--- a/python/sglang/srt/distributed/device_communicators/pynccl.py
+++ b/python/sglang/srt/distributed/device_communicators/pynccl.py
@@ -3,7 +3,10 @@
 # Adapted from https://github.com/vllm-project/vllm/blob/v0.6.4.post1/vllm/distributed/device_communicators/pynccl.py
 
 import logging
-from contextlib import contextmanager
+import json
+import os
+import weakref
+from contextlib import contextmanager, nullcontext
 from typing import Optional, Union
 
 # ===================== import region =====================
@@ -27,6 +30,25 @@
 logger = logging.getLogger(__name__)
 
 
+_roce_runtimes = weakref.WeakSet()
+
+
+def synchronize_and_check_roce(copy_done):
+    """Finish this result before checking transport health or consuming output."""
+    if copy_done is not None:
+        copy_done.synchronize()
+    elif _roce_runtimes:
+        # Non-overlap paths have no copy event. Complete all forward streams
+        # before reading the host failure word; an early check is insufficient.
+        torch.cuda.synchronize()
+    for runtime in tuple(_roce_runtimes):
+        runtime.check_health()
+        if not getattr(runtime, "_sglang_post_result_logged", False):
+            logger.info("ROCE_POST_RESULT_HEALTH %s", json.dumps(runtime.stats()))
+            runtime._sglang_post_result_logged = True
+
+
+
 class PyNcclCommunicator:
     def __init__(
         self,
@@ -34,6 +56,7 @@
         device: Union[int, str, torch.device],
         library_path: Optional[str] = None,
         is_symmetric_memory_enabled: bool = False,
+        roce_enabled: bool = False,
     ):
         """
         Args:
@@ -60,6 +83,8 @@
             self.world_size = group.world_size
 
         self.group = group
+        self.roce = None
+        self._roce_route_logged = False
 
         # if world_size == 1, no need to create communicator
         if self.world_size == 1:
@@ -131,11 +156,45 @@
             warmup_stream.synchronize()
             del data
 
+        if roce_enabled and os.environ.get("SGLANG8_ROCE_ALLREDUCE", "0") == "1":
+            if self.world_size != 8 or isinstance(group, StatelessProcessGroup):
+                raise RuntimeError("SG17 RoCEnante requires the TP8 Gloo exchange group")
+            from b12x.comm import roce
+
+            self.roce = roce.AllReduce.from_exchange_group(
+                exchange_group=group, device=self.device,
+                max_size=512 * 1024, max_gather_bytes=0,
+            )
+            self.roce.prepare((torch.bfloat16, torch.float32))
+            if len(self.roce.hca_names) != 2:
+                raise RuntimeError("SG17 RoCEnante requires two active RoCE interfaces")
+            _roce_runtimes.add(self.roce)
+            logger.info("ROCE_TP8_READY %s", json.dumps(self.roce.stats()))
+
+
         # by default it is disabled, e.g. in profiling models and prefill phase.
         # to use it, use under `with obj.change_state(enable=True)`, usually
         # when we are using CUDA graph.
         self.disabled = True
 
+    def roce_capture(self, stream):
+        return self.roce.capture(stream=stream) if self.roce is not None else nullcontext()
+
+    def _should_roce(self, tensor, op):
+        if self.roce is None:
+            return False
+        self.roce.check_health()
+        eligible = (
+            op == ReduceOp.SUM
+            and tensor.dtype in (torch.bfloat16, torch.float32)
+            and self.roce.should_allreduce(tensor)
+        )
+        if eligible and not self._roce_route_logged:
+            logger.info("ROCE_TP8_ROUTE rank=%d dtype=%s bytes=%d", self.rank,
+                        tensor.dtype, tensor.numel() * tensor.element_size())
+            self._roce_route_logged = True
+        return eligible
+
     def _resolve_stream(self) -> torch.cuda.Stream:
         """Return the current device stream used for NCCL calls."""
         return get_current_device_stream_fast()
@@ -151,6 +210,9 @@
             f"but the input tensor is on {tensor.device}"
         )
         stream = self._resolve_stream()
+        if self._should_roce(tensor, op):
+            self.roce.all_reduce(tensor, out=tensor, stream=stream)
+            return
         self.nccl.ncclAllReduce(
             buffer_type(tensor.data_ptr()),
             buffer_type(tensor.data_ptr()),
@@ -178,6 +240,8 @@
             out_tensor = torch.empty_like(in_tensor)
 
         stream = self._resolve_stream()
+        if self._should_roce(in_tensor, op):
+            return self.roce.all_reduce(in_tensor, out=out_tensor, stream=stream)
         self.nccl.ncclAllReduce(
             buffer_type(in_tensor.data_ptr()),  # sendbuff
             buffer_type(out_tensor.data_ptr()),  # recvbuff - DIFFERENT pointer
--- a/python/sglang/srt/distributed/parallel_state.py
+++ b/python/sglang/srt/distributed/parallel_state.py
@@ -458,6 +458,7 @@
                 group=self.cpu_group,
                 device=self.device,
                 is_symmetric_memory_enabled=self.is_symmetric_memory_enabled(),
+                roce_enabled=(group_name == "tp"),
             )
 
         self.pymscclpp_comm: Optional[PyMscclppCommunicator] = None
@@ -644,7 +645,10 @@
                 maybe_pymscclpp_context = nullcontext()
             else:
                 maybe_pymscclpp_context = pymscclpp_comm.change_state(enable=True)
-            with maybe_pynccl_context, maybe_pymscclpp_context:
+            maybe_roce_context = (
+                pynccl_comm.roce_capture(stream) if pynccl_comm else nullcontext()
+            )
+            with maybe_pynccl_context, maybe_pymscclpp_context, maybe_roce_context:
                 yield graph_capture_context
 
     def all_reduce(self, input_: torch.Tensor) -> torch.Tensor:
@@ -1850,6 +1854,8 @@
             torch.distributed.destroy_process_group(self.cpu_group)
             self.cpu_group = None
         if self.pynccl_comm is not None:
+            if self.pynccl_comm.roce is not None:
+                self.pynccl_comm.roce.close()
             self.pynccl_comm = None
         if self.pymscclpp_comm is not None:
             self.pymscclpp_comm.destroy()
--- a/python/sglang/srt/managers/scheduler_components/batch_result_processor.py
+++ b/python/sglang/srt/managers/scheduler_components/batch_result_processor.py
@@ -12,6 +12,8 @@
 )
 
 import torch
+
+from sglang.srt.distributed.device_communicators.pynccl import synchronize_and_check_roce
 
 from sglang.srt.disaggregation.utils import DisaggregationMode
 from sglang.srt.environ import envs
@@ -259,8 +261,7 @@
         self.token_to_kv_pool_allocator.free_group_begin()
 
         if self.is_generation:
-            if result.copy_done is not None:
-                result.copy_done.synchronize()
+            synchronize_and_check_roce(result.copy_done)
             auxiliary_output_starts = self.snapshot_auxiliary_output_starts(
                 batch, result
             )
@@ -412,8 +413,7 @@
                 )
 
         else:  # embedding or reward model
-            if result.copy_done is not None:
-                result.copy_done.synchronize()
+            synchronize_and_check_roce(result.copy_done)
 
             embeddings = self._convert_embeddings(result=result)
             phs = result.pooled_hidden_states
@@ -836,8 +836,7 @@
         is_decode = batch.forward_mode.is_decode()
         if not (is_decode or batch.forward_mode.is_extend()):
             return
-        if result.copy_done is not None:
-            result.copy_done.synchronize()
+        synchronize_and_check_roce(result.copy_done)
         next_token_ids = result.next_token_ids.tolist()
 
         if not is_decode:
@@ -879,8 +878,7 @@
         batch: ScheduleBatch,
         result: GenerationBatchResult,
     ):
-        if result.copy_done is not None:
-            result.copy_done.synchronize()
+        synchronize_and_check_roce(result.copy_done)
 
         self.output_streamer._stream_output_generation(
             batch.reqs, batch.return_logprob, is_idle_batch=True
@@ -891,8 +889,7 @@
         batch: ScheduleBatch,
         result: GenerationBatchResult,
     ):
-        if result.copy_done is not None:
-            result.copy_done.synchronize()
+        synchronize_and_check_roce(result.copy_done)
         auxiliary_output_starts = self.snapshot_auxiliary_output_starts(batch, result)
         auxiliary_output = result.auxiliary_host_output
         if result.routed_experts_output is not None: