diff --git a/vllm/v1/worker/gpu_model_runner.py b/vllm/v1/worker/gpu_model_runner.py
index 5d49bbaf270bb..4f6cf9a350706 100644
--- a/vllm/v1/worker/gpu_model_runner.py
+++ b/vllm/v1/worker/gpu_model_runner.py
@@ -316,6 +316,12 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
         # Cached outputs.
         self._draft_token_ids: Optional[Union[list[list[int]],
                                               torch.Tensor]] = None
+        self.transfer_event = torch.cuda.Event()
+        self.sampled_token_ids_pinned_cpu = torch.empty(
+            (self.max_model_len, 1),
+            dtype=torch.int64,
+            device="cpu",
+            pin_memory=True)
 
     def _make_buffer(self, *args, dtype: torch.dtype) -> CpuGpuBuffer:
         return CpuGpuBuffer(*args,
@@ -1691,7 +1697,7 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
         max_gen_len = sampled_token_ids.shape[-1]
         if max_gen_len == 1:
             # No spec decode tokens.
-            valid_sampled_token_ids = sampled_token_ids.tolist()
+            valid_sampled_token_ids = self._to_list(sampled_token_ids)
         else:
             # Includes spec decode tokens.
             valid_sampled_token_ids = self.rejection_sampler.parse_output(
@@ -2219,7 +2225,7 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
                 - CUDAGraphMode.PIECEWISE: Piecewise cudagraph.
                 - CUDAGraphMode.FULL: Full cudagraph, attention metadata is
                     needed.
-            force_attention: If True, always create attention metadata. Used to 
+            force_attention: If True, always create attention metadata. Used to
                 warm up attention backend when mode is NONE.
             uniform_decode: If True, the batch is a uniform decode batch.
             skip_eplb: If True, skip EPLB state update.
@@ -3233,3 +3239,18 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
                     mamba_type=mamba_module.mamba_type)
 
         return kv_cache_spec
+
+    def _to_list(self, sampled_token_ids: torch.Tensor) -> list[list[int]]:
+        # This is a short term mitigation for issue mentioned in
+        # https://github.com/vllm-project/vllm/issues/22754.
+        # `tolist` would trigger a cuda wise stream sync, which
+        # would block other copy ops from other cuda streams.
+        # A cuda event sync would avoid such a situation. Since
+        # this is in the critical path of every single model
+        # forward loop, this has caused perf issue for a disagg
+        # setup.
+        pinned = self.sampled_token_ids_pinned_cpu[:sampled_token_ids.shape[0]]
+        pinned.copy_(sampled_token_ids, non_blocking=True)
+        self.transfer_event.record()
+        self.transfer_event.synchronize()
+        return pinned.tolist()