sparse(self):
+        """In training mode, forward delegates to parent and returns sparse tensors."""
+        router = self._make_router()
+        router.train()
+        num_tokens = 16
+        num_experts = NANOV3_BASE["num_moe_experts"]
+
+        input_tensor = torch.randn(
+            num_tokens, NANOV3_BASE["hidden_size"], device="cuda", dtype=torch.bfloat16
+        )
+        probs, routing_map = router(input_tensor)
+
+        # Parent TopKRouter returns [num_tokens, num_experts] sparse routing_map
+        assert routing_map.shape == (num_tokens, num_experts)
+
+    def test_inference_vs_training_selects_same_experts(self):
+        """Inference and training modes should select the same top-k experts."""
+        router = self._make_router()
+        num_tokens = 16
+        topk = NANOV3_BASE["moe_router_topk"]
+
+        input_tensor = torch.randn(
+            num_tokens, NANOV3_BASE["hidden_size"], device="cuda", dtype=torch.bfloat16
+        )
+
+        # Training mode: get routing_map (sparse) and extract top expert indices
+        router.train()
+        _, routing_map = router(input_tensor.clone())
+        # routing_map is [num_tokens, num_experts] bool
+        training_experts = set()
+        for i in range(num_tokens):
+            experts_for_token = routing_map[i].nonzero(as_tuple=True)[0]
+            for e in experts_for_token:
+                training_experts.add((i, e.item()))
+
+        # Inference mode: get top_indices (dense)
+        router.eval()
+        router.set_inference_cuda_graphed_iteration()
+        _, top_indices = router(input_tensor.clone())
+
+        inference_experts = set()
+        for i in range(num_tokens):
+            for k in range(topk):
+                inference_experts.add((i, top_indices[i, k].item()))
+
+        # Same expert selections
+        assert training_experts == inference_experts
+
+
+# ──────────────────────────────────────────────────────────────────────
+# InferenceCUDAGraphTokenDispatcher
+# ──────────────────────────────────────────────────────────────────────
+
+
+@pytest.mark.internal
+class TestInferenceCUDAGraphTokenDispatcher:
+
+    @classmethod
+    def setup_class(cls):
+        from megatron.core.parallel_state import _set_global_symmetric_memory_buffer
+
+        Utils.initialize_model_parallel(1, 1, expert_model_parallel_size=Utils.world_size)
+        _set_random_seed(seed_=123, data_parallel_random_init=False)
+        _set_global_symmetric_memory_buffer()
+
+    @classmethod
+    def teardown_class(cls):
+        from megatron.core.parallel_state import destroy_global_symmetric_memory_buffer
+
+        destroy_global_symmetric_memory_buffer()
+        Utils.destroy_model_parallel()
+
+    def _make_dispatcher(self, **config_overrides):
+        from megatron.core.transformer.moe.moe_utils import get_default_pg_collection
+        from megatron.core.transformer.moe.token_dispatcher_inference import (
+            InferenceCUDAGraphTokenDispatcher,
+        )
+
+        config_overrides.setdefault("expert_model_parallel_size", Utils.world_size)
+        config = _make_base_config(**config_overrides)
+        num_local_experts = config.num_moe_experts // Utils.world_size
+        ep_rank = torch.distributed.get_rank() if Utils.world_size > 1 else 0
+        local_expert_indices = [ep_rank * num_local_experts + i for i in range(num_local_experts)]
+
+        return InferenceCUDAGraphTokenDispatcher(
+            num_local_experts=num_local_experts,
+            local_expert_indices=local_expert_indices,
+            config=config,
+            pg_collection=get_default_pg_collection(),
+        )
+
+    def test_init(self):
+        """Dispatcher can be constructed with nanov3-like config and EP=world_size."""
+        dispatcher = self._make_dispatcher()
+        assert dispatcher.topk == NANOV3_BASE["moe_router_topk"]
+        assert dispatcher.ep_size == Utils.world_size
+
+    def test_symmetric_memory_buffer_initialized(self):
+        """EP symmetric memory buffer is accessible after _set_global_symmetric_memory_buffer."""
+        from megatron.core.parallel_state import get_global_symmetric_memory_buffer_ep
+
+        buf = get_global_symmetric_memory_buffer_ep()
+        assert buf is not None
+
+    @pytest.mark.parametrize("seed", [42, 123, 7])
+    @pytest.mark.parametrize(
+        "num_local_tokens",
+        [
+            1,
+            2,
+            4,
+            8,
+            16,
+            24,
+            32,
+            40,
+            48,
+            56,
+            64,
+            72,
+            80,
+            88,
+            96,
+            104,
+            112,
+            120,
+            128,
+            136,
+            144,
+            152,
+            160,
+            168,
+            176,
+            184,
+            192,
+            200,
+            208,
+            216,
+            224,
+            232,
+            240,
+            248,
+            256,
+            272,
+            288,
+            304,
+            320,
+            336,
+            352,
+            368,
+            384,
+            400,
+            416,
+            432,
+            448,
+            464,
+            480,
+            496,
+            512,
+        ],
+    )
+    def test_cuda_graph_dispatch_combine(self, num_local_tokens, seed):
+        """Dispatch+combine can be captured in a CUDA graph and replayed.
+        Creates global buffers, shards per rank, and verifies:
+        - NVLS AllGather output matches the full globalwol buffer
+        - NVLS ReduceScatter output matches fp32-accumulated reference
+        All tensor byte sizes are 128-bit aligned for NVLS eligibility.
+        """
+
+        torch.manual_seed(seed)
+        torch.cuda.manual_seed(seed)
+
+        dispatcher = self._make_dispatcher()
+        ep_size = dispatcher.ep_size
+        hidden_size = NANOV3_BASE["hidden_size"]
+        topk = NANOV3_BASE["moe_router_topk"]
+        num_experts = NANOV3_BASE["num_moe_experts"]
+        rank = torch.distributed.get_rank() if ep_size > 1 else 0
+        num_global_tokens = num_local_tokens * ep_size
+
+        # Create global buffers on rank 0 and broadcast to all ranks
+        global_hidden = torch.randn(
+            num_global_tokens, hidden_size, device="cuda", dtype=torch.bfloat16
+        )
+        global_probs = torch.randn(num_global_tokens, topk, device="cuda", dtype=torch.float32)
+        global_routing_map = torch.randint(0, num_experts, (num_global_tokens, topk), device="cuda")
+        torch.distributed.broadcast(global_hidden, src=0)
+        torch.distributed.broadcast(global_probs, src=0)
+        torch.distributed.broadcast(global_routing_map, src=0)
+
+        # Each rank grabs their own shard
+        start = rank * num_local_tokens
+        end = start + num_local_tokens
+        static_hidden = global_hidden[start:end].contiguous()
+        static_probs = global_probs[start:end].contiguous()
+        static_routing_map = global_routing_map[start:end].contiguous()
+
+        if not are_tensors_nvls_eligible(static_hidden, static_probs, static_routing_map):
+            pytest.skip(
+                "Tensors are not NVLS-eligible (need SM>=9 and each tensor's memory to be a multiple of 16 bytes)"
+            )
+
+        # 3 warmup iterations on a side stream
+        with torch.no_grad():
+            s = torch.cuda.Stream()
+            s.wait_stream(torch.cuda.current_stream())
+            with torch.cuda.stream(s):
+                for _ in range(3):
+                    dispatcher.routing_map = static_routing_map
+                    d_hidden, d_probs = dispatcher.token_dispatch(static_hidden, static_probs)
+                    d_hidden = d_hidden.clone()
+                    d_probs = d_probs.clone()
+                    dispatcher.routing_map = dispatcher.routing_map.clone()
+                    dispatcher.token_combine(d_hidden.clone())
+            torch.cuda.current_stream().wait_stream(s)
+
+        # Capture
+        graph = torch.cuda.CUDAGraph()
+        with torch.cuda.graph(graph):
+            dispatcher.routing_map = static_routing_map
+            d_hidden, d_probs = dispatcher.token_dispatch(static_hidden, static_probs)
+            graph_hidden = d_hidden.clone()
+            graph_probs = d_probs.clone()
+            graph_routing_map = dispatcher.routing_map.clone()
+            graph_combined = dispatcher.token_combine(d_hidden.clone())
+
+        # Verify shapes: dispatch expands by ep_size, combine shrinks back
+        assert graph_hidden.shape == (num_global_tokens, hidden_size)
+        assert graph_probs.shape == (num_global_tokens, topk)
+        assert graph_combined.shape == (num_local_tokens, hidden_size)
+
+        # Replay
+        graph.replay()
+
+        # Verify AllGather: all gathered tensors should match global buffers
+        torch.testing.assert_close(graph_hidden, global_hidden, atol=0, rtol=0)
+        torch.testing.assert_close(graph_probs, global_probs, atol=0, rtol=0)
+        torch.testing.assert_close(graph_routing_map, global_routing_map, atol=0, rtol=0)
+
+        # Verify ReduceScatter: all ranks have the same all-gathered data, so
+        # rank r gets ep_size * chunk_r. Compute reference in fp32 then downcast.
+        # Exact match (atol=0, rtol=0) is possible because the NVLS triton kernels
+        # accumulate in fp32 before writing bf16 output.
+        expected_combined = (global_hidden[start:end].float() * ep_size).bfloat16()
+        torch.testing.assert_close(graph_combined, expected_combined, atol=0, rtol=0)
diff --git a/tests/unit_tests/inference/test_stop_words.py b/tests/unit_tests/inference/test_stop_words.py
index 31665c0bb81..6c3e484de86 100644
--- a/tests/unit_tests/inference/test_stop_words.py
+++ b/tests/unit_tests/inference/test_stop_words.py
@@ -31,132 +31,146 @@ class TestStopWordDetection:
     """Test stop word detection logic."""
 
     def _check_stop_words_for_request_post_append(
-        self, request: MockDynamicInferenceRequest
-    ) -> bool:
+        self, request: MockDynamicInferenceRequest, num_speculative_tokens: int = 0
+    ) -> tuple:
         """
         Check if a request should stop due to stop words (after token is appended).
 
-        This mirrors the logic in DynamicInferenceEngine._check_stop_words_for_request_post_append
+        This mirrors the logic in DynamicInferenceEngine._check_stop_words_for_request_post_append.
+        Returns (stop_word_hit, num_tokens_trimmed).
         """
-        # Check if request has stop words configured
         if request.stop_word_ids is None or len(request.stop_word_ids) == 0:
-            return False
+            return False, 0
 
         generated_tokens = request.generated_tokens
 
-        # Check if the sequence ends with any stop word
         for stop_word_ids in request.stop_word_ids:
             stop_len = len(stop_word_ids)
             if len(generated_tokens) >= stop_len:
-                # Check if the last stop_len tokens match the stop word
-                if list(generated_tokens[-stop_len:]) == stop_word_ids:
-                    return True
+                for i in range(num_speculative_tokens + 1):
+                    end_idx = -i if i > 0 else None
+                    if list(generated_tokens[-stop_len - i : end_idx]) == stop_word_ids:
+                        if i > 0:
+                            request.generated_tokens = request.generated_tokens[:-i]
+                        return True, i
 
-        return False
+        return False, 0
 
     def test_no_stop_words_configured(self):
         """Test that requests without stop words configured don't trigger stop."""
         request = MockDynamicInferenceRequest(
             request_id=1, generated_tokens=[100, 200, 300], stop_word_ids=None
         )
-        assert self._check_stop_words_for_request_post_append(request) is False
+        hit, trim = self._check_stop_words_for_request_post_append(request)
+        assert hit is False
+        assert trim == 0
 
     def test_empty_stop_words_list(self):
         """Test that empty stop words list doesn't trigger stop."""
         request = MockDynamicInferenceRequest(
             request_id=1, generated_tokens=[100, 200, 300], stop_word_ids=[]
         )
-        assert self._check_stop_words_for_request_post_append(request) is False
+        hit, trim = self._check_stop_words_for_request_post_append(request)
+        assert hit is False
 
     def test_single_token_stop_word_match(self):
         """Test detection of single-token stop word."""
-        # Stop word is token 300
         request = MockDynamicInferenceRequest(
             request_id=1, generated_tokens=[100, 200, 300], stop_word_ids=[[300]]
         )
-        assert self._check_stop_words_for_request_post_append(request) is True
+        hit, trim = self._check_stop_words_for_request_post_append(request)
+        assert hit is True
+        assert trim == 0
+        assert request.generated_tokens == [100, 200, 300]
 
     def test_single_token_stop_word_no_match(self):
         """Test no detection when single-token stop word doesn't match."""
         request = MockDynamicInferenceRequest(
             request_id=1, generated_tokens=[100, 200, 300], stop_word_ids=[[400]]
         )
-        assert self._check_stop_words_for_request_post_append(request) is False
+        hit, trim = self._check_stop_words_for_request_post_append(request)
+        assert hit is False
 
     def test_multi_token_stop_word_match(self):
         """Test detection of multi-token stop word."""
-        # Stop word is tokens [200, 300]
         request = MockDynamicInferenceRequest(
             request_id=1, generated_tokens=[100, 200, 300], stop_word_ids=[[200, 300]]
         )
-        assert self._check_stop_words_for_request_post_append(request) is True
+        hit, trim = self._check_stop_words_for_request_post_append(request)
+        assert hit is True
+        assert trim == 0
 
     def test_multi_token_stop_word_no_match_partial(self):
         """Test no detection when only partial stop word matches."""
-        # Stop word is [200, 300], but generated ends with [100, 200]
         request = MockDynamicInferenceRequest(
             request_id=1, generated_tokens=[100, 200], stop_word_ids=[[200, 300]]
         )
-        assert self._check_stop_words_for_request_post_append(request) is False
+        hit, trim = self._check_stop_words_for_request_post_append(request)
+        assert hit is False
 
     def test_multi_token_stop_word_no_match_wrong_order(self):
         """Test no detection when tokens are present but in wrong order."""
-        # Stop word is [200, 300], but generated ends with [300, 200]
         request = MockDynamicInferenceRequest(
             request_id=1, generated_tokens=[100, 300, 200], stop_word_ids=[[200, 300]]
         )
-        assert self._check_stop_words_for_request_post_append(request) is False
+        hit, trim = self._check_stop_words_for_request_post_append(request)
+        assert hit is False
 
     def test_multiple_stop_words_first_matches(self):
         """Test with multiple stop words where first one matches."""
         request = MockDynamicInferenceRequest(
             request_id=1, generated_tokens=[100, 200, 300], stop_word_ids=[[300], [400], [500]]
         )
-        assert self._check_stop_words_for_request_post_append(request) is True
+        hit, trim = self._check_stop_words_for_request_post_append(request)
+        assert hit is True
 
     def test_multiple_stop_words_second_matches(self):
         """Test with multiple stop words where second one matches."""
         request = MockDynamicInferenceRequest(
             request_id=1, generated_tokens=[100, 200, 400], stop_word_ids=[[300], [400], [500]]
         )
-        assert self._check_stop_words_for_request_post_append(request) is True
+        hit, trim = self._check_stop_words_for_request_post_append(request)
+        assert hit is True
 
     def test_multiple_stop_words_none_match(self):
         """Test with multiple stop words where none match."""
         request = MockDynamicInferenceRequest(
             request_id=1, generated_tokens=[100, 200, 600], stop_word_ids=[[300], [400], [500]]
         )
-        assert self._check_stop_words_for_request_post_append(request) is False
+        hit, trim = self._check_stop_words_for_request_post_append(request)
+        assert hit is False
 
     def test_stop_word_longer_than_generated(self):
         """Test that stop word longer than generated tokens doesn't crash."""
-        # Stop word is 5 tokens, but only 3 tokens generated
         request = MockDynamicInferenceRequest(
             request_id=1, generated_tokens=[100, 200, 300], stop_word_ids=[[1, 2, 3, 4, 5]]
         )
-        assert self._check_stop_words_for_request_post_append(request) is False
+        hit, trim = self._check_stop_words_for_request_post_append(request)
+        assert hit is False
 
     def test_stop_word_exact_length_match(self):
         """Test stop word that matches entire generated sequence."""
         request = MockDynamicInferenceRequest(
             request_id=1, generated_tokens=[100, 200, 300], stop_word_ids=[[100, 200, 300]]
         )
-        assert self._check_stop_words_for_request_post_append(request) is True
+        hit, trim = self._check_stop_words_for_request_post_append(request)
+        assert hit is True
 
     def test_empty_generated_tokens(self):
         """Test with no generated tokens."""
         request = MockDynamicInferenceRequest(
             request_id=1, generated_tokens=[], stop_word_ids=[[300]]
         )
-        assert self._check_stop_words_for_request_post_append(request) is False
+        hit, trim = self._check_stop_words_for_request_post_append(request)
+        assert hit is False
 
     def test_stop_word_in_middle_not_end(self):
         """Test that stop word in middle of sequence doesn't trigger (only end matters)."""
-        # Stop word is [200], which is in middle but not at end
         request = MockDynamicInferenceRequest(
             request_id=1, generated_tokens=[100, 200, 300], stop_word_ids=[[200]]
         )
-        assert self._check_stop_words_for_request_post_append(request) is False
+        hit, trim = self._check_stop_words_for_request_post_append(request)
+        assert hit is False
 
 
 class TestStopWordTrackingFlow:
diff --git a/tests/unit_tests/inference/text_generation_controllers/test_text_generation_controller.py b/tests/unit_tests/inference/text_generation_controllers/test_text_generation_controller.py
index bdf95c2d9bf..f39060d7a85 100644
--- a/tests/unit_tests/inference/text_generation_controllers/test_text_generation_controller.py
+++ b/tests/unit_tests/inference/text_generation_controllers/test_text_generation_controller.py
@@ -52,6 +52,11 @@ def setup_model(
         batch_size: int = 4,
         static: bool = True,
         use_training_random_init: bool = False,
+        materialize_only_last_token_logits: bool = False,
+        num_speculative_tokens: int = 0,
+        block_size_tokens: int = 256,
+        enable_prefix_caching: bool = False,
+        max_requests: int = None,
     ):
         Utils.initialize_model_parallel(
             tensor_model_parallel_size=tensor_model_parallel_size,
@@ -108,10 +113,14 @@ def setup_model(
                 inference_config=InferenceConfig(
                     max_sequence_length=2048,
                     buffer_size_gb=0.2,
-                    materialize_only_last_token_logits=False,
+                    materialize_only_last_token_logits=materialize_only_last_token_logits,
                     use_flashinfer_fused_rope=None,  # default to using flash-infer if available
                     # this is for compatibility with the LTS environment
                     unified_memory_level=0,  # unit tests currently broken with UVM
+                    num_speculative_tokens=num_speculative_tokens,
+                    block_size_tokens=block_size_tokens,
+                    enable_prefix_caching=enable_prefix_caching,
+                    max_requests=max_requests,
                 ),
             )
 
@@ -224,11 +233,15 @@ def test_sample_from_dynamic_logits(
         self, backend: str, materialize_only_last_token_logits: bool
     ):
         batch_size = 12
-        self.setup_model(torch.float32, batch_size=batch_size, static=False)
+        self.setup_model(
+            torch.float32,
+            batch_size=batch_size,
+            static=False,
+            materialize_only_last_token_logits=materialize_only_last_token_logits,
+        )
         self.mock_tokenizer.eod = self.vocab_size
 
         context = self.text_generation_controller.inference_wrapped_model.inference_context
-        context.materialize_only_last_token_logits = materialize_only_last_token_logits
 
         # Prepare sampling params in human-readable format, to aid with test maintenance.
         sampling_test_cases: List[Tuple[SamplingParams, List[int]]] = [
@@ -743,11 +756,15 @@ def test_dynamic_top_n_logprobs_calculation(
         3. Correct number of tokens are returned for each request
         """
         batch_size = 4
-        self.setup_model(torch.bfloat16, batch_size=batch_size, static=False)
+        self.setup_model(
+            torch.bfloat16,
+            batch_size=batch_size,
+            static=False,
+            materialize_only_last_token_logits=materialize_only_last_token_logits,
+        )
         self.mock_tokenizer.eod = self.vocab_size
 
         context = self.text_generation_controller.inference_wrapped_model.inference_context
-        context.materialize_only_last_token_logits = materialize_only_last_token_logits
 
         # Prepare sampling params
         top_n = 5
diff --git a/tests/unit_tests/models/test_gpt_model.py b/tests/unit_tests/models/test_gpt_model.py
index 87aba9c6ed9..336ff0552b0 100644
--- a/tests/unit_tests/models/test_gpt_model.py
+++ b/tests/unit_tests/models/test_gpt_model.py
@@ -3,6 +3,7 @@
 import inspect
 import os
 from datetime import timedelta
+from unittest.mock import MagicMock, patch
 
 import pytest
 import torch
@@ -122,7 +123,6 @@ def test_get_mlp_module_spec_interface():
         "num_experts": inspect.Parameter.POSITIONAL_OR_KEYWORD,
         "moe_grouped_gemm": inspect.Parameter.POSITIONAL_OR_KEYWORD,
         "fp8": inspect.Parameter.POSITIONAL_OR_KEYWORD,
-        "moe_use_legacy_grouped_gemm": inspect.Parameter.POSITIONAL_OR_KEYWORD,
         "use_te_op_fuser": inspect.Parameter.POSITIONAL_OR_KEYWORD,
     }
 
@@ -131,7 +131,6 @@ def test_get_mlp_module_spec_interface():
         "num_experts": None,
         "moe_grouped_gemm": False,
         "fp8": None,
-        "moe_use_legacy_grouped_gemm": False,
         "use_te_op_fuser": False,
     }
 
@@ -448,3 +447,41 @@ def test_dynamic_inference_padding_with_fp8(self):
 
         # Assert that all padding logits are zero.
         assert torch.all(padding_logits == 0.0), "Logits for padding tokens are not all zero."
+
+
+def test_get_transformer_layer_spec_forwards_use_te_activation_func():
+    """Test that _get_transformer_layer_spec forwards use_te_activation_func.
+
+    Regression test for https://github.com/NVIDIA/Megatron-LM/issues/2770
+    The --use-te-activation-func flag was silently ignored for non-MoE GPT
+    models because _get_transformer_layer_spec did not forward the parameter
+    to get_gpt_layer_with_transformer_engine_spec.
+    """
+    mock_config = MagicMock()
+    mock_config.use_te_activation_func = True
+    mock_config.use_kitchen = False
+    mock_config.use_kitchen_attention = False
+    mock_config.kitchen_attention_backend = "sdpa"
+
+    mock_args = MagicMock()
+    mock_args.num_experts = None
+    mock_args.moe_grouped_gemm = False
+    mock_args.qk_layernorm = False
+    mock_args.multi_latent_attention = False
+    mock_args.experimental_attention_variant = None
+    mock_args.moe_use_legacy_grouped_gemm = False
+    mock_args.qk_l2_norm = False
+
+    with (
+        patch('gpt_builders.get_args', return_value=mock_args),
+        patch('gpt_builders.get_gpt_layer_with_transformer_engine_spec') as mock_spec_fn,
+    ):
+        from gpt_builders import _get_transformer_layer_spec
+
+        _get_transformer_layer_spec(use_te=True, config=mock_config)
+
+        mock_spec_fn.assert_called_once()
+        _, call_kwargs = mock_spec_fn.call_args
+        assert (
+            call_kwargs.get('use_te_activation_func') is True
+        ), "use_te_activation_func must be forwarded from config"
diff --git a/tests/unit_tests/models/test_gpt_model_quantization.py b/tests/unit_tests/models/test_gpt_model_quantization.py
index e993c9be8d2..73f1e20558f 100644
--- a/tests/unit_tests/models/test_gpt_model_quantization.py
+++ b/tests/unit_tests/models/test_gpt_model_quantization.py
@@ -267,7 +267,6 @@ def test_kitchen_config_resolution_moe(self) -> None:
             moe_router_load_balancing_type="sinkhorn",
             moe_router_topk=1,
             moe_grouped_gemm=True,
-            moe_use_legacy_grouped_gemm=False,
             num_layers=2,
             hidden_size=12,
             num_attention_heads=4,
diff --git a/tests/unit_tests/models/test_mamba_model.py b/tests/unit_tests/models/test_mamba_model.py
index 29e3630d7bb..6f1c27ca70c 100644
--- a/tests/unit_tests/models/test_mamba_model.py
+++ b/tests/unit_tests/models/test_mamba_model.py
@@ -42,8 +42,7 @@ def setup_method(self, method):
             mamba_stack_spec=mamba_stack_spec,
             vocab_size=100,
             max_sequence_length=4,
-            hybrid_attention_ratio=0.3,
-            hybrid_mlp_ratio=0.3,
+            hybrid_layer_pattern="M*-",  # 1 Mamba, 1 attention, 1 MLP
         )
 
     def teardown_method(self, method):
@@ -111,8 +110,7 @@ def test_forward_packed_sequence(self):
             mamba_stack_spec=mamba_stack_spec,
             vocab_size=vocab_size,
             max_sequence_length=12,
-            hybrid_attention_ratio=0.3,
-            hybrid_mlp_ratio=0.3,
+            hybrid_layer_pattern="M*-",  # 1 Mamba, 1 attention, 1 MLP
         )
 
         sequence_length = model.max_sequence_length
@@ -142,6 +140,7 @@ def test_forward_packed_sequence(self):
             cu_seqlens_kv_padded=None,
             max_seqlen_q=max_seqlen,
             max_seqlen_kv=max_seqlen,
+            total_tokens=sequence_length,
         )
 
         logits = model.forward(
@@ -247,6 +246,9 @@ def test_with_custom_process_groups(self, tmp_path, tp_size, cp_size, pp_size):
             tp=tp_group, cp=cp_group, pp=pp_group, embd=embd_group
         )
 
+        # Build pattern with '|' pipeline stage separators: 3 layers per PP stage
+        hybrid_layer_pattern = "|".join(["M*-"] * pp_size)
+
         # Configure model with appropriate sizes for parallelism
         model_config = TransformerConfig(
             num_layers=3 * pp_size,  # Scale layers with PP size
@@ -264,8 +266,7 @@ def test_with_custom_process_groups(self, tmp_path, tp_size, cp_size, pp_size):
             mamba_stack_spec=mamba_stack_spec,
             vocab_size=128,
             max_sequence_length=4,
-            hybrid_attention_ratio=0.3,
-            hybrid_mlp_ratio=0.3,
+            hybrid_layer_pattern=hybrid_layer_pattern,
             pg_collection=pg_collection,
         )
 
@@ -319,8 +320,7 @@ def setup_method(self, method):
             mamba_stack_spec=mamba_stack_spec,
             vocab_size=128,
             max_sequence_length=DynamicInferenceContext.TOKEN_ROUNDER,
-            hybrid_attention_ratio=0.5,
-            hybrid_mlp_ratio=0.0,
+            hybrid_layer_pattern="M*",  # 1 Mamba, 1 attention
         )
         self.model = Float16Module(self.model.config, self.model)
 
diff --git a/tests/unit_tests/models/test_mamba_moe_model.py b/tests/unit_tests/models/test_mamba_moe_model.py
index a55042ee979..117ea33a4e9 100644
--- a/tests/unit_tests/models/test_mamba_moe_model.py
+++ b/tests/unit_tests/models/test_mamba_moe_model.py
@@ -115,6 +115,7 @@
     "fp8_quantizer_factory": None,
     "fp8_recipe": "delayed",
     "fp8_wgrad": True,
+    "fused_residual_rmsnorm": False,
     "fused_single_qkv_rope": False,
     "gated_linear_unit": False,
     "glu_linear_offset": 0.0,
@@ -193,10 +194,15 @@
     "moe_token_dispatcher_type": "alltoall",
     "moe_token_drop_policy": "probs",
     "moe_token_dropping": False,
-    "moe_use_legacy_grouped_gemm": False,
     "moe_z_loss_coeff": None,
     "moe_enable_routing_replay": False,
     "mrope_section": None,
+    "mup_attn_scale_power": 1.0,
+    "mup_base_head_dim": None,
+    "mup_base_hidden_size": None,
+    "mup_embedding_mult": 1.0,
+    "mup_output_mult": 1.0,
+    "mup_width_mult": 1.0,
     "mtp_hybrid_override_pattern": None,
     "mtp_loss_scaling_factor": 0.1,
     "mtp_num_layers": None,
@@ -268,6 +274,7 @@
     "use_kitchen": False,
     "use_kitchen_attention": False,
     "use_mamba_mem_eff_path": True,
+    "use_mup": False,
     "use_ring_exchange_p2p": False,
     "use_te_activation_func": False,
     "use_te_rng_tracker": False,
@@ -286,6 +293,8 @@
     "fallback_to_eager_attn": False,
     "linear_attention_type": None,
     "moe_router_force_biased": None,
+    "inference_disable_torch_grouped_mm": False,
+    "inference_disable_triton_nvls_kernels": False,
 }
 # Fields to ignore entirely (ephemeral, environment-specific, very large).
 SKIP_FIELDS = set()
@@ -399,7 +408,6 @@ def create_test_args(self):
         args = parse_args()
 
         # The following args would be set from the nano v3 checkpoint.
-        args.num_layers = 52
         args.hidden_size = 2688
         args.ffn_hidden_size = 1856
         args.num_attention_heads = 32
@@ -422,10 +430,9 @@ def create_test_args(self):
         args.apply_query_key_layer_scaling = False
         args.attention_dropout = 0.0
         args.hidden_dropout = 0.0
-        args.hybrid_override_pattern = "MEMEM*EMEMEM*EMEMEM*EMEMEM*EMEMEM*EMEMEMEM*EMEMEMEME"
+        args.hybrid_layer_pattern = "MEMEM*EMEMEM*EMEMEM*EMEMEM*EMEMEM*EMEMEMEM*EMEMEMEME"
+        args.hybrid_override_pattern = None
         args.spec = ["megatron.core.models.mamba.mamba_layer_specs", "mamba_stack_spec"]
-        args.hybrid_attention_ratio = 0.0
-        args.hybrid_mlp_ratio = 0.0
         args.num_experts = 128
         args.moe_layer_freq = 1
         args.moe_ffn_hidden_size = 1856
@@ -440,7 +447,6 @@ def create_test_args(self):
         args.mamba_head_dim = 64
         args.mamba_num_groups = 8
         args.mamba_num_heads = 64
-        args.is_hybrid_model = True
         args.tokenizer_type = "TikTokenizer"
         args.tiktoken_pattern = "v2"
         args.tokenizer_model = "/mnt/artifacts/model/nemotron6/tokenizers/multiMixV8.gpt4o_nc_sd.500000.128k.vocab.json"
@@ -504,9 +510,7 @@ def setup_method(self, method):
             mamba_stack_spec=mamba_stack_spec,
             vocab_size=args.vocab_size,
             max_sequence_length=args.seq_length,
-            hybrid_attention_ratio=args.hybrid_attention_ratio,
-            hybrid_mlp_ratio=args.hybrid_mlp_ratio,
-            hybrid_override_pattern=args.hybrid_override_pattern,
+            hybrid_layer_pattern=args.hybrid_layer_pattern,
             position_embedding_type=args.position_embedding_type,
             rotary_base=args.rotary_base,
             rotary_percent=args.rotary_percent,
@@ -524,11 +528,9 @@ def test_constructor(self):
 
         assert self.model.pre_process is True, "pre_process should be True"
         assert self.model.post_process is True, "post_process should be True"
-        assert self.model.hybrid_attention_ratio == 0.0, "hybrid_attention_ratio should be 0.0"
-        assert self.model.hybrid_mlp_ratio == 0.0, "hybrid_mlp_ratio should be 0.0"
         assert (
-            self.model.hybrid_override_pattern == args.hybrid_override_pattern
-        ), f"hybrid_override_pattern should be {args.hybrid_override_pattern}"
+            self.model.hybrid_layer_pattern == args.hybrid_layer_pattern
+        ), f"hybrid_layer_pattern should be {args.hybrid_layer_pattern}"
         num_weights = sum([p.numel() for p in self.model.parameters()])
         assert num_weights == 8449294624, f"Expected 8449294624 parameters, got {num_weights}"
 
diff --git a/tests/unit_tests/models/test_mimo_embedding_alignment.py b/tests/unit_tests/models/test_mimo_embedding_alignment.py
index 74596256774..688ebe4832b 100644
--- a/tests/unit_tests/models/test_mimo_embedding_alignment.py
+++ b/tests/unit_tests/models/test_mimo_embedding_alignment.py
@@ -20,7 +20,14 @@ class TestEmbeddingAlignment:
     def setup_method(self):
         """Set up for each test."""
         # Create a minimal MimoModelConfig
-        language_model_spec = ModuleSpec(module=MagicMock, params={'config': MagicMock()})
+        language_model_spec = ModuleSpec(
+            module=MagicMock,
+            params={
+                'config': MagicMock(
+                    context_parallel_size=1, sequence_parallel=False, pipeline_model_parallel_size=1
+                )
+            },
+        )
         self.mimo_config = MimoModelConfig(
             language_model_spec=language_model_spec,
             modality_submodules_spec={},
diff --git a/tests/unit_tests/models/test_mimo_model.py b/tests/unit_tests/models/test_mimo_model.py
index f786f118c68..3496087ac3b 100644
--- a/tests/unit_tests/models/test_mimo_model.py
+++ b/tests/unit_tests/models/test_mimo_model.py
@@ -5,6 +5,7 @@
 '''
 
 import math
+from unittest.mock import MagicMock, patch
 
 import pytest
 import torch
@@ -455,3 +456,174 @@ def test_state_dict(self):
         # Test checkpoint state dict
         checkpoint_dict = mimo_model.state_dict_for_save_checkpoint()
         assert len(checkpoint_dict) > 0
+
+    def test_pipeline_model_parallel_assertion(self):
+        """Test that MimoModel raises AssertionError when pipeline_model_parallel_size > 1."""
+        lm_config_pp2 = TransformerConfig(
+            num_layers=2,
+            hidden_size=self.hidden_size,
+            num_attention_heads=4,
+            use_cpu_initialization=True,
+            pipeline_model_parallel_size=2,
+            pipeline_dtype=torch.float32,
+        )
+        language_layer_spec = get_gpt_layer_with_transformer_engine_spec()
+        language_model_spec_pp2 = ModuleSpec(
+            module=GPTModel,
+            params={
+                "config": lm_config_pp2,
+                "transformer_layer_spec": language_layer_spec,
+                "vocab_size": self.vocab_size,
+                "max_sequence_length": self.seq_len,
+                "pre_process": True,
+                "post_process": True,
+            },
+        )
+        mimo_config = MimoModelConfig(
+            language_model_spec=language_model_spec_pp2,
+            modality_submodules_spec={},
+            special_token_ids=self.special_token_ids,
+        )
+
+        with pytest.raises(AssertionError, match="Pipeline parallelism is not supported"):
+            MimoModel(mimo_config)
+
+    def test_partition_adapter_none_by_default(self):
+        """Test that partition_adapter is None with default config (no CP/SP)."""
+        mimo_model = get_vlm_mimo_model(
+            self.hidden_size,
+            self.vocab_size,
+            self.seq_len,
+            self.img_h,
+            self.img_w,
+            self.patch_dim,
+            self.special_token_ids,
+        )
+        # TransformerConfig defaults: context_parallel_size=1, sequence_parallel=False
+        assert mimo_model.partition_adapter is None
+
+    def test_forward_with_packing_kwargs(self):
+        """Test that packing_kwargs builds PackedSeqParams with qkv_format='thd' and int32 seqlens."""
+        from megatron.core.packed_seq_params import PackedSeqParams
+
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        input_ids = torch.randint(
+            0, self.vocab_size, (self.batch_size, self.seq_len), device=device
+        )
+        position_ids = (
+            torch.arange(self.seq_len, device=device).unsqueeze(0).expand(self.batch_size, -1)
+        )
+
+        mimo_model = get_vlm_mimo_model(
+            self.hidden_size,
+            self.vocab_size,
+            self.seq_len,
+            self.img_h,
+            self.img_w,
+            self.patch_dim,
+            self.special_token_ids,
+        )
+        mimo_model = mimo_model.to(device)
+
+        # cu_seqlens covering full batch: [0, seq_len, 2*seq_len]
+        cu_seqlens = torch.tensor(
+            [0, self.seq_len, 2 * self.seq_len], dtype=torch.int64, device=device
+        )
+        packing_kwargs = {"cu_seqlens_q": cu_seqlens.clone(), "cu_seqlens_kv": cu_seqlens.clone()}
+
+        # Mock get_text_embeddings and align_embeddings_by_token_positions to avoid full forward
+        text_emb = torch.zeros(self.batch_size * self.seq_len, self.hidden_size, device=device)
+        combined_emb = torch.zeros(self.seq_len, self.batch_size, self.hidden_size, device=device)
+
+        # Capture packed_seq_params via a side_effect on language_model.forward.
+        # Direct assignment (mimo_model.language_model = MagicMock()) is rejected by
+        # PyTorch because language_model is a registered nn.Module child.
+        captured = {}
+
+        def capture_lm_forward(*args, **kwargs):
+            captured['packed_seq_params'] = kwargs.get('packed_seq_params')
+            return torch.zeros(self.batch_size, self.seq_len, self.vocab_size, device=device)
+
+        with (
+            patch.object(mimo_model, 'get_text_embeddings', return_value=text_emb),
+            patch.object(
+                mimo_model, 'align_embeddings_by_token_positions', return_value=combined_emb
+            ),
+            patch.object(mimo_model.language_model, 'forward', side_effect=capture_lm_forward),
+        ):
+            mimo_model(
+                input_ids=input_ids,
+                position_ids=position_ids,
+                modality_inputs=None,
+                packing_kwargs=packing_kwargs,
+            )
+
+        # Verify language model received a properly constructed PackedSeqParams
+        packed_seq_params = captured['packed_seq_params']
+
+        assert packed_seq_params is not None
+        assert isinstance(packed_seq_params, PackedSeqParams)
+        assert packed_seq_params.qkv_format == 'thd'
+        assert packed_seq_params.cu_seqlens_q.dtype == torch.int32
+        assert packed_seq_params.cu_seqlens_kv.dtype == torch.int32
+
+    def test_forward_with_partition_adapter(self):
+        """Test that partition_adapter.shard() is called and embeddings are transposed correctly."""
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        input_ids = torch.randint(
+            0, self.vocab_size, (self.batch_size, self.seq_len), device=device
+        )
+        position_ids = (
+            torch.arange(self.seq_len, device=device).unsqueeze(0).expand(self.batch_size, -1)
+        )
+
+        mimo_model = get_vlm_mimo_model(
+            self.hidden_size,
+            self.vocab_size,
+            self.seq_len,
+            self.img_h,
+            self.img_w,
+            self.patch_dim,
+            self.special_token_ids,
+        )
+        mimo_model = mimo_model.to(device)
+
+        # Inject a mock partition adapter that halves the sequence dimension
+        sharded_seq_len = self.seq_len // 2
+        sharded_emb = torch.zeros(self.batch_size, sharded_seq_len, self.hidden_size, device=device)
+        mock_adapter = MagicMock()
+        mock_adapter.shard.return_value = (sharded_emb, None, None, None, None)
+        mimo_model.partition_adapter = mock_adapter
+
+        text_emb = torch.zeros(self.batch_size * self.seq_len, self.hidden_size, device=device)
+        # align_embeddings_by_token_positions returns [S, B, H]
+        combined_emb = torch.zeros(self.seq_len, self.batch_size, self.hidden_size, device=device)
+
+        captured = {}
+
+        def capture_lm_forward(*args, **kwargs):
+            captured['decoder_input'] = kwargs.get('decoder_input')
+            return torch.zeros(self.batch_size, sharded_seq_len, self.vocab_size, device=device)
+
+        with (
+            patch.object(mimo_model, 'get_text_embeddings', return_value=text_emb),
+            patch.object(
+                mimo_model, 'align_embeddings_by_token_positions', return_value=combined_emb
+            ),
+            patch.object(mimo_model.language_model, 'forward', side_effect=capture_lm_forward),
+        ):
+            mimo_model(input_ids=input_ids, position_ids=position_ids, modality_inputs=None)
+
+        # shard() should have been called once
+        mock_adapter.shard.assert_called_once()
+
+        # The embeddings passed to shard() must be [B, S, H] (transposed from [S, B, H])
+        shard_kwargs = mock_adapter.shard.call_args[1]
+        assert shard_kwargs['embeddings'].shape == (self.batch_size, self.seq_len, self.hidden_size)
+
+        # The language model decoder_input must be [S/cp, B, H] (re-transposed after shard)
+        assert captured['decoder_input'].shape == (
+            sharded_seq_len,
+            self.batch_size,
+            self.hidden_size,
+        )
diff --git a/tests/unit_tests/models/test_mimo_partition.py b/tests/unit_tests/models/test_mimo_partition.py
new file mode 100644
index 00000000000..1527fb92935
--- /dev/null
+++ b/tests/unit_tests/models/test_mimo_partition.py
@@ -0,0 +1,434 @@
+# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
+
+'''
+WORLD_SIZE=1 LOCAL_RANK=0 python -m torch.distributed.run \
+    --nproc_per_node=1 -m pytest \
+    tests/unit_tests/models/test_mimo_partition.py -v
+'''
+
+from unittest.mock import MagicMock, patch
+
+import pytest
+import torch
+
+from megatron.core.models.mimo.partition.utils import PartitionAdapter, PartitionConfig
+from megatron.core.transformer.transformer_config import TransformerConfig
+
+
+@pytest.mark.experimental
+class TestPartitionConfig:
+    """Tests for PartitionConfig dataclass and factory method."""
+
+    def test_is_partitioning_enabled_cp_only(self):
+        cfg = PartitionConfig(
+            seq_parallel=False, use_cp=True, tp_comm_overlap=False, max_seq_len=128
+        )
+        assert cfg.is_partitioning_enabled is True
+
+    def test_is_partitioning_enabled_sp_only(self):
+        cfg = PartitionConfig(
+            seq_parallel=True, use_cp=False, tp_comm_overlap=False, max_seq_len=128
+        )
+        assert cfg.is_partitioning_enabled is True
+
+    def test_is_partitioning_enabled_both(self):
+        cfg = PartitionConfig(
+            seq_parallel=True, use_cp=True, tp_comm_overlap=False, max_seq_len=128
+        )
+        assert cfg.is_partitioning_enabled is True
+
+    def test_is_partitioning_enabled_neither(self):
+        cfg = PartitionConfig(
+            seq_parallel=False, use_cp=False, tp_comm_overlap=False, max_seq_len=128
+        )
+        assert cfg.is_partitioning_enabled is False
+
+    def test_from_mp_config_invalid_type_raises(self):
+        with pytest.raises(TypeError, match="mp must be a ModelParallelConfig instance"):
+            PartitionConfig.from_mp_config("not_a_config", max_seq_len=128)
+
+    def test_from_mp_config_no_parallelism(self):
+        mp = TransformerConfig(
+            num_layers=1,
+            hidden_size=64,
+            num_attention_heads=4,
+            context_parallel_size=1,
+            sequence_parallel=False,
+        )
+        with patch('megatron.core.models.mimo.partition.utils.get_pg_size', return_value=1):
+            cfg = PartitionConfig.from_mp_config(mp, max_seq_len=512)
+        assert cfg.use_cp is False
+        assert cfg.seq_parallel is False
+        assert cfg.cp_group is None
+        assert cfg.tp_group is None
+        assert cfg.max_seq_len == 512
+
+    def test_from_mp_config_kv_format_thd(self):
+        mp = TransformerConfig(num_layers=1, hidden_size=64, num_attention_heads=4)
+        with patch('megatron.core.models.mimo.partition.utils.get_pg_size', return_value=1):
+            cfg = PartitionConfig.from_mp_config(mp, max_seq_len=512, kv_format='thd')
+        assert cfg.kv_format == 'thd'
+
+    def test_from_mp_config_explicit_cp_group(self):
+        mock_cp_group = MagicMock()
+        mp = TransformerConfig(
+            num_layers=1, hidden_size=64, num_attention_heads=4, context_parallel_size=2
+        )
+        with patch('megatron.core.models.mimo.partition.utils.get_pg_size', return_value=2):
+            cfg = PartitionConfig.from_mp_config(mp, max_seq_len=512, cp_group=mock_cp_group)
+        assert cfg.use_cp is True
+        assert cfg.cp_group is mock_cp_group
+
+    def test_from_mp_config_explicit_tp_group(self):
+        mock_tp_group = MagicMock()
+        mp = TransformerConfig(
+            num_layers=1,
+            hidden_size=64,
+            num_attention_heads=4,
+            tensor_model_parallel_size=2,
+            sequence_parallel=True,
+        )
+        with patch('megatron.core.models.mimo.partition.utils.get_pg_size', return_value=1):
+            cfg = PartitionConfig.from_mp_config(mp, max_seq_len=512, tp_group=mock_tp_group)
+        assert cfg.seq_parallel is True
+        assert cfg.tp_group is mock_tp_group
+
+    def test_from_mp_config_auto_fetch_cp_group(self):
+        mock_group = MagicMock()
+        mp = TransformerConfig(
+            num_layers=1, hidden_size=64, num_attention_heads=4, context_parallel_size=2
+        )
+        with (
+            patch(
+                'megatron.core.models.mimo.partition.utils.get_context_parallel_group',
+                return_value=mock_group,
+            ),
+            patch('megatron.core.models.mimo.partition.utils.get_pg_size', return_value=2),
+        ):
+            cfg = PartitionConfig.from_mp_config(mp, max_seq_len=512)
+        assert cfg.cp_group is mock_group
+
+    def test_from_mp_config_auto_fetch_tp_group(self):
+        mock_group = MagicMock()
+        mp = TransformerConfig(
+            num_layers=1,
+            hidden_size=64,
+            num_attention_heads=4,
+            tensor_model_parallel_size=2,
+            sequence_parallel=True,
+        )
+        with (
+            patch(
+                'megatron.core.models.mimo.partition.utils.get_tensor_model_parallel_group',
+                return_value=mock_group,
+            ),
+            patch('megatron.core.models.mimo.partition.utils.get_pg_size', return_value=1),
+        ):
+            cfg = PartitionConfig.from_mp_config(mp, max_seq_len=512)
+        assert cfg.tp_group is mock_group
+
+
+@pytest.mark.experimental
+class TestPartitionAdapterShard:
+    """Tests for PartitionAdapter.shard()."""
+
+    def _make_cfg(
+        self,
+        use_cp=False,
+        seq_parallel=False,
+        tp_comm_overlap=False,
+        max_seq_len=128,
+        cp_group=None,
+        tp_group=None,
+    ):
+        return PartitionConfig(
+            use_cp=use_cp,
+            seq_parallel=seq_parallel,
+            tp_comm_overlap=tp_comm_overlap,
+            max_seq_len=max_seq_len,
+            cp_group=cp_group,
+            tp_group=tp_group,
+        )
+
+    def _make_tensors(self, B=2, S=8, H=16):
+        embeddings = torch.rand(B, S, H)
+        labels = torch.randint(0, 100, (B, S))
+        loss_mask = torch.ones(B, S)
+        attention_mask = torch.ones(B, S)
+        return embeddings, labels, loss_mask, attention_mask
+
+    def test_noop_when_both_disabled(self):
+        """No sharding when neither CP nor SP is enabled — inputs returned as-is."""
+        cfg = self._make_cfg(use_cp=False, seq_parallel=False)
+        adapter = PartitionAdapter(cfg)
+        embeddings, labels, loss_mask, attention_mask = self._make_tensors()
+        out = adapter.shard(embeddings, labels, loss_mask, attention_mask)
+        assert out[0] is embeddings
+        assert out[1] is labels
+        assert out[2] is loss_mask
+        assert out[3] is attention_mask
+        assert out[4] is None
+
+    def test_cp_only_shards_sequence(self):
+        mock_cp_group = MagicMock()
+        cfg = self._make_cfg(use_cp=True, max_seq_len=8, cp_group=mock_cp_group)
+        adapter = PartitionAdapter(cfg)
+        embeddings, labels, loss_mask, attention_mask = self._make_tensors(B=2, S=8, H=16)
+        sharded = {
+            'embeddings': embeddings[:, :4, :],
+            'labels': labels[:, :4],
+            'loss_mask': loss_mask[:, :4],
+            'attention_mask': attention_mask[:, :4],
+        }
+        with (
+            patch('megatron.core.models.mimo.partition.utils.get_pg_size', return_value=2),
+            patch(
+                'megatron.core.models.mimo.partition.utils.get_batch_on_this_cp_rank',
+                return_value=sharded,
+            ),
+        ):
+            out = adapter.shard(embeddings, labels, loss_mask, attention_mask)
+        assert out[0].shape == (2, 4, 16)
+        assert out[1].shape == (2, 4)
+
+    def test_sp_only_scatters(self):
+        mock_tp_group = MagicMock()
+        cfg = self._make_cfg(seq_parallel=True, max_seq_len=8, tp_group=mock_tp_group)
+        adapter = PartitionAdapter(cfg)
+        # SP uses seq_dim=0: embeddings shape [S, B, H]
+        embeddings = torch.rand(8, 2, 16)
+        labels = torch.randint(0, 100, (2, 8))
+        loss_mask = torch.ones(2, 8)
+        attention_mask = torch.ones(2, 8)
+        scattered = torch.rand(4, 2, 16)
+        with (
+            patch('megatron.core.models.mimo.partition.utils.get_pg_size', return_value=2),
+            patch(
+                'megatron.core.models.mimo.partition.utils.tensor_parallel.scatter_to_sequence_parallel_region',
+                return_value=scattered,
+            ),
+        ):
+            out = adapter.shard(embeddings, labels, loss_mask, attention_mask)
+        assert out[0].shape == (4, 2, 16)
+
+    def test_cp_and_sp_combined(self):
+        mock_cp_group = MagicMock()
+        mock_tp_group = MagicMock()
+        cfg = self._make_cfg(
+            use_cp=True,
+            seq_parallel=True,
+            max_seq_len=16,
+            cp_group=mock_cp_group,
+            tp_group=mock_tp_group,
+        )
+        adapter = PartitionAdapter(cfg)
+        # cp_size=2, tp_size=2 → shard_factor = 2*2*2 = 8; S=16 is divisible
+        embeddings = torch.rand(2, 16, 16)
+        labels = torch.randint(0, 100, (2, 16))
+        loss_mask = torch.ones(2, 16)
+        attention_mask = torch.ones(2, 16)
+        cp_sharded = {
+            'embeddings': embeddings[:, :8, :],
+            'labels': labels[:, :8],
+            'loss_mask': loss_mask[:, :8],
+            'attention_mask': attention_mask[:, :8],
+        }
+        scattered = torch.rand(2, 4, 16)
+
+        with (
+            patch('megatron.core.models.mimo.partition.utils.get_pg_size', return_value=2),
+            patch(
+                'megatron.core.models.mimo.partition.utils.get_batch_on_this_cp_rank',
+                return_value=cp_sharded,
+            ),
+            patch(
+                'megatron.core.models.mimo.partition.utils.tensor_parallel.scatter_to_sequence_parallel_region',
+                return_value=scattered,
+            ),
+        ):
+            out = adapter.shard(embeddings, labels, loss_mask, attention_mask)
+        assert out[0].shape == (2, 4, 16)
+
+    def test_seq_not_divisible_raises(self):
+        mock_cp_group = MagicMock()
+        cfg = self._make_cfg(use_cp=True, max_seq_len=7, cp_group=mock_cp_group)
+        adapter = PartitionAdapter(cfg)
+        embeddings = torch.rand(2, 7, 16)  # 7 % (2*2) != 0
+        labels = torch.randint(0, 100, (2, 7))
+        loss_mask = torch.ones(2, 7)
+        attention_mask = torch.ones(2, 7)
+        with (
+            patch('megatron.core.models.mimo.partition.utils.get_pg_size', return_value=2),
+            pytest.raises(AssertionError, match="divisible"),
+        ):
+            adapter.shard(embeddings, labels, loss_mask, attention_mask)
+
+    def test_tp_comm_overlap_seq_len_assertion(self):
+        mock_tp_group = MagicMock()
+        cfg = self._make_cfg(
+            seq_parallel=True, tp_comm_overlap=True, max_seq_len=16, tp_group=mock_tp_group
+        )
+        adapter = PartitionAdapter(cfg)
+        # S=8 but max_seq_len=16 → assertion fires
+        embeddings = torch.rand(8, 2, 16)  # [S, B, H] for SP
+        labels = torch.randint(0, 100, (2, 8))
+        loss_mask = torch.ones(2, 8)
+        attention_mask = torch.ones(2, 8)
+        with (
+            patch('megatron.core.models.mimo.partition.utils.get_pg_size', return_value=2),
+            pytest.raises(AssertionError, match="TP Comm overlap"),
+        ):
+            adapter.shard(embeddings, labels, loss_mask, attention_mask)
+
+    def test_thd_format_skips_divisibility_check(self):
+        """PackedSeqParams with qkv_format='thd' bypasses the divisibility assertion."""
+        from megatron.core.packed_seq_params import PackedSeqParams
+
+        mock_cp_group = MagicMock()
+        cfg = self._make_cfg(use_cp=True, max_seq_len=7, cp_group=mock_cp_group)
+        adapter = PartitionAdapter(cfg)
+        embeddings = torch.rand(2, 7, 16)  # seq_len=7 not divisible by cp*2, but THD skips check
+        labels = torch.randint(0, 100, (2, 7))
+        loss_mask = torch.ones(2, 7)
+        attention_mask = torch.ones(2, 7)
+        packed_seq_params = MagicMock(spec=PackedSeqParams)
+        packed_seq_params.qkv_format = 'thd'
+        packed_seq_params.cu_seqlens_q_padded = torch.tensor([0, 4, 7], dtype=torch.int32)
+
+        # THD path calls tex.thd_get_partitioned_indices — mock it to return first 4 indices
+        fake_index = torch.arange(4, dtype=torch.int32)
+        with (
+            patch('megatron.core.models.mimo.partition.utils.get_pg_size', return_value=2),
+            patch('megatron.core.models.mimo.partition.utils.get_pg_rank', return_value=0),
+            patch('megatron.core.models.mimo.partition.utils.tex') as mock_tex,
+        ):
+            mock_tex.thd_get_partitioned_indices.return_value = fake_index
+            # Should NOT raise AssertionError about divisibility
+            out = adapter.shard(embeddings, labels, loss_mask, attention_mask, packed_seq_params)
+        assert out[0] is not None
+
+    def test_none_embeddings_skips_shard_factor_check(self):
+        """When embeddings is None, the divisibility check is skipped."""
+        mock_cp_group = MagicMock()
+        cfg = self._make_cfg(use_cp=True, max_seq_len=7, cp_group=mock_cp_group)
+        adapter = PartitionAdapter(cfg)
+        labels = torch.randint(0, 100, (2, 7))
+        loss_mask = torch.ones(2, 7)
+        attention_mask = torch.ones(2, 7)
+        cp_sharded = {
+            'labels': labels[:, :4],
+            'loss_mask': loss_mask[:, :4],
+            'attention_mask': attention_mask[:, :4],
+        }
+        with (
+            patch('megatron.core.models.mimo.partition.utils.get_pg_size', return_value=2),
+            patch(
+                'megatron.core.models.mimo.partition.utils.get_batch_on_this_cp_rank',
+                return_value=cp_sharded,
+            ),
+        ):
+            out = adapter.shard(None, labels, loss_mask, attention_mask)
+        assert out[0] is None
+
+
+@pytest.mark.experimental
+class TestPartitionAdapterApplyContextParallel:
+    """Tests for PartitionAdapter._apply_context_parallel()."""
+
+    def _make_cfg(self, use_cp=True, cp_group=None):
+        return PartitionConfig(
+            use_cp=use_cp,
+            seq_parallel=False,
+            tp_comm_overlap=False,
+            max_seq_len=128,
+            cp_group=cp_group,
+        )
+
+    def test_returns_unchanged_when_cp_disabled(self):
+        cfg = self._make_cfg(use_cp=False)
+        adapter = PartitionAdapter(cfg)
+        embeddings = torch.rand(2, 8, 16)
+        labels = torch.randint(0, 100, (2, 8))
+        loss_mask = torch.ones(2, 8)
+        attention_mask = torch.ones(2, 8)
+        out = adapter._apply_context_parallel(embeddings, labels, loss_mask, attention_mask, None)
+        assert out[0] is embeddings
+        assert out[1] is labels
+        assert out[2] is loss_mask
+        assert out[3] is attention_mask
+
+    def test_sbhd_path_calls_get_batch_on_this_cp_rank(self):
+        mock_cp_group = MagicMock()
+        cfg = self._make_cfg(use_cp=True, cp_group=mock_cp_group)
+        adapter = PartitionAdapter(cfg)
+        embeddings = torch.rand(2, 8, 16)
+        labels = torch.randint(0, 100, (2, 8))
+        loss_mask = torch.ones(2, 8)
+        attention_mask = torch.ones(2, 8)
+        sharded = {
+            'embeddings': embeddings[:, :4, :],
+            'labels': labels[:, :4],
+            'loss_mask': loss_mask[:, :4],
+            'attention_mask': attention_mask[:, :4],
+        }
+        with patch(
+            'megatron.core.models.mimo.partition.utils.get_batch_on_this_cp_rank',
+            return_value=sharded,
+        ) as mock_fn:
+            out = adapter._apply_context_parallel(
+                embeddings, labels, loss_mask, attention_mask, None
+            )
+            mock_fn.assert_called_once()
+        assert out[0].shape == (2, 4, 16)
+        assert out[1].shape == (2, 4)
+
+    def test_all_none_inputs_produces_none_outputs(self):
+        mock_cp_group = MagicMock()
+        cfg = self._make_cfg(use_cp=True, cp_group=mock_cp_group)
+        adapter = PartitionAdapter(cfg)
+        with patch(
+            'megatron.core.models.mimo.partition.utils.get_batch_on_this_cp_rank', return_value={}
+        ):
+            out = adapter._apply_context_parallel(None, None, None, None, None)
+        assert all(v is None for v in out[:4])
+
+    def test_only_non_none_tensors_added_to_batch(self):
+        """None tensors must not appear in the batch dict passed to get_batch_on_this_cp_rank."""
+        mock_cp_group = MagicMock()
+        cfg = self._make_cfg(use_cp=True, cp_group=mock_cp_group)
+        adapter = PartitionAdapter(cfg)
+        embeddings = torch.rand(2, 8, 16)
+        sharded = {'embeddings': embeddings[:, :4, :]}
+        captured = {}
+
+        def mock_fn(batch):
+            captured.update(batch)
+            return sharded
+
+        with patch(
+            'megatron.core.models.mimo.partition.utils.get_batch_on_this_cp_rank',
+            side_effect=mock_fn,
+        ):
+            out = adapter._apply_context_parallel(embeddings, None, None, None, None)
+
+        assert 'embeddings' in captured
+        assert 'labels' not in captured
+        assert 'loss_mask' not in captured
+        assert out[0] is not None
+        assert out[1] is None
+
+    def test_thd_path_raises_when_te_unavailable(self):
+        """THD format must assert when Transformer Engine is not available."""
+        from megatron.core.packed_seq_params import PackedSeqParams
+
+        mock_cp_group = MagicMock()
+        cfg = self._make_cfg(use_cp=True, cp_group=mock_cp_group)
+        adapter = PartitionAdapter(cfg)
+        embeddings = torch.rand(2, 5, 16)
+        packed_seq_params = MagicMock(spec=PackedSeqParams)
+        packed_seq_params.qkv_format = 'thd'
+        with (
+            patch('megatron.core.models.mimo.partition.utils._HAVE_TEX', False),
+            pytest.raises(AssertionError, match="Transformer Engine"),
+        ):
+            adapter._apply_context_parallel(embeddings, None, None, None, packed_seq_params)
diff --git a/tests/unit_tests/pipeline_parallel/test_fine_grained_activation_offloading.py b/tests/unit_tests/pipeline_parallel/test_fine_grained_activation_offloading.py
index 558c6934a0c..524b22e2de5 100644
--- a/tests/unit_tests/pipeline_parallel/test_fine_grained_activation_offloading.py
+++ b/tests/unit_tests/pipeline_parallel/test_fine_grained_activation_offloading.py
@@ -73,7 +73,6 @@ def _build_gpt_model(
         transformer_layer_spec=get_gpt_layer_with_transformer_engine_spec(
             num_experts=num_experts,
             moe_grouped_gemm=num_experts is not None,
-            moe_use_legacy_grouped_gemm=False,
             multi_latent_attention=is_mla,
         ),
         vocab_size=vocab_size,
@@ -435,10 +434,7 @@ def _build_overlap_moe_gpt(
             GPTModel(
                 config=transformer_config,
                 transformer_layer_spec=get_gpt_layer_with_transformer_engine_spec(
-                    num_experts=num_experts,
-                    moe_grouped_gemm=True,
-                    moe_use_legacy_grouped_gemm=False,
-                    multi_latent_attention=is_mla,
+                    num_experts=num_experts, moe_grouped_gemm=True, multi_latent_attention=is_mla
                 ),
                 vocab_size=vocab_size,
                 max_sequence_length=seq_length,
diff --git a/tests/unit_tests/post_training/test_modelopt_module_spec.py b/tests/unit_tests/post_training/test_modelopt_module_spec.py
index 3f6491f835d..585be52f944 100644
--- a/tests/unit_tests/post_training/test_modelopt_module_spec.py
+++ b/tests/unit_tests/post_training/test_modelopt_module_spec.py
@@ -212,7 +212,7 @@ def setup_method(self, method):
             mamba_stack_spec=mamba_stack_spec,
             vocab_size=100,
             max_sequence_length=4,
-            hybrid_override_pattern="M*-",
+            hybrid_layer_pattern="M*-",
         )
 
         # A Hybrid MambaModel using ModelOpt spec (local + TENorm).
@@ -221,7 +221,7 @@ def setup_method(self, method):
             mamba_stack_spec=get_mamba_stack_modelopt_spec(remap_te_layernorm=True),
             vocab_size=100,
             max_sequence_length=4,
-            hybrid_override_pattern="M*-",
+            hybrid_layer_pattern="M*-",
         )
 
 
diff --git a/tests/unit_tests/resharding/test_mxfp8_refit.py b/tests/unit_tests/resharding/test_mxfp8_refit.py
new file mode 100644
index 00000000000..815d4eeedac
--- /dev/null
+++ b/tests/unit_tests/resharding/test_mxfp8_refit.py
@@ -0,0 +1,242 @@
+# Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+
+import pytest
+import torch
+
+_IS_BLACKWELL = torch.cuda.is_available() and (torch.cuda.get_device_properties(0).major >= 10)
+
+try:
+    from flashinfer import mxfp8_quantize
+
+    _HAVE_FLASHINFER = True
+except ImportError:
+    _HAVE_FLASHINFER = False
+
+pytestmark = [
+    pytest.mark.skipif(not _IS_BLACKWELL, reason="MXFP8 tests require Blackwell GPU (SM >= 10)"),
+    pytest.mark.skipif(not _HAVE_FLASHINFER, reason="MXFP8 tests require FlashInfer"),
+]
+
+
+# ===========================================================================
+# MXFP8ReshardTransform
+# ===========================================================================
+
+
+class TestMXFP8ReshardTransform:
+    """Tests for the core MXFP8 reshard transform (transforms.py).
+
+    These test the receiver-side BF16→MXFP8 conversion paths that run on
+    every refit iteration, including the critical 1D-scale accumulation
+    logic that avoids corrupting swizzled scales from partial updates.
+    """
+
+    def _make_persistent_buffers(self, shapes):
+        from megatron.core.inference.quantization.mxfp8_tensor import MXFP8Tensor
+
+        buffers = {}
+        for name, (M, K) in shapes.items():
+            x = torch.randn(M, K, dtype=torch.bfloat16, device="cuda")
+            buffers[name] = MXFP8Tensor.from_bf16(x)
+        return buffers
+
+    def test_finalize_recv_bf16_2d_scale(self):
+        """Receiver-side conversion with 2D scale: immediate per-slice quantization."""
+        from megatron.core.inference.quantization.mxfp8_tensor import MXFP8Tensor
+        from megatron.core.resharding.transforms import MXFP8ReshardTransform
+
+        M, K = 64, 128
+        buf = MXFP8Tensor.from_bf16(torch.randn(M, K, dtype=torch.bfloat16, device="cuda"))
+
+        if buf.scale.ndim != 2:
+            pytest.skip("FlashInfer produced 1D swizzled scale; 2D-scale test not applicable")
+
+        t = MXFP8ReshardTransform(
+            convertible_params={"decoder.weight"},
+            persistent_buffers={"weight": buf},
+            buffer_key_prefix="decoder.",
+            convert_on_send=False,
+        )
+
+        new_data = torch.randn(M, K, dtype=torch.bfloat16, device="cuda")
+        t.finalize_recv("decoder.weight", (slice(None), slice(None)), [new_data])
+
+        expected = MXFP8Tensor.from_bf16(new_data)
+        assert torch.equal(buf.data, expected.data)
+        assert torch.equal(buf.scale, expected.scale)
+
+    def test_finalize_recv_bf16_1d_scale_accumulation(self):
+        """Receiver-side conversion with 1D scale: accumulate slices then quantize."""
+        from megatron.core.inference.quantization.mxfp8_tensor import MXFP8Tensor
+        from megatron.core.resharding.transforms import MXFP8ReshardTransform
+
+        M, K = 64, 128
+        buf = MXFP8Tensor.from_bf16(torch.randn(M, K, dtype=torch.bfloat16, device="cuda"))
+
+        if buf.scale.ndim != 1:
+            pytest.skip("FlashInfer produced 2D scale; 1D-scale accumulation test not applicable")
+
+        t = MXFP8ReshardTransform(
+            convertible_params={"decoder.weight"},
+            persistent_buffers={"weight": buf},
+            buffer_key_prefix="decoder.",
+            convert_on_send=False,
+        )
+
+        full_data = torch.randn(M, K, dtype=torch.bfloat16, device="cuda")
+        half = M // 2
+
+        # First slice: should accumulate (not finalize yet)
+        t.finalize_recv("decoder.weight", (slice(0, half), slice(None)), [full_data[:half]])
+        assert "weight" in t._pending_1d, "Should be pending after partial slice"
+
+        # Second slice: should trigger final quantization
+        t.finalize_recv("decoder.weight", (slice(half, M), slice(None)), [full_data[half:]])
+        assert "weight" not in t._pending_1d, "Should be finalized after all slices"
+
+        expected = MXFP8Tensor.from_bf16(full_data)
+        assert torch.equal(buf.data, expected.data)
+        assert torch.equal(buf.scale, expected.scale)
+
+    def test_finalize_recv_1d_scale_wrong_element_count(self):
+        """1D accumulation should raise if total elements don't match (duplicate slices)."""
+        from megatron.core.inference.quantization.mxfp8_tensor import MXFP8Tensor
+        from megatron.core.resharding.transforms import MXFP8ReshardTransform
+
+        M, K = 64, 128
+        buf = MXFP8Tensor.from_bf16(torch.randn(M, K, dtype=torch.bfloat16, device="cuda"))
+        if buf.scale.ndim != 1:
+            pytest.skip("Need 1D scale for this test")
+
+        t = MXFP8ReshardTransform(
+            convertible_params={"decoder.weight"},
+            persistent_buffers={"weight": buf},
+            buffer_key_prefix="decoder.",
+            convert_on_send=False,
+        )
+
+        half_data = torch.randn(M // 2, K, dtype=torch.bfloat16, device="cuda")
+        t.finalize_recv("decoder.weight", (slice(0, M // 2), slice(None)), [half_data])
+
+        with pytest.raises(AssertionError, match="duplicate or missing"):
+            overlap = torch.randn(M // 2 + 1, K, dtype=torch.bfloat16, device="cuda")
+            t.finalize_recv("decoder.weight", (slice(M // 2 - 1, M), slice(None)), [overlap])
+
+
+# ===========================================================================
+# quantize_params_to_mxfp8
+# ===========================================================================
+
+
+class TestQuantizeParamsToMXFP8:
+    """Tests for persistent buffer quantization (quantization/utils.py).
+
+    The persistent buffer address stability is critical for CUDA graph
+    compatibility — if addresses change, captured graphs segfault.
+    """
+
+    def test_basic_quantization_replaces_param(self):
+        from megatron.core.inference.quantization.mxfp8_tensor import MXFP8Tensor
+        from megatron.core.inference.quantization.utils import quantize_params_to_mxfp8
+
+        model = torch.nn.Linear(128, 64, bias=False).to(dtype=torch.bfloat16, device="cuda")
+        buffers = quantize_params_to_mxfp8(model)
+
+        assert "weight" in buffers
+        assert isinstance(buffers["weight"], MXFP8Tensor)
+        assert buffers["weight"].data.shape == (64, 128)
+        assert "weight" not in model._parameters
+
+    def test_persistent_buffer_reuse_preserves_addresses(self):
+        """Second call must copy into existing buffers (CUDA graph address stability)."""
+        from megatron.core.inference.quantization.utils import quantize_params_to_mxfp8
+
+        model = torch.nn.Linear(128, 64, bias=False).to(dtype=torch.bfloat16, device="cuda")
+        buffers = quantize_params_to_mxfp8(model)
+        data_ptr = buffers["weight"].data.data_ptr()
+        scale_ptr = buffers["weight"].scale.data_ptr()
+
+        model2 = torch.nn.Linear(128, 64, bias=False).to(dtype=torch.bfloat16, device="cuda")
+        quantize_params_to_mxfp8(model2, persistent_buffers=buffers)
+
+        assert buffers["weight"].data.data_ptr() == data_ptr
+        assert buffers["weight"].scale.data_ptr() == scale_ptr
+
+    def test_nested_module_fqn(self):
+        """Recursive quantization should produce correct fully-qualified names."""
+        from megatron.core.inference.quantization.mxfp8_tensor import MXFP8Tensor
+        from megatron.core.inference.quantization.utils import quantize_params_to_mxfp8
+
+        model = torch.nn.Sequential(
+            torch.nn.Linear(128, 64, bias=False), torch.nn.Linear(64, 32, bias=False)
+        ).to(dtype=torch.bfloat16, device="cuda")
+        buffers = quantize_params_to_mxfp8(model)
+
+        assert "0.weight" in buffers and "1.weight" in buffers
+        assert isinstance(buffers["0.weight"], MXFP8Tensor)
+
+
+# ===========================================================================
+# End-to-end MXFP8 refit integration (single-GPU)
+# ===========================================================================
+
+
+class TestMXFP8RefitIntegration:
+    """Integration tests simulating the full send→recv→finalize refit flow."""
+
+    def test_full_transform_roundtrip_bf16_wire(self):
+        """Simulate sender sending BF16, receiver converting to MXFP8."""
+        from megatron.core.inference.quantization.mxfp8_tensor import MXFP8Tensor
+        from megatron.core.resharding.transforms import MXFP8ReshardTransform
+
+        M, K = 64, 128
+        src_weight = torch.randn(M, K, dtype=torch.bfloat16, device="cuda")
+        src_param = torch.nn.Parameter(src_weight.clone())
+
+        dst_buf = MXFP8Tensor.from_bf16(torch.randn(M, K, dtype=torch.bfloat16, device="cuda"))
+        t = MXFP8ReshardTransform(
+            convertible_params={"decoder.weight"},
+            persistent_buffers={"weight": dst_buf},
+            buffer_key_prefix="decoder.",
+            convert_on_send=False,
+        )
+
+        # Simulate: prepare_send → wire → prepare_recv → finalize_recv
+        sent = t.prepare_send("decoder.weight", (slice(None), slice(None)), src_param)
+        recv_bufs = t.prepare_recv("decoder.weight", (slice(None), slice(None)))
+        recv_bufs[0].copy_(sent[0])
+        t.finalize_recv("decoder.weight", (slice(None), slice(None)), recv_bufs)
+
+        expected = MXFP8Tensor.from_bf16(src_weight)
+        assert torch.equal(dst_buf.data, expected.data)
+        assert torch.equal(dst_buf.scale, expected.scale)
+
+    def test_multi_slice_assembly(self):
+        """Multiple row slices should correctly assemble the full quantized weight."""
+        from megatron.core.inference.quantization.mxfp8_tensor import MXFP8Tensor
+        from megatron.core.resharding.transforms import MXFP8ReshardTransform
+
+        M, K = 128, 256
+        full_weight = torch.randn(M, K, dtype=torch.bfloat16, device="cuda")
+        dst_buf = MXFP8Tensor.from_bf16(torch.zeros(M, K, dtype=torch.bfloat16, device="cuda"))
+
+        t = MXFP8ReshardTransform(
+            convertible_params={"decoder.weight"},
+            persistent_buffers={"weight": dst_buf},
+            buffer_key_prefix="decoder.",
+            convert_on_send=False,
+        )
+
+        # Send in 4 row-slices (simulates TP=4 refit)
+        chunk = M // 4
+        for i in range(4):
+            row_slice = (slice(i * chunk, (i + 1) * chunk), slice(None))
+            src_param = torch.nn.Parameter(full_weight.clone())
+            sent = t.prepare_send("decoder.weight", row_slice, src_param)
+            recv = t.prepare_recv("decoder.weight", row_slice)
+            recv[0].copy_(sent[0])
+            t.finalize_recv("decoder.weight", row_slice, recv)
+
+        expected = MXFP8Tensor.from_bf16(full_weight)
+        assert torch.equal(dst_buf.data, expected.data)
+        assert torch.equal(dst_buf.scale, expected.scale)
diff --git a/tests/unit_tests/rl/test_rl_utils.py b/tests/unit_tests/rl/test_rl_utils.py
index 6206fa93c61..740b74c8ec8 100644
--- a/tests/unit_tests/rl/test_rl_utils.py
+++ b/tests/unit_tests/rl/test_rl_utils.py
@@ -316,6 +316,10 @@ def test_prepare_data_for_update(self, initialize_model_parallel):
             logprobs=[[0.1, 0.2, 0.3]],
             env_id='MEGAENV',
             problem_id="2",
+            policy_staleness=[[0, 0, 0]],
+            kv_cache_staleness=[[0, 0, 0]],
+            completed_at_step=[1],
+            num_evictions=[0],
         )
         r2 = TokenRollout(
             trajectory=[[1, 2, 3, 4]],
@@ -324,6 +328,10 @@ def test_prepare_data_for_update(self, initialize_model_parallel):
             logprobs=[[0.1, 0.2, 0.3, -1.2]],
             env_id='MEGAENV',
             problem_id="2",
+            policy_staleness=[[0, 0, 0, 0]],
+            kv_cache_staleness=[[0, 0, 0, 0]],
+            completed_at_step=[1],
+            num_evictions=[0],
         )
 
         rollouts = [[r1, r2] for _ in range(dp)]
@@ -342,6 +350,10 @@ def test_prepare_data_for_update(self, initialize_model_parallel):
             logprobs=torch.tensor([[-0.2, -0.3, -3.2]]).cuda(),
             env_id='MEGAENV',
             problem_id="2",
+            policy_staleness=[[0, 0, 0, 0]],
+            kv_cache_staleness=[[0, 0, 0, 0]],
+            completed_at_step=[1],
+            num_evictions=[0],
         )
         r2 = TokenRollout(
             trajectory=torch.tensor([[1, 2, 234, tokenizer.eod]], dtype=torch.float).cuda(),
@@ -350,9 +362,13 @@ def test_prepare_data_for_update(self, initialize_model_parallel):
             logprobs=torch.tensor([[-0.2, -0.3, -1.2]]),
             env_id='MEGAENV',
             problem_id="2",
+            policy_staleness=[[0, 0, 0, 0]],
+            kv_cache_staleness=[[0, 0, 0, 0]],
+            completed_at_step=[1],
+            num_evictions=[0],
         )
         rollouts = [[r1, r2] for _ in range(dp)]
-        data_iter = rl_utils.prepare_data_for_update(
+        data_iter, _, _ = rl_utils.prepare_data_for_update(
             [model], {}, rollouts, tokenizer, sequence_packing=False, is_correction=False
         )
 
@@ -383,6 +399,10 @@ def test_prepare_trajectories(self, use_sequence_packing, num_turns):
             logprobs=[[0.1, 0.2, 0.3, 0.35]] * num_turns,
             env_id='MEGAENV',
             problem_id="1",
+            policy_staleness=[[0, 0, 0, 0]] * num_turns,
+            kv_cache_staleness=[[0, 0, 0, 0]] * num_turns,
+            completed_at_step=[0] * num_turns,
+            num_evictions=[0] * num_turns,
         )
         r2 = TokenRollout(
             trajectory=[[4, 5, 6, 7, tokenizer.eod]] * num_turns,
@@ -391,6 +411,10 @@ def test_prepare_trajectories(self, use_sequence_packing, num_turns):
             logprobs=[[0.4, 0.5, 0.6, 0.7, 0.75]] * num_turns,
             env_id='MEGAENV',
             problem_id="2",
+            policy_staleness=[[0, 0, 0, 0, 0]] * num_turns,
+            kv_cache_staleness=[[0, 0, 0, 0, 0]] * num_turns,
+            completed_at_step=[0] * num_turns,
+            num_evictions=[0] * num_turns,
         )
         r3 = TokenRollout(
             trajectory=[[8, 9, tokenizer.eod]] * num_turns,
@@ -399,6 +423,10 @@ def test_prepare_trajectories(self, use_sequence_packing, num_turns):
             logprobs=[[0.8, 0.9, 0.95]] * num_turns,
             env_id='MEGAENV',
             problem_id="3",
+            policy_staleness=[[0, 0, 0]] * num_turns,
+            kv_cache_staleness=[[0, 0, 0]] * num_turns,
+            completed_at_step=[0] * num_turns,
+            num_evictions=[0] * num_turns,
         )
 
         rollouts = [r1, r2, r3]
@@ -687,7 +715,7 @@ def test_gpt_logprobs(self, initialize_model_parallel):
         """
 
         world_size, dp, tp, pp = initialize_model_parallel
-        micro_batch_size = 2
+        micro_batch_size = 1
         self.create_test_args(
             tensor_model_parallel_size=tp,
             pipeline_model_parallel_size=pp,
@@ -868,6 +896,30 @@ def test_get_logprobs_cuda_graphs(self, initialize_model_parallel):
         CudaGraphManager.fwd_mempools = None
         CudaGraphManager.bwd_mempools = None
 
+    def test_compute_true_staleness(self):
+        # Single group, 2 turns: turn 1 has 1 token, turn 2 has 2 tokens
+        result = rl_utils.compute_true_staleness(
+            per_token_staleness=[[1, 2, 3]],
+            completed_at_steps=[[8, 7]],
+            turn_lens=[[1, 2]],
+            current_iteration=10,
+        )
+        # token 0: 1 + (10-8) = 3
+        # token 1: 2 + (10-7) = 5
+        # token 2: 3 + (10-7) = 6
+        assert result == [3, 5, 6]
+
+        # Multiple groups
+        result = rl_utils.compute_true_staleness(
+            per_token_staleness=[[0, 0], [1]],
+            completed_at_steps=[[5], [5]],
+            turn_lens=[[2], [1]],
+            current_iteration=6,
+        )
+        # group 1: [0+1, 0+1] = [1, 1]
+        # group 2: [1+1] = [2]
+        assert result == [1, 1, 2]
+
     @pytest.mark.parametrize(
         "initialize_model_parallel",
         [pytest.param((1, 1), id="tp1-pp1")],
@@ -881,8 +933,25 @@ def test_prep_wandb_metrics(self, initialize_model_parallel):
         rewards = [[1, 1], [-1, 2]]
         num_turns = [[42, 2], [10, 8]]
         advantages = [0, 1]
+        # Per-token staleness (6 tokens in group 1, 3 in group 2; matching turn_lens)
+        policy_staleness = [[1, 2, 2, 0, 0, 3], [0, 3, 3]]
+        kv_cache_staleness = [[1, 1, 1, 0, 0, 2], [0, 2, 2]]
+        num_evictions = [[0, 1], [0, 0]]
+        # Per-turn completed_at_steps (5 turns in group 1, 2 in group 2; matching turn_lens)
+        completed_at_steps = [[5, 4, 5, 5, 3], [5, 3]]
+        current_iteration = 6
         metrics = rl_utils.prep_wandb_metrics(
-            MagicMock(), traj_lens, turn_lens, rewards, num_turns, advantages
+            MagicMock(),
+            traj_lens,
+            turn_lens,
+            rewards,
+            num_turns,
+            advantages,
+            policy_staleness=policy_staleness,
+            kv_cache_staleness=kv_cache_staleness,
+            num_evictions=num_evictions,
+            completed_at_steps=completed_at_steps,
+            current_iteration=current_iteration,
         )
         assert metrics["mean_reward"] == 0.75
         assert metrics["mean_advantage"] == 0.5
@@ -898,3 +967,15 @@ def test_prep_wandb_metrics(self, initialize_model_parallel):
         assert metrics["mean_num_turns"] == 15.5
         assert metrics["max_num_turns"] == 42
         assert metrics["min_num_turns"] == 2
+        # true_policy_staleness = [2, 4, 4, 1, 1, 6, 1, 6, 6]
+        assert metrics["mean_policy_staleness"] == np.mean([2, 4, 4, 1, 1, 6, 1, 6, 6])
+        assert metrics["max_policy_staleness"] == 6
+        assert metrics["min_policy_staleness"] == 1
+        # true_kv_staleness = [2, 3, 3, 1, 1, 5, 1, 5, 5]
+        assert metrics["mean_kv_cache_staleness"] == np.mean([2, 3, 3, 1, 1, 5, 1, 5, 5])
+        assert metrics["max_kv_cache_staleness"] == 5
+        assert metrics["min_kv_cache_staleness"] == 1
+        assert metrics["total_eviction_count"] == 1
+        assert metrics["max_num_evictions"] == 1
+        # completion_gaps per-turn = [1, 2, 1, 1, 3, 1, 3]
+        assert metrics["mean_completion_gap"] == np.mean([1, 2, 1, 1, 3, 1, 3])
diff --git a/tests/unit_tests/ssm/test_mamba_block.py b/tests/unit_tests/ssm/test_mamba_block.py
index 909ee47e836..c65623e08e0 100644
--- a/tests/unit_tests/ssm/test_mamba_block.py
+++ b/tests/unit_tests/ssm/test_mamba_block.py
@@ -6,7 +6,7 @@
 from megatron.core.models.mamba.mamba_layer_specs import mamba_stack_spec
 from megatron.core.process_groups_config import ProcessGroupCollection
 from megatron.core.ssm.mamba_block import MambaStack
-from megatron.core.ssm.mamba_hybrid_layer_allocation import Symbols
+from megatron.core.ssm.mamba_hybrid_layer_allocation import Symbols, validate_segment_layers
 from megatron.core.ssm.mamba_layer import MambaLayer
 from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed
 from megatron.core.transformer import TransformerConfig
@@ -26,12 +26,13 @@ def setup_method(self, method):
     def get_pg_collection(self):
         return ProcessGroupCollection.use_mpu_process_groups(required_pgs=['tp', 'pp', 'cp'])
 
-    def get_mamba_block(self, hybrid_override_pattern):
+    def get_mamba_block(self, layer_pattern):
+        layer_type_list = validate_segment_layers(layer_pattern)
         transformer_config = TransformerConfig(
             hidden_size=256,  # The Mamba layer places several constraints on this
             # Need to specify num_attention_heads and num_layers or TransformerConfig
             # will generate errors.
-            num_layers=len(hybrid_override_pattern),
+            num_layers=len(layer_type_list),
             num_attention_heads=4,
             use_cpu_initialization=True,
         )
@@ -39,7 +40,8 @@ def get_mamba_block(self, hybrid_override_pattern):
         return MambaStack(
             transformer_config,
             modules,
-            hybrid_override_pattern=hybrid_override_pattern,
+            layer_type_list=layer_type_list,
+            pp_layer_offset=0,
             pg_collection=self.get_pg_collection(),
         )
 
@@ -48,8 +50,8 @@ def teardown_method(self, method):
 
     def test_gpu_forward(self):
         """Test GPU forward pass."""
-        hybrid_override_pattern = Symbols.MAMBA + Symbols.ATTENTION + Symbols.MLP
-        block = self.get_mamba_block(hybrid_override_pattern)
+        layer_pattern = Symbols.MAMBA + Symbols.ATTENTION + Symbols.MLP
+        block = self.get_mamba_block(layer_pattern)
         block.cuda()
         micro_batch_size = 2
         sequence_length = 32
@@ -67,13 +69,13 @@ def test_gpu_forward(self):
 
     def test_layer_types(self):
         """
-        Make sure that the layer types specified with hybrid_override_pattern
+        Make sure that the layer types specified with layer_pattern
         were honored.
         """
-        hybrid_override_pattern = Symbols.MAMBA + Symbols.ATTENTION + Symbols.MLP
-        block = self.get_mamba_block(hybrid_override_pattern)
+        layer_pattern = Symbols.MAMBA + Symbols.ATTENTION + Symbols.MLP
+        block = self.get_mamba_block(layer_pattern)
         layers = block.layers
-        # Note that this matches the order specified by hybrid_override_pattern in setup_method
+        # Note that this matches the order specified by layer_pattern above
         assert isinstance(layers[0], MambaLayer)
         assert isinstance(layers[1], TransformerLayer)
         assert isinstance(layers[1].self_attention, SelfAttention)
@@ -82,8 +84,8 @@ def test_layer_types(self):
 
     def test_invalid_layer_types_cause_failure(self):
         invalid_symbol = '+'
-        assert invalid_symbol not in Symbols.VALID  # sanity check.
-        hybrid_override_pattern = Symbols.MAMBA + Symbols.ATTENTION + Symbols.MLP + invalid_symbol
-        # _allocate_override() in mamba_hybrid_layer_allocation.py throws a ValueError.
+        assert invalid_symbol not in Symbols.VALID_LAYERS  # sanity check.
+        layer_pattern = Symbols.MAMBA + Symbols.ATTENTION + Symbols.MLP + invalid_symbol
+        # validate_segment_layers() in mamba_hybrid_layer_allocation.py throws a ValueError.
         with pytest.raises(ValueError):
-            block = self.get_mamba_block(hybrid_override_pattern)
+            block = self.get_mamba_block(layer_pattern)
diff --git a/tests/unit_tests/ssm/test_mamba_hybrid_layer_allocation.py b/tests/unit_tests/ssm/test_mamba_hybrid_layer_allocation.py
index 77c106c3bee..a0a0401d113 100644
--- a/tests/unit_tests/ssm/test_mamba_hybrid_layer_allocation.py
+++ b/tests/unit_tests/ssm/test_mamba_hybrid_layer_allocation.py
@@ -1,85 +1,135 @@
 # Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
 
-import math
-import re
+from unittest.mock import patch
 
 import pytest
-import torch
 
 from megatron.core.ssm.mamba_hybrid_layer_allocation import (
     ParsedHybridPattern,
     Symbols,
-    allocate_layers,
+    get_hybrid_layer_counts,
+    get_hybrid_total_layer_count,
+    get_hybrid_total_pipeline_segment_count,
     parse_hybrid_pattern,
+    pattern_from_ratios,
+    select_pipeline_segment,
+    validate_segment_layers,
 )
 
 
 @pytest.mark.internal
-class TestMambaHybridLayerAllocation:
+class TestPatternFromRatios:
 
-    def test_hybrid_layer_allocation(self):
-        # The format for the test cases is:
-        # (layers_count, attention_ratio, mlp_ratio, override_pattern).
+    def test_pure_mamba(self):
+        result = pattern_from_ratios(8, attention_ratio=0.0, mlp_ratio=0.0)
+        assert result == "MMMMMMMM"
+
+    def test_attention_only(self):
+        result = pattern_from_ratios(10, attention_ratio=0.3)
+        assert result.count(Symbols.ATTENTION) == 3
+        assert result.count(Symbols.MAMBA) == 7
+        assert len(result) == 10
+
+    def test_attention_and_mlp(self):
+        result = pattern_from_ratios(10, attention_ratio=0.3, mlp_ratio=0.3)
+        assert result.count(Symbols.ATTENTION) == 3
+        assert result.count(Symbols.MLP) == 3
+        assert result.count(Symbols.MAMBA) == 4
+        assert len(result) == 10
+
+    def test_attention_evenly_spaced(self):
+        result = pattern_from_ratios(10, attention_ratio=0.5)
+        assert result.count(Symbols.ATTENTION) == 5
+        assert result.count(Symbols.MAMBA) == 5
+        attn_positions = [i for i, ch in enumerate(result) if ch == Symbols.ATTENTION]
+        gaps = [attn_positions[i + 1] - attn_positions[i] for i in range(len(attn_positions) - 1)]
+        assert all(
+            g in (1, 2, 3) for g in gaps
+        ), f"Gaps between attention layers should be small, got {gaps}"
+
+    def test_mlp_does_not_replace_attention(self):
+        result = pattern_from_ratios(10, attention_ratio=0.3, mlp_ratio=0.3)
+        attn_positions = [i for i, c in enumerate(result) if c == Symbols.ATTENTION]
+        mlp_positions = [i for i, c in enumerate(result) if c == Symbols.MLP]
+        assert not set(attn_positions) & set(mlp_positions)
+
+    def test_single_layer(self):
+        assert pattern_from_ratios(1, 0.0, 0.0) == "M"
+        assert pattern_from_ratios(1, 1.0, 0.0) == "*"
+
+    def test_returns_string(self):
+        result = pattern_from_ratios(4, 0.5)
+        assert isinstance(result, str)
+
+
+@pytest.mark.internal
+class TestValidateSegmentLayers:
+
+    def test_valid_patterns(self):
+        """Test that valid segment patterns produce the correct layer type lists."""
         test_cases = [
-            (9, 0.0, 0.0, "M*-M*-M*-"),
-            (9, 0.0, 0.0, "MMMMMMMMM"),
-            (30, 0.0, 0.0, None),
-            (8, 0.25, 0.25, "MM*-MM*-"),
-            (8, 0.5, 0.25, "M**-M**-"),
-            (48, 0.5, 0.2, None),
+            ("M*-M*-M*-", ['M', '*', '-', 'M', '*', '-', 'M', '*', '-']),
+            ("MMMMMMMMM", ['M'] * 9),
+            ("MM*-MM*-", ['M', 'M', '*', '-', 'M', 'M', '*', '-']),
+            ("E", ['E']),
+            ("", []),
         ]
-        for test in test_cases:
-            (layers_count, attention_ratio, mlp_ratio, override_pattern) = test
+        for pattern, expected in test_cases:
+            result = validate_segment_layers(pattern)
+            assert result == expected, f"Failed for pattern: {pattern}"
+
+    def test_all_valid_symbols(self):
+        """Make sure all returned layers are valid."""
+        for pattern in ["M*-M*-M*-", "MMMMMMMMM", "MM*-", "MEME"]:
+            layer_types = validate_segment_layers(pattern)
+            for layer_type in layer_types:
+                assert layer_type in Symbols.VALID_LAYERS
 
-            layer_types = allocate_layers(*test)
+    def test_invalid_symbols_cause_failure(self):
+        """Test that invalid symbols raise ValueError."""
+        with pytest.raises(ValueError):
+            validate_segment_layers("M*X")
+        with pytest.raises(ValueError):
+            validate_segment_layers("M|M")  # pipe not valid in a segment
+        with pytest.raises(ValueError):
+            validate_segment_layers("M/M")  # MTP separator not valid in a segment
 
-            # Check that return value is in the right format.
-            assert isinstance(layer_types, list)
-            assert layers_count == len(layer_types)
 
-            # Make sure all the layers are valid.
-            for layer_type in layer_types:
-                assert layer_type in Symbols.VALID
-
-            # Make sure each layer is as requested by override_pattern.
-            if override_pattern is not None:
-                assert len(override_pattern) == len(layer_types)
-                for index, layer_type in enumerate(layer_types):
-                    assert override_pattern[index] == layer_types[index]
-            else:
-                # Make sure the count of each type of layer is correct.
-                counts = {layer_type: 0 for layer_type in Symbols.VALID}  # Initialize all to zero.
-                for layer_type in layer_types:
-                    assert layer_type in counts
-                    counts[layer_type] += 1
-                # Check the ratios.
-                remainder = 1.0 - attention_ratio - mlp_ratio
-                assert remainder >= 0
-                assert int(attention_ratio * layers_count + 0.5) == counts[Symbols.ATTENTION]
-                assert int(mlp_ratio * layers_count + 0.5) == counts[Symbols.MLP]
-                assert int(remainder * layers_count + 0.5) == counts[Symbols.MAMBA]
-
-            # Make sure the ratios are as requested.
-            # This code is not working yet because capsys seems broken in Megatron.
-            # captured = capsys.readouterr()  # Remove this output from the capture buffer.
-            # out = captured.out  # Get stdout.
-            # if attention_ratio != 0 or mlp_ratio != 0:
-            #     assert (
-            #             match := re.search(r'Actual attention ratio: (1\.0|0\.[0-9]+)\.', out)
-            #     ) and math.isclose(match.group(1), attention_ratio)
-            #     assert (
-            #             match := re.search(r'Actual mlp ratio: (1\.0|0\.[0-9]+)\.', out)
-            #     ) and math.isclose(match.group(1), mlp_ratio)
-
-    @pytest.mark.xfail(raises=ValueError)
-    def test_wrong_length_override_pattern(self):
-        # This override_pattern is too short.
-        layer_types = allocate_layers(9, 0.0, 0.0, "M*-M*-")
-
-    @pytest.mark.xfail(raises=ValueError)
-    def test_wrong_number_of_layer_types_in_override_pattern(self):
-        # This override_pattern has too many mlps and not enough attention
-        layer_types = allocate_layers(8, 0.5, 0.25, "M*--M**-")
+@pytest.mark.internal
+class TestGetHybridTotalLayerCount:
+
+    def test_simple_patterns(self):
+        assert get_hybrid_total_layer_count("M*M*") == 4
+        assert get_hybrid_total_layer_count("MMMM") == 4
+        assert get_hybrid_total_layer_count("M") == 1
+
+    def test_with_pipe_separators(self):
+        assert get_hybrid_total_layer_count("M-M-|M-M*-") == 9
+        assert get_hybrid_total_layer_count("M-M-|M-M*-|M-M-|M-M*-") == 18
+        assert get_hybrid_total_layer_count("||M") == 1
+        assert get_hybrid_total_layer_count("M|M") == 2
+
+    def test_with_mtp(self):
+        assert get_hybrid_total_layer_count("M*M*/MM/MM") == 4
+        assert get_hybrid_total_layer_count("M-M-|M-M*-/MM/MM") == 9
+
+    def test_empty(self):
+        assert get_hybrid_total_layer_count("") == 0
+
+
+@pytest.mark.internal
+class TestGetHybridTotalPipelineSegmentCount:
+
+    def test_no_pipe(self):
+        assert get_hybrid_total_pipeline_segment_count("M*M*") == 1
+
+    def test_with_pipes(self):
+        assert get_hybrid_total_pipeline_segment_count("M-M-|M-M*-") == 2
+        assert get_hybrid_total_pipeline_segment_count("M|M|M|M") == 4
+        assert get_hybrid_total_pipeline_segment_count("||M") == 3
+
+    def test_with_mtp(self):
+        assert get_hybrid_total_pipeline_segment_count("M-M-|M-M*-/MM/MM") == 2
 
 
 @pytest.mark.internal
@@ -108,6 +158,15 @@ def test_main_pattern_only(self):
             assert result.mtp_pattern is None
             assert result.mtp_num_depths == 0
 
+    def test_main_pattern_with_pipes(self):
+        """Test patterns with pipe separators (no MTP)."""
+        test_cases = [("M*|M*", "M*|M*"), ("M-M-|M-M*-", "M-M-|M-M*-"), ("M|M|M|M", "M|M|M|M")]
+        for pattern, expected_main in test_cases:
+            result = parse_hybrid_pattern(pattern)
+            assert result.main_pattern == expected_main, f"Failed for pattern: {pattern}"
+            assert result.mtp_pattern is None
+            assert result.mtp_num_depths == 0
+
     def test_main_with_single_mtp_depth(self):
         """Test patterns with 1 MTP depth."""
         test_cases = [
@@ -136,6 +195,13 @@ def test_main_with_multiple_mtp_depths(self):
             assert result.mtp_pattern == expected_mtp, f"Failed for pattern: {pattern}"
             assert result.mtp_num_depths == expected_depths, f"Failed for pattern: {pattern}"
 
+    def test_pipe_with_mtp(self):
+        """Test patterns with both pipe and MTP separators."""
+        result = parse_hybrid_pattern("M-M-|M-M*-/MM/MM")
+        assert result.main_pattern == "M-M-|M-M*-"
+        assert result.mtp_pattern == "MM"
+        assert result.mtp_num_depths == 2
+
     def test_mtp_patterns_must_be_identical(self):
         """Test that mismatched MTP patterns raise ValueError."""
         invalid_patterns = [
@@ -173,6 +239,11 @@ def test_invalid_symbols_in_mtp_pattern(self):
         with pytest.raises(ValueError, match="All MTP patterns must be identical"):
             parse_hybrid_pattern("M*M*/MM/Ma")
 
+    def test_pipe_not_allowed_in_mtp(self):
+        """Test that pipe symbol in MTP pattern raises ValueError."""
+        with pytest.raises(ValueError, match="not a valid layer symbol"):
+            parse_hybrid_pattern("M*M*/M|M/M|M")
+
     def test_empty_main_pattern_with_mtp(self):
         """Test pattern that starts with / (empty main pattern)."""
         result = parse_hybrid_pattern("/MM/MM")
@@ -212,3 +283,297 @@ def test_dataclass_equality(self):
         p1 = parse_hybrid_pattern("M*M*/MM/MM")
         p2 = ParsedHybridPattern(main_pattern="M*M*", mtp_pattern="MM", mtp_num_depths=2)
         assert p1 == p2
+
+
+@pytest.mark.internal
+class TestGetHybridLayerCounts:
+
+    def test_simple_pattern(self):
+        assert get_hybrid_layer_counts("M*M*") == {'*': 2, 'M': 2, '-': 0, 'E': 0}
+
+    def test_all_layer_types(self):
+        assert get_hybrid_layer_counts("M*-E") == {'*': 1, 'M': 1, '-': 1, 'E': 1}
+
+    def test_with_pipes(self):
+        # Pipes should be skipped in counting
+        assert get_hybrid_layer_counts("M*|M*") == {'*': 2, 'M': 2, '-': 0, 'E': 0}
+        assert get_hybrid_layer_counts("M-M-|M-M*-") == {'*': 1, 'M': 4, '-': 4, 'E': 0}
+
+    def test_with_mtp(self):
+        # MTP pattern "MM" repeated 2 depths -> 4 extra mamba layers
+        assert get_hybrid_layer_counts("M*M*/MM/MM") == {'*': 2, 'M': 6, '-': 0, 'E': 0}
+
+    def test_with_pipes_and_mtp(self):
+        # Main: M-M-|M-M*- -> 1 attn, 4 mamba, 4 mlp
+        # MTP: MM x 2 depths -> +4 mamba
+        assert get_hybrid_layer_counts("M-M-|M-M*-/MM/MM") == {'*': 1, 'M': 8, '-': 4, 'E': 0}
+
+    def test_moe_pattern(self):
+        assert get_hybrid_layer_counts("MEME") == {'*': 0, 'M': 2, '-': 0, 'E': 2}
+
+    def test_mtp_with_attention(self):
+        # MTP pattern "*M" repeated 3 depths -> 3 attn + 3 mamba from MTP
+        assert get_hybrid_layer_counts("MMMM/*M/*M/*M") == {'*': 3, 'M': 7, '-': 0, 'E': 0}
+
+    def test_empty_pattern(self):
+        assert get_hybrid_layer_counts("") == {'*': 0, 'M': 0, '-': 0, 'E': 0}
+
+
+@pytest.mark.internal
+class TestSelectPipelineSegment:
+    """Tests for select_pipeline_segment with pp_group=None (single rank).
+
+    When pp_group is None, pp_rank=0 and pp_size=1, so the segment index
+    is simply the vp_stage value.
+    """
+
+    @patch('megatron.core.ssm.mamba_hybrid_layer_allocation.log_on_each_pipeline_stage')
+    def test_single_segment_no_vp(self, mock_log):
+        """Single segment, no VPP."""
+        layer_types, offset = select_pipeline_segment("M*M*", pp_group=None, vp_stage=None)
+        assert layer_types == ['M', '*', 'M', '*']
+        assert offset == 0
+
+    @patch('megatron.core.ssm.mamba_hybrid_layer_allocation.log_on_each_pipeline_stage')
+    def test_two_segments_vp0(self, mock_log):
+        """Two segments, select first (vp_stage=0)."""
+        layer_types, offset = select_pipeline_segment("M-M-|M-M*-", pp_group=None, vp_stage=0)
+        assert layer_types == ['M', '-', 'M', '-']
+        assert offset == 0
+
+    @patch('megatron.core.ssm.mamba_hybrid_layer_allocation.log_on_each_pipeline_stage')
+    def test_two_segments_vp1(self, mock_log):
+        """Two segments, select second (vp_stage=1)."""
+        layer_types, offset = select_pipeline_segment("M-M-|M-M*-", pp_group=None, vp_stage=1)
+        assert layer_types == ['M', '-', 'M', '*', '-']
+        assert offset == 4
+
+    @patch('megatron.core.ssm.mamba_hybrid_layer_allocation.log_on_each_pipeline_stage')
+    def test_four_segments(self, mock_log):
+        """Four segments, verify each vp_stage selects correctly."""
+        pattern = "MM|M*|M-|ME"
+        expected = [(['M', 'M'], 0), (['M', '*'], 2), (['M', '-'], 4), (['M', 'E'], 6)]
+        for vp_stage, (expected_layers, expected_offset) in enumerate(expected):
+            layer_types, offset = select_pipeline_segment(pattern, pp_group=None, vp_stage=vp_stage)
+            assert layer_types == expected_layers, f"Failed for vp_stage={vp_stage}"
+            assert offset == expected_offset, f"Failed for vp_stage={vp_stage}"
+
+    @patch('megatron.core.ssm.mamba_hybrid_layer_allocation.log_on_each_pipeline_stage')
+    def test_empty_segment(self, mock_log):
+        """Empty segments are allowed for pipeline balancing."""
+        layer_types, offset = select_pipeline_segment("||M*", pp_group=None, vp_stage=0)
+        assert layer_types == []
+        assert offset == 0
+
+        layer_types, offset = select_pipeline_segment("||M*", pp_group=None, vp_stage=2)
+        assert layer_types == ['M', '*']
+        assert offset == 0
+
+    @patch('megatron.core.ssm.mamba_hybrid_layer_allocation.log_on_each_pipeline_stage')
+    def test_uneven_segments(self, mock_log):
+        """Segments of different lengths."""
+        pattern = "MMM|M|MMMMM"
+        layer_types, offset = select_pipeline_segment(pattern, pp_group=None, vp_stage=0)
+        assert len(layer_types) == 3
+        assert offset == 0
+
+        layer_types, offset = select_pipeline_segment(pattern, pp_group=None, vp_stage=1)
+        assert len(layer_types) == 1
+        assert offset == 3
+
+        layer_types, offset = select_pipeline_segment(pattern, pp_group=None, vp_stage=2)
+        assert len(layer_types) == 5
+        assert offset == 4
+
+    @patch('megatron.core.ssm.mamba_hybrid_layer_allocation.log_on_each_pipeline_stage')
+    def test_empty_main_pattern(self, mock_log):
+        """Empty main pattern produces one empty segment."""
+        layer_types, offset = select_pipeline_segment("", pp_group=None, vp_stage=None)
+        assert layer_types == []
+        assert offset == 0
+
+    @patch('megatron.core.ssm.mamba_hybrid_layer_allocation.log_on_each_pipeline_stage')
+    def test_invalid_segment_raises(self, mock_log):
+        """Invalid layer symbols in a segment should raise ValueError."""
+        with pytest.raises(ValueError):
+            select_pipeline_segment("MX|M*", pp_group=None, vp_stage=0)
+
+    @patch('megatron.core.ssm.mamba_hybrid_layer_allocation.log_on_each_pipeline_stage')
+    def test_out_of_range_segment_raises(self, mock_log):
+        """Segment index out of range should raise ValueError."""
+        with pytest.raises(ValueError, match="out of range"):
+            select_pipeline_segment("M*|M*", pp_group=None, vp_stage=5)
+
+    @patch('megatron.core.ssm.mamba_hybrid_layer_allocation.log_on_each_pipeline_stage')
+    def test_logging_is_called(self, mock_log):
+        """Verify that log_on_each_pipeline_stage is called."""
+        select_pipeline_segment("M*M*", pp_group=None, vp_stage=None)
+        mock_log.assert_called_once()
+
+    @patch('megatron.core.ssm.mamba_hybrid_layer_allocation.log_on_each_pipeline_stage')
+    def test_mutual_exclusivity_pipes_with_first_stage(self, mock_log):
+        """Pipe separators + first_stage_layers should raise ValueError."""
+        with pytest.raises(ValueError, match="Cannot specify"):
+            select_pipeline_segment("M*|M*", pp_group=None, vp_stage=0, first_stage_layers=1)
+
+    @patch('megatron.core.ssm.mamba_hybrid_layer_allocation.log_on_each_pipeline_stage')
+    def test_mutual_exclusivity_pipes_with_last_stage(self, mock_log):
+        """Pipe separators + last_stage_layers should raise ValueError."""
+        with pytest.raises(ValueError, match="Cannot specify"):
+            select_pipeline_segment("M*|M*", pp_group=None, vp_stage=0, last_stage_layers=1)
+
+    @patch('megatron.core.ssm.mamba_hybrid_layer_allocation.log_on_each_pipeline_stage')
+    def test_segment_count_not_divisible_by_pp_size(self, mock_log):
+        """Segment count not divisible by pp_size should raise ValueError."""
+        mock_group = object()
+        with (
+            patch('torch.distributed.get_rank', return_value=0),
+            patch('torch.distributed.get_world_size', return_value=2),
+        ):
+            with pytest.raises(ValueError, match="evenly divisible"):
+                select_pipeline_segment("M|M|M", pp_group=mock_group, vp_stage=None)
+
+
+@pytest.mark.internal
+class TestSelectPipelineSegmentLegacyFallback:
+    """Tests for the no-pipes fallback path in select_pipeline_segment.
+
+    These tests exercise the backwards-compatible auto-split logic that
+    activates when the pattern has no pipe separators but pp_size > 1.
+    """
+
+    def _call_for_rank(
+        self,
+        pattern,
+        pp_rank,
+        pp_size,
+        vp_stage=None,
+        first_stage_layers=None,
+        last_stage_layers=None,
+    ):
+        """Call select_pipeline_segment with mocked PP group for a given rank."""
+        mock_group = object()
+        with (
+            patch('torch.distributed.get_rank', return_value=pp_rank),
+            patch('torch.distributed.get_world_size', return_value=pp_size),
+            patch('megatron.core.ssm.mamba_hybrid_layer_allocation.log_on_each_pipeline_stage'),
+            patch('megatron.core.ssm.mamba_hybrid_layer_allocation.log_single_rank'),
+        ):
+            return select_pipeline_segment(
+                pattern,
+                pp_group=mock_group,
+                vp_stage=vp_stage,
+                first_stage_layers=first_stage_layers,
+                last_stage_layers=last_stage_layers,
+            )
+
+    def test_even_split_2_ranks(self):
+        """4 layers across 2 ranks -> 2 each."""
+        layers0, off0 = self._call_for_rank("M*M-", pp_rank=0, pp_size=2)
+        assert layers0 == ['M', '*']
+        assert off0 == 0
+
+        layers1, off1 = self._call_for_rank("M*M-", pp_rank=1, pp_size=2)
+        assert layers1 == ['M', '-']
+        assert off1 == 2
+
+    def test_even_split_4_ranks(self):
+        """8 layers across 4 ranks -> 2 each."""
+        pattern = "M*M*M*M*"
+        for rank in range(4):
+            layers, offset = self._call_for_rank(pattern, pp_rank=rank, pp_size=4)
+            assert len(layers) == 2
+            assert offset == rank * 2
+
+    def test_even_split_not_divisible_raises(self):
+        """6 layers across 4 ranks with no uneven PP -> ValueError."""
+        with pytest.raises(ValueError, match="evenly divisible"):
+            self._call_for_rank("M*M*M*", pp_rank=0, pp_size=4)
+
+    def test_uneven_pp_first_stage(self):
+        """6 layers, pp_size=4, first_stage=3 -> first gets 3, others get 1."""
+        pattern = "M*M*M*"
+        layers0, off0 = self._call_for_rank(pattern, pp_rank=0, pp_size=4, first_stage_layers=3)
+        assert len(layers0) == 3
+        assert off0 == 0
+
+        layers1, off1 = self._call_for_rank(pattern, pp_rank=1, pp_size=4, first_stage_layers=3)
+        assert len(layers1) == 1
+        assert off1 == 3
+
+        layers3, off3 = self._call_for_rank(pattern, pp_rank=3, pp_size=4, first_stage_layers=3)
+        assert len(layers3) == 1
+        assert off3 == 5
+
+    def test_uneven_pp_last_stage(self):
+        """6 layers, pp_size=4, last_stage=3 -> last gets 3, others get 1."""
+        pattern = "M*M*M*"
+        layers0, off0 = self._call_for_rank(pattern, pp_rank=0, pp_size=4, last_stage_layers=3)
+        assert len(layers0) == 1
+        assert off0 == 0
+
+        layers3, off3 = self._call_for_rank(pattern, pp_rank=3, pp_size=4, last_stage_layers=3)
+        assert len(layers3) == 3
+        assert off3 == 3
+
+    def test_uneven_pp_first_and_last(self):
+        """8 layers, pp_size=4, first=1, last=1 -> first 1, middle 3 each, last 1."""
+        pattern = "M*M*M*M*"
+        layers0, off0 = self._call_for_rank(
+            pattern, pp_rank=0, pp_size=4, first_stage_layers=1, last_stage_layers=1
+        )
+        assert len(layers0) == 1
+        assert off0 == 0
+
+        layers1, off1 = self._call_for_rank(
+            pattern, pp_rank=1, pp_size=4, first_stage_layers=1, last_stage_layers=1
+        )
+        assert len(layers1) == 3
+        assert off1 == 1
+
+        layers2, off2 = self._call_for_rank(
+            pattern, pp_rank=2, pp_size=4, first_stage_layers=1, last_stage_layers=1
+        )
+        assert len(layers2) == 3
+        assert off2 == 4
+
+        layers3, off3 = self._call_for_rank(
+            pattern, pp_rank=3, pp_size=4, first_stage_layers=1, last_stage_layers=1
+        )
+        assert len(layers3) == 1
+        assert off3 == 7
+
+    def test_uneven_pp_middle_not_divisible_raises(self):
+        """Middle layers not divisible by middle stages -> ValueError."""
+        with pytest.raises(ValueError, match="Middle layers"):
+            self._call_for_rank("M*M*M*M*M", pp_rank=0, pp_size=4, first_stage_layers=2)
+
+    def test_vpp_with_no_pipes_raises(self):
+        """VPP (vp_stage != None) without pipe separators -> ValueError."""
+        with pytest.raises(ValueError, match="Virtual pipeline parallelism"):
+            self._call_for_rank("M*M*", pp_rank=0, pp_size=2, vp_stage=0)
+
+    def test_deprecation_warning_logged(self):
+        """Legacy path should log a deprecation warning via log_single_rank."""
+        mock_group = object()
+        with (
+            patch('torch.distributed.get_rank', return_value=0),
+            patch('torch.distributed.get_world_size', return_value=2),
+            patch('megatron.core.ssm.mamba_hybrid_layer_allocation.log_on_each_pipeline_stage'),
+            patch('megatron.core.ssm.mamba_hybrid_layer_allocation.log_single_rank') as mock_warn,
+        ):
+            select_pipeline_segment("M*M*", pp_group=mock_group, vp_stage=None)
+            mock_warn.assert_called_once()
+            call_args = mock_warn.call_args
+            assert "DEPRECATION" in call_args[0][2]
+
+    def test_all_ranks_cover_full_pattern(self):
+        """All ranks together should reconstruct the original layer list."""
+        pattern = "M*M*M*"
+        pp_size = 3
+        all_layers = []
+        for rank in range(pp_size):
+            layers, offset = self._call_for_rank(pattern, pp_rank=rank, pp_size=pp_size)
+            assert offset == len(all_layers)
+            all_layers.extend(layers)
+        assert all_layers == ['M', '*', 'M', '*', 'M', '*']
diff --git a/tests/unit_tests/test_fp8_param.py b/tests/unit_tests/test_fp8_param.py
index e15d2440d99..e0a71526297 100644
--- a/tests/unit_tests/test_fp8_param.py
+++ b/tests/unit_tests/test_fp8_param.py
@@ -11,7 +11,7 @@
 
 from megatron.core.distributed import DistributedDataParallel as DDP
 from megatron.core.enums import ModelType
-from megatron.core.fp8_utils import is_float8tensor
+from megatron.core.fp8_utils import is_float8tensor, is_mxfp8tensor
 from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_with_transformer_engine_spec
 from megatron.core.models.gpt.gpt_model import GPTModel
 from megatron.core.num_microbatches_calculator import destroy_num_microbatches_calculator
@@ -246,6 +246,28 @@ def _run_test_helper(
         if fp8_param_gather:
             assert num_fp8_params == 4 * fp8_layers
 
+        # Verify that bf16 params (embedding, LN, etc.) in the MXFP8 model are mapped
+        # to the param buffer (shared with grad buffer) rather than allocated separately.
+        if args.reuse_grad_buf_for_mxfp8_param_ag:
+            for buffer in gpt_model[0].buffers:
+                if buffer.param_data is None:
+                    continue
+                buf_start = buffer.param_data.data_ptr()
+                buf_end = buf_start + buffer.param_data.numel() * buffer.param_data.element_size()
+                for param in buffer.param_to_bucket:
+                    if is_mxfp8tensor(param):
+                        # MXFP8 params keep their own quantized storage.
+                        assert not (
+                            buf_start <= param.data.data_ptr() < buf_end
+                        ), "MXFP8 param should not be mapped to the param buffer"
+                    else:
+                        # BF16 params should be views into the param buffer
+                        # (no double allocation).
+                        assert buf_start <= param.data.data_ptr() < buf_end, (
+                            "BF16 param should be a view into the param buffer "
+                            "(no separate allocation)"
+                        )
+
         loss_list = []
 
         for i in range(100):
diff --git a/tests/unit_tests/test_layer_wise_optimizer.py b/tests/unit_tests/test_layer_wise_optimizer.py
index 9b404b388b4..d8b0e97b524 100644
--- a/tests/unit_tests/test_layer_wise_optimizer.py
+++ b/tests/unit_tests/test_layer_wise_optimizer.py
@@ -10,12 +10,13 @@
 
 from megatron.core import parallel_state
 from megatron.core.distributed import DistributedDataParallel, DistributedDataParallelConfig
-from megatron.core.optimizer import OptimizerConfig, get_megatron_optimizer
+from megatron.core.optimizer import OptimizerConfig
 from megatron.core.optimizer.layer_wise_optimizer import LayerWiseDistributedOptimizer
-from megatron.core.optimizer.optimizer import Float16OptimizerWithFloat16Params, FP32Optimizer
+from megatron.core.optimizer.muon import get_megatron_muon_optimizer
+from megatron.core.optimizer.optimizer import Float16OptimizerWithFloat16Params
 from megatron.core.process_groups_config import ProcessGroupCollection
 from megatron.core.transformer import TransformerConfig
-from megatron.core.utils import get_pg_size
+from megatron.core.utils import get_pg_rank, get_pg_size
 from tests.unit_tests.test_utilities import Utils
 
 # Skip all tests in this file for LTS versions
@@ -88,8 +89,8 @@ def create_model_and_optimizer(
             model_class: Model class to instantiate
             clip_grad: Optional gradient clipping value
             model_kwargs: Optional kwargs for model initialization
-            use_layer_wise: If True, wrap optimizer in LayerWiseDistributedOptimizer;
-                          if False, use get_megatron_optimizer instead (for reference)
+            use_layer_wise: If True, use LayerWiseDistributedOptimizer via dist_muon;
+                          if False, use standard muon ChainedOptimizer (for reference)
 
         Returns:
             tuple: (model, optimizer, pg_collection)
@@ -110,26 +111,99 @@ def create_model_and_optimizer(
             model.broadcast_params()
 
         optimizer_config = OptimizerConfig(
-            optimizer='adam',
+            optimizer='muon',
             lr=0.01,
             weight_decay=0.01,
-            bf16=not use_layer_wise,
+            bf16=True,
             use_distributed_optimizer=False,
             clip_grad=clip_grad,
+            muon_tp_mode="duplicated",
         )
 
         pg_collection = ProcessGroupCollection.use_mpu_process_groups()
         pg_collection.dp_cp = parallel_state.get_data_parallel_group(with_context_parallel=True)
         pg_collection.expt_dp = parallel_state.get_expert_data_parallel_group()
 
-        optimizer = get_megatron_optimizer(optimizer_config, [model])
-        if use_layer_wise:
-            # Extract base torch optimizers from the FP32Optimizer wrappers.
-            base_optimizers = [opt.optimizer for opt in optimizer.chained_optimizers]
-            optimizer_config.bf16 = True
-            optimizer = LayerWiseDistributedOptimizer(
-                base_optimizers, optimizer_config, pg_collection
-            )
+        optimizer = get_megatron_muon_optimizer(
+            config=optimizer_config,
+            model_chunks=[model],
+            use_gloo_process_groups=True,
+            layer_wise_distributed_optimizer=use_layer_wise,
+            pg_collection=pg_collection,
+        )
+        return model, optimizer, pg_collection
+
+    def create_model_and_optimizer_with_overlap_param_gather(
+        self,
+        model_class=SimpleModel,
+        clip_grad=1.0,
+        model_kwargs=None,
+        copy_from=None,
+        async_allgather=True,
+        grad_reduce_in_fp32=False,
+        bucket_size=None,
+    ):
+        """Create model, DDP wrapper, and optimizer with overlap-param-gather enabled.
+
+        This variant sets overlap_param_gather=True in DDP config and uses
+        get_megatron_muon_optimizer with layer_wise_distributed_optimizer=True,
+        enabling the bucket-based async param gather path.
+
+        Args:
+            model_class: Model class to instantiate
+            clip_grad: Optional gradient clipping value
+            model_kwargs: Optional kwargs for model initialization
+            copy_from: Optional DDP model to copy weights from
+            async_allgather: If True, defer param all-gather to bucket infrastructure
+            grad_reduce_in_fp32: If True, reduce grads in fp32 (regression test for dtype fix)
+            bucket_size: Maximum number of parameters per bucket (None = single bucket)
+
+        Returns:
+            tuple: (model, optimizer, pg_collection)
+        """
+        if model_kwargs is None:
+            model_kwargs = {}
+
+        model = model_class(**model_kwargs).bfloat16().cuda()
+        model.requires_grad_(True)
+
+        ddp_config = DistributedDataParallelConfig(
+            use_distributed_optimizer=False,
+            overlap_param_gather=True,
+            overlap_grad_reduce=True,
+            grad_reduce_in_fp32=grad_reduce_in_fp32,
+            bucket_size=bucket_size,
+        )
+        model = DistributedDataParallel(
+            TransformerConfig(num_attention_heads=1, num_layers=1), ddp_config, model
+        )
+        if copy_from:
+            model.module.load_state_dict(copy_from.module.state_dict())
+        else:
+            model.broadcast_params()
+
+        optimizer_config = OptimizerConfig(
+            optimizer='muon',
+            lr=0.01,
+            weight_decay=0.01,
+            bf16=True,
+            use_distributed_optimizer=False,
+            clip_grad=clip_grad,
+            overlap_param_gather=async_allgather,
+            muon_tp_mode="duplicated",
+        )
+
+        pg_collection = ProcessGroupCollection.use_mpu_process_groups()
+        pg_collection.dp_cp = parallel_state.get_data_parallel_group(with_context_parallel=True)
+        pg_collection.expt_dp = parallel_state.get_expert_data_parallel_group()
+
+        optimizer = get_megatron_muon_optimizer(
+            config=optimizer_config,
+            model_chunks=[model],
+            use_gloo_process_groups=True,
+            layer_wise_distributed_optimizer=True,
+            pg_collection=pg_collection,
+        )
         return model, optimizer, pg_collection
 
     def create_reference_model(self, model):
@@ -241,11 +315,19 @@ def test_sharded_state_dict(self):
         # Test sharded_state_dict
         sharded_state_dict = optimizer.sharded_state_dict(model_sharded_state_dict)
 
-        # Verify the sharded_state_dict is not None and has expected structure
+        # Verify the sharded_state_dict is not None and has expected structure.
+        # With multiple chained optimizers (muon + adam), the top-level keys are
+        # integer indices; each sub-dict should contain an 'optimizer' key.
         assert sharded_state_dict is not None, "Sharded state dict should not be None"
-        assert (
-            'optimizer' in sharded_state_dict
-        ), "Sharded state dict should contain 'optimizer' key"
+        if isinstance(sharded_state_dict, dict) and all(
+            isinstance(k, int) for k in sharded_state_dict.keys()
+        ):
+            for idx, sub_dict in sharded_state_dict.items():
+                assert 'optimizer' in sub_dict, f"Sub-dict {idx} should contain 'optimizer' key"
+        else:
+            assert (
+                'optimizer' in sharded_state_dict
+            ), "Sharded state dict should contain 'optimizer' key"
 
         # Verify that replica_id is set correctly (should be 0 for DP dimension)
         from megatron.core.dist_checkpointing import ShardedTensor
@@ -263,39 +345,13 @@ def test_sharded_state_dict(self):
     def test_multiple_optimizers(self):
         """Test LayerWiseDistributedOptimizer with multiple chained optimizers.
 
-        This test properly tests allgather functionality with multiple ranks.
+        Uses get_megatron_muon_optimizer which produces multiple chained optimizers
+        (muon for 2D weights + adam for 1D biases). Tests allgather with multiple ranks.
         """
-        model = SimpleModel().bfloat16().cuda()
-        model.requires_grad_(True)
-
-        ddp_config = DistributedDataParallelConfig(use_distributed_optimizer=False)
-        model = DistributedDataParallel(
-            TransformerConfig(num_attention_heads=1, num_layers=1), ddp_config, model
-        )
-
-        optimizer_config = OptimizerConfig(
-            optimizer='adam', lr=0.01, bf16=True, use_distributed_optimizer=False
-        )
-
-        # Split parameters into two groups for testing multiple optimizers
-        params = list(model.parameters())
-        mid_point = len(params) // 2
-        param_groups_1 = [{'params': params[:mid_point]}]
-        param_groups_2 = [{'params': params[mid_point:]}]
-
-        # Create two separate plain base optimizers (LayerWise wraps them itself)
-        base_optimizer_1 = torch.optim.Adam(param_groups_1, lr=optimizer_config.lr)
-        base_optimizer_2 = torch.optim.Adam(param_groups_2, lr=optimizer_config.lr)
-
-        pg_collection = ProcessGroupCollection.use_mpu_process_groups()
-        pg_collection.dp_cp = parallel_state.get_data_parallel_group(with_context_parallel=True)
-        pg_collection.expt_dp = parallel_state.get_expert_data_parallel_group()
-
-        optimizer = LayerWiseDistributedOptimizer(
-            [base_optimizer_1, base_optimizer_2], optimizer_config, pg_collection
-        )
+        model, optimizer, pg_collection = self.create_model_and_optimizer()
 
-        assert len(optimizer.chained_optimizers) == 2, "Should have two chained optimizers"
+        # get_megatron_muon_optimizer produces muon + adam chained optimizers
+        assert len(optimizer.chained_optimizers) >= 2, "Should have multiple chained optimizers"
 
         # Set gradients and test optimizer step - this will trigger allgather
         for param in model.parameters():
@@ -331,26 +387,39 @@ def test_bf16_error(self):
             TransformerConfig(num_attention_heads=1, num_layers=1), ddp_config, model
         )
 
+        pg_collection = ProcessGroupCollection.use_mpu_process_groups()
+        pg_collection.dp_cp = parallel_state.get_data_parallel_group(with_context_parallel=True)
+        pg_collection.expt_dp = parallel_state.get_expert_data_parallel_group()
+
+        # Create muon optimizer (non-layer-wise) — produces Float16-wrapped chained optimizers
         optimizer_config = OptimizerConfig(
-            optimizer='adam', lr=0.01, bf16=True, use_distributed_optimizer=False
+            optimizer='muon',
+            lr=0.01,
+            bf16=True,
+            use_distributed_optimizer=False,
+            muon_tp_mode="duplicated",
         )
-
-        # Create base optimizer and manually wrap in Float16 optimizer
-        param_groups = [{'params': list(model.parameters())}]
-        base_optimizer = torch.optim.Adam(param_groups, lr=optimizer_config.lr)
-        wrapped_optimizer = Float16OptimizerWithFloat16Params(
-            base_optimizer, optimizer_config, None, None
+        muon_optimizer = get_megatron_muon_optimizer(
+            config=optimizer_config,
+            model_chunks=[model],
+            use_gloo_process_groups=True,
+            layer_wise_distributed_optimizer=False,
+            pg_collection=pg_collection,
         )
 
-        pg_collection = ProcessGroupCollection.use_mpu_process_groups()
-        pg_collection.dp_cp = parallel_state.get_data_parallel_group(with_context_parallel=True)
-        pg_collection.expt_dp = parallel_state.get_expert_data_parallel_group()
+        # Extract a Float16-wrapped chained optimizer
+        wrapped_optimizer = muon_optimizer.chained_optimizers[0]
+        assert isinstance(wrapped_optimizer, Float16OptimizerWithFloat16Params)
 
-        # Should raise TypeError when receiving already-wrapped optimizer
+        # Should raise TypeError when receiving already-wrapped Float16 optimizer
+        # Use a fresh config since get_megatron_muon_optimizer mutates config.optimizer
+        lw_config = OptimizerConfig(
+            optimizer='muon', lr=0.01, bf16=True, use_distributed_optimizer=False
+        )
         with pytest.raises(
             TypeError, match='LayerWiseDistributedOptimizer expects base torch optimizers'
         ):
-            LayerWiseDistributedOptimizer([wrapped_optimizer], optimizer_config, pg_collection)
+            LayerWiseDistributedOptimizer([wrapped_optimizer], lw_config, pg_collection)
 
     def _run_parameter_update_test(self, model_class=SimpleModel):
         """Helper method to test parameter updates with a given model class.
@@ -437,3 +506,662 @@ def test_broadcast_vs_allgather(self):
         # Verify updated values match reference optimizer
         for param, ref_param in zip(model.parameters(), reference_model.parameters()):
             torch.testing.assert_close(param.data, ref_param.data, rtol=0, atol=0)
+
+    # ---- Overlap-param-gather tests ----
+
+    def test_overlap_param_gather_basic(self):
+        """Test overlap-param-gather path: init, forward/backward/step, bucket-based param sync."""
+        model, optimizer, pg_collection = (
+            self.create_model_and_optimizer_with_overlap_param_gather()
+        )
+
+        assert optimizer is not None, "Optimizer should not be None"
+        assert optimizer.async_allgather, "async_allgather should be True"
+
+        reference_model = self.create_reference_model(model)
+
+        input_tensor = torch.randn(16, 80, dtype=torch.bfloat16, device='cuda')
+        output = model(input_tensor)
+        loss = output.sum()
+        loss.backward()
+
+        # step() updates local params but skips allgather (async_allgather=True)
+        update_successful, grad_norm, num_zeros = optimizer.step()
+
+        assert update_successful, "Optimizer step should be successful"
+
+        # Manually sync params through the bucket-based param sync path
+        # force_sync=True does synchronous allgather via bucket infrastructure
+        model.start_param_sync(force_sync=True)
+
+        # Verify parameters were updated
+        params_updated = 0
+        for param, ref_param in zip(model.parameters(), reference_model.parameters()):
+            if not torch.equal(param.data, ref_param.data):
+                params_updated += 1
+
+        assert params_updated > 0, "At least some parameters should be updated"
+
+        # Verify all ranks have the same updated parameters
+        dp_size = get_pg_size(pg_collection.dp_cp)
+
+        if dp_size > 1:
+            for name, param in model.named_parameters():
+                param_list = [torch.zeros_like(param.data) for _ in range(dp_size)]
+                torch.distributed.all_gather(param_list, param.data, group=pg_collection.dp_cp)
+
+                for i in range(1, dp_size):
+                    torch.testing.assert_close(
+                        param_list[0],
+                        param_list[i],
+                        msg=f"Parameter {name} differs between rank 0 and rank {i}",
+                    )
+
+    def test_overlap_param_gather_parameter_updates(self):
+        """Test overlap-param-gather produces same parameter updates as standard optimizer."""
+        model, optimizer, pg_collection = (
+            self.create_model_and_optimizer_with_overlap_param_gather()
+        )
+
+        # Create reference model with standard (non-layer-wise) optimizer
+        reference_model, reference_optimizer, _ = self.create_model_and_optimizer(
+            use_layer_wise=False, copy_from=model
+        )
+
+        # Set same gradients on both models
+        for param, ref_param in zip(model.parameters(), reference_model.parameters()):
+            assert torch.equal(param.data, ref_param.data)
+            grad_value = torch.randn_like(param)
+            torch.distributed.broadcast(grad_value, src=0, group=pg_collection.dp_cp)
+            param.main_grad = grad_value.clone().detach()
+            ref_param.main_grad = grad_value.clone().detach()
+
+        # step() with async_allgather=True: updates but no allgather
+        optimizer.step()
+        # Manually sync params via bucket infrastructure
+        model.start_param_sync(force_sync=True)
+
+        reference_optimizer.step()
+
+        # Verify updated values match reference optimizer
+        for param, ref_param in zip(model.parameters(), reference_model.parameters()):
+            torch.testing.assert_close(param.data, ref_param.data, rtol=1e-5, atol=1e-5)
+
+    def test_overlap_param_gather_vs_sync_allgather(self):
+        """Key correctness test: overlap path and sync allgather produce identical updates.
+
+        Compares:
+        - Overlap path: async_allgather=True, bucket-based param sync
+        - Sync path: async_allgather=False, optimizer.allgather_params() in step()
+        """
+        # Create overlap model
+        overlap_model, overlap_optimizer, pg_collection = (
+            self.create_model_and_optimizer_with_overlap_param_gather(async_allgather=True)
+        )
+
+        # Create sync model with same weights (overlap_param_gather=True but sync allgather)
+        sync_model, sync_optimizer, _ = self.create_model_and_optimizer_with_overlap_param_gather(
+            async_allgather=False, copy_from=overlap_model
+        )
+
+        # Verify initial parameters match
+        for op, sp in zip(overlap_model.parameters(), sync_model.parameters()):
+            assert torch.equal(op.data, sp.data)
+
+        # Set identical gradients on both
+        for op, sp in zip(overlap_model.parameters(), sync_model.parameters()):
+            grad_value = torch.randn_like(op)
+            torch.distributed.broadcast(grad_value, src=0, group=pg_collection.dp_cp)
+            op.main_grad = grad_value.clone().detach()
+            sp.main_grad = grad_value.clone().detach()
+
+        # Overlap path: step + manual sync
+        overlap_optimizer.step()
+        overlap_model.start_param_sync(force_sync=True)
+
+        # Sync path: step (includes allgather_params)
+        sync_optimizer.step()
+
+        # Both paths should produce identical parameter values
+        for op, sp in zip(overlap_model.parameters(), sync_model.parameters()):
+            torch.testing.assert_close(
+                op.data,
+                sp.data,
+                rtol=0,
+                atol=0,
+                msg="Overlap and sync allgather paths produced different parameter updates",
+            )
+
+    def test_overlap_param_gather_bucket_lw_params(self):
+        """Verify bucket.layerwise_params_list is populated when async_allgather is enabled."""
+        model, optimizer, pg_collection = (
+            self.create_model_and_optimizer_with_overlap_param_gather()
+        )
+
+        dp_size = get_pg_size(pg_collection.dp_cp)
+
+        for bucket_group in model.bucket_groups:
+            for bucket in bucket_group.buckets:
+                # layerwise_params_list should be populated by set_bucket_layerwise_params_list
+                assert (
+                    bucket.layerwise_params_list is not None
+                ), "bucket.layerwise_params_list should be populated"
+                assert (
+                    len(bucket.layerwise_params_list) == dp_size
+                ), f"Expected {dp_size} per-rank lists, got {len(bucket.layerwise_params_list)}"
+
+                # The union of all per-rank param lists should cover all bucket params
+                all_lw_params = set()
+                for rank_params in bucket.layerwise_params_list:
+                    for p in rank_params:
+                        all_lw_params.add(p)
+                assert (
+                    all_lw_params == bucket.params
+                ), "Union of per-rank layerwise_params should equal bucket params"
+
+                # layerwise_param_flat_sizes should be populated and have correct length
+                assert bucket.layerwise_param_flat_sizes is not None
+                assert len(bucket.layerwise_param_flat_sizes) == dp_size
+
+                # Each flat size should equal the sum of param numels for that rank
+                for rank_idx in range(dp_size):
+                    expected_size = sum(p.numel() for p in bucket.layerwise_params_list[rank_idx])
+                    assert bucket.layerwise_param_flat_sizes[rank_idx] == expected_size, (
+                        f"Rank {rank_idx}: expected flat_size {expected_size}, "
+                        f"got {bucket.layerwise_param_flat_sizes[rank_idx]}"
+                    )
+
+    def test_overlap_param_gather_vs_standard_ddp(self):
+        """Verify DDP with overlap_param_gather=True produces same results as standard DDP.
+
+        Both use LayerWiseDistributedOptimizer but with different DDP configs:
+        - Overlap path: overlap_param_gather=True (padded buffers)
+        - Standard path: overlap_param_gather=False (unpadded buffers)
+        """
+        # Create overlap-param-gather model (sync allgather for simpler comparison)
+        opg_model, opg_optimizer, pg_collection = (
+            self.create_model_and_optimizer_with_overlap_param_gather(async_allgather=False)
+        )
+
+        # Create standard model with same weights
+        std_model, std_optimizer, _ = self.create_model_and_optimizer(copy_from=opg_model)
+
+        # Set identical gradients
+        for op, sp in zip(opg_model.parameters(), std_model.parameters()):
+            assert torch.equal(op.data, sp.data)
+            grad_value = torch.randn_like(op)
+            torch.distributed.broadcast(grad_value, src=0, group=pg_collection.dp_cp)
+            op.main_grad = grad_value.clone().detach()
+            sp.main_grad = grad_value.clone().detach()
+
+        opg_optimizer.step()
+        std_optimizer.step()
+
+        # Both should produce identical parameter values
+        for op, sp in zip(opg_model.parameters(), std_model.parameters()):
+            torch.testing.assert_close(
+                op.data,
+                sp.data,
+                rtol=1e-5,
+                atol=1e-5,
+                msg="Overlap-param-gather and standard paths produced different updates",
+            )
+
+    def test_overlap_param_gather_insufficient_parameters(self):
+        """Test overlap-param-gather with TinyModel (only 2 params).
+
+        Many ranks will have no assigned params when world_size > 2.
+        """
+        model, optimizer, pg_collection = self.create_model_and_optimizer_with_overlap_param_gather(
+            model_class=TinyModel
+        )
+
+        # Create reference model with standard (non-layer-wise) optimizer
+        reference_model, reference_optimizer, _ = self.create_model_and_optimizer(
+            model_class=TinyModel, use_layer_wise=False, copy_from=model
+        )
+
+        # Set same gradients on both models
+        for param, ref_param in zip(model.parameters(), reference_model.parameters()):
+            assert torch.equal(param.data, ref_param.data)
+            grad_value = torch.randn_like(param)
+            torch.distributed.broadcast(grad_value, src=0, group=pg_collection.dp_cp)
+            param.main_grad = grad_value.clone().detach()
+            ref_param.main_grad = grad_value.clone().detach()
+
+        optimizer.step()
+        model.start_param_sync(force_sync=True)
+
+        reference_optimizer.step()
+
+        # Verify updated values match reference optimizer
+        for param, ref_param in zip(model.parameters(), reference_model.parameters()):
+            torch.testing.assert_close(param.data, ref_param.data, rtol=1e-5, atol=1e-5)
+
+    def test_overlap_param_gather_broadcast_vs_allgather(self):
+        """Test overlap-param-gather: allgather vs broadcast produce same results."""
+        model, optimizer, pg_collection = self.create_model_and_optimizer_with_overlap_param_gather(
+            model_class=SimpleModel, async_allgather=False
+        )
+
+        # Create reference model with overlap-param-gather path too
+        reference_model, reference_optimizer, _ = (
+            self.create_model_and_optimizer_with_overlap_param_gather(
+                model_class=SimpleModel, async_allgather=False, copy_from=model
+            )
+        )
+
+        # Set same gradients on both models
+        for param, ref_param in zip(model.parameters(), reference_model.parameters()):
+            assert torch.equal(param.data, ref_param.data)
+            torch.testing.assert_close(param.data, ref_param.data, rtol=0, atol=0)
+            grad_value = torch.randn_like(param)
+            torch.distributed.broadcast(grad_value, src=0, group=pg_collection.dp_cp)
+            param.main_grad = grad_value.clone().detach()
+            ref_param.main_grad = grad_value.clone().detach()
+
+        optimizer.step()
+
+        # Verify at least some parameters were updated
+        params_updated = 0
+        for param, ref_param in zip(model.parameters(), reference_model.parameters()):
+            if not torch.equal(param.data, ref_param.data):
+                params_updated += 1
+
+        assert params_updated > 0, "At least some parameters should be updated"
+
+        # step() internally calls allgather_params. Replace reference with broadcast.
+        reference_optimizer.allgather_params = reference_optimizer.broadcast_params
+        reference_optimizer.step()
+
+        # Verify updated values match reference optimizer
+        for param, ref_param in zip(model.parameters(), reference_model.parameters()):
+            torch.testing.assert_close(param.data, ref_param.data, rtol=0, atol=0)
+
+    def test_overlap_param_gather_multi_iteration(self):
+        """Test overlap-param-gather correctness over multiple training iterations.
+
+        Runs multiple forward/backward/step iterations using the async allgather path.
+        After each iteration, manually syncs params and verifies they match a reference
+        model using the sync path.
+        """
+        model, optimizer, pg_collection = self.create_model_and_optimizer_with_overlap_param_gather(
+            async_allgather=True
+        )
+
+        # Create reference model with sync allgather for comparison
+        ref_model, ref_optimizer, _ = self.create_model_and_optimizer_with_overlap_param_gather(
+            async_allgather=False, copy_from=model
+        )
+
+        for iteration in range(3):
+            # Set identical gradients on both models
+            for param, ref_param in zip(model.parameters(), ref_model.parameters()):
+                grad_value = torch.randn_like(param)
+                torch.distributed.broadcast(grad_value, src=0, group=pg_collection.dp_cp)
+                param.main_grad = grad_value.clone().detach()
+                ref_param.main_grad = grad_value.clone().detach()
+
+            # Async path: step (no allgather) + manual sync
+            optimizer.step()
+            model.start_param_sync(force_sync=True)
+
+            # Sync path: step (includes allgather)
+            ref_optimizer.step()
+
+            # Verify parameters match after each iteration
+            for param, ref_param in zip(model.parameters(), ref_model.parameters()):
+                torch.testing.assert_close(
+                    param.data,
+                    ref_param.data,
+                    rtol=0,
+                    atol=0,
+                    msg=f"Parameters diverged at iteration {iteration}",
+                )
+
+    def test_overlap_param_gather_async_dispatch_and_finish(self):
+        """Test async dispatch + finish_param_sync cycle (the actual runtime path).
+
+        start_param_sync() (no force_sync) dispatches async all-gathers, then
+        finish_param_sync() waits on the handle and unflattens gathered params.
+        """
+        model, optimizer, pg_collection = self.create_model_and_optimizer_with_overlap_param_gather(
+            async_allgather=True
+        )
+        ref_model, ref_optimizer, _ = self.create_model_and_optimizer_with_overlap_param_gather(
+            async_allgather=False, copy_from=model
+        )
+
+        # Set identical gradients on both models
+        for param, ref_param in zip(model.parameters(), ref_model.parameters()):
+            grad_value = torch.randn_like(param)
+            torch.distributed.broadcast(grad_value, src=0, group=pg_collection.dp_cp)
+            param.main_grad = grad_value.clone().detach()
+            ref_param.main_grad = grad_value.clone().detach()
+
+        # Async path: step (no allgather) + async dispatch + explicit finish
+        optimizer.step()
+        model.start_param_sync()  # async dispatch to all bucket groups
+        for bucket_group in model.bucket_groups:
+            bucket_group.finish_param_sync(skip_next_bucket_dispatch=True)
+
+        # Sync path: step (includes allgather)
+        ref_optimizer.step()
+
+        # Verify params match sync path
+        for param, ref_param in zip(model.parameters(), ref_model.parameters()):
+            torch.testing.assert_close(
+                param.data,
+                ref_param.data,
+                rtol=0,
+                atol=0,
+                msg="Async dispatch + finish path produced different params than sync path",
+            )
+
+        # Verify all ranks have identical parameters
+        dp_size = get_pg_size(pg_collection.dp_cp)
+        if dp_size > 1:
+            for name, param in model.named_parameters():
+                param_list = [torch.zeros_like(param.data) for _ in range(dp_size)]
+                torch.distributed.all_gather(param_list, param.data, group=pg_collection.dp_cp)
+                for i in range(1, dp_size):
+                    torch.testing.assert_close(
+                        param_list[0],
+                        param_list[i],
+                        msg=f"Parameter {name} differs between rank 0 and rank {i}",
+                    )
+
+    def test_overlap_param_gather_finish_chains_next_bucket(self):
+        """Test that finish_param_sync() dispatches next_param_gather_bucket_group.
+
+        Uses a small bucket_size to force multiple bucket groups, then dispatches
+        only the last bucket group and verifies that finishing it chains to the next.
+        """
+        model, optimizer, pg_collection = self.create_model_and_optimizer_with_overlap_param_gather(
+            async_allgather=True, bucket_size=2000
+        )
+
+        bucket_groups = model.bucket_groups
+        if len(bucket_groups) <= 1:
+            pytest.skip("Need multiple bucket groups to test chaining")
+
+        ref_model, ref_optimizer, _ = self.create_model_and_optimizer_with_overlap_param_gather(
+            async_allgather=False, copy_from=model, bucket_size=2000
+        )
+
+        # Set identical gradients on both models
+        for param, ref_param in zip(model.parameters(), ref_model.parameters()):
+            grad_value = torch.randn_like(param)
+            torch.distributed.broadcast(grad_value, src=0, group=pg_collection.dp_cp)
+            param.main_grad = grad_value.clone().detach()
+            ref_param.main_grad = grad_value.clone().detach()
+
+        optimizer.step()
+
+        # Dispatch ONLY the last bucket group (which has next_param_gather_bucket_group set)
+        last_bg = bucket_groups[-1]
+        last_bg.start_param_sync()
+
+        # Verify: next bucket group has NOT been dispatched yet
+        next_bg = last_bg.next_param_gather_bucket_group
+        assert next_bg is not None, "Last bucket group should have a next"
+        assert not next_bg.param_gather_dispatched, "Next bucket should not be dispatched yet"
+
+        # Finish the last bucket group — should chain-dispatch the next one
+        last_bg.finish_param_sync()
+
+        # Verify: next bucket group IS now dispatched via chaining
+        assert (
+            next_bg.param_gather_dispatched
+        ), "finish_param_sync should have dispatched next bucket group"
+
+        # Finish remaining bucket groups through the chain
+        for bg in reversed(bucket_groups[:-1]):
+            bg.finish_param_sync()
+
+        # Reference: sync step
+        ref_optimizer.step()
+
+        # Verify params match
+        for param, ref_param in zip(model.parameters(), ref_model.parameters()):
+            torch.testing.assert_close(
+                param.data,
+                ref_param.data,
+                rtol=0,
+                atol=0,
+                msg="Chained bucket finish produced different params than sync path",
+            )
+
+    def test_overlap_param_gather_forward_pre_hook(self):
+        """Test forward pre-hooks trigger finish_param_sync during model(input).
+
+        After async dispatch, running model(input) fires forward pre-hooks that
+        call finish_param_sync() on each bucket group, completing the param sync.
+        """
+        model, optimizer, pg_collection = self.create_model_and_optimizer_with_overlap_param_gather(
+            async_allgather=True
+        )
+        ref_model, ref_optimizer, _ = self.create_model_and_optimizer_with_overlap_param_gather(
+            async_allgather=False, copy_from=model
+        )
+
+        # Set identical gradients on both models
+        for param, ref_param in zip(model.parameters(), ref_model.parameters()):
+            grad_value = torch.randn_like(param)
+            torch.distributed.broadcast(grad_value, src=0, group=pg_collection.dp_cp)
+            param.main_grad = grad_value.clone().detach()
+            ref_param.main_grad = grad_value.clone().detach()
+
+        # Async path: step (no allgather) + async dispatch
+        optimizer.step()
+        model.start_param_sync()  # dispatch async broadcasts
+
+        # Forward pass triggers hooks that call finish_param_sync()
+        input_tensor = torch.randn(16, 80, dtype=torch.bfloat16, device='cuda')
+        output = model(input_tensor)
+
+        # Sync path: step (includes allgather)
+        ref_optimizer.step()
+
+        # Verify params match
+        for param, ref_param in zip(model.parameters(), ref_model.parameters()):
+            torch.testing.assert_close(
+                param.data,
+                ref_param.data,
+                rtol=0,
+                atol=0,
+                msg="Forward pre-hook path produced different params than sync path",
+            )
+
+    def test_overlap_param_gather_grad_reduce_in_fp32(self):
+        """Regression test: grad_reduce_in_fp32 must not cause dtype mismatch in broadcasts.
+
+        When grad_reduce_in_fp32=True, the grad buffer dtype is fp32 but broadcast
+        buffers must use param dtype (bf16). Without the fix (commit cbed167fc), this
+        would cause a dtype mismatch error in the per-rank broadcast calls.
+        """
+        model, optimizer, pg_collection = self.create_model_and_optimizer_with_overlap_param_gather(
+            async_allgather=True, grad_reduce_in_fp32=True
+        )
+        ref_model, ref_optimizer, _ = self.create_model_and_optimizer_with_overlap_param_gather(
+            async_allgather=False, copy_from=model, grad_reduce_in_fp32=True
+        )
+
+        # Set identical gradients on both models
+        for param, ref_param in zip(model.parameters(), ref_model.parameters()):
+            grad_value = torch.randn_like(param)
+            torch.distributed.broadcast(grad_value, src=0, group=pg_collection.dp_cp)
+            param.main_grad = grad_value.clone().detach()
+            ref_param.main_grad = grad_value.clone().detach()
+
+        # Async path: step + force_sync
+        optimizer.step()
+        model.start_param_sync(force_sync=True)
+
+        # Sync path: step (includes allgather)
+        ref_optimizer.step()
+
+        # Verify params match
+        for param, ref_param in zip(model.parameters(), ref_model.parameters()):
+            torch.testing.assert_close(
+                param.data,
+                ref_param.data,
+                rtol=1e-5,
+                atol=1e-5,
+                msg="grad_reduce_in_fp32 path produced different params than reference",
+            )
+
+    def test_overlap_param_gather_hook_enable_disable_cycle(self):
+        """Test the training loop's hook lifecycle: disable → manual sync → enable → forward.
+
+        The training loop disables hooks before iteration 1 (for initialization),
+        then enables them for subsequent iterations. This test exercises that cycle.
+        """
+        model, optimizer, pg_collection = self.create_model_and_optimizer_with_overlap_param_gather(
+            async_allgather=True
+        )
+        ref_model, ref_optimizer, _ = self.create_model_and_optimizer_with_overlap_param_gather(
+            async_allgather=False, copy_from=model
+        )
+
+        input_tensor = torch.randn(16, 80, dtype=torch.bfloat16, device='cuda')
+
+        # Iteration 1: hooks disabled, manual sync
+        model.disable_forward_pre_hook(param_sync=False)
+
+        for param, ref_param in zip(model.parameters(), ref_model.parameters()):
+            grad_value = torch.randn_like(param)
+            torch.distributed.broadcast(grad_value, src=0, group=pg_collection.dp_cp)
+            param.main_grad = grad_value.clone().detach()
+            ref_param.main_grad = grad_value.clone().detach()
+
+        optimizer.step()
+        model.start_param_sync(force_sync=True)  # manual sync
+
+        ref_optimizer.step()
+
+        for param, ref_param in zip(model.parameters(), ref_model.parameters()):
+            torch.testing.assert_close(
+                param.data,
+                ref_param.data,
+                rtol=0,
+                atol=0,
+                msg="Params diverged after iteration 1 (hooks disabled)",
+            )
+
+        # Iteration 2: hooks re-enabled, forward pass triggers sync
+        model.enable_forward_pre_hook()
+
+        for param, ref_param in zip(model.parameters(), ref_model.parameters()):
+            grad_value = torch.randn_like(param)
+            torch.distributed.broadcast(grad_value, src=0, group=pg_collection.dp_cp)
+            param.main_grad = grad_value.clone().detach()
+            ref_param.main_grad = grad_value.clone().detach()
+
+        optimizer.step()
+        model.start_param_sync()  # async dispatch
+        output = model(input_tensor)  # hooks finish sync
+
+        ref_optimizer.step()
+
+        for param, ref_param in zip(model.parameters(), ref_model.parameters()):
+            torch.testing.assert_close(
+                param.data,
+                ref_param.data,
+                rtol=0,
+                atol=0,
+                msg="Params diverged after iteration 2 (hooks re-enabled)",
+            )
+
+    def test_overlap_param_gather_multi_iteration_with_hooks(self):
+        """Test multiple iterations using forward pre-hooks (not manual force_sync).
+
+        Runs 3 iterations where each iteration uses: set grads → step → async dispatch →
+        forward pass (hooks wait+unflatten). Compares against reference model using sync
+        allgather after each iteration.
+        """
+        model, optimizer, pg_collection = self.create_model_and_optimizer_with_overlap_param_gather(
+            async_allgather=True
+        )
+        ref_model, ref_optimizer, _ = self.create_model_and_optimizer_with_overlap_param_gather(
+            async_allgather=False, copy_from=model
+        )
+
+        input_tensor = torch.randn(16, 80, dtype=torch.bfloat16, device='cuda')
+
+        for iteration in range(3):
+            # Set identical gradients on both models
+            for param, ref_param in zip(model.parameters(), ref_model.parameters()):
+                grad_value = torch.randn_like(param)
+                torch.distributed.broadcast(grad_value, src=0, group=pg_collection.dp_cp)
+                param.main_grad = grad_value.clone().detach()
+                ref_param.main_grad = grad_value.clone().detach()
+
+            # Async path: step + dispatch + forward (hooks wait+unflatten)
+            optimizer.step()
+            model.start_param_sync()  # async dispatch
+            output = model(input_tensor)  # hooks trigger finish_param_sync
+
+            # Sync path: step (includes allgather)
+            ref_optimizer.step()
+
+            # Verify parameters match after each iteration
+            for param, ref_param in zip(model.parameters(), ref_model.parameters()):
+                torch.testing.assert_close(
+                    param.data,
+                    ref_param.data,
+                    rtol=0,
+                    atol=0,
+                    msg=f"Parameters diverged at iteration {iteration}",
+                )
+
+    def test_overlap_param_gather_start_sync_with_autograd(self):
+        """Regression test: start_param_sync must work when autograd is active.
+
+        _flatten_dense_tensors on params with requires_grad=True produces a tensor
+        that also requires grad.  Since all_gather writes into gather_list entries
+        in-place and the local rank's slot reuses src, this triggers:
+            RuntimeError: a view of a leaf Variable that requires grad is being
+            used in an in-place operation.
+        The fix is to .detach() the flattened tensor before using it as src.
+
+        This test calls start_param_sync (synchronous via force_sync) WITHOUT
+        torch.no_grad() to reproduce the exact scenario that occurs during the
+        forward pass when finish_param_sync chains to start_param_sync for the
+        next bucket group.
+        """
+        model, optimizer, pg_collection = self.create_model_and_optimizer_with_overlap_param_gather(
+            async_allgather=True
+        )
+        ref_model, ref_optimizer, _ = self.create_model_and_optimizer_with_overlap_param_gather(
+            async_allgather=False, copy_from=model
+        )
+
+        # Confirm params require grad (the precondition for this bug).
+        for param in model.parameters():
+            assert param.requires_grad, "Test requires params with requires_grad=True"
+
+        # Set identical gradients on both models.
+        for param, ref_param in zip(model.parameters(), ref_model.parameters()):
+            grad_value = torch.randn_like(param)
+            torch.distributed.broadcast(grad_value, src=0, group=pg_collection.dp_cp)
+            param.main_grad = grad_value.clone().detach()
+            ref_param.main_grad = grad_value.clone().detach()
+
+        # Step both optimizers (async path skips allgather, ref path includes it).
+        optimizer.step()
+        ref_optimizer.step()
+
+        # Call start_param_sync with autograd ENABLED (no torch.no_grad()).
+        # Before the .detach() fix, this would raise RuntimeError.
+        model.start_param_sync(force_sync=True)
+
+        # Verify gathered params match the reference.
+        for param, ref_param in zip(model.parameters(), ref_model.parameters()):
+            torch.testing.assert_close(
+                param.data,
+                ref_param.data,
+                rtol=0,
+                atol=0,
+                msg="Params incorrect after start_param_sync with autograd enabled",
+            )
diff --git a/tests/unit_tests/transformer/experimental_attention_variant/test_absorbed_mla.py b/tests/unit_tests/transformer/experimental_attention_variant/test_absorbed_mla.py
index 4ed9ff8af46..89061ad4219 100644
--- a/tests/unit_tests/transformer/experimental_attention_variant/test_absorbed_mla.py
+++ b/tests/unit_tests/transformer/experimental_attention_variant/test_absorbed_mla.py
@@ -1,7 +1,6 @@
 # Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 
 import random
-from types import SimpleNamespace
 from typing import List, Optional, Tuple
 
 import pytest
@@ -22,6 +21,7 @@
     MLASelfAttention,
     MLASelfAttentionSubmodules,
 )
+from megatron.core.transformer.transformer_config import MLATransformerConfig
 from megatron.core.utils import init_method_normal, scaled_init_method_normal
 from tests.unit_tests.test_utilities import Utils
 
@@ -125,9 +125,9 @@ def get_mock_mla_config(
     context_parallel_size: int,
     sequence_parallel: bool,
     recompute_mla_up_proj: bool,
-) -> SimpleNamespace:
+) -> MLATransformerConfig:
     """Create test config with all attributes used in MLA."""
-    return SimpleNamespace(
+    return MLATransformerConfig(
         multi_latent_attention=True,
         hidden_size=7168,
         num_attention_heads=128,
diff --git a/tests/unit_tests/transformer/moe/test_grouped_mlp.py b/tests/unit_tests/transformer/moe/test_grouped_mlp.py
index 67543168480..58ca0cf1e3f 100644
--- a/tests/unit_tests/transformer/moe/test_grouped_mlp.py
+++ b/tests/unit_tests/transformer/moe/test_grouped_mlp.py
@@ -9,7 +9,6 @@
     get_gpt_layer_with_transformer_engine_submodules,
 )
 from megatron.core.transformer.module import Float16Module
-from megatron.core.transformer.moe import grouped_gemm_util as gg
 from megatron.core.transformer.moe.experts import TEGroupedMLP
 from megatron.core.transformer.moe.moe_layer import MoELayer
 from megatron.core.transformer.transformer_config import TransformerConfig
@@ -18,208 +17,6 @@
 from megatron.training.initialize import _set_random_seed
 from tests.unit_tests.test_utilities import Utils
 
-DEVICE_CAPABILITY = None
-if torch.cuda.is_available():
-    DEVICE_CAPABILITY = torch.cuda.get_device_capability()
-
-
-@pytest.mark.skipif(is_te_min_version("1.9.0.dev0"), reason="Switch to TEGroupedMLP when TE>1.9.")
-class TestParallelGroupedMLP:
-
-    def setup_method(self, method, use_cpu_initialization=False, swiglu=True):
-        print("============")
-        print(
-            "Test for use_cpu_initilization={} and swiglu={}.".format(
-                use_cpu_initialization, swiglu
-            )
-        )
-        print("============")
-        Utils.initialize_model_parallel(1, 1)
-        num_layers = 1  # 2
-        self.hidden_size = (
-            16  # must be an multiple of 16, otherwise trigger CUTLASS misaligned issue
-        )
-        self.num_experts = 2
-        self.gated_linear_unit = swiglu
-        self.activation_func = F.silu if swiglu else F.gelu
-        self.use_cpu_initialization = use_cpu_initialization
-
-        tf_config = TransformerConfig(
-            num_layers=num_layers,
-            hidden_size=self.hidden_size,
-            num_attention_heads=4,
-            num_moe_experts=self.num_experts,
-            use_cpu_initialization=self.use_cpu_initialization,
-            add_bias_linear=False,
-            gated_linear_unit=self.gated_linear_unit,
-            activation_func=self.activation_func,
-            bias_activation_fusion=False,
-            bf16=True,
-            params_dtype=torch.bfloat16,
-            moe_router_load_balancing_type="sinkhorn",
-            moe_router_topk=1,
-        )
-
-        self.fc1_ffn_hidden_size = tf_config.ffn_hidden_size
-        self.fc2_ffn_hidden_size = tf_config.ffn_hidden_size
-        # If using swiglu double the output width, see https://arxiv.org/pdf/2002.05202.pdf
-        if self.gated_linear_unit:
-            self.fc1_ffn_hidden_size *= 2
-
-        ## Vanilla sequential GEMM
-        # Set random seed for reproducability
-        _set_random_seed(seed_=123, data_parallel_random_init=False)
-        submodules = get_gpt_layer_local_submodules(self.num_experts, moe_grouped_gemm=False)
-        self.sequential_mlp = MoELayer(tf_config, submodules.mlp.submodules)
-
-        self.args = parse_args(ignore_unknown_args=True)
-        self.args.bf16 = True
-        # Bias is not supported in grouped gemm currently, thus we disable the
-        # bias in the linear layer.
-        self.args.add_bias_linear = False
-        self.sequential_mlp = Float16Module(self.sequential_mlp.config, self.sequential_mlp).module
-        print("done intializing for sequential gemm")
-
-        ## Grouped GEMM
-        _set_random_seed(seed_=123, data_parallel_random_init=False)
-        tf_config.moe_grouped_gemm = True
-        self.grouped_mlp = MoELayer(
-            tf_config,
-            get_gpt_layer_with_transformer_engine_submodules(
-                self.num_experts, moe_grouped_gemm=True
-            ).mlp.submodules,
-        )
-        self.grouped_mlp = Float16Module(self.grouped_mlp.config, self.grouped_mlp).module
-        print("done intializing for grouped gemm")
-
-    def teardown_method(self, method):
-        Utils.destroy_model_parallel()
-
-    @pytest.mark.internal
-    def test_constructor(self):
-        assert isinstance(self.sequential_mlp, MoELayer)
-        assert isinstance(self.grouped_mlp, MoELayer)
-
-        num_weights_smm = sum([p.numel() for p in self.sequential_mlp.parameters()])
-        num_weights_gmm = sum([p.numel() for p in self.grouped_mlp.parameters()])
-
-        # For the same hyper-parm model configs except the `moe_grouped_gemm`,
-        # GroupedGEMM and sequential GEMMs should hold the same number of parms.
-        assert num_weights_smm == num_weights_gmm
-        # expected num weights: router linear weights+bias + MLP weights(no bias) of all experts
-        expected_num_weights = (
-            self.hidden_size * self.num_experts
-            + self.hidden_size
-            * (self.fc1_ffn_hidden_size + self.fc2_ffn_hidden_size)
-            * self.num_experts
-        )
-        assert num_weights_smm == expected_num_weights
-
-        assert torch.equal(self.sequential_mlp.router.weight, self.grouped_mlp.router.weight)
-
-        # weight1: [h, num_experts*4h]
-        # weight2: [num_experts*4h, h]
-        assert self.grouped_mlp.experts.weight1.shape[0] == self.hidden_size
-        assert (
-            self.grouped_mlp.experts.weight1.shape[1] == self.num_experts * self.fc1_ffn_hidden_size
-        )
-        if self.gated_linear_unit:
-            assert (
-                self.grouped_mlp.experts.weight2.shape[0]
-                == self.num_experts * self.fc2_ffn_hidden_size
-            )
-            assert self.grouped_mlp.experts.weight2.shape[1] == self.hidden_size
-        else:
-            assert (
-                self.grouped_mlp.experts.weight1.shape == self.grouped_mlp.experts.weight2.t().shape
-            )
-
-    @pytest.mark.internal
-    def test_weight_init_value_the_same(self):
-        gmm_w1 = self.grouped_mlp.experts.weight1.view(self.num_experts, -1, self.hidden_size)
-        gmm_w2 = self.grouped_mlp.experts.weight2.view(self.num_experts, self.hidden_size, -1)
-        gmm_expert1_fc1 = gmm_w1[0]
-        gmm_expert1_fc2 = gmm_w2[0]
-        gmm_expert2_fc1 = gmm_w1[1]
-        gmm_expert2_fc2 = gmm_w2[1]
-
-        smm_expert1_fc1 = self.sequential_mlp.experts.local_experts[0].linear_fc1.weight
-        smm_expert1_fc2 = self.sequential_mlp.experts.local_experts[0].linear_fc2.weight
-        smm_expert2_fc1 = self.sequential_mlp.experts.local_experts[1].linear_fc1.weight
-        smm_expert2_fc2 = self.sequential_mlp.experts.local_experts[1].linear_fc2.weight
-
-        assert torch.equal(gmm_expert1_fc1, smm_expert1_fc1)
-        if not self.use_cpu_initialization:
-            assert torch.equal(gmm_expert1_fc2, smm_expert1_fc2)
-        # the param init value is not exactly the same between gmm and smm (refer to test_weight_init_value_the_same.)
-        # TODO: is it necessary to keep smm and gmm share exactly the same init params?
-        # assert torch.equal(gmm_expert2_fc1, smm_expert2_fc1)
-        if self.use_cpu_initialization:
-            assert torch.equal(gmm_expert2_fc2, smm_expert2_fc2)
-
-    @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
-    @pytest.mark.internal
-    @pytest.mark.skipif(
-        not DEVICE_CAPABILITY or DEVICE_CAPABILITY[0] < 8,
-        reason='GroupedGEMM kernels are not supported on this device.',
-    )
-    def test_gpu_forward(self):
-        self.sequential_mlp.cuda()
-        self.grouped_mlp.cuda()
-        # [sequence length, batch size, hidden size]
-        seq_len = 3  # 32
-        batch_size = 2
-        hidden_states = torch.rand(
-            (seq_len, batch_size, self.sequential_mlp.config.hidden_size), dtype=torch.bfloat16
-        )
-        hidden_states = hidden_states.cuda()
-        output_smm, _ = self.sequential_mlp(hidden_states)
-        output_gmm, _ = self.grouped_mlp(hidden_states)
-
-        # The following assert fails due to the param init value is not exactly
-        # the same between gmm and smm (refer to test_weight_init_value_the_same.)
-        # assert torch.equal(output_smm, output_gmm)
-
-    @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
-    @pytest.mark.internal
-    @pytest.mark.skipif(
-        not DEVICE_CAPABILITY or DEVICE_CAPABILITY[0] < 8,
-        reason='GroupedGEMM kernels are not supported on this device.',
-    )
-    def test_gpu_forward_with_no_tokens_allocated(self):
-        """Test the case when no token is allocated for groupedGEMM kernels."""
-        w1 = self.grouped_mlp.experts.weight1.view(self.num_experts, -1, self.hidden_size)
-        num_allocated_tokens = 0
-        tokens_per_expert = torch.zeros(self.num_experts)
-        hidden_states = torch.rand((num_allocated_tokens, self.hidden_size), dtype=torch.bfloat16)
-        hidden_states = hidden_states.cuda()
-        try:
-            gg.ops.gmm(hidden_states, w1, tokens_per_expert, trans_b=False)
-        except Exception as e:
-            print("Expected error message from groupedGEMM:", e)
-            assert str(e) == "Input batch_sizes should not be all zeros!"
-
-    @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
-    @pytest.mark.internal
-    @pytest.mark.skipif(
-        not DEVICE_CAPABILITY or DEVICE_CAPABILITY[0] < 8,
-        reason='GroupedGEMM kernels are not supported on this device.',
-    )
-    def test_gradient_with_no_tokens_allocated(self):
-        """Test that when no token is passed in, the parameters of the grouped MLP will also have gradients."""
-        self.grouped_mlp.cuda()
-        num_allocated_tokens = 0
-        tokens_per_expert = torch.zeros(self.num_experts)
-        hidden_states = torch.rand((num_allocated_tokens, self.hidden_size), dtype=torch.bfloat16)
-        hidden_states = hidden_states.cuda()
-        probs = torch.rand((num_allocated_tokens,), dtype=torch.float32)
-        probs = probs.cuda()
-        output_gmm, _ = self.grouped_mlp.experts(
-            hidden_states, tokens_per_expert=tokens_per_expert, permuted_probs=probs
-        )
-        output_gmm.mean().backward()
-        assert self.grouped_mlp.experts.weight1.grad is not None
-
 
 @pytest.mark.skipif(
     not is_te_min_version("1.9.0.dev0"),
@@ -384,17 +181,3 @@ def test_gpu_forward_backward_with_no_tokens_allocated(self):
         for i in range(self.num_experts):
             assert getattr(self.grouped_mlp.experts.linear_fc1, f"weight{i}").grad is not None
             assert getattr(self.grouped_mlp.experts.linear_fc2, f"weight{i}").grad is not None
-
-
-if __name__ == "__main__":
-    for use_cpu_unitilization in [True, False]:
-        for swiglu in [True, False]:
-            GMLP_test = TestParallelGroupedMLP()
-            GMLP_test.setup_method(
-                method=None, use_cpu_initialization=use_cpu_unitilization, swiglu=swiglu
-            )
-            GMLP_test.test_constructor()
-            GMLP_test.test_weight_init_value_the_same()
-            GMLP_test.test_gpu_forward()
-            GMLP_test.test_gpu_forward_with_no_tokens_allocated()
-            GMLP_test.teardown_method(method=None)
diff --git a/tests/unit_tests/transformer/test_cuda_graphs.py b/tests/unit_tests/transformer/test_cuda_graphs.py
index 956b4897bc8..bfde9ff9cf1 100644
--- a/tests/unit_tests/transformer/test_cuda_graphs.py
+++ b/tests/unit_tests/transformer/test_cuda_graphs.py
@@ -23,6 +23,7 @@
 from megatron.core.pipeline_parallel.schedules import set_current_microbatch
 from megatron.core.process_groups_config import ProcessGroupCollection
 from megatron.core.ssm.mamba_block import MambaStack
+from megatron.core.ssm.mamba_hybrid_layer_allocation import validate_segment_layers
 from megatron.core.tensor_parallel.random import (
     HAVE_TE,
     initialize_rng_tracker,
@@ -474,12 +475,13 @@ def setup_method(self, method):
         def get_pg_collection():
             return ProcessGroupCollection.use_mpu_process_groups(required_pgs=['tp', 'pp', 'cp'])
 
-        def get_mamba_block(hybrid_override_pattern):
+        def get_mamba_block(hybrid_layer_pattern):
+            layer_type_list = validate_segment_layers(hybrid_layer_pattern)
             transformer_config = TransformerConfig(
                 hidden_size=256,  # The Mamba layer places several constraints on this
                 # Need to specify num_attention_heads and num_layers or TransformerConfig
                 # will generate errors.
-                num_layers=len(hybrid_override_pattern),
+                num_layers=len(layer_type_list),
                 num_attention_heads=4,
                 use_cpu_initialization=True,
                 cuda_graph_impl="local",
@@ -488,11 +490,12 @@ def get_mamba_block(hybrid_override_pattern):
             return MambaStack(
                 transformer_config,
                 modules,
-                hybrid_override_pattern=hybrid_override_pattern,
+                layer_type_list=layer_type_list,
+                pp_layer_offset=0,
                 pg_collection=get_pg_collection(),
             )
 
-        self.mamba_block = get_mamba_block(hybrid_override_pattern="M-M*-")
+        self.mamba_block = get_mamba_block(hybrid_layer_pattern="M-M*-")
         self.transformer_config = self.mamba_block.config
 
     def teardown_method(self, method):
diff --git a/tests/unit_tests/transformer/test_multi_latent_attention.py b/tests/unit_tests/transformer/test_multi_latent_attention.py
index 83c65877fbb..51c36268eb4 100644
--- a/tests/unit_tests/transformer/test_multi_latent_attention.py
+++ b/tests/unit_tests/transformer/test_multi_latent_attention.py
@@ -22,6 +22,7 @@
 from megatron.core.transformer.attention import Attention
 from megatron.core.transformer.enums import AttnMaskType
 from megatron.core.transformer.multi_latent_attention import (
+    FusedMLASelfAttention,
     MLASelfAttention,
     MLASelfAttentionSubmodules,
     MultiLatentAttention,
@@ -106,6 +107,16 @@ def get_mla_self_attn_submodules(linear_qkv_down_proj=None):
     return submodules
 
 
+def get_fused_mla_submodules():
+    """Get submodules for FusedMLASelfAttention via the mla_down_proj_fusion spec path."""
+    submodules = get_gpt_layer_with_transformer_engine_submodules(
+        multi_latent_attention=True, mla_down_proj_fusion=True
+    ).self_attention.submodules
+    assert isinstance(submodules, MLASelfAttentionSubmodules)
+    assert submodules.linear_qkv_down_proj is not None
+    return submodules
+
+
 backend = TESpecProvider()
 linear_qkv_down_proj_options = [backend.linear(), backend.column_parallel_linear()]
 
@@ -1543,3 +1554,274 @@ def get_tensor_on_this_rank(tensor):
 
     os.environ.clear()
     os.environ.update(_environ)
+
+
+@pytest.mark.parametrize("rope_type", ('yarn', 'rope'))
+class TestFusedMLASelfAttention:
+
+    @pytest.fixture(scope='function', autouse=True)
+    def setup_and_teardown(self, rope_type):
+        Utils.initialize_model_parallel(1, 1)
+        model_parallel_cuda_manual_seed(123)
+        self.transformer_config = MLATransformerConfig(
+            num_layers=2,
+            hidden_size=12,
+            num_attention_heads=4,
+            use_cpu_initialization=True,
+            q_lora_rank=32,
+            kv_lora_rank=32,
+            qk_head_dim=128,
+            v_head_dim=128,
+            qk_pos_emb_head_dim=64,
+            rope_type=rope_type,
+            rotary_base=10000,
+            original_max_position_embeddings=32,
+        )
+        self.fused_attention = FusedMLASelfAttention(
+            self.transformer_config,
+            get_fused_mla_submodules(),
+            layer_number=1,
+            attn_mask_type=AttnMaskType.causal,
+        )
+
+    def teardown_method(self, method):
+        Utils.destroy_model_parallel()
+
+    def test_constructor(self):
+        assert isinstance(self.fused_attention, FusedMLASelfAttention)
+        assert isinstance(self.fused_attention, MLASelfAttention)
+        assert self.fused_attention.layer_number == 1
+        assert hasattr(self.fused_attention, 'linear_qkv_down_proj')
+
+    def test_fused_weight_shape(self):
+        config = self.transformer_config
+        expected_out = config.q_lora_rank + config.kv_lora_rank + config.qk_pos_emb_head_dim
+        weight = self.fused_attention.linear_qkv_down_proj.weight
+        assert weight.shape[0] == expected_out
+        assert weight.shape[1] == config.hidden_size
+
+    def test_qkv_down_projection_split(self):
+        if not is_te_min_version("1.10.0"):
+            pytest.skip("Requires TE >= 1.10.0")
+        config = self.transformer_config
+        self.fused_attention.cuda()
+
+        seq_len, batch = 16, 2
+        hidden = torch.randn(seq_len, batch, config.hidden_size).cuda()
+        q_compressed, kv_combined = self.fused_attention._qkv_down_projection(hidden)
+
+        assert q_compressed.shape == (seq_len, batch, config.q_lora_rank)
+        assert kv_combined.shape == (
+            seq_len,
+            batch,
+            config.kv_lora_rank + config.qk_pos_emb_head_dim,
+        )
+
+    def test_gpu_forward(self):
+        if not is_te_min_version("1.10.0"):
+            pytest.skip("Requires TE >= 1.10.0")
+
+        config = self.fused_attention.config
+        sequence_length = 32
+        micro_batch_size = 2
+
+        self.fused_attention.cuda()
+
+        hidden_states = torch.ones((sequence_length, micro_batch_size, config.hidden_size)).cuda()
+        attention_mask = torch.ones((1, 1, sequence_length, sequence_length), dtype=bool).cuda()
+
+        output, bias = self.fused_attention(hidden_states, attention_mask)
+
+        assert output.shape[0] == sequence_length
+        assert output.shape[1] == micro_batch_size
+        assert output.shape[2] == config.hidden_size
+        assert bias.shape[0] == config.hidden_size
+
+    def test_gpu_forward_bf16(self):
+        if not is_te_min_version("1.10.0"):
+            pytest.skip("Requires TE >= 1.10.0")
+
+        config = self.fused_attention.config
+        sequence_length = 32
+        micro_batch_size = 2
+
+        self.fused_attention.cuda().bfloat16()
+
+        hidden_states = (
+            torch.ones((sequence_length, micro_batch_size, config.hidden_size)).cuda().bfloat16()
+        )
+        attention_mask = torch.ones((1, 1, sequence_length, sequence_length), dtype=bool).cuda()
+
+        output, bias = self.fused_attention(hidden_states, attention_mask)
+
+        assert output.shape[0] == sequence_length
+        assert output.shape[1] == micro_batch_size
+        assert output.shape[2] == config.hidden_size
+        assert output.dtype == torch.bfloat16
+
+
+class TestFusedMLAGradientFlow:
+
+    @pytest.fixture(scope='function', autouse=True)
+    def setup_and_teardown(self):
+        Utils.initialize_model_parallel(1, 1)
+        model_parallel_cuda_manual_seed(123)
+        self.transformer_config = MLATransformerConfig(
+            num_layers=2,
+            hidden_size=12,
+            num_attention_heads=4,
+            use_cpu_initialization=True,
+            q_lora_rank=32,
+            kv_lora_rank=32,
+            qk_head_dim=128,
+            v_head_dim=128,
+            qk_pos_emb_head_dim=64,
+            rope_type="rope",
+            rotary_base=10000,
+            original_max_position_embeddings=32,
+        )
+
+    def teardown_method(self, method):
+        Utils.destroy_model_parallel()
+
+    def test_backward_pass(self):
+        if not is_te_min_version("1.10.0"):
+            pytest.skip("Requires TE >= 1.10.0")
+
+        config = self.transformer_config
+        fused = FusedMLASelfAttention(
+            config, get_fused_mla_submodules(), layer_number=1, attn_mask_type=AttnMaskType.causal
+        )
+        fused.cuda()
+
+        seq_len, batch = 32, 2
+        hidden_states = torch.randn(
+            seq_len, batch, config.hidden_size, device='cuda', requires_grad=True
+        )
+        attention_mask = torch.ones((1, 1, seq_len, seq_len), dtype=bool, device='cuda')
+
+        output, bias = fused(hidden_states, attention_mask)
+        loss = output.sum()
+        loss.backward()
+
+        assert fused.linear_qkv_down_proj.weight.grad is not None
+        assert (
+            fused.linear_qkv_down_proj.weight.grad.shape == fused.linear_qkv_down_proj.weight.shape
+        )
+        assert hidden_states.grad is not None
+
+
+class TestFusedMLALoadFromStateDict:
+
+    @pytest.fixture(scope='function', autouse=True)
+    def setup_and_teardown(self):
+        Utils.initialize_model_parallel(1, 1)
+        model_parallel_cuda_manual_seed(123)
+        self.transformer_config = MLATransformerConfig(
+            num_layers=2,
+            hidden_size=12,
+            num_attention_heads=4,
+            use_cpu_initialization=True,
+            q_lora_rank=32,
+            kv_lora_rank=32,
+            qk_head_dim=128,
+            v_head_dim=128,
+            qk_pos_emb_head_dim=64,
+            rope_type="rope",
+            rotary_base=10000,
+            original_max_position_embeddings=32,
+        )
+
+    def teardown_method(self, method):
+        Utils.destroy_model_parallel()
+
+    def test_load_unfused_state_dict(self):
+        if not is_te_min_version("1.10.0"):
+            pytest.skip("Requires TE >= 1.10.0")
+
+        unfused = MLASelfAttention(
+            self.transformer_config,
+            get_mla_self_attn_submodules(),
+            layer_number=1,
+            attn_mask_type=AttnMaskType.causal,
+        )
+        fused = FusedMLASelfAttention(
+            self.transformer_config,
+            get_fused_mla_submodules(),
+            layer_number=1,
+            attn_mask_type=AttnMaskType.causal,
+        )
+
+        unfused_sd = unfused.state_dict()
+
+        q_down_keys = [k for k in unfused_sd if 'linear_q_down_proj' in k]
+        kv_down_keys = [k for k in unfused_sd if 'linear_kv_down_proj' in k]
+        assert len(q_down_keys) > 0, "Expected q_down_proj keys in unfused state dict"
+        assert len(kv_down_keys) > 0, "Expected kv_down_proj keys in unfused state dict"
+
+        fused.load_state_dict(unfused_sd, strict=False)
+
+        config = self.transformer_config
+        expected_out = config.q_lora_rank + config.kv_lora_rank + config.qk_pos_emb_head_dim
+        assert fused.linear_qkv_down_proj.weight.shape[0] == expected_out
+
+        q_w = unfused_sd['linear_q_down_proj.weight']
+        kv_w = unfused_sd['linear_kv_down_proj.weight']
+        expected_fused = torch.cat([q_w, kv_w], dim=0)
+        torch.testing.assert_close(fused.linear_qkv_down_proj.weight.data, expected_fused)
+
+    def test_sharded_state_dict_splits_back(self):
+        if not is_te_min_version("1.10.0"):
+            pytest.skip("Requires TE >= 1.10.0")
+
+        fused = FusedMLASelfAttention(
+            self.transformer_config,
+            get_fused_mla_submodules(),
+            layer_number=1,
+            attn_mask_type=AttnMaskType.causal,
+        )
+
+        sharded_sd = fused.sharded_state_dict(prefix="")
+        assert any(
+            'linear_q_down_proj.weight' in k for k in sharded_sd
+        ), f"Expected linear_q_down_proj.weight in sharded state dict, got keys: {list(sharded_sd.keys())}"
+        assert any(
+            'linear_kv_down_proj.weight' in k for k in sharded_sd
+        ), f"Expected linear_kv_down_proj.weight in sharded state dict, got keys: {list(sharded_sd.keys())}"
+        assert not any(
+            'linear_qkv_down_proj.weight' in k for k in sharded_sd
+        ), f"Unexpected linear_qkv_down_proj.weight in sharded state dict"
+
+
+class TestFusedMLARequiresQLora:
+
+    @pytest.fixture(scope='function', autouse=True)
+    def setup_and_teardown(self):
+        Utils.initialize_model_parallel(1, 1)
+        model_parallel_cuda_manual_seed(123)
+
+    def teardown_method(self, method):
+        Utils.destroy_model_parallel()
+
+    def test_raises_without_q_lora_rank(self):
+        config = MLATransformerConfig(
+            num_layers=2,
+            hidden_size=12,
+            num_attention_heads=4,
+            use_cpu_initialization=True,
+            q_lora_rank=None,
+            kv_lora_rank=32,
+            qk_head_dim=128,
+            v_head_dim=128,
+            qk_pos_emb_head_dim=64,
+            rope_type="rope",
+            rotary_base=10000,
+            original_max_position_embeddings=32,
+        )
+        with pytest.raises(AssertionError, match="q_lora_rank"):
+            FusedMLASelfAttention(
+                config,
+                get_fused_mla_submodules(),
+                layer_number=1,
+                attn_mask_type=AttnMaskType.causal,
+            )
diff --git a/tests/unit_tests/transformer/test_multi_token_prediction.py b/tests/unit_tests/transformer/test_multi_token_prediction.py
index 1b8fc48c415..6859388fd79 100644
--- a/tests/unit_tests/transformer/test_multi_token_prediction.py
+++ b/tests/unit_tests/transformer/test_multi_token_prediction.py
@@ -703,7 +703,7 @@ def teardown_method(self, method):
     def model_provider(self, pre_process=True, post_process=True, **config_kwargs):
         """Model provider for Mamba hybrid models with MTP.
 
-        Uses the unified pattern syntax where MTP is configured via hybrid_override_pattern:
+        Uses the unified pattern syntax where MTP is configured via hybrid_layer_pattern:
         Format: "<main_pattern>/<mtp_pattern>/<mtp_pattern>/..."
         Example: "M*M*/M*/M*" = main decoder "M*M*", MTP pattern "M*" with 2 depths
         """
@@ -711,7 +711,7 @@ def model_provider(self, pre_process=True, post_process=True, **config_kwargs):
         args = get_args()
         config = core_transformer_config_from_args(args)
 
-        # MTP is configured via unified pattern in hybrid_override_pattern
+        # MTP is configured via unified pattern in hybrid_layer_pattern
         # MambaModel creates the MTP block internally based on the parsed pattern
         model = MambaModel(
             config=config,
@@ -720,9 +720,7 @@ def model_provider(self, pre_process=True, post_process=True, **config_kwargs):
             max_sequence_length=args.max_position_embeddings,
             pre_process=pre_process,
             post_process=post_process,
-            hybrid_attention_ratio=args.hybrid_attention_ratio,
-            hybrid_mlp_ratio=args.hybrid_mlp_ratio,
-            hybrid_override_pattern=args.hybrid_override_pattern,
+            hybrid_layer_pattern=args.hybrid_layer_pattern,
             fp16_lm_cross_entropy=args.fp16_lm_cross_entropy,
             parallel_output=True,
             share_embeddings_and_output_weights=not args.untie_embeddings_and_output_weights,
@@ -739,7 +737,6 @@ def create_test_args(
 
         sys.argv = ['test_multi_token_prediction_mamba.py']
         args = parse_args()
-        args.num_layers = 4
         args.mtp_num_layers = 2
         args.mtp_loss_scaling_factor = 0.1
         args.vocab_size = 128800
@@ -764,10 +761,8 @@ def create_test_args(
         args.no_load_optim = True
         args.no_load_rng = True
         args.bf16 = True
-        args.hybrid_attention_ratio = 0.5
-        args.hybrid_mlp_ratio = 0.0
         # Unified pattern: "main/mtp/mtp" - main decoder "M*M*", MTP pattern "M*" with 2 depths
-        args.hybrid_override_pattern = "M*M*/M*/M*"
+        args.hybrid_layer_pattern = "M*M*/M*/M*"
         args.spec = "megatron.core.models.mamba.mamba_layer_specs.mamba_stack_spec"
 
         if fp8 is not None:
diff --git a/tests/unit_tests/transformer/test_mup.py b/tests/unit_tests/transformer/test_mup.py
new file mode 100644
index 00000000000..605a4e2607b
--- /dev/null
+++ b/tests/unit_tests/transformer/test_mup.py
@@ -0,0 +1,559 @@
+# Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+
+"""Tests for Maximal Update Parameterization (MuP) implementation.
+
+These tests verify that MuP is correctly implemented in Megatron-LM:
+1. Config validation and width_mult computation
+2. Initialization scaling
+3. Attention scaling
+4. LR override computation
+"""
+
+import math
+
+import pytest
+import torch
+
+from megatron.core.optimizer import get_mup_config_overrides, get_standard_config_overrides
+from megatron.core.optimizer.optimizer_config import OptimizerConfig
+from megatron.core.optimizer_param_scheduler import combine_param_group_overrides
+from megatron.core.transformer.multi_token_prediction import process_mtp_loss
+from megatron.core.transformer.transformer_config import TransformerConfig
+from megatron.core.utils import init_method_normal, mup_scaled_init_method_normal
+
+
+class TestMuPConfigValidation:
+    """Tests for MuP config validation and width_mult computation."""
+
+    def test_mup_defaults_base_hidden_size(self):
+        """use_mup without base_hidden_size defaults to hidden_size (width_mult=1.0)."""
+        config = TransformerConfig(
+            hidden_size=512,
+            num_layers=4,
+            num_attention_heads=8,
+            use_mup=True,
+            # mup_base_hidden_size not set - should default to hidden_size
+        )
+        assert config.mup_base_hidden_size == 512
+        assert config.mup_width_mult == 1.0
+
+    def test_mup_width_mult_calculation(self):
+        """width_mult = hidden_size / base_hidden_size."""
+        config = TransformerConfig(
+            hidden_size=1024,
+            num_layers=4,
+            num_attention_heads=16,
+            use_mup=True,
+            mup_base_hidden_size=256,
+        )
+        assert config.mup_width_mult == 4.0
+
+    def test_mup_width_mult_fractional(self):
+        """width_mult can be fractional (smaller than base)."""
+        config = TransformerConfig(
+            hidden_size=128,
+            num_layers=4,
+            num_attention_heads=2,
+            use_mup=True,
+            mup_base_hidden_size=256,
+        )
+        assert config.mup_width_mult == 0.5
+
+    def test_mup_backward_compatible(self):
+        """Default config unchanged when MuP disabled."""
+        config = TransformerConfig(hidden_size=512, num_layers=4, num_attention_heads=8)
+        assert config.use_mup is False
+        assert config.mup_width_mult == 1.0
+        assert config.mup_base_hidden_size is None
+
+    def test_mup_base_hidden_size_must_be_positive(self):
+        """mup_base_hidden_size must be positive."""
+        with pytest.raises(AssertionError) as exc_info:
+            TransformerConfig(
+                hidden_size=512,
+                num_layers=4,
+                num_attention_heads=8,
+                use_mup=True,
+                mup_base_hidden_size=0,
+            )
+        assert "positive" in str(exc_info.value).lower()
+
+
+class TestMuPInitMethods:
+    """Tests for MuP initialization methods."""
+
+    def test_mup_init_variance_scaling(self):
+        """Init std scales as 1/sqrt(width_mult)."""
+        base_sigma = 0.02
+        width_mult = 4.0
+
+        init_fn = init_method_normal(base_sigma / math.sqrt(width_mult))
+        tensor = torch.empty(1000, 1000)
+        init_fn(tensor)
+
+        expected_std = base_sigma / math.sqrt(width_mult)  # 0.02/sqrt(4) = 0.01
+        actual_std = tensor.std().item()
+
+        # Allow 5% tolerance for statistical variance
+        assert (
+            abs(actual_std - expected_std) < 0.002
+        ), f"Expected std ~{expected_std:.4f}, got {actual_std:.4f}"
+
+    def test_mup_scaled_init_variance(self):
+        """MuP scaled init combines depth and width scaling."""
+        sigma = 0.02
+        num_layers = 8
+        width_mult = 4.0
+
+        init_fn = mup_scaled_init_method_normal(sigma, num_layers, width_mult)
+        tensor = torch.empty(1000, 1000)
+        init_fn(tensor)
+
+        # std = sigma / (sqrt(2*num_layers) * sqrt(width_mult))
+        expected_std = sigma / (math.sqrt(2 * num_layers) * math.sqrt(width_mult))
+        actual_std = tensor.std().item()
+
+        # Allow 10% tolerance
+        assert (
+            abs(actual_std - expected_std) < expected_std * 0.1
+        ), f"Expected std ~{expected_std:.6f}, got {actual_std:.6f}"
+
+
+class TestMuPAttentionScaling:
+    """Tests for MuP attention scaling."""
+
+    def test_mup_attention_scale_power_1(self):
+        """MuP uses 1/d instead of 1/sqrt(d) when power=1.0."""
+        config = TransformerConfig(
+            hidden_size=512,
+            num_layers=4,
+            num_attention_heads=8,
+            use_mup=True,
+            mup_base_hidden_size=128,
+            mup_attn_scale_power=1.0,  # MuP default
+        )
+        kv_channels = config.kv_channels  # 512 / 8 = 64
+
+        expected_scale = 1.0 / kv_channels  # 1/64 = 0.015625
+        assert config.softmax_scale == expected_scale
+
+    def test_standard_attention_scale_power_05(self):
+        """Standard uses 1/sqrt(d) when power=0.5."""
+        config = TransformerConfig(
+            hidden_size=512,
+            num_layers=4,
+            num_attention_heads=8,
+            use_mup=True,
+            mup_base_hidden_size=128,
+            mup_attn_scale_power=0.5,  # Standard default
+        )
+        kv_channels = config.kv_channels  # 64
+
+        expected_scale = 1.0 / math.sqrt(kv_channels)  # 1/8 = 0.125
+        assert abs(config.softmax_scale - expected_scale) < 1e-6
+
+    def test_attention_scale_not_set_when_disabled(self):
+        """softmax_scale should not be auto-set when use_mup=False."""
+        config = TransformerConfig(
+            hidden_size=512, num_layers=4, num_attention_heads=8, use_mup=False  # MuP disabled
+        )
+        # softmax_scale defaults to None when MuP is disabled
+        # (actual scaling is done in the attention layer)
+        assert config.softmax_scale is None
+
+
+class TestMuPWarnings:
+    """Tests for MuP configuration warnings."""
+
+    def test_mup_warns_with_custom_init_method(self):
+        """Warn when MuP is enabled and init_method is user-provided."""
+        with pytest.warns(UserWarning, match="use_mup is enabled"):
+            TransformerConfig(
+                hidden_size=512,
+                num_layers=4,
+                num_attention_heads=8,
+                use_mup=True,
+                mup_base_hidden_size=128,
+                init_method=init_method_normal(0.01),
+            )
+
+    def test_mup_warns_with_custom_output_layer_init_method(self):
+        """Warn when MuP is enabled and output_layer_init_method is user-provided."""
+        with pytest.warns(UserWarning, match="use_mup is enabled"):
+            TransformerConfig(
+                hidden_size=512,
+                num_layers=4,
+                num_attention_heads=8,
+                use_mup=True,
+                mup_base_hidden_size=128,
+                output_layer_init_method=init_method_normal(0.01),
+            )
+
+
+class TestMuPLRScaling:
+    """Tests for MuP learning rate and Adam epsilon scaling."""
+
+    def test_mup_lr_override_computation(self):
+        """Hidden LR and Adam eps scale as 1/width_mult."""
+        optimizer_config = OptimizerConfig(lr=1e-3, min_lr=1e-5)
+        width_mult = 4.0
+
+        overrides = get_mup_config_overrides(optimizer_config, width_mult)
+
+        # Should have one override for hidden layers
+        assert len(overrides) == 1
+
+        # Get the override values
+        for param_key, override in overrides.items():
+            expected_max_lr = 1e-3 / width_mult  # 2.5e-4
+            expected_min_lr = 1e-5 / width_mult  # 2.5e-6
+            expected_eps = optimizer_config.adam_eps / width_mult
+
+            assert abs(override['max_lr'] - expected_max_lr) < 1e-10
+            assert abs(override['min_lr'] - expected_min_lr) < 1e-10
+            assert abs(override['eps'] - expected_eps) < 1e-15
+
+    def test_mup_lr_no_scaling_at_unity(self):
+        """No LR scaling when width_mult=1.0."""
+        optimizer_config = OptimizerConfig(lr=1e-3, min_lr=1e-5)
+        width_mult = 1.0
+
+        overrides = get_mup_config_overrides(optimizer_config, width_mult)
+
+        # Should return empty dict when no scaling needed
+        assert len(overrides) == 0
+
+    def test_mup_lr_override_has_correct_predicate(self):
+        """LR override predicate correctly identifies hidden vs vector-like params.
+
+        Per MuP paper and Microsoft's mup library:
+        - Embedding layer: base LR (fan_in is vocab, finite dimension)
+        - Hidden layers: scaled LR (fan_in is hidden, infinite dimension)
+        - Output layer: base LR when tagged as embedding-class (Table 8 symmetry)
+        """
+        optimizer_config = OptimizerConfig(lr=1e-3)
+        width_mult = 4.0
+
+        overrides = get_mup_config_overrides(optimizer_config, width_mult)
+
+        # Check the predicate is set and works correctly
+        for param_key, override in overrides.items():
+            assert param_key.with_name_predicate is not None
+            predicate_fn = param_key.with_name_predicate.fn
+
+            # Create mock parameters
+            hidden_param = torch.nn.Parameter(torch.zeros(10, 10))
+
+            # Hidden param with hidden-layer name should match (scaled LR)
+            assert predicate_fn(hidden_param, 'decoder.layer.0.weight') is True
+
+            # Output layer should NOT match when tagged as embedding-class.
+            output_param = torch.nn.Parameter(torch.zeros(10, 10))
+            output_param.is_embedding_parameter = True
+            assert predicate_fn(output_param, 'output_layer.weight') is False
+
+            # Embedding layer should NOT match (base LR) when attribute is set.
+            embedding_param = torch.nn.Parameter(torch.zeros(10, 10))
+            embedding_param.is_embedding_parameter = True
+            assert predicate_fn(embedding_param, 'decoder.layer.0.weight') is False
+
+            # 1D vector-like params (biases/LN) should also keep base LR.
+            vector_like_param = torch.nn.Parameter(torch.zeros(10))
+            assert predicate_fn(vector_like_param, 'decoder.layer.0.bias') is False
+
+            # Shared embedding copies on tied output stages should also use base LR.
+            shared_embedding_param = torch.nn.Parameter(torch.zeros(10, 10))
+            shared_embedding_param.shared_embedding = True
+            assert predicate_fn(shared_embedding_param, 'output_layer.weight') is False
+
+            # Backward-compatible fallback for older modules without the attribute.
+            assert predicate_fn(hidden_param, 'embedding.word_embeddings.weight') is False
+
+    def test_mup_with_decoupled_lr_scales_hidden_only_for_lr(self):
+        """With decoupled_lr, MuP scales hidden params only; embedding/output stay decoupled."""
+        optimizer_config = OptimizerConfig(
+            lr=1e-3, min_lr=1e-5, decoupled_lr=2e-4, decoupled_min_lr=2e-6
+        )
+        width_mult = 4.0
+
+        standard_overrides = get_standard_config_overrides(optimizer_config)
+        mup_overrides = get_mup_config_overrides(optimizer_config, width_mult)
+        combined_overrides = {**standard_overrides, **mup_overrides}
+
+        hidden_param = torch.nn.Parameter(torch.zeros(10, 10))
+        bias_param = torch.nn.Parameter(torch.zeros(10))
+        embedding_param = torch.nn.Parameter(torch.zeros(10, 10))
+        embedding_param.is_embedding_parameter = True
+        embedding_param.is_embedding_or_output_parameter = True
+        output_param = torch.nn.Parameter(torch.zeros(10, 10))
+        output_param.is_embedding_or_output_parameter = True
+        output_param.is_embedding_parameter = True
+        shared_output_param = torch.nn.Parameter(torch.zeros(10, 10))
+        shared_output_param.is_embedding_or_output_parameter = True
+        shared_output_param.shared_embedding = True
+
+        hidden_matches = [
+            override
+            for param_key, override in combined_overrides.items()
+            if param_key.matches(hidden_param, 'decoder.layer.0.weight')
+        ]
+        bias_matches = [
+            override
+            for param_key, override in combined_overrides.items()
+            if param_key.matches(bias_param, 'decoder.layer.0.bias')
+        ]
+        embedding_matches = [
+            override
+            for param_key, override in combined_overrides.items()
+            if param_key.matches(embedding_param, 'embedding.word_embeddings.weight')
+        ]
+        output_matches = [
+            override
+            for param_key, override in combined_overrides.items()
+            if param_key.matches(output_param, 'output_layer.weight')
+        ]
+        shared_output_matches = [
+            override
+            for param_key, override in combined_overrides.items()
+            if param_key.matches(shared_output_param, 'output_layer.weight')
+        ]
+
+        hidden_override = combine_param_group_overrides(hidden_matches)
+        bias_override = combine_param_group_overrides(bias_matches)
+        embedding_override = combine_param_group_overrides(embedding_matches)
+        output_override = combine_param_group_overrides(output_matches)
+        shared_output_override = combine_param_group_overrides(shared_output_matches)
+
+        # Hidden params keep MuP scaling.
+        assert hidden_override['max_lr'] == pytest.approx(1e-3 / width_mult)
+        assert hidden_override['min_lr'] == pytest.approx(1e-5 / width_mult)
+        assert hidden_override['eps'] == pytest.approx(optimizer_config.adam_eps / width_mult)
+
+        # Biases are vector-like; MuP should not override LR/eps.
+        assert 'max_lr' not in bias_override
+        assert 'min_lr' not in bias_override
+        assert 'eps' not in bias_override
+
+        # Embeddings keep decoupled LR and unscaled eps.
+        assert embedding_override['max_lr'] == pytest.approx(2e-4)
+        assert embedding_override['min_lr'] == pytest.approx(2e-6)
+        assert 'eps' not in embedding_override
+
+        # Output params keep decoupled LR and unscaled eps (Table 8 symmetry).
+        assert output_override['max_lr'] == pytest.approx(2e-4)
+        assert output_override['min_lr'] == pytest.approx(2e-6)
+        assert 'eps' not in output_override
+
+        # Shared embedding output copies stay on embedding-class (unscaled) eps.
+        assert shared_output_override['max_lr'] == pytest.approx(2e-4)
+        assert shared_output_override['min_lr'] == pytest.approx(2e-6)
+        assert 'eps' not in shared_output_override
+
+
+class TestMuPConfigIntegration:
+    """Integration tests for MuP config with init methods."""
+
+    def test_mup_output_layer_init(self):
+        """Output layer init should also scale with MuP."""
+        config = TransformerConfig(
+            hidden_size=1024,
+            num_layers=8,
+            num_attention_heads=16,
+            use_mup=True,
+            mup_base_hidden_size=256,
+        )
+
+        tensor = torch.empty(1000, 1000)
+        config.output_layer_init_method(tensor)
+
+        # Expected: sigma / (sqrt(2*L) * sqrt(m)) = 0.02 / (sqrt(16) * sqrt(4))
+        expected_std = config.init_method_std / (
+            math.sqrt(2 * config.num_layers) * math.sqrt(config.mup_width_mult)
+        )
+        actual_std = tensor.std().item()
+
+        assert abs(actual_std - expected_std) < expected_std * 0.15
+
+
+class TestMuPOptimizerTypeHandling:
+    """Tests for MuP optimizer-specific override behavior."""
+
+    def test_sgd_scales_vector_like_lr_only(self):
+        """SGD scales vector-like params by width_mult; hidden params keep base LR."""
+        optimizer_config = OptimizerConfig(lr=1e-3, min_lr=1e-5)
+        width_mult = 4.0
+
+        for sgd_variant in ['sgd', 'SGD', 'Sgd']:
+            overrides = get_mup_config_overrides(
+                optimizer_config, width_mult, optimizer_type=sgd_variant
+            )
+            assert len(overrides) == 1
+
+            param_key, override = next(iter(overrides.items()))
+            assert override['max_lr'] == pytest.approx(1e-3 * width_mult)
+            assert override['min_lr'] == pytest.approx(1e-5 * width_mult)
+            assert 'eps' not in override
+
+            hidden_param = torch.nn.Parameter(torch.zeros(10, 10))
+            bias_param = torch.nn.Parameter(torch.zeros(10))
+            embedding_param = torch.nn.Parameter(torch.zeros(10, 10))
+            embedding_param.is_embedding_parameter = True
+            output_param = torch.nn.Parameter(torch.zeros(10, 10))
+            output_param.is_embedding_parameter = True
+
+            assert param_key.matches(hidden_param, 'decoder.layer.0.weight') is False
+            assert param_key.matches(bias_param, 'decoder.layer.0.bias') is True
+            assert param_key.matches(embedding_param, 'embedding.word_embeddings.weight') is True
+            assert param_key.matches(output_param, 'output_layer.weight') is True
+
+    def test_sgd_with_decoupled_lr_preserves_embedding_output_precedence(self):
+        """With decoupled_lr, embedding/output keep decoupled LR under SGD MuP."""
+        optimizer_config = OptimizerConfig(
+            lr=1e-3, min_lr=1e-5, decoupled_lr=2e-4, decoupled_min_lr=2e-6
+        )
+        width_mult = 4.0
+
+        standard_overrides = get_standard_config_overrides(optimizer_config)
+        mup_overrides = get_mup_config_overrides(optimizer_config, width_mult, optimizer_type='sgd')
+        combined_overrides = {**standard_overrides, **mup_overrides}
+
+        hidden_param = torch.nn.Parameter(torch.zeros(10, 10))
+        bias_param = torch.nn.Parameter(torch.zeros(10))
+        embedding_param = torch.nn.Parameter(torch.zeros(10, 10))
+        embedding_param.is_embedding_parameter = True
+        embedding_param.is_embedding_or_output_parameter = True
+        output_param = torch.nn.Parameter(torch.zeros(10, 10))
+        output_param.is_embedding_parameter = True
+        output_param.is_embedding_or_output_parameter = True
+        shared_output_param = torch.nn.Parameter(torch.zeros(10, 10))
+        shared_output_param.shared_embedding = True
+        shared_output_param.is_embedding_or_output_parameter = True
+
+        hidden_matches = [
+            override
+            for param_key, override in combined_overrides.items()
+            if param_key.matches(hidden_param, 'decoder.layer.0.weight')
+        ]
+        bias_matches = [
+            override
+            for param_key, override in combined_overrides.items()
+            if param_key.matches(bias_param, 'decoder.layer.0.bias')
+        ]
+        embedding_matches = [
+            override
+            for param_key, override in combined_overrides.items()
+            if param_key.matches(embedding_param, 'embedding.word_embeddings.weight')
+        ]
+        output_matches = [
+            override
+            for param_key, override in combined_overrides.items()
+            if param_key.matches(output_param, 'output_layer.weight')
+        ]
+        shared_output_matches = [
+            override
+            for param_key, override in combined_overrides.items()
+            if param_key.matches(shared_output_param, 'output_layer.weight')
+        ]
+
+        hidden_override = combine_param_group_overrides(hidden_matches)
+        bias_override = combine_param_group_overrides(bias_matches)
+        embedding_override = combine_param_group_overrides(embedding_matches)
+        output_override = combine_param_group_overrides(output_matches)
+        shared_output_override = combine_param_group_overrides(shared_output_matches)
+
+        # Hidden params keep base LR under SGD in current uniform-width setup.
+        assert 'max_lr' not in hidden_override
+        assert 'min_lr' not in hidden_override
+        assert 'eps' not in hidden_override
+
+        # Biases are vector-like and scale up by width_mult.
+        assert bias_override['max_lr'] == pytest.approx(1e-3 * width_mult)
+        assert bias_override['min_lr'] == pytest.approx(1e-5 * width_mult)
+        assert 'eps' not in bias_override
+
+        # Embedding/output params keep explicit decoupled LR precedence.
+        assert embedding_override['max_lr'] == pytest.approx(2e-4)
+        assert embedding_override['min_lr'] == pytest.approx(2e-6)
+        assert output_override['max_lr'] == pytest.approx(2e-4)
+        assert output_override['min_lr'] == pytest.approx(2e-6)
+        assert shared_output_override['max_lr'] == pytest.approx(2e-4)
+        assert shared_output_override['min_lr'] == pytest.approx(2e-6)
+        assert 'eps' not in embedding_override
+        assert 'eps' not in output_override
+        assert 'eps' not in shared_output_override
+
+    def test_adam_scales_lr_by_default(self):
+        """Adam optimizer should scale LR and eps; default optimizer_type is adam."""
+        optimizer_config = OptimizerConfig(lr=1e-3, min_lr=1e-5)
+        width_mult = 4.0
+
+        # Explicit adam
+        overrides_explicit = get_mup_config_overrides(
+            optimizer_config, width_mult, optimizer_type='adam'
+        )
+        assert len(overrides_explicit) == 1
+
+        # Default (should behave like adam for backward compat)
+        overrides_default = get_mup_config_overrides(optimizer_config, width_mult)
+        assert len(overrides_default) == 1
+
+        for param_key, override in overrides_explicit.items():
+            expected_max_lr = 1e-3 / width_mult  # 2.5e-4
+            expected_eps = optimizer_config.adam_eps / width_mult
+            assert abs(override['max_lr'] - expected_max_lr) < 1e-10
+            assert abs(override['eps'] - expected_eps) < 1e-15
+
+    def test_non_adam_does_not_set_eps_override(self):
+        """Non-Adam optimizers should not receive MuP epsilon overrides."""
+        optimizer_config = OptimizerConfig(lr=1e-3, min_lr=1e-5)
+        width_mult = 4.0
+
+        for non_adam_optimizer in ['muon', 'dist_muon']:
+            overrides = get_mup_config_overrides(
+                optimizer_config, width_mult, optimizer_type=non_adam_optimizer
+            )
+            assert len(overrides) == 1
+            for _, override in overrides.items():
+                assert override['max_lr'] == pytest.approx(1e-3 / width_mult)
+                assert override['min_lr'] == pytest.approx(1e-5 / width_mult)
+                assert 'eps' not in override
+
+
+class TestMuPMTPLossScaling:
+    """Tests for MuP scaling integration with MTP loss processing."""
+
+    def test_process_mtp_loss_applies_scale_hook(self):
+        config = TransformerConfig(
+            hidden_size=8, num_layers=2, num_attention_heads=2, mtp_num_layers=1
+        )
+        hidden_states = torch.ones(2, 1, 4)
+        labels = torch.ones(1, 4, dtype=torch.long)
+        loss_mask = torch.ones_like(labels, dtype=torch.float32)
+        observed_logits_mean = {'value': None}
+
+        def output_layer(hidden, weight=None, runtime_gather_output=None):
+            return hidden.clone(), None
+
+        def scale_logits_fn(logits):
+            return logits * 3.0
+
+        def compute_language_model_loss(mtp_labels, mtp_logits):
+            observed_logits_mean['value'] = mtp_logits.mean().item()
+            return torch.ones_like(mtp_labels, dtype=mtp_logits.dtype)
+
+        process_mtp_loss(
+            hidden_states=hidden_states,
+            labels=labels,
+            loss_mask=loss_mask,
+            output_layer=output_layer,
+            output_weight=None,
+            runtime_gather_output=None,
+            is_training=False,
+            compute_language_model_loss=compute_language_model_loss,
+            config=config,
+            cp_group=None,
+            packed_seq_params=None,
+            scale_logits_fn=scale_logits_fn,
+        )
+
+        assert observed_logits_mean['value'] == pytest.approx(3.0)
diff --git a/tests/unit_tests/transformer/test_vision_cuda_graphs.py b/tests/unit_tests/transformer/test_vision_cuda_graphs.py
new file mode 100644
index 00000000000..d8990e0d973
--- /dev/null
+++ b/tests/unit_tests/transformer/test_vision_cuda_graphs.py
@@ -0,0 +1,680 @@
+# Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+
+import gc
+import os
+from copy import deepcopy
+from types import SimpleNamespace
+from unittest.mock import MagicMock
+
+import pytest
+import torch
+
+from megatron.core import parallel_state
+from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_with_transformer_engine_spec
+from megatron.core.models.vision.vit_layer_specs import get_vit_layer_with_transformer_engine_spec
+from megatron.core.tensor_parallel.random import (
+    HAVE_TE,
+    initialize_rng_tracker,
+    model_parallel_cuda_manual_seed,
+)
+from megatron.core.transformer.cuda_graphs import (
+    HAVE_TE_GRAPHS,
+    VisionTECudaGraphHelper,
+    _layer_is_graphable,
+    _wrap_graph_for_vision,
+    get_vision_cuda_graph_seq_length,
+    set_current_microbatch,
+)
+from megatron.core.transformer.transformer_config import TransformerConfig
+from megatron.core.utils import is_te_min_version
+from tests.unit_tests.test_utilities import Utils
+
+TE_MIN_VERSION = "2.13.0"
+_te_version_ok = HAVE_TE and is_te_min_version(TE_MIN_VERSION)
+if not _te_version_ok and __name__ != "__main__":
+    pytest.skip(
+        f"Vision CUDA graph tests require TransformerEngine >= {TE_MIN_VERSION}",
+        allow_module_level=True,
+    )
+
+
+# ---------------------------------------------------------------------------
+# Tests for _layer_is_graphable
+# ---------------------------------------------------------------------------
+class TestVisionLayerIsGraphable:
+    def test_non_transformer_layer_returns_false(self):
+        config = SimpleNamespace(cuda_graph_impl="transformer_engine")
+        layer = torch.nn.Linear(4, 4)
+        assert _layer_is_graphable(layer, config) is False
+
+    @pytest.mark.flaky
+    @pytest.mark.flaky_in_dev
+    def test_wrong_cuda_graph_impl_returns_false(self):
+        from megatron.core.transformer.transformer_layer import TransformerLayer
+
+        config = SimpleNamespace(cuda_graph_impl="local")
+        layer = MagicMock(spec=TransformerLayer)
+        # isinstance check with MagicMock(spec=...) should pass
+        assert _layer_is_graphable(layer, config) is False
+
+    def test_correct_config_with_transformer_layer(self):
+        """Real TransformerLayer + cuda_graph_impl='transformer_engine' -> True."""
+        initialize_rng_tracker(use_te_rng_tracker=True, force_reset=True)
+        Utils.initialize_model_parallel(
+            tensor_model_parallel_size=1, pipeline_model_parallel_size=1
+        )
+        model_parallel_cuda_manual_seed(123)
+
+        config = TransformerConfig(
+            num_layers=1,
+            hidden_size=16,
+            num_attention_heads=2,
+            use_cpu_initialization=True,
+            cuda_graph_impl="transformer_engine",
+        )
+        from megatron.core.transformer.transformer_block import TransformerBlock
+
+        block = TransformerBlock(config, get_vit_layer_with_transformer_engine_spec())
+        layer = block.layers[0]
+        assert _layer_is_graphable(layer, config) is True
+
+        Utils.destroy_model_parallel()
+
+
+# ---------------------------------------------------------------------------
+# Tests for _wrap_graph_for_vision
+# ---------------------------------------------------------------------------
+class TestWrapGraphForVision:
+    def test_filters_none_from_tuple(self):
+        def fake_graph(*args, **kwargs):
+            return (torch.tensor(1.0), None)
+
+        wrapped = _wrap_graph_for_vision(fake_graph)
+        result = wrapped()
+        assert result == (torch.tensor(1.0),)
+
+    def test_returns_non_tuple_unchanged(self):
+        t = torch.tensor(42.0)
+
+        def fake_graph(*args, **kwargs):
+            return t
+
+        wrapped = _wrap_graph_for_vision(fake_graph)
+        result = wrapped()
+        assert result is t
+
+    def test_preserves_all_non_none(self):
+        a, b = torch.tensor(1.0), torch.tensor(2.0)
+
+        def fake_graph(*args, **kwargs):
+            return (a, b)
+
+        wrapped = _wrap_graph_for_vision(fake_graph)
+        result = wrapped()
+        assert result == (a, b)
+
+    def test_all_none_returns_original(self):
+        def fake_graph(*args, **kwargs):
+            return (None, None)
+
+        wrapped = _wrap_graph_for_vision(fake_graph)
+        result = wrapped()
+        # filtered is empty -> returns original tuple
+        assert result == (None, None)
+
+    def test_preserves_te_attributes(self):
+        def fake_graph(*args, **kwargs):
+            return (torch.tensor(1.0),)
+
+        fake_graph.backward_dw = "bwd_dw_fn"
+        fake_graph.reset = "reset_fn"
+
+        wrapped = _wrap_graph_for_vision(fake_graph)
+        assert wrapped.backward_dw == "bwd_dw_fn"
+        assert wrapped.reset == "reset_fn"
+
+    def test_missing_te_attributes_not_set(self):
+        def fake_graph(*args, **kwargs):
+            return (torch.tensor(1.0),)
+
+        wrapped = _wrap_graph_for_vision(fake_graph)
+        assert not hasattr(wrapped, 'backward_dw')
+        assert not hasattr(wrapped, 'reset')
+
+
+# ---------------------------------------------------------------------------
+# Tests for get_vision_cuda_graph_seq_length
+# ---------------------------------------------------------------------------
+class TestGetVisionCudaGraphSeqLength:
+    def test_explicit_max_seq_length(self):
+        config = SimpleNamespace(max_vision_cuda_graph_seq_length=2048)
+        assert get_vision_cuda_graph_seq_length(config) == 2048
+
+    def test_explicit_max_seq_length_zero_falls_through(self):
+        """max_vision_cuda_graph_seq_length=0 is falsy, should fall through."""
+        config = SimpleNamespace(max_vision_cuda_graph_seq_length=0)
+        assert get_vision_cuda_graph_seq_length(config, default_seq_length=999) == 999
+
+    def test_num_position_embeddings_only(self):
+        config = SimpleNamespace(num_position_embeddings=1024)
+        assert get_vision_cuda_graph_seq_length(config) == 1024
+
+    def test_num_position_embeddings_with_spatial_merge(self):
+        config = SimpleNamespace(num_position_embeddings=1024, spatial_merge_size=2)
+        # merge_factor = 2**2 = 4, seq = 1024 // 4 = 256
+        assert get_vision_cuda_graph_seq_length(config) == 256
+
+    def test_spatial_merge_size_3(self):
+        config = SimpleNamespace(num_position_embeddings=900, spatial_merge_size=3)
+        # merge_factor = 9, seq = 900 // 9 = 100
+        assert get_vision_cuda_graph_seq_length(config) == 100
+
+    def test_default_seq_length(self):
+        config = SimpleNamespace()
+        assert get_vision_cuda_graph_seq_length(config) == 4096
+
+    def test_custom_default(self):
+        config = SimpleNamespace()
+        assert get_vision_cuda_graph_seq_length(config, default_seq_length=512) == 512
+
+    def test_explicit_overrides_position_embeddings(self):
+        config = SimpleNamespace(
+            max_vision_cuda_graph_seq_length=8192, num_position_embeddings=1024
+        )
+        assert get_vision_cuda_graph_seq_length(config) == 8192
+
+
+# ---------------------------------------------------------------------------
+# Integration test for VisionTECudaGraphHelper with LLaVA model
+# ---------------------------------------------------------------------------
+@pytest.mark.skipif(
+    not (HAVE_TE and is_te_min_version("1.5.0")),
+    reason="use_te_rng_tracker requires TransformerEngine version >= 1.5",
+)
+class TestVisionTECudaGraphHelper:
+    """Test VisionTECudaGraphHelper initialization, sample args, and graph lifecycle."""
+
+    def setup_method(self, method):
+        initialize_rng_tracker(use_te_rng_tracker=True, force_reset=True)
+        Utils.initialize_model_parallel(
+            tensor_model_parallel_size=1,
+            pipeline_model_parallel_size=1,
+            virtual_pipeline_model_parallel_size=None,
+        )
+        model_parallel_cuda_manual_seed(123)
+
+        from megatron.core.models.multimodal.llava_model import LLaVAModel
+
+        self.language_hidden_size = 64
+        self.vision_hidden_size = 16
+        self.vision_num_layers = 2
+
+        language_config = TransformerConfig(
+            num_layers=2,
+            hidden_size=self.language_hidden_size,
+            num_attention_heads=4,
+            use_cpu_initialization=True,
+        )
+
+        self.vision_config = TransformerConfig(
+            num_layers=self.vision_num_layers,
+            hidden_size=self.vision_hidden_size,
+            num_attention_heads=2,
+            use_cpu_initialization=True,
+            cuda_graph_impl="transformer_engine",
+            bf16=True,
+            pipeline_dtype=torch.bfloat16,
+        )
+
+        vision_projection_config = TransformerConfig(
+            num_layers=1,
+            hidden_size=self.language_hidden_size,
+            ffn_hidden_size=32,
+            num_attention_heads=1,
+            use_cpu_initialization=True,
+            bf16=True,
+            pipeline_dtype=torch.bfloat16,
+        )
+
+        language_layer_spec = get_gpt_layer_with_transformer_engine_spec()
+        vision_layer_spec = get_vit_layer_with_transformer_engine_spec()
+        vision_projection_spec = deepcopy(language_layer_spec.submodules.mlp.submodules)
+
+        self.vision_config.vision_model_type = "clip"
+        language_config.language_model_type = "dummy"
+
+        self.llava_model = LLaVAModel(
+            language_transformer_config=language_config,
+            language_transformer_layer_spec=language_layer_spec,
+            language_vocab_size=8192,
+            language_max_sequence_length=4096,
+            vision_transformer_config=self.vision_config,
+            vision_transformer_layer_spec=vision_layer_spec,
+            drop_vision_class_token=False,
+            vision_projection_config=vision_projection_config,
+            vision_projection_layer_spec=vision_projection_spec,
+            img_h=336,
+            img_w=336,
+            patch_dim=14,
+            pre_process=True,
+            post_process=True,
+            add_encoder=True,
+            add_decoder=True,
+        )
+        self.llava_model.bfloat16()
+
+        self.vision_seq_length = 576
+        self.micro_batch_size = 2
+
+    def teardown_method(self, method):
+        Utils.destroy_model_parallel()
+        gc.collect()
+
+    def _make_helper(self, num_microbatches=1):
+        return VisionTECudaGraphHelper(
+            model=[self.llava_model],
+            vision_config=self.vision_config,
+            vision_seq_length=self.vision_seq_length,
+            micro_batch_size=self.micro_batch_size,
+            num_microbatches=num_microbatches,
+        )
+
+    # -- Initialization tests --
+
+    def test_init_finds_vision_layers(self):
+        helper = self._make_helper()
+        assert helper.vision_model is not None, "Should find vision_model"
+        assert helper.num_layers == self.vision_num_layers
+        assert len(helper.callables) == self.vision_num_layers
+        assert helper.graphs_created() is False
+
+    def test_init_no_vision_model_warns(self):
+        """When model has no vision_model attr, helper should degrade gracefully."""
+        dummy_model = torch.nn.Linear(4, 4)
+        helper = VisionTECudaGraphHelper(
+            model=[dummy_model],
+            vision_config=self.vision_config,
+            vision_seq_length=self.vision_seq_length,
+            micro_batch_size=self.micro_batch_size,
+        )
+        assert helper.vision_model is None
+        assert len(helper.callables) == 0
+        assert helper.graphs_created() is False
+
+    # -- _get_sample_arguments tests --
+
+    def test_get_sample_arguments_shapes(self):
+        helper = self._make_helper(num_microbatches=1)
+        # order is unused by vision override; pass a dummy
+        sample_args, sample_kwargs_list = helper._get_sample_arguments(order=[1, -1])
+
+        expected_count = self.vision_num_layers * 1  # layers * microbatches
+        assert len(sample_args) == expected_count
+        assert len(sample_kwargs_list) == expected_count
+
+        for i, (args_item, kwargs_item) in enumerate(zip(sample_args, sample_kwargs_list)):
+            assert isinstance(args_item, tuple), f"sample_args[{i}] should be tuple"
+            assert len(args_item) == 1, f"sample_args[{i}] should have one element (hidden_states)"
+            hs = args_item[0]
+            assert hs.shape == (self.vision_seq_length, 1, self.vision_hidden_size), (
+                f"Expected ({self.vision_seq_length}, 1, {self.vision_hidden_size}), "
+                f"got {hs.shape}"
+            )
+            assert hs.dtype == torch.bfloat16
+            assert hs.device.type == 'cuda'
+            assert hs.requires_grad is True
+
+    def test_get_sample_arguments_multi_microbatch(self):
+        helper = self._make_helper(num_microbatches=3)
+        sample_args, sample_kwargs_list = helper._get_sample_arguments(order=[1, -1])
+
+        expected_count = self.vision_num_layers * 3
+        assert len(sample_args) == expected_count
+        assert len(sample_kwargs_list) == expected_count
+
+    def test_get_sample_arguments_empty_when_no_callables(self):
+        dummy_model = torch.nn.Linear(4, 4)
+        helper = VisionTECudaGraphHelper(
+            model=[dummy_model],
+            vision_config=self.vision_config,
+            vision_seq_length=self.vision_seq_length,
+            micro_batch_size=self.micro_batch_size,
+        )
+        sample_args, sample_kwargs_list = helper._get_sample_arguments(order=[1, -1])
+        assert sample_args == []
+        assert sample_kwargs_list == []
+
+    # -- create_cudagraphs / delete_cuda_graphs lifecycle --
+    @pytest.mark.flaky
+    @pytest.mark.flaky_in_dev
+    @pytest.mark.skipif(
+        not (HAVE_TE_GRAPHS and is_te_min_version("2.7.0")),
+        reason="TE CUDA graph capture requires TransformerEngine >= 2.7.0",
+    )
+    def test_create_and_delete_cudagraphs(self):
+        """Full lifecycle: create graphs, verify state, delete, verify cleanup."""
+        self.llava_model.cuda()
+        helper = self._make_helper(num_microbatches=1)
+
+        assert not helper.graphs_created()
+
+        helper.create_cudagraphs()
+        assert helper.graphs_created()
+
+        # Each vision layer should have cuda_graphs attached
+        for layer in helper.callables:
+            assert hasattr(layer, 'cuda_graphs'), "Layer should have cuda_graphs after capture"
+            assert len(layer.cuda_graphs) == 1  # 1 microbatch
+
+        # cudagraph_manager should have been removed during capture
+        for layer in helper.callables:
+            assert not hasattr(
+                layer, 'cudagraph_manager'
+            ), "cudagraph_manager should be removed before TE capture"
+
+        helper.delete_cuda_graphs()
+        assert not helper.graphs_created()
+
+        # cuda_graphs should be empty after delete
+        for layer in helper.callables:
+            assert layer.cuda_graphs == [], "cuda_graphs should be empty after delete"
+
+    @pytest.mark.skipif(
+        not (HAVE_TE_GRAPHS and is_te_min_version("2.7.0")),
+        reason="TE CUDA graph capture requires TransformerEngine >= 2.7.0",
+    )
+    @pytest.mark.flaky
+    @pytest.mark.flaky_in_dev
+    def test_create_cudagraphs_multi_microbatch(self):
+        """Verify that graphs are created per-microbatch per-layer."""
+        self.llava_model.cuda()
+        num_mb = 2
+        helper = self._make_helper(num_microbatches=num_mb)
+
+        helper.create_cudagraphs()
+        assert helper.graphs_created()
+
+        for layer in helper.callables:
+            assert hasattr(layer, 'cuda_graphs')
+            # PP=1 collapses to 1 microbatch internally
+            assert len(layer.cuda_graphs) == helper.num_microbatches
+
+        helper.delete_cuda_graphs()
+
+    def test_create_cudagraphs_no_callables_is_noop(self):
+        """create_cudagraphs on empty helper should not crash."""
+        dummy_model = torch.nn.Linear(4, 4)
+        helper = VisionTECudaGraphHelper(
+            model=[dummy_model],
+            vision_config=self.vision_config,
+            vision_seq_length=self.vision_seq_length,
+            micro_batch_size=self.micro_batch_size,
+        )
+        helper.create_cudagraphs()
+        assert not helper.graphs_created()
+
+    def test_delete_cudagraphs_before_create_asserts(self):
+        """delete_cuda_graphs before creation should raise AssertionError."""
+        helper = self._make_helper()
+        with pytest.raises(AssertionError):
+            helper.delete_cuda_graphs()
+
+
+# ---------------------------------------------------------------------------
+# Integration test with PP=2: vision encoder on first pipeline stage only
+# ---------------------------------------------------------------------------
+@pytest.mark.skipif(
+    not (HAVE_TE and is_te_min_version("1.5.0")),
+    reason="use_te_rng_tracker requires TransformerEngine version >= 1.5",
+)
+class TestVisionTECudaGraphHelperPP2:
+    """Test VisionTECudaGraphHelper with PP=2.
+
+    With pipeline_model_parallel_size=2 the LLaVA model is split so that the
+    vision encoder lives exclusively on the first pipeline stage:
+      - pp_rank 0: add_encoder=True,  pre_process=True,  post_process=False
+      - pp_rank 1: add_encoder=False, pre_process=False, post_process=True
+
+    This test verifies that:
+      1. On stage 0 the helper finds and captures vision layers.
+      2. On stage 1 the helper gracefully finds no vision layers.
+      3. With PP>1, num_microbatches is NOT collapsed to 1.
+    """
+
+    def setup_method(self, method):
+        initialize_rng_tracker(use_te_rng_tracker=True, force_reset=True)
+        Utils.initialize_model_parallel(
+            tensor_model_parallel_size=1,
+            pipeline_model_parallel_size=2,
+            virtual_pipeline_model_parallel_size=None,
+        )
+        model_parallel_cuda_manual_seed(123)
+
+        from megatron.core.models.multimodal.llava_model import LLaVAModel
+
+        self.language_hidden_size = 64
+        self.vision_hidden_size = 16
+        self.vision_num_layers = 2
+        self.language_num_layers = 4
+
+        pp_rank = parallel_state.get_pipeline_model_parallel_rank()
+        is_first_stage = pp_rank == 0
+        is_last_stage = pp_rank == (parallel_state.get_pipeline_model_parallel_world_size() - 1)
+
+        language_config = TransformerConfig(
+            num_layers=self.language_num_layers,
+            hidden_size=self.language_hidden_size,
+            num_attention_heads=4,
+            use_cpu_initialization=True,
+            pipeline_model_parallel_size=2,
+            bf16=True,
+            pipeline_dtype=torch.bfloat16,
+        )
+
+        self.vision_config = TransformerConfig(
+            num_layers=self.vision_num_layers,
+            hidden_size=self.vision_hidden_size,
+            num_attention_heads=2,
+            use_cpu_initialization=True,
+            cuda_graph_impl="transformer_engine",
+            bf16=True,
+            pipeline_dtype=torch.bfloat16,
+        )
+
+        vision_projection_config = TransformerConfig(
+            num_layers=1,
+            hidden_size=self.language_hidden_size,
+            ffn_hidden_size=32,
+            num_attention_heads=1,
+            use_cpu_initialization=True,
+            bf16=True,
+            pipeline_dtype=torch.bfloat16,
+        )
+
+        language_layer_spec = get_gpt_layer_with_transformer_engine_spec()
+        vision_layer_spec = get_vit_layer_with_transformer_engine_spec()
+        vision_projection_spec = deepcopy(language_layer_spec.submodules.mlp.submodules)
+
+        self.vision_config.vision_model_type = "clip"
+        language_config.language_model_type = "dummy"
+
+        self.is_first_stage = is_first_stage
+        self.llava_model = LLaVAModel(
+            language_transformer_config=language_config,
+            language_transformer_layer_spec=language_layer_spec,
+            language_vocab_size=8192,
+            language_max_sequence_length=4096,
+            vision_transformer_config=self.vision_config,
+            vision_transformer_layer_spec=vision_layer_spec,
+            drop_vision_class_token=False,
+            vision_projection_config=vision_projection_config,
+            vision_projection_layer_spec=vision_projection_spec,
+            img_h=336,
+            img_w=336,
+            patch_dim=14,
+            pre_process=is_first_stage,
+            post_process=is_last_stage,
+            add_encoder=is_first_stage,
+            add_decoder=True,
+        )
+        self.llava_model.bfloat16()
+
+        self.vision_seq_length = 576
+        self.micro_batch_size = 2
+
+    def teardown_method(self, method):
+        Utils.destroy_model_parallel()
+        gc.collect()
+
+    def _make_helper(self, num_microbatches=4):
+        return VisionTECudaGraphHelper(
+            model=[self.llava_model],
+            vision_config=self.vision_config,
+            vision_seq_length=self.vision_seq_length,
+            micro_batch_size=self.micro_batch_size,
+            num_microbatches=num_microbatches,
+        )
+
+    def test_pp2_first_stage_finds_vision_layers(self):
+        """Stage 0 should discover all vision encoder layers."""
+        if not self.is_first_stage:
+            pytest.skip("This assertion is only for pp_rank 0")
+
+        helper = self._make_helper(num_microbatches=4)
+        assert helper.vision_model is not None
+        assert helper.num_layers == self.vision_num_layers
+        assert len(helper.callables) == self.vision_num_layers
+
+    def test_pp2_last_stage_has_no_vision_layers(self):
+        """Stage 1 should find no vision model (encoder lives on stage 0)."""
+        if self.is_first_stage:
+            pytest.skip("This assertion is only for pp_rank 1")
+
+        helper = self._make_helper(num_microbatches=4)
+        assert helper.vision_model is None
+        assert len(helper.callables) == 0
+        assert not helper.graphs_created()
+
+    def test_pp2_num_microbatches_preserved(self):
+        """With PP>1, num_microbatches should NOT be collapsed to 1."""
+        if not self.is_first_stage:
+            pytest.skip("Vision layers only on pp_rank 0")
+
+        num_mb = 8
+        helper = self._make_helper(num_microbatches=num_mb)
+        # _get_sample_arguments generates layers * microbatches entries
+        sample_args, sample_kwargs_list = helper._get_sample_arguments(order=[1, -1])
+        expected_count = self.vision_num_layers * num_mb
+        assert len(sample_args) == expected_count, (
+            f"With PP>1, expected {expected_count} sample_args "
+            f"(layers={self.vision_num_layers} * mb={num_mb}), got {len(sample_args)}"
+        )
+
+    @pytest.mark.skipif(
+        not (HAVE_TE_GRAPHS and is_te_min_version("2.7.0")),
+        reason="TE CUDA graph capture requires TransformerEngine >= 2.7.0",
+    )
+    @pytest.mark.flaky
+    @pytest.mark.flaky_in_dev
+    def test_pp2_create_cudagraphs_first_stage(self):
+        """On stage 0, CUDA graphs should be captured with the full pipeline order."""
+        if not self.is_first_stage:
+            pytest.skip("Vision layers only on pp_rank 0")
+
+        self.llava_model.cuda()
+        num_mb = 4
+        helper = self._make_helper(num_microbatches=num_mb)
+
+        assert not helper.graphs_created()
+
+        helper.create_cudagraphs()
+        assert helper.graphs_created()
+
+        # num_microbatches should be preserved (PP>1 does not collapse)
+        assert helper.num_microbatches == num_mb
+
+        # Each layer should have one graph per microbatch
+        for layer in helper.callables:
+            assert hasattr(layer, 'cuda_graphs')
+            assert (
+                len(layer.cuda_graphs) == num_mb
+            ), f"Expected {num_mb} graphs per layer, got {len(layer.cuda_graphs)}"
+
+        # Cleanup
+        helper.delete_cuda_graphs()
+        assert not helper.graphs_created()
+        for layer in helper.callables:
+            assert layer.cuda_graphs == []
+
+    @pytest.mark.skipif(
+        not (HAVE_TE_GRAPHS and is_te_min_version("2.7.0")),
+        reason="TE CUDA graph capture requires TransformerEngine >= 2.7.0",
+    )
+    def test_pp2_create_cudagraphs_last_stage_noop(self):
+        """On stage 1 (no vision model), create_cudagraphs should be a no-op."""
+        if self.is_first_stage:
+            pytest.skip("This assertion is only for pp_rank 1")
+
+        helper = self._make_helper(num_microbatches=4)
+        helper.create_cudagraphs()
+        assert not helper.graphs_created()
+
+
+if __name__ == "__main__":
+    if not _te_version_ok:
+        print(f"SKIPPED: Vision CUDA graph tests require TransformerEngine >= {TE_MIN_VERSION}")
+        exit(0)
+
+    from _pytest.outcomes import Skipped
+
+    def run_test(test_obj, test_fn_name):
+        """Run a test method, treating pytest.skip() as a non-error."""
+        test_obj.setup_method(method=None)
+        try:
+            getattr(test_obj, test_fn_name)()
+        except Skipped as e:
+            print(f"  SKIPPED {test_fn_name}: {e}")
+        finally:
+            test_obj.teardown_method(method=None)
+
+    # Quick smoke tests for pure functions
+    t = TestWrapGraphForVision()
+    t.test_filters_none_from_tuple()
+    t.test_returns_non_tuple_unchanged()
+    t.test_preserves_all_non_none()
+    t.test_all_none_returns_original()
+    t.test_preserves_te_attributes()
+    t.test_missing_te_attributes_not_set()
+    print("_wrap_graph_for_vision tests passed.")
+
+    t2 = TestGetVisionCudaGraphSeqLength()
+    t2.test_explicit_max_seq_length()
+    t2.test_explicit_max_seq_length_zero_falls_through()
+    t2.test_num_position_embeddings_only()
+    t2.test_num_position_embeddings_with_spatial_merge()
+    t2.test_spatial_merge_size_3()
+    t2.test_default_seq_length()
+    t2.test_custom_default()
+    t2.test_explicit_overrides_position_embeddings()
+    print("get_vision_cuda_graph_seq_length tests passed.")
+
+    # Integration tests (require GPU + distributed init)
+    t3 = TestVisionTECudaGraphHelper()
+    run_test(t3, "test_init_finds_vision_layers")
+    run_test(t3, "test_get_sample_arguments_shapes")
+    run_test(t3, "test_create_and_delete_cudagraphs")
+    print("TestVisionTECudaGraphHelper tests passed.")
+
+    # PP=2 integration tests (require 2+ GPUs)
+    if Utils.world_size >= 2:
+        t4 = TestVisionTECudaGraphHelperPP2()
+        run_test(t4, "test_pp2_first_stage_finds_vision_layers")
+        run_test(t4, "test_pp2_last_stage_has_no_vision_layers")
+        run_test(t4, "test_pp2_num_microbatches_preserved")
+        run_test(t4, "test_pp2_create_cudagraphs_first_stage")
+        run_test(t4, "test_pp2_create_cudagraphs_last_stage_noop")
+        print("TestVisionTECudaGraphHelperPP2 tests passed.")
+    else:
+        print("SKIPPED TestVisionTECudaGraphHelperPP2 (requires 2+ GPUs)")
+
+    print("All vision CUDA graph tests passed.")
diff --git a/tools/preprocess_data.py b/tools/preprocess_data.py
index 3ac06c412b6..f472dd50dbf 100644
--- a/tools/preprocess_data.py
+++ b/tools/preprocess_data.py
@@ -11,9 +11,8 @@
 import time
 import gzip
 import glob
-import torch
-import numpy as np
 import multiprocessing
+import numpy as np
 try:
     import nltk
     from nltk.tokenize.punkt import PunktLanguageVars
@@ -113,6 +112,7 @@ class Partition(object):
     def __init__(self, args, workers):
         self.args = args
         self.workers = workers
+        self.performance = []
 
     def print_processing_stats(self, count, proc_start, total_bytes_processed):
         if count % self.args.log_interval == 0:
@@ -122,6 +122,8 @@ def print_processing_stats(self, count, proc_start, total_bytes_processed):
             print(f"Processed {count} documents",
                   f"({count/elapsed} docs/s, {mbs} MB/s).",
                   file=sys.stderr)
+            if self.args.find_optimal_num_workers:
+                self.performance.append(count/elapsed)
 
     def split_sentences(self, file_name):
         input_file_name, output_file_name = file_name
@@ -143,6 +145,8 @@ def split_sentences(self, file_name):
         fin.close()
         fout.close()
 
+        pool.close()
+        pool.join()
 
     def process_json_file(self, file_name):
         input_file_name, output_prefix = file_name
@@ -178,14 +182,22 @@ def process_json_file(self, file_name):
         total_bytes_processed = 0
         print("Time to startup:", startup_end - startup_start)
         for i, (doc, sentence_lens, bytes_processed) in enumerate(encoded_docs, start=1):
-            total_bytes_processed += bytes_processed
-            for key in doc.keys():
-                builders[key].add_document(doc[key], sentence_lens[key])
-            self.print_processing_stats(i, proc_start, total_bytes_processed)
+            if self.args.find_optimal_num_workers and i > self.args.max_documents:
+                break
+            else:
+                total_bytes_processed += bytes_processed
+                for key in doc.keys():
+                    builders[key].add_document(doc[key], sentence_lens[key])
+                self.print_processing_stats(i, proc_start, total_bytes_processed)
 
         fin.close()
         builders[key].finalize(output_idx_files[key])
 
+        pool.close()
+        pool.join()
+
+        return self.performance
+
 
 def get_args():
     parser = argparse.ArgumentParser()
@@ -212,6 +224,19 @@ def get_args():
                        help=('Number of worker processes to launch.'
                              'A good default for fast pre-processing '
                              'is: (workers * partitions) = available CPU cores.'))
+    group.add_argument('--find-optimal-num-workers', action='store_true',
+                       help=('Find optimal number of workers.'
+                             'Script will run few small jobs with '
+                             'different number of workers to define '
+                             'optimal number of workers in terms of performance.'))
+    group.add_argument('--workers-to-check', nargs='+', type=int, default=[16, 32, 64],
+                       help=('list of workers to run data processing with '
+                             'to find optimal number of workers. '
+                             'Works only when --find-optimal-num-workers is enabled. '))
+    group.add_argument('--max-documents', type=int, default=100_000,
+                       help=('Maximum number of documents to preprocess '
+                             'to find  optimal number of workers.'
+                             'Works only when --find-optimal-num-workers is enabled.'))
     group.add_argument('--partitions', type=int, default=1,
                         help='Number of file partitions')
     group.add_argument('--log-interval', type=int, default=1000,
@@ -253,143 +278,197 @@ def check_files_exist(in_ss_out_names, key, num_partitions):
     return True
 
 
+def find_optimal_num_workers(performance, partitions):
+    """Parses saved .json files with perf. numbers and prints optimal number of workers"""
+    results = []
+
+    # each file assumed to contain a single {workers: [perf_list]}
+    for workers, perf_list in performance.items():
+        workers = int(workers)
+        avg_perf = np.mean(perf_list)
+        results.append((workers, avg_perf))
+
+    # sort by average performance (descending: fastest first)
+    results.sort(key=lambda x: x[1], reverse=True)
+    
+    print("\n-----------------------------------")
+    print("Performance results (fastest → slowest):")
+    for i, (workers, avg_perf) in enumerate(results):
+        print(f"{i+1}. {workers * partitions} workers → avg. docs/s: {avg_perf:.4f}")
+    
+    best_workers, best_perf = results[0]
+
+    print("\n-----------------------------------")
+    print(
+        f"The most optimal num of workers is {best_workers * partitions} "
+        f"with avg. preprocessed docs/s: {best_perf:.4f}."
+    )
+    print("-----------------------------------")
+
+
 def main():
     args = get_args()
 
-    if args.split_sentences:
-        if nltk_available:
-            nltk.download("punkt", quiet=True, download_dir=os.environ.get("NLTK_DATA"))
+    workers = args.workers_to_check if args.find_optimal_num_workers else [args.workers]
+    for num_workers in workers:
+        if num_workers % args.partitions != 0:
+            print(
+                f"Removing num_workers ({num_workers}) from workers list "
+                f"because it's not divisible by num_partitions ({args.partitions})"
+            )
+            workers.remove(num_workers)
+    assert workers, "Please, provide valid number of workers which is divisible by number of partitions."
+    if args.find_optimal_num_workers:
+        args.log_interval = 1000
+
+    performance = {}
+    for num_workers in workers:
+        print(f"Processing data with {num_workers} workers.")
+        if args.split_sentences:
+            if nltk_available:
+                nltk.download("punkt", quiet=True, download_dir=os.environ.get("NLTK_DATA"))
+            else:
+                raise Exception(
+                    "nltk library required for sentence splitting is not available.")
+
+        in_ss_out_names = []
+        if args.partitions == 1:
+            file_name, extension = os.path.splitext(args.input)
+            sentence_split_file = file_name + "_ss" + extension
+            file_names = {
+                'partition': args.input,
+                'sentence_split': sentence_split_file,
+                'output_prefix': args.output_prefix}
+            in_ss_out_names.append(file_names)
         else:
-            raise Exception(
-                "nltk library required for sentence splitting is not available.")
-
-    in_ss_out_names = []
-    if args.partitions == 1:
-        file_name, extension = os.path.splitext(args.input)
-        sentence_split_file = file_name + "_ss" + extension
-        file_names = {
-            'partition': args.input,
-            'sentence_split': sentence_split_file,
-            'output_prefix': args.output_prefix}
-        in_ss_out_names.append(file_names)
-    else:
-        in_file_names = glob.glob(args.input)
-
-        # Count total number of lines across .jsonl files
-        if args.keep_sequential_samples:
-            total_sample_count = 0
-            for filename in in_file_names:
-                with open(filename, "r") as fin:
-                    for fc, _ in enumerate(fin):
-                        pass
-                total_sample_count += (fc + 1)
-            partition_size = math.ceil(total_sample_count / args.partitions)
-
-        # create .jsonl parition files
-        for idx in range(args.partitions):
-            in_ss_out_name = get_file_name(args, idx)
-            in_ss_out_names.append(in_ss_out_name)
-
-        # check to see if paritions were already created
-        partitions_present = check_files_exist(in_ss_out_names, 'partition', args.partitions)
+            in_file_names = glob.glob(args.input)
+
+            # Count total number of lines across .jsonl files
+            if args.keep_sequential_samples:
+                total_sample_count = 0
+                for filename in in_file_names:
+                    with open(filename, "r") as fin:
+                        for fc, _ in enumerate(fin):
+                            pass
+                    total_sample_count += (fc + 1)
+                partition_size = math.ceil(total_sample_count / args.partitions)
+
+            # create .jsonl parition files
+            for idx in range(args.partitions):
+                in_ss_out_name = get_file_name(args, idx)
+                in_ss_out_names.append(in_ss_out_name)
+
+            # check to see if paritions were already created
+            partitions_present = check_files_exist(in_ss_out_names, 'partition', args.partitions)
+
+            # check to see if paritions with split sentences already created
+            split_sentences_present = check_files_exist(in_ss_out_names, 'sentence_split', args.partitions)
+
+            if not partitions_present and not split_sentences_present:
+                # populate .jsonl partition files from parent files
+                partitioned_input_files = []
+                for idx in range(args.partitions):
+                    partitioned_input_file = open(in_ss_out_names[idx]['partition'], 'w')
+                    partitioned_input_files.append(partitioned_input_file)
+
+                index = 0
+                if args.keep_sequential_samples: line_count = 0
+                for in_file_name in in_file_names:
+                    # support for gzip files
+                    if in_file_name.endswith(".gz"):
+                        fin = gzip.open(in_file_name, 'rt')
+                    else:
+                        fin = open(in_file_name, 'r', encoding='utf-8')
+
+                    for line in fin:
+                        partitioned_input_files[index].write(line)
+                        if args.keep_sequential_samples:
+                            line_count += 1
+                            if line_count % partition_size == 0:
+                                index += 1
+                        else:
+                            index = (index + 1)%args.partitions
+
+                    fin.close()
+
+                for idx in range(args.partitions):
+                    partitioned_input_files[idx].close()
+
+        partition = Partition(args, num_workers//args.partitions)
 
         # check to see if paritions with split sentences already created
         split_sentences_present = check_files_exist(in_ss_out_names, 'sentence_split', args.partitions)
 
-        if not partitions_present and not split_sentences_present:
-            # populate .jsonl partition files from parent files
-            partitioned_input_files = []
-            for idx in range(args.partitions):
-                partitioned_input_file = open(in_ss_out_names[idx]['partition'], 'w')
-                partitioned_input_files.append(partitioned_input_file)
-
-            index = 0
-            if args.keep_sequential_samples: line_count = 0
-            for in_file_name in in_file_names:
-                # support for gzip files
-                if in_file_name.endswith(".gz"):
-                    fin = gzip.open(in_file_name, 'rt')
-                else:
-                    fin = open(in_file_name, 'r', encoding='utf-8')
-
-                for line in fin:
-                    partitioned_input_files[index].write(line)
-                    if args.keep_sequential_samples:
-                        line_count += 1
-                        if line_count % partition_size == 0:
-                            index += 1
-                    else:
-                        index = (index + 1)%args.partitions
-
-                fin.close()
+        # split sentences in partition files
+        if args.split_sentences and not split_sentences_present:
+            processes = []
+            for name in in_ss_out_names:
+                p = multiprocessing.Process(target=partition.split_sentences,
+                                            args=((name['partition'], name['sentence_split']),))
+                p.start()
+                processes.append(p)
 
-            for idx in range(args.partitions):
-                partitioned_input_files[idx].close()
+            for p in processes:
+                p.join()
 
-    assert args.workers % args.partitions == 0
-    partition = Partition(args, args.workers//args.partitions)
+            if args.partitions == 1:
+                continue
 
-    # check to see if paritions with split sentences already created
-    split_sentences_present = check_files_exist(in_ss_out_names, 'sentence_split', args.partitions)
+        def process_json_file(name, q, input_key):
+            worker_performance = partition.process_json_file((name[input_key], name['output_prefix']))
+            q.put(worker_performance)
 
-    # split sentences in partition files
-    if args.split_sentences and not split_sentences_present:
+        # encode partition files in parallel
         processes = []
+        input_key = 'sentence_split' if args.split_sentences else 'partition'
+        q = multiprocessing.Queue()
         for name in in_ss_out_names:
-            p = multiprocessing.Process(target=partition.split_sentences,
-                                        args=((name['partition'], name['sentence_split']),))
+            p = multiprocessing.Process(target=process_json_file, args=(name, q, input_key))
+
             p.start()
             processes.append(p)
 
+        for _ in processes:
+            worker_performance = q.get()
+            if args.find_optimal_num_workers:
+                performance[num_workers] = worker_performance
+
         for p in processes:
             p.join()
 
         if args.partitions == 1:
-            return
-
-
-    # encode partition files in parallel
-    processes = []
-    input_key = 'sentence_split' if args.split_sentences else 'partition'
-    for name in in_ss_out_names:
-        p = multiprocessing.Process(target=partition.process_json_file,
-                                    args=((name[input_key], name['output_prefix']),))
-        p.start()
-        processes.append(p)
-
-    for p in processes:
-        p.join()
-
-    if args.partitions == 1:
-        return
-
-    # merge bin/idx partitions
-    level = "document"
-    if args.split_sentences:
-        level = "sentence"
-
-    output_bin_files = {}
-    output_idx_files = {}
-    builders = {}
-    tokenizer = build_tokenizer(args)
-
-    for key in args.json_keys:
-        output_bin_files[key] = "{}_{}_{}.bin".format(args.output_prefix,
-                                                      key, level)
-        output_idx_files[key] = "{}_{}_{}.idx".format(args.output_prefix,
-                                                      key, level)
-        builders[key] = indexed_dataset.IndexedDatasetBuilder(
-            output_bin_files[key],
-            dtype=indexed_dataset.DType.optimal_dtype(tokenizer.vocab_size),
-        )
+            continue
 
-        for name in in_ss_out_names:
-            parition_output_prefix = name['output_prefix']
-            full_partition_output_prefix = "{}_{}_{}".format(parition_output_prefix,
-                                                             key, level)
-            builders[key].add_index(full_partition_output_prefix)
-        builders[key].finalize(output_idx_files[key])
+        # merge bin/idx partitions
+        level = "document"
+        if args.split_sentences:
+            level = "sentence"
+
+        output_bin_files = {}
+        output_idx_files = {}
+        builders = {}
+        tokenizer = build_tokenizer(args)
+
+        for key in args.json_keys:
+            output_bin_files[key] = "{}_{}_{}.bin".format(args.output_prefix,
+                                                        key, level)
+            output_idx_files[key] = "{}_{}_{}.idx".format(args.output_prefix,
+                                                        key, level)
+            builders[key] = indexed_dataset.IndexedDatasetBuilder(
+                output_bin_files[key],
+                dtype=indexed_dataset.DType.optimal_dtype(tokenizer.vocab_size),
+            )
+
+            for name in in_ss_out_names:
+                parition_output_prefix = name['output_prefix']
+                full_partition_output_prefix = "{}_{}_{}".format(parition_output_prefix,
+                                                                key, level)
+                builders[key].add_index(full_partition_output_prefix)
+            builders[key].finalize(output_idx_files[key])
 
+    # Find the most optimal number of workers
+    if args.find_optimal_num_workers:
+        find_optimal_num_workers(performance, args.partitions)
 
 if __name__ == '__main__':
 
diff --git a/tools/run_dynamic_text_generation_server.py b/tools/run_dynamic_text_generation_server.py
index d054b542f90..1f99939a184 100644
--- a/tools/run_dynamic_text_generation_server.py
+++ b/tools/run_dynamic_text_generation_server.py
@@ -6,7 +6,10 @@
 import torch
 
 from megatron.core.inference.engines import DynamicInferenceEngine
-from megatron.core.inference.text_generation_server.dynamic_text_gen_server import run_flask_server
+from megatron.core.inference.text_generation_server.dynamic_text_gen_server import (
+    start_text_gen_server,
+    stop_text_gen_server,
+)
 from megatron.core.utils import trace_async_exceptions
 from megatron.inference.utils import add_inference_args, get_dynamic_inference_engine
 from megatron.post_training.arguments import add_modelopt_args
@@ -19,20 +22,24 @@ def add_text_generation_server_args(parser: argparse.ArgumentParser):
     parser = add_modelopt_args(parser)
     parser = add_inference_args(parser)
     parser.add_argument("--port", type=int, default=5000, help="Port for Flask server to run on")
-    parser.add_argument("--parsers", type=str, nargs="+", default=[], help="Parsers to use for parsing the response")
+    parser.add_argument(
+        "--parsers", type=str, nargs="+", default=[], help="Parsers to use for parsing the response"
+    )
     return parser
 
 
 @trace_async_exceptions
 async def run_text_generation_server(
-    engine: DynamicInferenceEngine, coordinator_port: int, flask_port: int
+    engine: DynamicInferenceEngine, coordinator_port: int, server_port: int
 ):
-    """Runs the Flask server from rank 0 and initializes the DynamicInferenceEngine on all ranks.
+    """
+    Runs the text generation server from rank 0 and initializes the
+    DynamicInferenceEngine on all ranks.
 
     Args:
         engine (DynamicInferenceEngine): The dynamic inference engine.
         coordinator_port (int): The network port for the dynamic inference DP coordinator.
-        flask_port (int): The network for port the frontend Flask server.
+        server_port (int): The network for port the frontend text generation server.
     """
 
     rank = torch.distributed.get_rank()
@@ -41,27 +48,24 @@ async def run_text_generation_server(
         inference_coordinator_port=coordinator_port, launch_inference_coordinator=True
     )
 
-    server_task = None
-    if rank == 0:
-        server_task = asyncio.create_task(
-            run_flask_server(
+    try:
+        if rank == 0:
+            start_text_gen_server(
                 coordinator_addr=coordinator_addr,
                 tokenizer=engine.controller.tokenizer,
                 parsers=args.parsers,
                 rank=rank,
-                flask_port=flask_port,
-                verbose=args.inference_flask_server_logging,
+                server_port=server_port,
+                verbose=args.inference_text_gen_server_logging,
             )
-        )
-    engine_task = engine.engine_loop_task
 
-    tasks_to_run = [engine_task]
-    if server_task:
-        assert rank == 0
+        # Await the engine loop directly since the server is running in a separate process
+        await engine.engine_loop_task
 
-        tasks_to_run.append(server_task)
-
-    await asyncio.gather(*tasks_to_run)
+    finally:
+        # Guarantee that the separate process is terminated when the engine loop stops or is interrupted
+        if rank == 0:
+            stop_text_gen_server()
 
 
 if __name__ == "__main__":
@@ -79,4 +83,14 @@ async def run_text_generation_server(
 
         engine = get_dynamic_inference_engine()
 
-        asyncio.run(run_text_generation_server(engine, args.inference_coordinator_port, args.port))
+        try:
+            asyncio.run(
+                run_text_generation_server(engine, args.inference_coordinator_port, args.port)
+            )
+        except KeyboardInterrupt:
+            # Catching at the top level ensures clean stdout without spamming the traceback
+            print("Server process interrupted by user.")
+        finally:
+            # Clean up PyTorch distributed groups properly
+            if torch.distributed.is_initialized():
+                torch.distributed.destroy_process_group()
diff --git a/tools/trigger_internal_ci.md b/tools/trigger_internal_ci.md
new file mode 100644
index 00000000000..340a9ff9bfb
--- /dev/null
+++ b/tools/trigger_internal_ci.md
@@ -0,0 +1,76 @@
+# Trigger Internal CI
+
+:warning: This is only useful to NVIDIANs.
+
+Pushes the current branch to the internal GitLab remote and triggers a CI
+pipeline — without touching the GitLab UI.
+
+## Prerequisites
+
+**1. Add the internal GitLab as a git remote** (skip if you already have one configured):
+
+```bash
+git remote add gitlab git@<gitlab-hostname>:ADLR/Megatron-LM.git
+```
+
+To check existing remotes: `git remote -v`
+
+**The name of the origin will be required later!**
+
+**2. Obtain a personal access token:**
+
+1. Open your internal GitLab profile: **User menu → Edit profile → Access tokens**.
+2. Click **Add new token**, give it a description, set an expiry, and select the **`api`** scope.
+3. Click **Create personal access token** and copy the generated token (starts with `glpat-`).
+4. Store it in your environment to avoid passing it on every invocation:
+
+```bash
+export GITLAB_TOKEN=glpat-<your-token>
+```
+
+**Tip: Store this in your .env or .bashrc file**
+
+## Usage
+
+```bash
+python tools/trigger_internal_ci.py \
+  --gitlab-origin gitlab \
+  [--access-token glpat-<your-token>] \
+  [--functional-test-scope mr] \
+  [--functional-test-repeat 5] \
+  [--functional-test-cases all] \
+  [--dry-run]
+```
+
+| Argument | Default | Description |
+|---|---|---|
+| `--gitlab-origin` | *(required)* | Git remote name for the internal GitLab |
+| `--access-token` | `$GITLAB_TOKEN` | Personal access token with `api` scope |
+| `--functional-test-scope` | `mr` | `FUNCTIONAL_TEST_SCOPE` pipeline variable |
+| `--functional-test-repeat` | `5` | `FUNCTIONAL_TEST_REPEAT` pipeline variable |
+| `--functional-test-cases` | `all` | `FUNCTIONAL_TEST_CASES` pipeline variable |
+| `--dry-run` | off | Print what would happen without pushing or triggering |
+
+## Example
+
+```bash
+# Dry run — no push, no trigger
+python tools/trigger_internal_ci.py --gitlab-origin gitlab --dry-run
+
+# Real run — uses token from environment
+python tools/trigger_internal_ci.py --gitlab-origin gitlab
+```
+
+## Expected behavior
+
+```
+Current branch: my-feature-branch
+Everything up-to-date
+Triggering pipeline on https://<gitlab-hostname> project 19378 @ pull-request/my-feature-branch
+Pipeline triggered: https://<gitlab-hostname>/<namespace>/<project>/-/pipelines/123456
+```
+
+1. The current branch is detected from git.
+2. The branch is force-pushed to the GitLab remote as `pull-request/<branch>`.
+3. A pipeline is triggered on that ref with the configured test variables.
+4. The URL of the newly created pipeline is printed.
diff --git a/tools/trigger_internal_ci.py b/tools/trigger_internal_ci.py
new file mode 100644
index 00000000000..4b0c19f09bd
--- /dev/null
+++ b/tools/trigger_internal_ci.py
@@ -0,0 +1,174 @@
+#!/usr/bin/env python3
+# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""CLI tool to trigger the internal GitLab CI pipeline from a local branch.
+
+Pushes the current branch to the internal GitLab remote under the
+pull-request/<branch> naming convention and triggers a pipeline with
+the specified test configuration.
+
+Requires a GitLab personal access token with at least the 'api' scope.
+Set the GITLAB_TOKEN environment variable or pass --access-token.
+"""
+
+import argparse
+import logging
+import os
+import subprocess
+import sys
+from urllib.parse import urlparse
+
+import gitlab  # python-gitlab
+
+GITLAB_PROJECT_ID = 19378
+GITLAB_BRANCH_PREFIX = "pull-request"
+
+PIPELINE_VARIABLES_FIXED = {
+    "UNIT_TEST": "no",
+    "INTEGRATION_TEST": "no",
+}
+
+logger = logging.getLogger(__name__)
+
+
+def get_remote_url(origin):
+    """Return the fetch URL configured for the given git remote name."""
+    result = subprocess.run(
+        ["git", "remote", "get-url", origin],
+        capture_output=True,
+        text=True,
+        check=True,
+    )
+    return result.stdout.strip()
+
+
+def get_gitlab_hostname(remote_url):
+    """Extract the hostname (without port) from an SSH or HTTPS remote URL."""
+    if remote_url.startswith("git@"):
+        hostname = remote_url.split("@", 1)[1].split(":")[0]
+    else:
+        hostname = urlparse(remote_url).hostname
+    return hostname.split(":")[0]
+
+
+def get_current_branch():
+    """Return the name of the currently checked-out git branch."""
+    result = subprocess.run(
+        ["git", "rev-parse", "--abbrev-ref", "HEAD"],
+        capture_output=True,
+        text=True,
+        check=True,
+    )
+    return result.stdout.strip()
+
+
+def git_push(origin, target_branch, dry_run=False):
+    """Force-push HEAD to the given branch on the named git remote."""
+    if dry_run:
+        logger.info("[DRY RUN] Would push HEAD to remote '%s' as %s", origin, target_branch)
+        return
+    subprocess.run(
+        ["git", "push", origin, f"HEAD:{target_branch}", "--force"],
+        check=True,
+    )
+
+
+def trigger_pipeline(gitlab_url, access_token, ref, pipeline_vars, dry_run=False):
+    """Trigger a GitLab pipeline on the given ref with the provided variables."""
+    if dry_run:
+        logger.info(
+            "[DRY RUN] Would trigger pipeline on https://%s project %s @ %s",
+            gitlab_url,
+            GITLAB_PROJECT_ID,
+            ref,
+        )
+        return
+    logger.info(
+        "Triggering pipeline on https://%s project %s @ %s", gitlab_url, GITLAB_PROJECT_ID, ref
+    )
+    gl = gitlab.Gitlab(f"https://{gitlab_url}", private_token=access_token)
+    project = gl.projects.get(GITLAB_PROJECT_ID, lazy=True)
+    variables = [{"key": k, "value": v} for k, v in pipeline_vars.items()]
+    pipeline = project.pipelines.create({"ref": ref, "variables": variables})
+    logger.info("Pipeline triggered: %s", pipeline.web_url)
+
+
+def main():
+    """Parse arguments and orchestrate the push and pipeline trigger flow."""
+    parser = argparse.ArgumentParser(
+        description="Trigger the internal GitLab CI pipeline for the current branch."
+    )
+    parser.add_argument(
+        "--gitlab-origin",
+        required=True,
+        help="Name of the git remote pointing to the internal GitLab (e.g. gitlab)",
+    )
+    parser.add_argument(
+        "--access-token",
+        default=os.environ.get("GITLAB_TOKEN"),
+        help="GitLab personal access token with 'api' scope (or set GITLAB_TOKEN env var)",
+    )
+    parser.add_argument(
+        "--functional-test-scope",
+        default="mr",
+        help="FUNCTIONAL_TEST_SCOPE pipeline variable (default: mr)",
+    )
+    parser.add_argument(
+        "--functional-test-repeat",
+        type=int,
+        default=5,
+        help="FUNCTIONAL_TEST_REPEAT pipeline variable (default: 5)",
+    )
+    parser.add_argument(
+        "--functional-test-cases",
+        default="all",
+        help="FUNCTIONAL_TEST_CASES pipeline variable (default: all)",
+    )
+    parser.add_argument(
+        "--dry-run",
+        action="store_true",
+        help="Print actions without executing git push or pipeline trigger",
+    )
+    args = parser.parse_args()
+    logging.basicConfig(level=logging.INFO, format="%(message)s")
+
+    if not args.access_token:
+        logger.error("--access-token or GITLAB_TOKEN not set")
+        sys.exit(1)
+
+    branch = get_current_branch()
+    logger.info("Current branch: %s", branch)
+
+    remote_url = get_remote_url(args.gitlab_origin)
+    gitlab_hostname = get_gitlab_hostname(remote_url)
+
+    target_branch = f"{GITLAB_BRANCH_PREFIX}/{branch}"
+
+    git_push(args.gitlab_origin, target_branch, dry_run=args.dry_run)
+
+    pipeline_vars = {
+        **PIPELINE_VARIABLES_FIXED,
+        "FUNCTIONAL_TEST_SCOPE": args.functional_test_scope,
+        "FUNCTIONAL_TEST_REPEAT": str(args.functional_test_repeat),
+        "FUNCTIONAL_TEST_CASES": args.functional_test_cases,
+    }
+
+    trigger_pipeline(
+        gitlab_hostname, args.access_token, target_branch, pipeline_vars, dry_run=args.dry_run
+    )
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/train_rl.py b/train_rl.py
index 645e78ba986..f75bba12997 100644
--- a/train_rl.py
+++ b/train_rl.py
@@ -22,9 +22,11 @@
     load_packed_data_by_index,
 )
 from megatron.training import get_args, get_timers, pretrain, print_rank_0
+from megatron.training.utils import is_hybrid_model
 from megatron.training.arguments import core_transformer_config_from_args
 from model_provider import model_provider
 
+from megatron.core.packed_seq_params import PackedSeqParams
 from megatron.rl.sequence_packing_utils import get_default_packed_seq_params
 
 stimer = StragglerDetector()
@@ -257,11 +259,22 @@ def forward_step(data_iterator, model: GPTModel, loss_only: bool = False):
     model_to_use = model[0] if isinstance(model, list) else model
 
     if packed_seq_params is None:
-        packed_seq_params = get_default_packed_seq_params(
-            seq_length=tokens.shape[1],
-            max_sequences_per_bin=args.rl_sequence_packing_max_sequences_per_bin,
-            device=tokens.device,
-        )
+        if args.rl_use_sequence_packing:
+            packed_seq_params = get_default_packed_seq_params(
+                seq_length=tokens.shape[1],
+                max_sequences_per_bin=args.rl_sequence_packing_max_sequences_per_bin,
+                device=tokens.device,
+            )
+        else:
+            cu_seqlens = torch.tensor([0, tokens.shape[1]], dtype=torch.int32, device=tokens.device)
+            packed_seq_params = PackedSeqParams(
+                qkv_format='thd',
+                cu_seqlens_q=cu_seqlens,
+                cu_seqlens_kv=cu_seqlens,
+                max_seqlen_q=tokens.shape[1],
+                max_seqlen_kv=tokens.shape[1],
+                total_tokens=tokens.shape[1],
+            )
 
     # Clear RoPE cache to avoid inference tensor errors
     try:
@@ -276,7 +289,8 @@ def forward_step(data_iterator, model: GPTModel, loss_only: bool = False):
     # Get current logprobs and calculate loss with straggler detection
     with stimer:
         logprobs_or_hidden_states = get_logprobs(
-            model_to_use, tokens, position_ids, no_grad=False, packed_seq_params=packed_seq_params
+            model_to_use, tokens, position_ids, no_grad=False,
+            packed_seq_params=packed_seq_params
         )
 
         if not is_pipeline_last_stage():
@@ -377,7 +391,7 @@ def __getitem__(self, idx):
     def _model_builder(
         args, pre_process, post_process, vp_stage=None, config=None, pg_collection=None
     ):
-        if getattr(args, "is_hybrid_model", False):
+        if is_hybrid_model(args):
             return mamba_builder(
                 args,
                 pre_process,
diff --git a/uv.lock b/uv.lock
index 129f94b4288..88fae8f7204 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,5 +1,5 @@
 version = 1
-revision = 3
+revision = 2
 requires-python = ">=3.10"
 resolution-markers = [
     "python_full_version >= '3.14' and sys_platform == 'linux'",
@@ -91,6 +91,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/b7/58/3bf0b7d474607dc7fd67dd1365c4e0f392c8177eaf4054e5ddee3ebd53b5/aiobotocore-2.26.0-py3-none-any.whl", hash = "sha256:a793db51c07930513b74ea7a95bd79aaa42f545bdb0f011779646eafa216abec", size = 87333, upload-time = "2025-11-28T07:54:58.457Z" },
 ]
 
+[[package]]
+name = "aiofiles"
+version = "25.1.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/41/c3/534eac40372d8ee36ef40df62ec129bee4fdb5ad9706e58a29be53b2c970/aiofiles-25.1.0.tar.gz", hash = "sha256:a8d728f0a29de45dc521f18f07297428d56992a742f0cd2701ba86e44d23d5b2", size = 46354, upload-time = "2025-10-09T20:51:04.358Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/bc/8a/340a1555ae33d7354dbca4faa54948d76d89a27ceef032c8c3bc661d003e/aiofiles-25.1.0-py3-none-any.whl", hash = "sha256:abe311e527c862958650f9438e859c1fa7568a141b22abcd015e120e86a85695", size = 14668, upload-time = "2025-10-09T20:51:03.174Z" },
+]
+
 [[package]]
 name = "aiohappyeyeballs"
 version = "2.6.1"
@@ -107,7 +116,7 @@ source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohappyeyeballs" },
     { name = "aiosignal" },
-    { name = "async-timeout", marker = "python_full_version < '3.11' or (extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" },
+    { name = "async-timeout", marker = "python_full_version < '3.11'" },
     { name = "attrs" },
     { name = "frozenlist" },
     { name = "multidict" },
@@ -247,7 +256,7 @@ version = "1.4.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "frozenlist" },
-    { name = "typing-extensions", marker = "python_full_version < '3.13' or (extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" },
+    { name = "typing-extensions", marker = "python_full_version < '3.13'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/61/62/06741b579156360248d1ec624842ad0edf697050bbaf7c3e46394e106ad1/aiosignal-1.4.0.tar.gz", hash = "sha256:f47eecd9468083c2029cc99945502cb7708b082c232f9aca65da147157b251c7", size = 25007, upload-time = "2025-07-03T22:54:43.528Z" }
 wheels = [
@@ -301,10 +310,10 @@ name = "anyio"
 version = "4.9.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "exceptiongroup", marker = "python_full_version < '3.11' or (extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" },
+    { name = "exceptiongroup", marker = "python_full_version < '3.11'" },
     { name = "idna" },
     { name = "sniffio" },
-    { name = "typing-extensions", marker = "python_full_version < '3.13' or (extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" },
+    { name = "typing-extensions", marker = "python_full_version < '3.13'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/95/7d/4c1bd541d4dffa1b52bd83fb8527089e097a106fc90b467a7313b105f840/anyio-4.9.0.tar.gz", hash = "sha256:673c0c244e15788651a4ff38710fea9675823028a6f08a5eda409e0c9840a028", size = 190949, upload-time = "2025-03-17T00:02:54.77Z" }
 wheels = [
@@ -749,7 +758,7 @@ name = "cffi"
 version = "2.0.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "pycparser", marker = "implementation_name != 'PyPy' or (extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" },
+    { name = "pycparser", marker = "implementation_name != 'PyPy'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/eb/56/b1ba7935a17738ae8453301356628e8147c79dbb825bcbc73dc7401f9846/cffi-2.0.0.tar.gz", hash = "sha256:44d1b5909021139fe36001ae048dbdde8214afa20200eda0f64c068cac5d5529", size = 523588, upload-time = "2025-09-08T23:24:04.541Z" }
 wheels = [
@@ -920,7 +929,7 @@ name = "click"
 version = "8.3.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "colorama", marker = "sys_platform == 'win32' or (extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" },
+    { name = "colorama", marker = "sys_platform == 'win32'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/3d/fa/656b739db8587d7b5dfa22e22ed02566950fbfbcdc20311993483657a5c0/click-8.3.1.tar.gz", hash = "sha256:12ff4785d337a1bb490bb7e9c2b1ee5da3112e94a8622f26a6c77f5d2fc6842a", size = 295065, upload-time = "2025-11-15T20:45:42.706Z" }
 wheels = [
@@ -1413,8 +1422,8 @@ name = "emerging-optimizers"
 version = "0.2.0"
 source = { git = "https://github.com/NVIDIA-NeMo/Emerging-Optimizers.git?rev=v0.2.0#1effa026ff096b7fa1063ca2fba19d98be6e6cdf" }
 dependencies = [
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-    { name = "torch", marker = "(python_full_version >= '3.12' and sys_platform == 'never') or (python_full_version < '3.12' and extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts') or (sys_platform != 'never' and extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" },
+    { name = "absl-py", marker = "python_full_version >= '3.12'" },
+    { name = "torch", marker = "python_full_version >= '3.12' and sys_platform == 'never'" },
 ]
 
 [[package]]
@@ -1422,7 +1431,7 @@ name = "exceptiongroup"
 version = "1.3.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "typing-extensions", marker = "python_full_version < '3.13' or (extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" },
+    { name = "typing-extensions", marker = "python_full_version < '3.13'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/50/79/66800aadf48771f6b62f7eb014e352e5d06856655206165d775e675a02c9/exceptiongroup-1.3.1.tar.gz", hash = "sha256:8b412432c6055b0b7d14c310000ae93352ed6754f70fa8f7c34141f91c4e3219", size = 30371, upload-time = "2025-11-21T23:01:54.787Z" }
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@@ -1444,16 +1453,6 @@ wheels = [
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-[[package]]
-name = "fast-hadamard-transform"
-version = "1.0.4.post1"
-source = { git = "https://github.com/Dao-AILab/fast-hadamard-transform.git?rev=f134af63deb2df17e1171a9ec1ea4a7d8604d5ca#f134af63deb2df17e1171a9ec1ea4a7d8604d5ca" }
-dependencies = [
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-    { name = "packaging" },
-    { name = "torch", marker = "sys_platform == 'never' or (extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" },
-]
-
 [[package]]
 name = "fastapi"
 version = "0.133.0"
@@ -2018,6 +2017,19 @@ http2 = [
     { name = "h2" },
 ]
 
+[[package]]
+name = "httpx-aiohttp"
+version = "0.1.12"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "aiohttp" },
+    { name = "httpx" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/63/2c/b894861cecf030fb45675ea24aa55b5722e97c602a163d872fca66c5a6d8/httpx_aiohttp-0.1.12.tar.gz", hash = "sha256:81feec51fd82c0ecfa0e9aaf1b1a6c2591260d5e2bcbeb7eb0277a78e610df2c", size = 275945, upload-time = "2025-12-12T10:12:15.283Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/16/8d/85c9701e9af72ca132a1783e2a54364a90c6da832304416a30fc11196ab2/httpx_aiohttp-0.1.12-py3-none-any.whl", hash = "sha256:5b0eac39a7f360fa7867a60bcb46bb1024eada9c01cbfecdb54dc1edb3fb7141", size = 6367, upload-time = "2025-12-12T10:12:14.018Z" },
+]
+
 [[package]]
 name = "huggingface-hub"
 version = "0.36.2"
@@ -2026,7 +2038,7 @@ dependencies = [
     { name = "filelock" },
     { name = "fsspec", version = "2025.10.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.14' or sys_platform != 'linux' or extra == 'extra-13-megatron-core-dev' or extra == 'extra-13-megatron-core-lts'" },
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-    { name = "hf-xet", marker = "platform_machine == 'aarch64' or platform_machine == 'amd64' or platform_machine == 'arm64' or platform_machine == 'x86_64' or (extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" },
+    { name = "hf-xet", marker = "platform_machine == 'aarch64' or platform_machine == 'amd64' or platform_machine == 'arm64' or platform_machine == 'x86_64'" },
     { name = "packaging" },
     { name = "pyyaml" },
     { name = "requests" },
@@ -2461,7 +2473,7 @@ resolution-markers = [
     "python_full_version < '3.11' and sys_platform != 'linux'",
 ]
 dependencies = [
-    { name = "mdurl", marker = "python_full_version < '3.11' or (extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" },
+    { name = "mdurl", marker = "python_full_version < '3.11'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/38/71/3b932df36c1a044d397a1f92d1cf91ee0a503d91e470cbd670aa66b07ed0/markdown-it-py-3.0.0.tar.gz", hash = "sha256:e3f60a94fa066dc52ec76661e37c851cb232d92f9886b15cb560aaada2df8feb", size = 74596, upload-time = "2023-06-03T06:41:14.443Z" }
 wheels = [
@@ -2491,7 +2503,7 @@ resolution-markers = [
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 ]
 dependencies = [
-    { name = "mdurl", marker = "python_full_version >= '3.11' or (extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" },
+    { name = "mdurl", marker = "python_full_version >= '3.11'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/5b/f5/4ec618ed16cc4f8fb3b701563655a69816155e79e24a17b651541804721d/markdown_it_py-4.0.0.tar.gz", hash = "sha256:cb0a2b4aa34f932c007117b194e945bd74e0ec24133ceb5bac59009cda1cb9f3", size = 73070, upload-time = "2025-08-11T12:57:52.854Z" }
 wheels = [
@@ -2644,8 +2656,10 @@ dev = [
     { name = "nvidia-resiliency-ext" },
     { name = "nvtx" },
     { name = "onnxscript" },
-    { name = "openai" },
+    { name = "openai", extra = ["aiohttp"], marker = "extra == 'extra-13-megatron-core-dev'" },
     { name = "opentelemetry-api", version = "1.33.1", source = { registry = "https://pypi.org/simple" } },
+    { name = "orjson" },
+    { name = "quart" },
     { name = "tensorstore", version = "0.1.78", source = { registry = "https://pypi.org/simple" }, marker = "(python_full_version < '3.11' and extra == 'extra-13-megatron-core-dev') or (extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" },
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     { name = "tqdm" },
@@ -2680,6 +2694,14 @@ mlm = [
     { name = "transformers" },
     { name = "wandb" },
 ]
+training = [
+    { name = "accelerate" },
+    { name = "flask-restful" },
+    { name = "sentencepiece" },
+    { name = "tiktoken" },
+    { name = "transformers" },
+    { name = "wandb" },
+]
 
 [package.dev-dependencies]
 build = [
@@ -2718,10 +2740,10 @@ linting = [
 ]
 no-pypi-wheels = [
     { name = "emerging-optimizers", marker = "python_full_version >= '3.12' or (extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" },
-    { name = "fast-hadamard-transform" },
 ]
 test = [
     { name = "coverage" },
+    { name = "mock" },
     { name = "nemo-run" },
     { name = "nltk" },
     { name = "pydantic" },
@@ -2740,6 +2762,7 @@ test = [
 [package.metadata]
 requires-dist = [
     { name = "accelerate", marker = "extra == 'mlm'" },
+    { name = "accelerate", marker = "extra == 'training'" },
     { name = "av", marker = "extra == 'dev'" },
     { name = "av", marker = "extra == 'lts'" },
     { name = "causal-conv1d", marker = "extra == 'dev'", specifier = "~=1.5" },
@@ -2757,6 +2780,7 @@ requires-dist = [
     { name = "flashinfer-python", marker = "extra == 'lts'", specifier = "~=0.5.0" },
     { name = "flask", extras = ["async"], marker = "extra == 'dev'" },
     { name = "flask-restful", marker = "extra == 'mlm'" },
+    { name = "flask-restful", marker = "extra == 'training'" },
     { name = "hypercorn", marker = "extra == 'dev'" },
     { name = "mamba-ssm", marker = "extra == 'dev'", specifier = "~=2.2" },
     { name = "mamba-ssm", marker = "extra == 'lts'", specifier = "~=2.2" },
@@ -2773,24 +2797,30 @@ requires-dist = [
     { name = "nvtx", marker = "extra == 'lts'", specifier = "~=0.2" },
     { name = "onnxscript", marker = "extra == 'dev'" },
     { name = "onnxscript", marker = "extra == 'lts'" },
-    { name = "openai", marker = "extra == 'dev'" },
+    { name = "openai", extras = ["aiohttp"], marker = "extra == 'dev'" },
     { name = "opentelemetry-api", marker = "extra == 'dev'", specifier = "~=1.33.1" },
     { name = "opentelemetry-api", marker = "extra == 'lts'", specifier = "~=1.33.1" },
+    { name = "orjson", marker = "extra == 'dev'" },
     { name = "packaging", specifier = ">=24.2" },
+    { name = "quart", marker = "extra == 'dev'" },
     { name = "sentencepiece", marker = "extra == 'mlm'" },
+    { name = "sentencepiece", marker = "extra == 'training'" },
     { name = "tensorstore", marker = "extra == 'dev'", specifier = "~=0.1,!=0.1.46,!=0.1.72" },
     { name = "tensorstore", marker = "extra == 'lts'", specifier = "~=0.1,!=0.1.46,!=0.1.72" },
     { name = "tiktoken", marker = "extra == 'mlm'" },
+    { name = "tiktoken", marker = "extra == 'training'" },
     { name = "torch", specifier = ">=2.6.0" },
     { name = "tqdm", marker = "extra == 'dev'" },
     { name = "tqdm", marker = "extra == 'lts'" },
     { name = "transformer-engine", extras = ["core-cu13", "pytorch"], marker = "extra == 'dev'", git = "https://github.com/NVIDIA/TransformerEngine.git?rev=5671fd3675906cda1ade26c24a65d3dedd88eb89" },
     { name = "transformers", marker = "extra == 'mlm'" },
+    { name = "transformers", marker = "extra == 'training'" },
     { name = "wandb", marker = "extra == 'mlm'" },
+    { name = "wandb", marker = "extra == 'training'" },
     { name = "wget", marker = "extra == 'dev'" },
     { name = "wget", marker = "extra == 'lts'" },
 ]
-provides-extras = ["mlm", "dev", "lts"]
+provides-extras = ["training", "mlm", "dev", "lts"]
 
 [package.metadata.requires-dev]
 build = [
@@ -2822,12 +2852,10 @@ linting = [
     { name = "pylint", specifier = "==3.2.6" },
     { name = "ruff", specifier = "~=0.9.0" },
 ]
-no-pypi-wheels = [
-    { name = "emerging-optimizers", marker = "python_full_version >= '3.12'", git = "https://github.com/NVIDIA-NeMo/Emerging-Optimizers.git?rev=v0.2.0" },
-    { name = "fast-hadamard-transform", git = "https://github.com/Dao-AILab/fast-hadamard-transform.git?rev=f134af63deb2df17e1171a9ec1ea4a7d8604d5ca" },
-]
+no-pypi-wheels = [{ name = "emerging-optimizers", marker = "python_full_version >= '3.12'", git = "https://github.com/NVIDIA-NeMo/Emerging-Optimizers.git?rev=v0.2.0" }]
 test = [
     { name = "coverage" },
+    { name = "mock" },
     { name = "nemo-run", git = "https://github.com/NVIDIA-NeMo/Run.git?rev=01a9a8ba360f7b2908728ad0516e0ad9d936966d" },
     { name = "nltk" },
     { name = "pydantic" },
@@ -2920,6 +2948,15 @@ wheels = [
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 ]
 
+[[package]]
+name = "mock"
+version = "5.2.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/07/8c/14c2ae915e5f9dca5a22edd68b35be94400719ccfa068a03e0fb63d0f6f6/mock-5.2.0.tar.gz", hash = "sha256:4e460e818629b4b173f32d08bf30d3af8123afbb8e04bb5707a1fd4799e503f0", size = 92796, upload-time = "2025-03-03T12:31:42.911Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/bd/d9/617e6af809bf3a1d468e0d58c3997b1dc219a9a9202e650d30c2fc85d481/mock-5.2.0-py3-none-any.whl", hash = "sha256:7ba87f72ca0e915175596069dbbcc7c75af7b5e9b9bc107ad6349ede0819982f", size = 31617, upload-time = "2025-03-03T12:31:41.518Z" },
+]
+
 [[package]]
 name = "mpmath"
 version = "1.3.0"
@@ -3029,7 +3066,7 @@ name = "multidict"
 version = "6.7.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "typing-extensions", marker = "python_full_version < '3.11' or (extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" },
+    { name = "typing-extensions", marker = "python_full_version < '3.11'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/1a/c2/c2d94cbe6ac1753f3fc980da97b3d930efe1da3af3c9f5125354436c073d/multidict-6.7.1.tar.gz", hash = "sha256:ec6652a1bee61c53a3e5776b6049172c53b6aaba34f18c9ad04f82712bac623d", size = 102010, upload-time = "2026-01-26T02:46:45.979Z" }
 wheels = [
@@ -3559,18 +3596,6 @@ wheels = [
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 ]
 
-[[package]]
-name = "nvdlfw-inspect"
-version = "0.2.2"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "pyyaml" },
-    { name = "torch", marker = "sys_platform == 'never'" },
-]
-wheels = [
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-]
-
 [[package]]
 name = "nvidia-cudnn-frontend"
 version = "1.18.0"
@@ -3836,6 +3861,12 @@ wheels = [
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 ]
 
+[package.optional-dependencies]
+aiohttp = [
+    { name = "aiohttp" },
+    { name = "httpx-aiohttp" },
+]
+
 [[package]]
 name = "opencensus"
 version = "0.11.4"
@@ -3894,7 +3925,7 @@ wheels = [
 
 [[package]]
 name = "opentelemetry-api"
-version = "1.39.1"
+version = "1.40.0"
 source = { registry = "https://pypi.org/simple" }
 resolution-markers = [
     "python_full_version >= '3.14' and sys_platform == 'linux'",
@@ -3920,9 +3951,9 @@ dependencies = [
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 sdist = { url = "https://files.pythonhosted.org/packages/09/a9/6ba95a270c6f1fbcd8dac228323f2777d886cb206987444e4bce66338dd4/tqdm-4.67.3.tar.gz", hash = "sha256:7d825f03f89244ef73f1d4ce193cb1774a8179fd96f31d7e1dcde62092b960bb", size = 169598, upload-time = "2026-02-03T17:35:53.048Z" }
 wheels = [
@@ -6752,14 +6884,9 @@ name = "transformer-engine"
 version = "2.12.0+5671fd36"
 source = { git = "https://github.com/NVIDIA/TransformerEngine.git?rev=5671fd3675906cda1ade26c24a65d3dedd88eb89#5671fd3675906cda1ade26c24a65d3dedd88eb89" }
 dependencies = [
-    { name = "einops" },
     { name = "importlib-metadata" },
-    { name = "nvdlfw-inspect" },
-    { name = "onnx" },
-    { name = "onnxscript" },
     { name = "packaging" },
     { name = "pydantic" },
-    { name = "torch", marker = "sys_platform == 'never'" },
 ]
 
 [[package]]