diff --git a/python/sglang/srt/managers/scheduler_pp_mixin.py b/python/sglang/srt/managers/scheduler_pp_mixin.py
index e70536e0199..77079c7763c 100644
--- a/python/sglang/srt/managers/scheduler_pp_mixin.py
+++ b/python/sglang/srt/managers/scheduler_pp_mixin.py
@@ -17,7 +17,12 @@
     get_logprob_from_pp_outputs,
 )
 from sglang.srt.model_executor.forward_batch_info import PPProxyTensors
-from sglang.srt.utils import DynamicGradMode, broadcast_pyobj, point_to_point_pyobj, require_mlp_sync
+from sglang.srt.utils import (
+    DynamicGradMode,
+    broadcast_pyobj,
+    point_to_point_pyobj,
+    require_mlp_sync,
+)
 
 logger = logging.getLogger(__name__)
 
@@ -161,7 +166,9 @@ def _pp_send_output_to_next_stage(
         pp_outputs: PPProxyTensors | None,
     ) -> List[P2PWork]:
         send_output_work = []
-        if self.pp_group.is_last_rank:
+        if self.pp_group.is_last_rank and (
+            self.server_args.pp_async_batch_depth == 0 or self.server_args.pp_size <= 2
+        ):
             # send ready PP output to rank 0
             if mbs[next_first_rank_mb_id] is not None:
                 q_event, pp_outputs_to_send = last_rank_comm_queue.popleft()
@@ -173,7 +180,10 @@ def _pp_send_output_to_next_stage(
                     )
         # send the outputs from the last round to let the next stage worker run post processing
         if not self.pp_group.is_last_rank:
-            if pp_outputs:
+            if pp_outputs and (
+                self.server_args.pp_size <= 2
+                or self.server_args.pp_async_batch_depth > 0
+            ):
                 with torch.profiler.record_function("send_res_dict_to_next_stage"):
                     send_output_work = self._pp_send_dict_to_next_stage(
                         pp_outputs.tensors,
@@ -354,6 +364,34 @@ def event_loop_pp(self: Scheduler):
                                 result.pp_hidden_states_proxy_tensors.tensors,
                                 async_send=True,
                             )
+                elif (
+                    self.server_args.pp_async_batch_depth > 0
+                    and mbs[mb_id] is not None
+                    and self.server_args.pp_size > 2
+                ):
+                    # send ready PP output to rank 0
+                    q_event, pp_outputs_to_send = last_rank_comm_queue.popleft()
+                    torch.cuda.current_stream().wait_event(q_event)
+                    with torch.profiler.record_function("send_res_dict_to_next_stage"):
+                        send_output_work = self._pp_send_dict_to_next_stage(
+                            pp_outputs_to_send.tensors,
+                            async_send=True,
+                        )
+                # send the outputs from the last round to let the next stage worker run post processing
+                if not self.pp_group.is_last_rank:
+                    if next_pp_outputs and (
+                        self.server_args.pp_size > 2
+                        and self.server_args.pp_async_batch_depth == 0
+                    ):
+                        self._pp_commit_comm_work(work=send_output_work)
+                        with torch.profiler.record_function(
+                            "send_res_dict_to_next_stage"
+                        ):
+                            send_output_work = self._pp_send_dict_to_next_stage(
+                                next_pp_outputs.tensors,
+                                async_send=True,
+                            )
+                            next_pp_outputs = None
 
                 # if self.delayed_weight_sync_fn:
                 #     self.delayed_weight_sync_fn()