vllm-project/vllm · error · RuntimeError

NIXL EPLB nbytes_per_expert mismatch for {key} with rank {pe

Error message

NIXL EPLB nbytes_per_expert mismatch for {key} with rank {peer}: local={local_stride}, peer={peer_stride}

What it means

Error "NIXL EPLB nbytes_per_expert mismatch for {key} with rank {peer}: local={local_stride}, peer={peer_stride}" thrown in vllm-project/vllm.

Source

Thrown at vllm/distributed/eplb/eplb_communicator.py:495

        torch.distributed.all_gather_object(
            gathered_meta, local_meta, group=self._cpu_group
        )

        local_keys = set(local_meta.keys())
        for peer in self._remote_agents:
            peer_meta = gathered_meta[peer]
            assert peer_meta is not None
            peer_keys = set(peer_meta.keys())
            if peer_keys != local_keys:
                raise RuntimeError(
                    f"NIXL EPLB metadata key mismatch with rank {peer}: "
                    f"local={sorted(local_keys)}, peer={sorted(peer_keys)}"
                )
            for key in local_keys:
                _, local_stride, _ = local_meta[key]
                _, peer_stride, _ = peer_meta[key]
                if local_stride != peer_stride:
                    raise RuntimeError(
                        f"NIXL EPLB nbytes_per_expert mismatch for {key} "
                        f"with rank {peer}: "
                        f"local={local_stride}, peer={peer_stride}"
                    )
            self._remote_send_meta[peer] = peer_meta

    def _wait_for_all_transfers(self, handles: list[int]) -> None:
        pending = set(handles)
        while pending:
            completed: list[int] = []
            for handle in pending:
                state = self._nixl_wrapper.check_xfer_state(handle)
                if state == "DONE":
                    completed.append(handle)
                    continue
                if state != "PROC":
                    raise RuntimeError(f"NIXL transfer failed with state={state}")
            for handle in completed:

View on GitHub (pinned to c794754062)

Solutions

1. Ensure expert tensor shapes (nbytes_per_expert) are identical across all ranks for the key named in the error.

When it happens

Trigger: Raised at vllm/distributed/eplb/eplb_communicator.py:495 when validation fails: NIXL EPLB nbytes_per_expert mismatch with peer rank. Typically triggered by an incompatible or incomplete vLLM configuration, an unsupported platform/backend combination, or a runtime resource/dependency that is missing.

Common situations: Commonly encountered at vllm/distributed/eplb/eplb_communicator.py:495 during vLLM startup/config validation or runtime setup when: (1) conflicting CLI flags or config fields are combined, (2) the current platform (CUDA/ROCm/CPU/XPU) or installed optional packages do not support the requested feature, or (3) a required value is absent or out of range. Resolve by correcting the configuration as described in the message, or by selecting a supported alternative.

AI-assisted analysis of vllm-project/vllm@c794754062 (2026-08-14). Data as JSON: /api/errors/60ccd83cff175d51. Report an issue: GitHub.