diff --git a/vllm/v1/worker/gpu_model_runner.py b/vllm/v1/worker/gpu_model_runner.py
index 9d0f26266f0c5..3539f75612050 100644
--- a/vllm/v1/worker/gpu_model_runner.py
+++ b/vllm/v1/worker/gpu_model_runner.py
@@ -1903,7 +1903,7 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
                 **self._init_model_kwargs(num_scheduled_tokens),
                 **self._extract_mm_kwargs(scheduler_output),
             }
-        elif (self.enable_prompt_embeds and get_pp_group().is_first_rank):
+        elif self.enable_prompt_embeds and get_pp_group().is_first_rank:
             # Get the input embeddings for the tokens that are not input embeds,
             # then put them into the appropriate positions.
             # TODO(qthequartermasterman): Since even when prompt embeds are
@@ -2125,6 +2125,21 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
             invalid_req_indices,
         )
 
+    @contextmanager
+    def synchronize_input_prep(self):
+        if self.prepare_inputs_event is None:
+            yield
+            return
+
+        # Ensure prior step has finished with reused CPU tensors.
+        # This is required in the async scheduling case because
+        # the CPU->GPU transfer happens async.
+        self.prepare_inputs_event.synchronize()
+        try:
+            yield
+        finally:
+            self.prepare_inputs_event.record()
+
     @torch.inference_mode()
     def execute_model(
         self,
@@ -2132,33 +2147,28 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
         intermediate_tensors: Optional[IntermediateTensors] = None,
     ) -> Union[ModelRunnerOutput, AsyncModelRunnerOutput, IntermediateTensors]:
         with record_function_or_nullcontext("Preprocess"):
-            self._update_states(scheduler_output)
-            if not scheduler_output.total_num_scheduled_tokens:
-                if not has_kv_transfer_group():
-                    # Return empty ModelRunnerOutput if there's no work to do.
-                    return EMPTY_MODEL_RUNNER_OUTPUT
-                return self.kv_connector_no_forward(scheduler_output,
-                                                    self.vllm_config)
-            if self.cache_config.kv_sharing_fast_prefill:
-                assert not self.input_batch.num_prompt_logprobs, (
-                    "--kv-sharing-fast-prefill produces incorrect logprobs for "
-                    "prompt tokens, tokens, please disable it when the requests"
-                    " need prompt logprobs")
+            with self.synchronize_input_prep():
+                # Update persistent batch states.
+                self._update_states(scheduler_output)
+
+                if not scheduler_output.total_num_scheduled_tokens:
+                    if not has_kv_transfer_group():
+                        # Return empty ModelRunnerOutput if no work to do.
+                        return EMPTY_MODEL_RUNNER_OUTPUT
+                    return self.kv_connector_no_forward(
+                        scheduler_output, self.vllm_config)
+                if self.cache_config.kv_sharing_fast_prefill:
+                    assert not self.input_batch.num_prompt_logprobs, (
+                        "--kv-sharing-fast-prefill produces incorrect "
+                        "logprobs for prompt tokens, tokens, please disable "
+                        "it when the requests need prompt logprobs")
 
-            if self.prepare_inputs_event is not None:
-                # Ensure prior step has finished with reused CPU tensors.
-                self.prepare_inputs_event.synchronize()
-            try:
                 # Prepare the decoder inputs.
                 (attn_metadata, logits_indices, spec_decode_metadata,
                  num_scheduled_tokens_np, spec_decode_common_attn_metadata,
                  max_query_len, ubatch_slices, num_tokens_after_padding
                  ) = self._prepare_inputs(scheduler_output)
 
-            finally:
-                if self.prepare_inputs_event is not None:
-                    self.prepare_inputs_event.record()
-
             (
                 num_scheduled_tokens,
                 num_input_tokens,