diff --git a/llmfoundry/callbacks/hf_checkpointer.py b/llmfoundry/callbacks/hf_checkpointer.py
index e8866b9f93..bad9084235 100644
--- a/llmfoundry/callbacks/hf_checkpointer.py
+++ b/llmfoundry/callbacks/hf_checkpointer.py
@@ -470,33 +470,6 @@ def _save_checkpoint(self, state: State, logger: Logger):
         #     return state_dict
 
         # Add hook to move tensors to cpu to avoid CUDA OOM
-        # def tensor_hook(
-        #     module: nn.Module,
-        #     state_dict: Dict[str, Any],
-        #     prefix: str,
-        #     *args: Any,
-        # ) -> Dict[str, Any]:
-        #     dtensor_fqns = []
-        #     for fqn in state_dict.keys():
-        #         tensor = state_dict[fqn]
-        #         if isinstance(tensor, DTensor):
-        #             dtensor_fqns.append(fqn)
-        #             tensor = tensor.full_tensor()  # type: ignore
-        #             if dist.get_global_rank() == 0:
-        #                 # Offload any DTensors to CPU
-        #                 if cpu_offload:
-        #                     tensor = tensor.cpu()
-        #                     tensor = tensor.to(dtype=self.dtype)
-        #                 state_dict[fqn] = tensor
-        #             else:
-        #                 state_dict[fqn] = None
-        #         elif isinstance(tensor, torch.Tensor):
-        #             state_dict[fqn] = tensor.to(dtype=self.dtype)
-        #         del tensor
-        #     if dist.get_global_rank() != 0:
-        #         state_dict = {}
-        #     return state_dict
-    
         def tensor_hook(
             module: nn.Module,
             state_dict: Dict[str, Any],
@@ -510,18 +483,45 @@ def tensor_hook(
                     dtensor_fqns.append(fqn)
                     tensor = tensor.full_tensor()  # type: ignore
                     if dist.get_global_rank() == 0:
+                        # Offload any DTensors to CPU
                         if cpu_offload:
                             tensor = tensor.cpu()
+                            tensor = tensor.to(dtype=self.dtype)
                         state_dict[fqn] = tensor
+                    else:
+                        state_dict[fqn] = None
+                elif isinstance(tensor, torch.Tensor):
+                    state_dict[fqn] = tensor.to(dtype=self.dtype)
+                del tensor
             if dist.get_global_rank() != 0:
-                for fqn in dtensor_fqns:
-                    del state_dict[fqn]
+                state_dict = {}
+            return state_dict
+    
+        # def tensor_hook(
+        #     module: nn.Module,
+        #     state_dict: Dict[str, Any],
+        #     prefix: str,
+        #     *args: Any,
+        # ) -> Dict[str, Any]:
+        #     dtensor_fqns = []
+        #     for fqn in state_dict.keys():
+        #         tensor = state_dict[fqn]
+        #         if isinstance(tensor, DTensor):
+        #             dtensor_fqns.append(fqn)
+        #             tensor = tensor.full_tensor()  # type: ignore
+        #             if dist.get_global_rank() == 0:
+        #                 if cpu_offload:
+        #                     tensor = tensor.cpu()
+        #                 state_dict[fqn] = tensor
+        #     if dist.get_global_rank() != 0:
+        #         for fqn in dtensor_fqns:
+        #             del state_dict[fqn]
 
-            for fqn in state_dict.keys():
-                if isinstance(state_dict[fqn], torch.Tensor):
-                    state_dict[fqn] = state_dict[fqn].to(dtype=self.dtype)
+        #     for fqn in state_dict.keys():
+        #         if isinstance(state_dict[fqn], torch.Tensor):
+        #             state_dict[fqn] = state_dict[fqn].to(dtype=self.dtype)
 
-            return state_dict
+        #     return state_dict
 
         hooks = []
         for _, module in state_dict_model.named_modules():