diff --git a/nemo_automodel/components/checkpoint/checkpointing.py b/nemo_automodel/components/checkpoint/checkpointing.py
index 6b1c320ee9..b7c9e91924 100644
--- a/nemo_automodel/components/checkpoint/checkpointing.py
+++ b/nemo_automodel/components/checkpoint/checkpointing.py
@@ -520,6 +520,17 @@ def initialize_model_weights(
             and getattr(model.config, "n_routed_experts", None)  # is Nemotron V3
             and hasattr(model, "backbone")  # is HF remote code
         )
+        # HF's _init_weights calls init.zeros_(weight[padding_idx]) on
+        # nn.Embedding layers.  When the weight is a DTensor (TP-sharded),
+        # the integer index triggers a redistribute that fails.  Temporarily
+        # clear padding_idx so the zeroing is skipped, then restore it and
+        # zero the row via local-tensor ops instead.
+        has_padding_idx = any(
+            isinstance(mod, nn.Embedding)
+            and type(mod.weight).__name__ == "DTensor"
+            and getattr(mod, "padding_idx", None) is not None
+            for mod in model.modules()
+        )
         skip_initialize_weights = (
             model_class
             in [
@@ -528,6 +539,7 @@ def initialize_model_weights(
             ]
             or is_nemotron_v2
             or is_nemotron_v3_hf
+            or has_padding_idx
         )
         if not skip_initialize_weights:
             for _, module in model.named_modules():
@@ -538,7 +550,8 @@ def initialize_model_weights(
                 model.initialize_weights()
             else:
                 logging.warning(
-                    "Warning: Model does not have initialize_weights method. Requires custom initialization to be implemented."
+                    "Warning: Model does not have initialize_weights method."
+                    " Requires custom initialization to be implemented."
                 )
 
         if peft_init_method is not None: