diff --git a/vllm/model_executor/utils.py b/vllm/model_executor/utils.py
index d7eec818cbba..c27b1cf6ac7b 100644
--- a/vllm/model_executor/utils.py
+++ b/vllm/model_executor/utils.py
@@ -3,6 +3,7 @@
 
 import torch
 
+from vllm.platforms import current_platform
 from vllm.utils import seed_everything
 
 
@@ -28,4 +29,25 @@ def set_weight_attrs(
     for key, value in weight_attrs.items():
         assert not hasattr(
             weight, key), (f"Overwriting existing tensor attribute: {key}")
+
+        # NOTE(woosuk): During weight loading, we often do something like:
+        # narrowed_tensor = param.data.narrow(0, offset, len)
+        # narrowed_tensor.copy_(real_weight)
+        # expecting narrowed_tensor and param.data to share the same storage.
+        # However, on TPUs, narrowed_tensor will lazily propagate to the base
+        # tensor, which is param.data, leading to the redundant memory usage.
+        # This sometimes causes OOM errors during model loading. To avoid this,
+        # we sync the param tensor after its weight loader is called.
+        # TODO(woosuk): Remove this hack once we have a better solution.
+        if current_platform.is_tpu() and key == "weight_loader":
+            value = _make_synced_weight_loader(value)
         setattr(weight, key, value)
+
+
+def _make_synced_weight_loader(original_weight_loader):
+
+    def _synced_weight_loader(param, *args, **kwargs):
+        original_weight_loader(param, *args, **kwargs)
+        torch._sync(param)
+
+    return _synced_weight_loader