diff --git a/modelopt/torch/opt/plugins/mcore_dist_checkpointing.py b/modelopt/torch/opt/plugins/mcore_dist_checkpointing.py
index 3e5b35946..16ace511f 100644
--- a/modelopt/torch/opt/plugins/mcore_dist_checkpointing.py
+++ b/modelopt/torch/opt/plugins/mcore_dist_checkpointing.py
@@ -22,7 +22,6 @@
 from typing import Any
 
 import torch
-import yaml
 from megatron.core import dist_checkpointing, mpu
 from megatron.core.dist_checkpointing.serialization import get_default_load_sharded_strategy
 from megatron.core.dist_checkpointing.strategies.common import COMMON_STATE_FNAME
@@ -36,21 +35,6 @@
 
 SUPPORTED_WRAPPERS[Float16Module] = "module"
 
-DROP_SUBSTRINGS = [
-    "fp4",
-    "fp8",
-    "tp_",
-    "parallel",
-    "cuda_graph",
-    "init_",
-    "cpu",
-    "recompute",
-    "inference",
-    "pipeline",
-    "comm",
-    "batch",
-]
-
 
 def remove_per_module_state(
     modelopt_state: dict[str, Any],
@@ -138,29 +122,6 @@ def save_sharded_modelopt_state(
         sharded_strategy: configures sharded tensors saving behavior and backend
         prefix: the prefix to add to the modelopt_state keys ("model." for NeMo)
     """
-
-    def _parse_transformer_config(transformer_config: dict) -> dict:
-        config = {}
-
-        for k, v in transformer_config.items():
-            if any(substring in k for substring in DROP_SUBSTRINGS):
-                continue
-            if isinstance(v, (bool, int, str)):
-                config[k] = v
-            else:
-                config[k] = str(v)
-
-        return config
-
-    # Save own version of run config, if not already saved by the framework.
-    if dist.is_master() and not os.path.exists(f"{checkpoint_name}/run_config.yaml"):
-        run_config_name = f"{checkpoint_name}/modelopt_run_config.yaml"
-        # We avoid deepcopy since some attributes in Megatron-Bridge config cannot be deepcopied.
-        config_dict = _parse_transformer_config(model[0].config.__dict__)
-        config_dict["nvidia_modelopt_version"] = modelopt.__version__
-        with open(run_config_name, "w") as f:
-            yaml.dump(config_dict, f, default_flow_style=False)
-
     if not mto.ModeloptStateManager.is_converted(model[0]):
         return
     if len(model) > 1: