diff --git a/python/sglang/srt/managers/tp_worker_overlap_thread.py b/python/sglang/srt/managers/tp_worker_overlap_thread.py
index 5d78b97ce43..8b27d2a69a9 100644
--- a/python/sglang/srt/managers/tp_worker_overlap_thread.py
+++ b/python/sglang/srt/managers/tp_worker_overlap_thread.py
@@ -55,7 +55,7 @@ def __init__(
             (self.max_running_requests * 5,), dtype=torch.int32, device=self.device
         )
 
-        # Launch a thread
+        # Launch threads
         self.input_queue = Queue()
         self.output_queue = Queue()
         self.forward_stream = torch.cuda.Stream()
@@ -64,6 +64,12 @@ def __init__(
         )
         self.forward_thread.start()
 
+        self.copy_queue = Queue()
+        self.copy_thread = threading.Thread(
+            target=self.copy_thread_func,
+        )
+        self.copy_thread.start()
+
     def get_worker_info(self):
         return self.worker.get_worker_info()
 
@@ -86,7 +92,10 @@ def forward_thread_func(self):
     @torch.inference_mode()
     def forward_thread_func_(self):
         while True:
+            self.has_inflight_batch = False
             model_worker_batch, future_token_ids_ct = self.input_queue.get()
+            self.has_inflight_batch = True
+            self.launch_event = threading.Event()
 
             # Resolve future tokens in the input
             input_ids = model_worker_batch.input_ids
@@ -100,6 +109,7 @@ def forward_thread_func_(self):
             logits_output, next_token_ids = self.worker.forward_batch_generation(
                 model_worker_batch
             )
+            self.launch_event.set()
 
             # Update the future token ids map
             bs = len(model_worker_batch.seq_lens)
@@ -113,13 +123,23 @@ def forward_thread_func_(self):
                 torch.int32
             )
 
-            # Set the result
-            next_token_ids = next_token_ids.tolist()
-            assert logits_output.next_token_logprobs is None, "Not supported"
-            self.output_queue.put((None, next_token_ids))
+            next_token_ids = next_token_ids.to("cpu", non_blocking=True)
+            copy_event = torch.cuda.Event(blocking=True)
+            copy_event.record()
+            self.copy_queue.put((copy_event, next_token_ids))
+
+    def copy_thread_func(self):
+        while True:
+            copy_event, next_token_ids = self.copy_queue.get()
+            while not copy_event.query():
+                time.sleep(1e-5)
+            self.output_queue.put((None, next_token_ids.tolist()))
 
     def resulve_batch_result(self, bid: int):
         logits_output, next_token_ids = self.output_queue.get()
+        if self.has_inflight_batch:
+            # Wait until the batch is launched
+            self.launch_event.wait()
         return logits_output, next_token_ids
 
     def forward_batch_generation(self, model_worker_batch: ModelWorkerBatch):