diff --git a/pyproject.toml b/pyproject.toml
index 3eebc26b46..690e9270b7 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -129,6 +129,7 @@ test = [
     "pytest-runner>=6.0.1",
     "pygithub",
     "click",
+    "multi-storage-client",
 ]
 dev = [
     "pre-commit>=3.6.0",
diff --git a/src/megatron/bridge/training/checkpointing.py b/src/megatron/bridge/training/checkpointing.py
index 486ebc7371..b0758710a1 100644
--- a/src/megatron/bridge/training/checkpointing.py
+++ b/src/megatron/bridge/training/checkpointing.py
@@ -21,7 +21,6 @@
 import sys
 import threading
 from enum import Enum, auto
-from functools import lru_cache
 from logging import getLogger
 from pathlib import Path
 from time import time
@@ -29,7 +28,6 @@
 
 import numpy as np
 import torch
-import yaml
 from megatron.core import dist_checkpointing, mpu, tensor_parallel
 from megatron.core.dist_checkpointing.mapping import ShardedObject, ShardedStateDict
 from megatron.core.dist_checkpointing.serialization import (
@@ -42,6 +40,7 @@
     FullyParallelLoadStrategyWrapper,
     FullyParallelSaveStrategyWrapper,
 )
+from megatron.core.msc_utils import MultiStorageClientFeature
 from megatron.core.num_microbatches_calculator import update_num_microbatches
 from megatron.core.optimizer import MegatronOptimizer
 from megatron.core.rerun_state_machine import get_rerun_state_machine
@@ -52,10 +51,20 @@
 from megatron.bridge.training.config import CheckpointConfig
 from megatron.bridge.training.state import GlobalState, TrainState
 from megatron.bridge.training.utils import wandb_utils
+from megatron.bridge.training.utils.checkpoint_utils import (
+    checkpoint_exists,
+    ensure_directory_exists,
+    file_exists,
+    get_checkpoint_name,
+    get_checkpoint_run_config_filename,
+    get_checkpoint_tracker_filename,
+    get_checkpoint_train_state_filename,
+    read_run_config,
+    read_train_state,
+)
 from megatron.bridge.training.utils.log_utils import append_to_progress_log
 from megatron.bridge.utils.common_utils import (
     get_rank_safe,
-    get_world_size_safe,
     is_last_rank,
     print_rank_0,
     unwrap_model,
@@ -78,9 +87,7 @@
 except Exception:
     has_nvidia_modelopt = False
 
-TRAIN_STATE_FILE = "train_state.pt"
 TRACKER_PREFIX = "latest"
-CONFIG_FILE = "run_config.yaml"
 _CHECKPOINT_VERSION = None
 
 logger = getLogger(__name__)
@@ -114,40 +121,6 @@ def get_checkpoint_version() -> Optional[float]:
     return _CHECKPOINT_VERSION
 
 
-def ensure_directory_exists(filename: str, check_parent: bool = True) -> None:
-    """Ensure that the directory for a given filename exists.
-
-    Args:
-        filename: The path whose directory should be checked/created.
-        check_parent: If True (default), checks the parent directory of the filename.
-                      If False, treats the filename itself as the directory path.
-    """
-    dirname = os.path.dirname(filename) if check_parent else filename
-    os.makedirs(dirname, exist_ok=True)
-
-
-def get_checkpoint_name(checkpoints_path: str, iteration: int, release: bool = False) -> str:
-    """Determine the directory name for a specific checkpoint.
-
-    Constructs the path based on iteration number or release flag.
-
-    Args:
-        checkpoints_path: Base directory where checkpoints are stored.
-        iteration: The training iteration number.
-        release: If True, uses 'release' as the directory name instead of iteration.
-
-    Returns:
-        The full path to the checkpoint directory.
-    """
-    if release:
-        directory = "release"
-    else:
-        directory = "iter_{:07d}".format(iteration)
-
-    common_path = os.path.join(checkpoints_path, directory)
-    return common_path
-
-
 def find_checkpoint_rank_0(checkpoints_path: str, iteration: int, release: bool = False) -> Optional[str]:
     """Find the checkpoint directory for a given iteration, assuming distributed checkpoints.
 
@@ -169,105 +142,6 @@ def find_checkpoint_rank_0(checkpoints_path: str, iteration: int, release: bool
     return None
 
 
-def get_checkpoint_train_state_filename(checkpoints_path: str, prefix: Optional[str] = None) -> str:
-    """Get the filename for the train state tracker file.
-
-    This file typically stores metadata about the latest checkpoint, like the iteration number.
-
-    Args:
-        checkpoints_path: Base directory where checkpoints are stored.
-        prefix: Optional prefix (e.g., 'latest') to prepend to the filename.
-
-    Returns:
-        The full path to the train state tracker file.
-    """
-    if prefix is None:
-        return os.path.join(checkpoints_path, TRAIN_STATE_FILE)
-    else:
-        return os.path.join(checkpoints_path, f"{prefix}_{TRAIN_STATE_FILE}")
-
-
-def get_checkpoint_run_config_filename(checkpoints_path: str) -> str:
-    """Get the filename for the run configuration file within a checkpoint directory.
-
-    Args:
-        checkpoints_path: Base directory where checkpoints are stored.
-
-    Returns:
-        The full path to the run configuration file (e.g., run_config.yaml).
-    """
-    return os.path.join(checkpoints_path, CONFIG_FILE)
-
-
-def get_checkpoint_tracker_filename(checkpoints_path: str) -> str:
-    """Tracker file rescords the latest chckpoint during training to restart from.
-
-    Supports checkpoints produced by Megatron-LM.
-
-    Args:
-        checkpoints_path: Base directory where checkpoints are stored.
-
-    Returns:
-        The full path to the checkpoint tracker file (e.g., latest_checkpointed_iteration.txt).
-    """
-    return os.path.join(checkpoints_path, "latest_checkpointed_iteration.txt")
-
-
-def checkpoint_exists(checkpoints_path: Optional[str]) -> bool:
-    """Check if a checkpoint directory exists.
-
-    Args:
-        checkpoints_path: Path to the potential checkpoint directory.
-
-    Returns:
-        True if the path exists, False otherwise.
-    """
-    if checkpoints_path is None:
-        return False
-
-    train_state_filename = os.path.join(checkpoints_path, f"{TRACKER_PREFIX}_{TRAIN_STATE_FILE}")
-    if os.path.exists(train_state_filename):
-        return True
-
-    # Fallback to the Megatron-LM tracker file
-    path = get_checkpoint_tracker_filename(checkpoints_path)
-    return os.path.isfile(path)
-
-
-@lru_cache()
-def read_train_state(train_state_filename: str) -> TrainState:
-    """Read the train state metadata from a YAML file (rank 0 only).
-
-    Reads the file on rank 0 and broadcasts the result to other ranks.
-
-    Args:
-        train_state_filename: Path to the train state YAML file.
-
-    Returns:
-        An initialized TrainState object.
-    """
-    state_obj = [None]
-    if get_rank_safe() == 0:
-        try:
-            state_dict = torch.load(train_state_filename, map_location="cpu")
-            ts = TrainState()
-            ts.load_state_dict(state_dict)
-            state_obj[0] = ts
-        except Exception as e:
-            error_msg = f"ERROR: Unable to load train state file {train_state_filename}: {e}"
-            sys.stderr.write(error_msg + "\n")
-            state_obj[0] = {"error": True, "msg": error_msg}
-
-    if torch.distributed.is_initialized():
-        print_rank_0(f"Broadcasting TrainState from rank 0 to all {get_world_size_safe()} ranks")
-        torch.distributed.broadcast_object_list(state_obj, src=0)
-
-    if isinstance(state_obj[0], dict) and state_obj[0].get("error", False):
-        raise RuntimeError(state_obj[0]["msg"])
-
-    return state_obj[0]
-
-
 def read_metadata(tracker_filename: str) -> tuple[int, bool]:
     """Read the metadata from the Megatron-LM tracker file.
 
@@ -280,7 +154,13 @@ def read_metadata(tracker_filename: str) -> tuple[int, bool]:
     iteration = 0
     release = False
 
-    with open(tracker_filename, "r") as f:
+    if MultiStorageClientFeature.is_enabled():
+        msc = MultiStorageClientFeature.import_package()
+        open_file = msc.open
+    else:
+        open_file = open
+
+    with open_file(tracker_filename, "r") as f:
         metastring = f.read().strip()
         try:
             iteration = int(metastring)
@@ -316,43 +196,6 @@ def read_metadata(tracker_filename: str) -> tuple[int, bool]:
     return max_iter, release
 
 
-@lru_cache()
-def read_run_config(run_config_filename: str) -> dict[str, Any]:
-    """Read the run configuration from a YAML file (rank 0 only).
-
-    Reads the file on rank 0 and broadcasts the result to other ranks.
-
-    Args:
-        run_config_filename: Path to the run config YAML file.
-
-    Returns:
-        A dictionary containing the run configuration.
-
-    Raises:
-        RuntimeError: If reading the config file fails on rank 0.
-    """
-    config_obj = [None]
-
-    if get_rank_safe() == 0:
-        try:
-            with open(run_config_filename, "r") as f:
-                config_dict = yaml.safe_load(f)
-            config_obj[0] = config_dict
-        except Exception as e:
-            error_msg = f"ERROR: Unable to load config file {run_config_filename}: {e}"
-            sys.stderr.write(error_msg + "\n")
-            config_obj[0] = {"error": True, "msg": error_msg}
-
-    if torch.distributed.is_initialized():
-        print_rank_0(f"Broadcasting config from rank 0 to all {get_world_size_safe()} ranks")
-        torch.distributed.broadcast_object_list(config_obj, src=0)
-
-    if isinstance(config_obj[0], dict) and config_obj[0].get("error", False):
-        raise RuntimeError(config_obj[0]["msg"])
-
-    return config_obj[0]
-
-
 def _extract_megatron_lm_args_from_state_dict(state_dict: dict[str, Any]) -> dict[str, Any]:
     """Extract and convert legacy Megatron-LM args from checkpoint state_dict to Megatron-Bridge config format.
 
@@ -735,8 +578,14 @@ def train_state_finalize_fn() -> None:
                 train_state_dict["floating_point_operations_so_far"] = torch.tensor(
                     num_floating_point_operations_so_far, dtype=torch.float32
                 )
-                torch.save(train_state_dict, train_state_local_filename)
-                shutil.copy(train_state_local_filename, train_state_global_filename)
+                if MultiStorageClientFeature.is_enabled():
+                    msc = MultiStorageClientFeature.import_package()
+                    msc.torch.save(train_state_dict, train_state_local_filename)
+                    msc.torch.save(train_state_dict, train_state_global_filename)
+                else:
+                    torch.save(train_state_dict, train_state_local_filename)
+                    shutil.copy(train_state_local_filename, train_state_global_filename)
+
                 cfg.to_yaml(config_filename)
 
                 tp_rank = (tensor_rank if tensor_rank is not None else mpu.get_tensor_model_parallel_rank()) + 1
@@ -1128,7 +977,8 @@ def _load_checkpoint_from_path(
     # If that fails, we are loading from a Megatron-LM checkpoint, so extract the corresponding values
     # from args in the state_dict
     run_config_filename = get_checkpoint_run_config_filename(checkpoint_name)
-    if os.path.exists(run_config_filename):
+
+    if file_exists(run_config_filename):
         run_config = read_run_config(run_config_filename)
     else:
         # Fallback to legacy Megatron-LM args extraction
@@ -1274,7 +1124,8 @@ def _load_checkpoint_from_path(
         # Try to read train_state.pt from checkpoint directory
         # If it doesn't exist (checkpoint generated by Megatron-LM), create from available information
         train_state_filename = get_checkpoint_train_state_filename(checkpoint_name)
-        if os.path.exists(train_state_filename):
+
+        if file_exists(train_state_filename):
             state.train_state = read_train_state(train_state_filename)
         else:
             # Legacy Megatron-LM checkpoint - create TrainState from checkpoint iteration
@@ -1597,7 +1448,7 @@ def _get_non_persistent_iteration(
         return -1
     elif non_persistent_ckpt_type == "global":
         train_state_filename = get_checkpoint_train_state_filename(non_persistent_global_dir, prefix=TRACKER_PREFIX)
-        if os.path.isfile(train_state_filename):
+        if file_exists(train_state_filename):
             train_state = read_train_state(train_state_filename)
             iteration = train_state.step
             # if train_state.release:
@@ -1702,14 +1553,14 @@ def _load_base_checkpoint(
     tracker_filename = "because load directory is not defined"
     if load_dir is not None:
         tracker_filename = get_checkpoint_train_state_filename(load_dir, prefix=TRACKER_PREFIX)
-        if os.path.isfile(tracker_filename):
+        if file_exists(tracker_filename):
             train_state = read_train_state(tracker_filename)
             iteration = train_state.step
             # release = train_state.release
         else:
             # Fallback to legacy Megatron-LM tracker file format
             legacy_tracker_filename = get_checkpoint_tracker_filename(load_dir)
-            if os.path.isfile(legacy_tracker_filename):
+            if file_exists(legacy_tracker_filename):
                 print_rank_0(f"Loading from legacy Megatron-LM checkpoint format: {legacy_tracker_filename}")
                 iteration, release = read_metadata(legacy_tracker_filename)
                 tracker_filename = legacy_tracker_filename  # Update for error messages
diff --git a/src/megatron/bridge/training/model_load_save.py b/src/megatron/bridge/training/model_load_save.py
index 8fa28b9f01..e03c0b11ea 100644
--- a/src/megatron/bridge/training/model_load_save.py
+++ b/src/megatron/bridge/training/model_load_save.py
@@ -31,6 +31,7 @@
 from megatron.bridge.training.config import CheckpointConfig, ConfigContainer, LoggerConfig
 from megatron.bridge.training.state import GlobalState
 from megatron.bridge.training.tokenizers.tokenizer import MegatronTokenizer, build_tokenizer
+from megatron.bridge.training.utils.checkpoint_utils import file_exists
 
 
 logger = logging.getLogger(__name__)
@@ -133,7 +134,8 @@ def load_tokenizer(checkpoint_path: str) -> MegatronTokenizer:
     from megatron.bridge.utils.instantiate_utils import instantiate
 
     run_config_filename = get_checkpoint_run_config_filename(checkpoint_path)
-    if os.path.exists(run_config_filename):
+
+    if file_exists(run_config_filename):
         run_config = read_run_config(run_config_filename)
         mbridge_ckpt = True
     else:
@@ -194,7 +196,8 @@ def load_megatron_model(
     from megatron.bridge.utils.instantiate_utils import instantiate
 
     run_config_filename = get_checkpoint_run_config_filename(checkpoint_path)
-    if os.path.exists(run_config_filename):
+
+    if file_exists(run_config_filename):
         run_config = read_run_config(run_config_filename)
         mbridge_ckpt = True
     else:
diff --git a/src/megatron/bridge/training/utils/checkpoint_utils.py b/src/megatron/bridge/training/utils/checkpoint_utils.py
index 3f6037c7a5..6dcfe87598 100644
--- a/src/megatron/bridge/training/utils/checkpoint_utils.py
+++ b/src/megatron/bridge/training/utils/checkpoint_utils.py
@@ -16,12 +16,13 @@
 import os
 import sys
 from functools import lru_cache
-from typing import Any, Optional, Type, TypeVar
+from typing import Any, Optional
 
 import torch
 import yaml
+from megatron.core.msc_utils import MultiStorageClientFeature
 
-from megatron.bridge.training.utils.log_utils import log_single_rank
+from megatron.bridge.training.state import TrainState
 from megatron.bridge.utils.common_utils import get_rank_safe, get_world_size_safe, print_rank_0
 
 
@@ -30,33 +31,60 @@
 CONFIG_FILE = "run_config.yaml"
 
 logger = logging.getLogger(__name__)
-_T = TypeVar("_T")
 
 
-def checkpoint_exists(checkpoints_path: str) -> bool:
-    """Check if a checkpoint directory exists.
+def file_exists(path: str) -> bool:
+    """Check if a file exists.
 
     Args:
-        checkpoints_path: Path to the potential checkpoint directory.
+        path: The path to the file. Can be a local path or an MSC URL.
 
     Returns:
-        True if the path exists, False otherwise.
+        True if the file exists, False otherwise.
     """
-    if checkpoints_path is None:
-        return False
-    return os.path.exists(os.path.join(checkpoints_path, f"{TRACKER_PREFIX}_{TRAIN_STATE_FILE}"))
+    if MultiStorageClientFeature.is_enabled():
+        msc = MultiStorageClientFeature.import_package()
+        return msc.os.path.exists(path)
+    else:
+        return os.path.exists(path)
 
 
-def get_checkpoint_run_config_filename(checkpoints_path: str) -> str:
-    """Get the filename for the run configuration file within a checkpoint directory.
+def ensure_directory_exists(filename: str, check_parent: bool = True) -> None:
+    """Ensure that the directory for a given filename exists.
+
+    Args:
+        filename: The path whose directory should be checked/created.
+        check_parent: If True (default), checks the parent directory of the filename.
+                      If False, treats the filename itself as the directory path.
+    """
+    dirname = os.path.dirname(filename) if check_parent else filename
+    if MultiStorageClientFeature.is_enabled():
+        msc = MultiStorageClientFeature.import_package()
+        msc.os.makedirs(dirname, exist_ok=True)
+    else:
+        os.makedirs(dirname, exist_ok=True)
+
+
+def get_checkpoint_name(checkpoints_path: str, iteration: int, release: bool = False) -> str:
+    """Determine the directory name for a specific checkpoint.
+
+    Constructs the path based on iteration number or release flag.
 
     Args:
         checkpoints_path: Base directory where checkpoints are stored.
+        iteration: The training iteration number.
+        release: If True, uses 'release' as the directory name instead of iteration.
 
     Returns:
-        The full path to the run configuration file (e.g., run_config.yaml).
+        The full path to the checkpoint directory.
     """
-    return os.path.join(checkpoints_path, CONFIG_FILE)
+    if release:
+        directory = "release"
+    else:
+        directory = "iter_{:07d}".format(iteration)
+
+    common_path = os.path.join(checkpoints_path, directory)
+    return common_path
 
 
 def get_checkpoint_train_state_filename(checkpoints_path: str, prefix: Optional[str] = None) -> str:
@@ -77,6 +105,58 @@ def get_checkpoint_train_state_filename(checkpoints_path: str, prefix: Optional[
         return os.path.join(checkpoints_path, f"{prefix}_{TRAIN_STATE_FILE}")
 
 
+def get_checkpoint_run_config_filename(checkpoints_path: str) -> str:
+    """Get the filename for the run configuration file within a checkpoint directory.
+
+    Args:
+        checkpoints_path: Base directory where checkpoints are stored.
+
+    Returns:
+        The full path to the run configuration file (e.g., run_config.yaml).
+    """
+    return os.path.join(checkpoints_path, CONFIG_FILE)
+
+
+def get_checkpoint_tracker_filename(checkpoints_path: str) -> str:
+    """Tracker file rescords the latest chckpoint during training to restart from.
+
+    Supports checkpoints produced by Megatron-LM.
+
+    Args:
+        checkpoints_path: Base directory where checkpoints are stored.
+
+    Returns:
+        The full path to the checkpoint tracker file (e.g., latest_checkpointed_iteration.txt).
+    """
+    return os.path.join(checkpoints_path, "latest_checkpointed_iteration.txt")
+
+
+def checkpoint_exists(checkpoints_path: Optional[str]) -> bool:
+    """Check if a checkpoint directory exists.
+
+    Args:
+        checkpoints_path: Path to the potential checkpoint directory.
+
+    Returns:
+        True if the path exists, False otherwise.
+    """
+    if checkpoints_path is None:
+        return False
+
+    train_state_filename = os.path.join(checkpoints_path, f"{TRACKER_PREFIX}_{TRAIN_STATE_FILE}")
+
+    if file_exists(train_state_filename):
+        return True
+
+    # Fallback to the Megatron-LM tracker file
+    path = get_checkpoint_tracker_filename(checkpoints_path)
+    if MultiStorageClientFeature.is_enabled():
+        msc = MultiStorageClientFeature.import_package()
+        return msc.os.path.isfile(path)
+    else:
+        return os.path.isfile(path)
+
+
 @lru_cache()
 def read_run_config(run_config_filename: str) -> dict[str, Any]:
     """Read the run configuration from a YAML file (rank 0 only).
@@ -96,8 +176,13 @@ def read_run_config(run_config_filename: str) -> dict[str, Any]:
 
     if get_rank_safe() == 0:
         try:
-            with open(run_config_filename, "r") as f:
-                config_dict = yaml.safe_load(f)
+            if MultiStorageClientFeature.is_enabled():
+                msc = MultiStorageClientFeature.import_package()
+                with msc.open(run_config_filename, "r") as f:
+                    config_dict = yaml.safe_load(f)
+            else:
+                with open(run_config_filename, "r") as f:
+                    config_dict = yaml.safe_load(f)
             config_obj[0] = config_dict
         except Exception as e:
             error_msg = f"ERROR: Unable to load config file {run_config_filename}: {e}"
@@ -105,7 +190,7 @@ def read_run_config(run_config_filename: str) -> dict[str, Any]:
             config_obj[0] = {"error": True, "msg": error_msg}
 
     if torch.distributed.is_initialized():
-        log_single_rank(logger, logging.INFO, f"Broadcasting config from rank 0 to all {get_world_size_safe()} ranks")
+        print_rank_0(f"Broadcasting config from rank 0 to all {get_world_size_safe()} ranks")
         torch.distributed.broadcast_object_list(config_obj, src=0)
 
     if isinstance(config_obj[0], dict) and config_obj[0].get("error", False):
@@ -115,7 +200,7 @@ def read_run_config(run_config_filename: str) -> dict[str, Any]:
 
 
 @lru_cache()
-def read_train_state(train_state_filename: str, train_state_cls: Type[_T]) -> _T:
+def read_train_state(train_state_filename: str) -> TrainState:
     """Read the train state metadata from a YAML file (rank 0 only).
 
     Reads the file on rank 0 and broadcasts the result to other ranks.
@@ -129,8 +214,12 @@ def read_train_state(train_state_filename: str, train_state_cls: Type[_T]) -> _T
     state_obj = [None]
     if get_rank_safe() == 0:
         try:
-            state_dict = torch.load(train_state_filename, map_location="cpu")
-            ts = train_state_cls()
+            if MultiStorageClientFeature.is_enabled():
+                msc = MultiStorageClientFeature.import_package()
+                state_dict = msc.torch.load(train_state_filename, map_location="cpu")
+            else:
+                state_dict = torch.load(train_state_filename, map_location="cpu")
+            ts = TrainState()
             ts.load_state_dict(state_dict)
             state_obj[0] = ts
         except Exception as e:
diff --git a/src/megatron/bridge/training/utils/config_utils.py b/src/megatron/bridge/training/utils/config_utils.py
index 91e6cf8115..ff68473792 100644
--- a/src/megatron/bridge/training/utils/config_utils.py
+++ b/src/megatron/bridge/training/utils/config_utils.py
@@ -19,6 +19,7 @@
 from typing import Any, Optional, Type, TypeVar
 
 import yaml
+from megatron.core.msc_utils import MultiStorageClientFeature
 from omegaconf import OmegaConf
 
 from megatron.bridge.utils.instantiate_utils import InstantiationMode, instantiate
@@ -93,11 +94,22 @@ def from_yaml(cls: Type[T], yaml_path: str, mode: InstantiationMode = Instantiat
         Returns:
             A new instance of this class initialized with the YAML file values
         """
-        if not os.path.exists(yaml_path):
+        if MultiStorageClientFeature.is_enabled():
+            msc = MultiStorageClientFeature.import_package()
+            yaml_path_exists = msc.os.path.exists(yaml_path)
+        else:
+            yaml_path_exists = os.path.exists(yaml_path)
+
+        if not yaml_path_exists:
             raise FileNotFoundError(f"YAML file not found: {yaml_path}")
 
-        with open(yaml_path, "r") as f:
-            config_dict = yaml.safe_load(f)
+        if MultiStorageClientFeature.is_enabled():
+            msc = MultiStorageClientFeature.import_package()
+            with msc.open(yaml_path, "r") as f:
+                config_dict = yaml.safe_load(f)
+        else:
+            with open(yaml_path, "r") as f:
+                config_dict = yaml.safe_load(f)
 
         # Convert to OmegaConf first for better compatibility with instantiate
         conf = OmegaConf.create(config_dict)
@@ -182,8 +194,13 @@ def to_yaml(self, yaml_path: Optional[str] = None) -> None:
             if yaml_path is None:
                 print(yaml.safe_dump(config_dict, default_flow_style=False))
             else:
-                with open(yaml_path, "w") as f:
-                    yaml.safe_dump(config_dict, f, default_flow_style=False)
+                if MultiStorageClientFeature.is_enabled():
+                    msc = MultiStorageClientFeature.import_package()
+                    with msc.open(yaml_path, "w") as f:
+                        yaml.safe_dump(config_dict, f, default_flow_style=False)
+                else:
+                    with open(yaml_path, "w") as f:
+                        yaml.safe_dump(config_dict, f, default_flow_style=False)
 
     def __deepcopy__(self, memo):
         """Support for deep copying."""
diff --git a/tests/unit_tests/conftest.py b/tests/unit_tests/conftest.py
index 34adb5ddec..09c336ccfe 100644
--- a/tests/unit_tests/conftest.py
+++ b/tests/unit_tests/conftest.py
@@ -18,6 +18,7 @@
 from unittest.mock import patch
 
 import pytest
+from megatron.core.msc_utils import MultiStorageClientFeature
 
 from tests.unit_tests.download_unit_tests_dataset import get_oldest_release_and_assets
 
@@ -71,6 +72,12 @@ def ensure_test_data(tmp_path_factory):
     yield data_path
 
 
+@pytest.fixture(scope="function", autouse=True)
+def disable_msc():
+    """Disable MSC for the tests."""
+    MultiStorageClientFeature.disable()
+
+
 @pytest.fixture(autouse=True)
 def reset_env_vars():
     """Reset environment variables"""
diff --git a/tests/unit_tests/training/test_checkpointing.py b/tests/unit_tests/training/test_checkpointing.py
index f9ddf5909b..5b34af89b4 100644
--- a/tests/unit_tests/training/test_checkpointing.py
+++ b/tests/unit_tests/training/test_checkpointing.py
@@ -13,12 +13,14 @@
 # limitations under the License.
 """Unit tests for megatron.bridge.training.checkpointing module."""
 
+import os
 import tempfile
 from pathlib import Path
 from unittest.mock import Mock, patch
 
 import pytest
 import torch
+from megatron.core.msc_utils import MultiStorageClientFeature
 
 from megatron.bridge.training.checkpointing import (
     CheckpointType,
@@ -193,6 +195,18 @@ def test_ensure_directory_exists(self, mock_makedirs):
         ensure_directory_exists("/path/to/dir", check_parent=False)
         mock_makedirs.assert_called_with("/path/to/dir", exist_ok=True)
 
+    def test_ensure_directory_exists_with_msc_url(self):
+        """Test directory creation with MSC URL."""
+        MultiStorageClientFeature.enable()
+
+        # Verify that the parent directory is created
+        with tempfile.TemporaryDirectory() as temp_dir:
+            ensure_directory_exists(f"msc://default{temp_dir}/checkpoints/iter_0000001", check_parent=True)
+            assert os.path.exists(f"{temp_dir}/checkpoints")
+
+            ensure_directory_exists(f"msc://default{temp_dir}/checkpoints/iter_0000001", check_parent=False)
+            assert os.path.exists(f"{temp_dir}/checkpoints/iter_0000001")
+
 
 class TestCheckpointTypes:
     """Test CheckpointType enum and related logic."""
@@ -645,7 +659,7 @@ class TestNonPersistentCheckpoints:
     """Test non-persistent checkpoint functionality."""
 
     @patch("megatron.bridge.training.checkpointing.read_train_state")
-    @patch("os.path.isfile")
+    @patch("os.path.exists")
     def test_get_non_persistent_iteration_global(self, mock_isfile, mock_read_state):
         """Test getting iteration from global non-persistent checkpoint."""
         non_persistent_ckpt_type = "global"
@@ -795,7 +809,7 @@ def test_load_base_checkpoint_no_checkpoint(self, mock_isfile, mock_read_state,
     @patch("megatron.bridge.training.checkpointing._get_non_persistent_iteration")
     @patch("megatron.bridge.training.checkpointing.read_train_state")
     @patch("megatron.bridge.training.checkpointing.dist_checkpointing")
-    @patch("os.path.isfile")
+    @patch("os.path.exists")
     def test_load_base_checkpoint_non_distributed_error(
         self, mock_isfile, mock_dist_ckpt, mock_read_state, mock_get_np_iter, base_config
     ):
@@ -1181,7 +1195,7 @@ def test_extract_megatron_lm_args_from_state_dict_missing_args(self):
         assert "Legacy checkpoint missing 'args' field" in str(exc_info.value)
 
     @patch("megatron.bridge.training.checkpointing.read_metadata")
-    @patch("os.path.isfile")
+    @patch("os.path.exists")
     def test_load_base_checkpoint_legacy_tracker(self, mock_isfile, mock_read_metadata):
         """Test loading checkpoint with legacy Megatron-LM tracker file."""
         mock_cfg = Mock()
diff --git a/tests/unit_tests/training/utils/test_checkpoint_utils.py b/tests/unit_tests/training/utils/test_checkpoint_utils.py
index 63c0ab1f05..0733d63595 100644
--- a/tests/unit_tests/training/utils/test_checkpoint_utils.py
+++ b/tests/unit_tests/training/utils/test_checkpoint_utils.py
@@ -216,7 +216,8 @@ def test_read_train_state_rank_0_success(self, mock_torch_load, mock_is_initiali
         state_dict = {"iteration": 100, "epoch": 5, "step": 1000}
         mock_torch_load.return_value = state_dict
 
-        result = read_train_state("train_state.pt", MockTrainState)
+        with patch("megatron.bridge.training.utils.checkpoint_utils.TrainState", return_value=MockTrainState()):
+            result = read_train_state("train_state.pt")
 
         assert isinstance(result, MockTrainState)
         assert result.iteration == 100
@@ -250,7 +251,7 @@ def broadcast_side_effect(obj_list, src):
 
         mock_broadcast.side_effect = broadcast_side_effect
 
-        result = read_train_state("train_state.pt", MockTrainState)
+        result = read_train_state("train_state.pt")
 
         assert result == train_state
         assert result.iteration == 200
@@ -267,23 +268,7 @@ def test_read_train_state_load_error(self, mock_torch_load, mock_is_initialized,
         mock_torch_load.side_effect = RuntimeError("Corrupted file")
 
         with pytest.raises(RuntimeError, match="Unable to load train state file"):
-            read_train_state("corrupted.pt", MockTrainState)
-
-    @patch("megatron.bridge.training.utils.checkpoint_utils.get_rank_safe")
-    @patch("megatron.bridge.training.utils.checkpoint_utils.torch.distributed.is_initialized")
-    @patch("megatron.bridge.training.utils.checkpoint_utils.torch.load")
-    def test_read_train_state_class_instantiation_error(self, mock_torch_load, mock_is_initialized, mock_get_rank):
-        """Test read_train_state handles train state class instantiation error."""
-        mock_get_rank.return_value = 0
-        mock_is_initialized.return_value = False
-        mock_torch_load.return_value = {"iteration": 100}
-
-        # Mock a class that raises an error during instantiation
-        def bad_class():
-            raise ValueError("Cannot instantiate")
-
-        with pytest.raises(RuntimeError, match="Unable to load train state file"):
-            read_train_state("train_state.pt", bad_class)
+            read_train_state("corrupted.pt")
 
     def test_caching_behavior_read_run_config(self):
         """Test that read_run_config uses caching properly."""
@@ -319,9 +304,11 @@ def test_caching_behavior_read_train_state(self):
             mock_load.return_value = state_dict
 
             # First call
-            result1 = read_train_state("train_state.pt", MockTrainState)
+            with patch("megatron.bridge.training.utils.checkpoint_utils.TrainState", return_value=MockTrainState()):
+                result1 = read_train_state("train_state.pt")
             # Second call should use cache
-            result2 = read_train_state("train_state.pt", MockTrainState)
+            with patch("megatron.bridge.training.utils.checkpoint_utils.TrainState", return_value=MockTrainState()):
+                result2 = read_train_state("train_state.pt")
 
             assert result1 == result2
             assert result1.iteration == 100
@@ -501,8 +488,9 @@ def test_memory_usage_with_complex_train_state(self):
                 "megatron.bridge.training.utils.checkpoint_utils.torch.distributed.is_initialized", return_value=False
             ),
             patch("megatron.bridge.training.utils.checkpoint_utils.torch.load", return_value=complex_state_dict),
+            patch("megatron.bridge.training.utils.checkpoint_utils.TrainState", return_value=ComplexTrainState()),
         ):
-            result = read_train_state("complex_state.pt", ComplexTrainState)
+            result = read_train_state("complex_state.pt")
 
             # Verify the complex state is loaded correctly
             assert result.iteration == 10000
@@ -568,8 +556,9 @@ def test_out_of_memory_error_handling(self, mock_torch_load, mock_is_initialized
         mock_is_initialized.return_value = False
         mock_torch_load.side_effect = RuntimeError("CUDA out of memory")
 
-        with pytest.raises(RuntimeError, match="Unable to load train state file"):
-            read_train_state("large_state.pt", MockTrainState)
+        with patch("megatron.bridge.training.utils.checkpoint_utils.TrainState", return_value=MockTrainState()):
+            with pytest.raises(RuntimeError, match="Unable to load train state file"):
+                read_train_state("large_state.pt")
 
     def test_unicode_and_special_characters_in_config(self, tmp_path):
         """Test handling of Unicode and special characters in config files."""
diff --git a/tests/unit_tests/training/utils/test_config_utils.py b/tests/unit_tests/training/utils/test_config_utils.py
index 9e55f0f4ef..29f89cae2b 100644
--- a/tests/unit_tests/training/utils/test_config_utils.py
+++ b/tests/unit_tests/training/utils/test_config_utils.py
@@ -17,11 +17,14 @@
 
 import copy
 import functools
+import os
+import tempfile
 from dataclasses import dataclass
 from unittest.mock import MagicMock, mock_open, patch
 
 import pytest
 import torch
+from megatron.core.msc_utils import MultiStorageClientFeature
 
 from megatron.bridge.training.utils.config_utils import _ConfigContainerBase
 from megatron.bridge.utils.instantiate_utils import InstantiationMode
@@ -477,6 +480,20 @@ def test_to_yaml_save_to_file(self, mock_file, mock_yaml_dump, mock_safe_represe
         call_args = mock_yaml_dump.call_args
         assert call_args[1]["default_flow_style"] is False
 
+    def test_to_yaml_with_msc_url(self):
+        """Test to_yaml with MSC URL."""
+        config = TestConfigContainer(name="msc_test", value=999)
+
+        MultiStorageClientFeature.enable()
+
+        # Verify that the file is created in the temporary directory
+        with tempfile.TemporaryDirectory() as temp_dir:
+            config.to_yaml(f"msc://default{temp_dir}/test_output.yaml")
+            assert os.path.exists(f"{temp_dir}/test_output.yaml")
+
+            loaded_config = TestConfigContainer.from_yaml(f"msc://default{temp_dir}/test_output.yaml")
+            assert config.to_dict() == loaded_config.to_dict()
+
 
 class TestConfigContainer_DeepCopy:
     """Test ConfigContainer.__deepcopy__ method."""
diff --git a/uv.lock b/uv.lock
index 6ed8b251a7..476fb7d272 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,5 +1,4 @@
 version = 1
-revision = 2
 requires-python = ">=3.10"
 resolution-markers = [
     "python_full_version >= '3.13' and sys_platform == 'win32'",
@@ -31,9 +30,9 @@ prerelease-mode = "allow"
 name = "absl-py"
 version = "2.3.1"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/10/2a/c93173ffa1b39c1d0395b7e842bbdc62e556ca9d8d3b5572926f3e4ca752/absl_py-2.3.1.tar.gz", hash = "sha256:a97820526f7fbfd2ec1bce83f3f25e3a14840dac0d8e02a0b71cd75db3f77fc9", size = 116588, upload-time = "2025-07-03T09:31:44.05Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/10/2a/c93173ffa1b39c1d0395b7e842bbdc62e556ca9d8d3b5572926f3e4ca752/absl_py-2.3.1.tar.gz", hash = "sha256:a97820526f7fbfd2ec1bce83f3f25e3a14840dac0d8e02a0b71cd75db3f77fc9", size = 116588 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/8f/aa/ba0014cc4659328dc818a28827be78e6d97312ab0cb98105a770924dc11e/absl_py-2.3.1-py3-none-any.whl", hash = "sha256:eeecf07f0c2a93ace0772c92e596ace6d3d3996c042b2128459aaae2a76de11d", size = 135811, upload-time = "2025-07-03T09:31:42.253Z" },
+    { url = "https://files.pythonhosted.org/packages/8f/aa/ba0014cc4659328dc818a28827be78e6d97312ab0cb98105a770924dc11e/absl_py-2.3.1-py3-none-any.whl", hash = "sha256:eeecf07f0c2a93ace0772c92e596ace6d3d3996c042b2128459aaae2a76de11d", size = 135811 },
 ]
 
 [[package]]
@@ -49,9 +48,9 @@ dependencies = [
     { name = "safetensors" },
     { name = "torch" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/f7/66/be171836d86dc5b8698b3a9bf4b9eb10cb53369729939f88bf650167588b/accelerate-1.10.0.tar.gz", hash = "sha256:8270568fda9036b5cccdc09703fef47872abccd56eb5f6d53b54ea5fb7581496", size = 392261, upload-time = "2025-08-07T10:54:51.664Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/f7/66/be171836d86dc5b8698b3a9bf4b9eb10cb53369729939f88bf650167588b/accelerate-1.10.0.tar.gz", hash = "sha256:8270568fda9036b5cccdc09703fef47872abccd56eb5f6d53b54ea5fb7581496", size = 392261 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/30/dd/0107f0aa179869ee9f47ef5a2686abd5e022fdc82af901d535e52fe91ce1/accelerate-1.10.0-py3-none-any.whl", hash = "sha256:260a72b560e100e839b517a331ec85ed495b3889d12886e79d1913071993c5a3", size = 374718, upload-time = "2025-08-07T10:54:49.988Z" },
+    { url = "https://files.pythonhosted.org/packages/30/dd/0107f0aa179869ee9f47ef5a2686abd5e022fdc82af901d535e52fe91ce1/accelerate-1.10.0-py3-none-any.whl", hash = "sha256:260a72b560e100e839b517a331ec85ed495b3889d12886e79d1913071993c5a3", size = 374718 },
 ]
 
 [[package]]
@@ -61,18 +60,18 @@ source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "pygments" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/bc/c1/bbac6a50d02774f91572938964c582fff4270eee73ab822a4aeea4d8b11b/accessible_pygments-0.0.5.tar.gz", hash = "sha256:40918d3e6a2b619ad424cb91e556bd3bd8865443d9f22f1dcdf79e33c8046872", size = 1377899, upload-time = "2024-05-10T11:23:10.216Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/bc/c1/bbac6a50d02774f91572938964c582fff4270eee73ab822a4aeea4d8b11b/accessible_pygments-0.0.5.tar.gz", hash = "sha256:40918d3e6a2b619ad424cb91e556bd3bd8865443d9f22f1dcdf79e33c8046872", size = 1377899 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/8d/3f/95338030883d8c8b91223b4e21744b04d11b161a3ef117295d8241f50ab4/accessible_pygments-0.0.5-py3-none-any.whl", hash = "sha256:88ae3211e68a1d0b011504b2ffc1691feafce124b845bd072ab6f9f66f34d4b7", size = 1395903, upload-time = "2024-05-10T11:23:08.421Z" },
+    { url = "https://files.pythonhosted.org/packages/8d/3f/95338030883d8c8b91223b4e21744b04d11b161a3ef117295d8241f50ab4/accessible_pygments-0.0.5-py3-none-any.whl", hash = "sha256:88ae3211e68a1d0b011504b2ffc1691feafce124b845bd072ab6f9f66f34d4b7", size = 1395903 },
 ]
 
 [[package]]
 name = "aiohappyeyeballs"
 version = "2.6.1"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/26/30/f84a107a9c4331c14b2b586036f40965c128aa4fee4dda5d3d51cb14ad54/aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558", size = 22760, upload-time = "2025-03-12T01:42:48.764Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/26/30/f84a107a9c4331c14b2b586036f40965c128aa4fee4dda5d3d51cb14ad54/aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558", size = 22760 }
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+    { url = "https://files.pythonhosted.org/packages/0f/15/5bf3b99495fb160b63f95972b81750f18f7f4e02ad051373b669d17d44f2/aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8", size = 15265 },
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