diff --git a/vllm/worker/tpu_model_runner.py b/vllm/worker/tpu_model_runner.py
index 14f14e40b4c0b..01daa64b5a32f 100644
--- a/vllm/worker/tpu_model_runner.py
+++ b/vllm/worker/tpu_model_runner.py
@@ -516,27 +516,19 @@ class TPUModelRunner(ModelRunnerBase[ModelInputForTPU]):
             raise ValueError(
                 "TPUModelRunner does not support multi-step execution.")
 
-        def _execute_model(*args, clone: bool = False) -> torch.Tensor:
+        def _execute_model(*args):
             """Move input args from CPU to device and execute the model."""
 
-            def _copy_to_device(x: torch.Tensor) -> torch.Tensor:
-                if clone:
-                    # When x is a slice of a CPU tensor, XLA may copy the whole
-                    # original tensor to TPU instead of only copying x.
-                    # To avoid this, we copy x after cloning.
-                    x = x.clone()
-                return x.to(self.device)
-
             new_args = []
             for arg in args:
                 if isinstance(arg, torch.Tensor):
-                    arg = _copy_to_device(arg)
+                    arg = arg.to(self.device)
                 elif isinstance(arg, AttentionMetadata):
-                    arg.slot_mapping = _copy_to_device(arg.slot_mapping)
+                    arg.slot_mapping = arg.slot_mapping.to(self.device)
                     if getattr(arg, "block_tables", None) is not None:
-                        arg.block_tables = _copy_to_device(arg.block_tables)
+                        arg.block_tables = arg.block_tables.to(self.device)
                     if getattr(arg, "context_lens", None) is not None:
-                        arg.context_lens = _copy_to_device(arg.context_lens)
+                        arg.context_lens = arg.context_lens.to(self.device)
                 new_args.append(arg)
             return self.model(*new_args)
 
@@ -563,13 +555,9 @@ class TPUModelRunner(ModelRunnerBase[ModelInputForTPU]):
                 output_token_ids = _execute_model(
                     model_input.token_ids[None, start_idx:end_idx],
                     model_input.position_ids[None, start_idx:end_idx],
-                    model_input.attn_metadata,
-                    model_input.input_lens[i:i + 1],
-                    model_input.t[i:i + 1],
-                    model_input.p[i:i + 1],
-                    model_input.num_samples,
-                    kv_caches,
-                    clone=True)
+                    model_input.attn_metadata, model_input.input_lens[i:i + 1],
+                    model_input.t[i:i + 1], model_input.p[i:i + 1],
+                    model_input.num_samples, kv_caches)
                 # Retrieve the outputs to CPU.
                 next_token_ids += output_token_ids.cpu().tolist()
                 start_idx = end_idx