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A high-throughput and memory-efficient inference and serving engine for LLMs

[EP] Enable DBO with NIXL EP (#45275)

Signed-off-by: Itay Alroy <ialroy@nvidia.com>

Itay Alroy committed Jun 16, 2026 at 02:37 UTC 3afe659b6bb90b961bf09984166393824a893af9
2 files changed +8 -6
vllm/config/compilation.py
+1 -1
@@ -1197,7 +1197,7 @@ class CompilationConfig:
1197 "are optimized for prefill and are incompatible with CUDA Graphs. "
1198 "In order to use CUDA Graphs for decode-optimized workloads, "
1199 "use --all2all-backend with another option, such as "
1200 - "deepep_low_latency or allgather_reducescatter."
1200 + "deepep_low_latency, nixl_ep, or allgather_reducescatter."
1201 )
1202 self.cudagraph_mode = CUDAGraphMode.NONE
1203
vllm/config/vllm.py
+7 -5
@@ -1457,12 +1457,14 @@ class VllmConfig:
1457 assert a2a_backend in [
1458 "deepep_low_latency",
1459 "deepep_high_throughput",
1460 + "nixl_ep",
1461 ], (
1461 - "Microbatching currently only supports the deepep_low_latency and "
1462 - f"deepep_high_throughput all2all backend. {a2a_backend} is not "
1463 - "supported. To fix use --all2all-backend=deepep_low_latency or "
1464 - "--all2all-backend=deepep_high_throughput and install the DeepEP"
1465 - " kernels."
1462 + "Microbatching currently only supports the deepep_low_latency, "
1463 + "deepep_high_throughput, and nixl_ep all2all backends. "
1464 + f"{a2a_backend} is not supported. To fix use "
1465 + "--all2all-backend=deepep_low_latency, "
1466 + "--all2all-backend=deepep_high_throughput, or "
1467 + "--all2all-backend=nixl_ep and install the matching kernels."
1468 )
1469
1470 if not self.model_config.disable_cascade_attn: