diff --git a/vllm/v1/worker/gpu_worker.py b/vllm/v1/worker/gpu_worker.py
index 6411874883ef2..3c828a2110973 100644
--- a/vllm/v1/worker/gpu_worker.py
+++ b/vllm/v1/worker/gpu_worker.py
@@ -15,7 +15,8 @@ from vllm.config import VllmConfig
 from vllm.distributed import (ensure_model_parallel_initialized,
                               init_distributed_environment,
                               set_custom_all_reduce)
-from vllm.distributed.kv_transfer import ensure_kv_transfer_initialized
+from vllm.distributed.kv_transfer import (ensure_kv_transfer_initialized,
+                                          has_kv_transfer_group)
 from vllm.distributed.parallel_state import get_pp_group, get_tp_group
 from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
@@ -333,19 +334,20 @@ class Worker(WorkerBase):
             assert isinstance(output, IntermediateTensors)
             get_pp_group().send_tensor_dict(output.tensors,
                                             all_gather_group=get_tp_group())
+            if not has_kv_transfer_group():
+                return None
 
             # In case of PP with kv transfer, we need to pass through the
             # finished_sending and finished_recving buffers.
-            empty_output = EMPTY_MODEL_RUNNER_OUTPUT
+            new_output = EMPTY_MODEL_RUNNER_OUTPUT
             if output.finished_sending or output.finished_recving:
-                empty_output = copy.copy(empty_output)
-                empty_output.finished_sending = output.finished_sending
-                empty_output.finished_recving = output.finished_recving
-            output = empty_output
+                new_output = copy.copy(new_output)
+                new_output.finished_sending = output.finished_sending
+                new_output.finished_recving = output.finished_recving
+            output = new_output
 
         assert isinstance(output, ModelRunnerOutput)
-        # return output only from the driver worker
-        return output if self.is_driver_worker else None
+        return output
 
     def profile(self, is_start: bool = True):
         if self.profiler is None: