[None][doc] Clarify the perf best practice and supported hardware for gptoss

[dongfengy]

… gptoss

Summary by CodeRabbit

• Documentation
  • Added support for new devices to MoE Backend Support Matrix.
  • Restructured and clarified backend selection guidance with performance optimization focus.

Description

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[dongfengy]

/bot run

[coderabbitai]

📝 Walkthrough

Walkthrough

Updated documentation for TensorRT-LLM GPU-OSS deployment guide. The MoE Backend Support Matrix was revised to include additional GPU devices (B200/GB200/B300/GB300) and consolidate backend use cases into a unified column. Descriptive guidance about MOE backend selection was rephrased to emphasize performance optimization and added clarification about TRTLLM MOE throughput advantages.

Changes

Cohort / File(s)	Summary
Documentation Updates docs/source/deployment-guide/quick-start-recipe-for-gpt-oss-on-trtllm.md	Updated MoE Backend Support Matrix to include B200/GB200/B300/GB300 devices and merge use case entries. Revised MOE backend selection guidance to emphasize performance optimization and clarified TRTLLM MOE throughput benefits.

Estimated code review effort

🎯 2 (Simple) | ⏱️ ~8 minutes

• Review focus: Verify accuracy of GPU device listings in the support matrix and validate correctness of performance claims in the updated guidance text.

Pre-merge checks and finishing touches

❌ Failed checks (2 warnings)

Check name	Status	Explanation	Resolution
Description Check	⚠️ Warning	The PR description is largely incomplete. The repository template requires three key sections to be filled: Description (explaining the issue and solution), Test Coverage (listing relevant tests), and a completed PR Checklist. The provided description contains only a fragment ("… gptoss" and "@coderabbitai summary"), with both the Description and Test Coverage sections entirely empty and most checklist items unchecked. While this is a documentation-only change which may have minimal test requirements, the complete absence of a Description explaining what and why violates the template's fundamental expectations.	The author should complete the Description section with a clear explanation of the documentation updates (e.g., explaining why the hardware support and performance guidance needed clarification), and address the Test Coverage section (noting that documentation changes may not require traditional testing, or specifying manual verification steps if applicable). Additionally, the author should review and check the appropriate PR Checklist items before merging to confirm compliance with repository standards.
Docstring Coverage	⚠️ Warning	Docstring coverage is 0.00% which is insufficient. The required threshold is 80.00%.	You can run @coderabbitai generate docstrings to improve docstring coverage.

✅ Passed checks (1 passed)

Check name	Status	Explanation
Title Check	✅ Passed	The PR title "[None][doc] Clarify the perf best practice and supported hardware for gptoss" directly aligns with the changeset, which updates MoE backend documentation to clarify performance best practices (emphasizing universal throughput optimization of TRTLLM MOE) and adds support for new hardware (B200/GB200/B300/GB300). The title follows the required template format with [None] for no ticket, [doc] for documentation type, and a clear, specific summary that accurately reflects the primary changes. The phrasing is concise, avoids vague terms, and would help teammates understand the main change when scanning history.

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[coderabbitai]

Actionable comments posted: 1

🧹 Nitpick comments (1)

docs/source/deployment-guide/quick-start-recipe-for-gpt-oss-on-trtllm.md (1)

30-31: Improve clarity of backend guidance and soften absolutist language.

Line 31 uses informal phrasing ("was better... but now we have optimized") and states TRTLLM is "universally faster" as an absolute claim. For a documentation guide, consider rephrasing to be more precise about:

• When TRTLLM should be used (e.g., "For low-latency workloads...")
• Whether there are any remaining use cases where CUTLASS is preferred
• The basis for the performance comparison

Consider revising lines 30-31 to:

-The default moe backend is `CUTLASS`, so for the best possible perf, one must set the `moe_config.backend` explicitly to run the model.
-`CUTLASS` was better for max throughput at first but now we have optimized `TRTLLM` moe to be universally faster.
+The default moe backend is `CUTLASS`. To achieve the best performance for your workload, explicitly set `moe_config.backend` based on your use case:
+- For low-latency inference: use TRTLLM (recommended for latency-sensitive applications)
+- For maximum throughput: [clarify whether TRTLLM or CUTLASS is recommended]

📜 Review details

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Review profile: CHILL

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📥 Commits

Reviewing files that changed from the base of the PR and between a6d20f6 and ff7a29f.

📒 Files selected for processing (1)

• docs/source/deployment-guide/quick-start-recipe-for-gpt-oss-on-trtllm.md (1 hunks)

⏰ Context from checks skipped due to timeout of 90000ms. You can increase the timeout in your CodeRabbit configuration to a maximum of 15 minutes (900000ms). (1)

• GitHub Check: Pre-commit Check

[tensorrt-cicd]

PR_Github #22512 [ run ] triggered by Bot. Commit: ff7a29f

[tensorrt-cicd]

PR_Github #22512 [ run ] completed with state SUCCESS. Commit: ff7a29f
/LLM/main/L0_MergeRequest_PR pipeline #16969 completed with status: 'SUCCESS'

[dongfengy]

/bot run

[tensorrt-cicd]

PR_Github #22678 [ run ] triggered by Bot. Commit: 58d6cf1

[tensorrt-cicd]

PR_Github #22678 [ run ] completed with state SUCCESS. Commit: 58d6cf1
/LLM/main/L0_MergeRequest_PR pipeline #17097 completed with status: 'SUCCESS'