diff --git a/vllm/v1/executor/multiproc_executor.py b/vllm/v1/executor/multiproc_executor.py
index b700f0631392..e715a1d767fa 100644
--- a/vllm/v1/executor/multiproc_executor.py
+++ b/vllm/v1/executor/multiproc_executor.py
@@ -597,17 +597,6 @@ def __init__(
         wrapper.init_worker(all_kwargs)
         self.worker = wrapper
 
-        scheduler_config = vllm_config.scheduler_config
-        self.use_async_scheduling = scheduler_config.async_scheduling
-        if self.use_async_scheduling:
-            self.async_output_queue: queue.Queue = queue.Queue()
-            self.async_output_copy_thread = Thread(
-                target=self.async_output_busy_loop,
-                daemon=True,
-                name="WorkerAsyncOutputCopy",
-            )
-            self.async_output_copy_thread.start()
-
         self.setup_proc_title_and_log_prefix(
             enable_ep=vllm_config.parallel_config.enable_expert_parallel
         )
@@ -622,6 +611,17 @@ def __init__(
             )
             self.worker.load_model()
 
+        scheduler_config = vllm_config.scheduler_config
+        self.use_async_scheduling = scheduler_config.async_scheduling
+        if self.use_async_scheduling:
+            self.async_output_queue: queue.Queue = queue.Queue()
+            self.async_output_copy_thread = Thread(
+                target=self.async_output_busy_loop,
+                daemon=True,
+                name="WorkerAsyncOutputCopy",
+            )
+            self.async_output_copy_thread.start()
+
         # Set block size based on the attention backends
         current_platform.update_block_size_for_backend(vllm_config)
 
@@ -911,6 +911,18 @@ def handle_output(self, output: Any):
 
     def async_output_busy_loop(self):
         """Entrypoint for the thread which handles outputs asynchronously."""
+
+        # set device to the worker device for the thread.
+        # a thread will not inherit the context of the main thread.
+        # when calling any cuda runtime functions, it will implicitly
+        # create a new cuda context on device 0, consuming extra memory.
+        # here we set the device to the worker device for the thread,
+        # enforcing the context to be the same as the main thread.
+        from vllm.platforms import current_platform
+
+        if hasattr(self.worker, "device"):
+            current_platform.set_device(self.worker.device)
+
         while True:
             output = self.async_output_queue.get()
             self.enqueue_output(output)