XiaomiMiMo/MiMo-V2.6-Pro-RL

Xiaomi's flagship omnimodal MoE (1.02T total / 42B active) with hybrid attention, FP8-compute/mxfp4-stored weights, 1M context, and a DFlash speculative decoder — the default MOPD checkpoint fixes the RL release's tool-call repetition

View on HuggingFace

moe1T / 42B1,048,576 ctxvLLM 0.31.0+textmultimodal

Guide

Overview

This recipe serves the MiMo-V2.6-Pro family from two checkpoints on the Variant row:

MOPD (default) — XiaomiMiMo/MiMo-V2.6-Pro-MOPD, the MOPD2-distilled upgrade and the recommended starting point for new deployments. It fixes the RL release's most visible agentic failure mode — tool-call repetition, where the model keeps re-issuing the same or highly similar tool calls while making no progress — with benchmark quality unchanged, folded into a regular MOPD2 pass via a short specialized-teacher run (diagnosis blog).
RL (original) — XiaomiMiMo/MiMo-V2.6-Pro-RL, the 2026-09-22 RL release, kept for reproducing earlier results. It can get stuck re-issuing the same tool calls in agentic workloads; prefer MOPD unless you are reproducing a specific earlier run.

MOPD2 (Technical Report §5.6) is multi-teacher on-policy distillation: domain-specialized mixRL and SFT teachers supervise the student on three on-policy streams (standard MOPD, teacher-prefix OPD, and SFT-prefix OPD).

The architecture is shared by both checkpoints: a sparse MoE model with 1.02T total parameters and 42B active per token — 70 layers (1 dense + 69 MoE) with 384 routed experts (top-8), hybrid attention (sliding-window 128 and global attention at a 6:1 ratio), and a 5-layer DFlash-style MTP drafter that predicts 7 tokens per pass. The model is natively omnimodal (text, image, video, audio via a 681M-param MiMo ViT and audio encoders) and supports up to 1M tokens of context.

Weights are stored as mxfp4 and computed as FP8 (block-wise e4m3, 128x128), so the checkpoint is 566 GB on disk. Loading this mixed storage format requires vLLM with the MiMo V2 mxfp4/bf16-router support (vllm-project/vllm#57784), first shipped in the v0.31.0 stable release.

Prerequisites

Hardware: 8x H200 (TP8), 4x MI355X (TP4), or equivalent aggregate VRAM (>= 680 GB)

Pull the vLLM docker image

vLLM 0.31.0 is the first stable release that can load the mxfp4-stored weights (vllm-project/vllm#57784); earlier stable releases cannot:

docker pull vllm/vllm-openai:v0.31.0

A vLLM nightly wheel built after 2026-09-20 also works (uv pip install -U vllm --extra-index-url https://wheels.vllm.ai/nightly/cu130).

The Hugging Face model cards point at vllm/vllm-openai:mimov25-cu129, the V2.5-era image; that tag predates the mxfp4-stored-weight support linked above, so use the v0.31.0 image recommended above.

This release also registers the mimo tool-call and reasoning parsers with the Rust frontend (vllm-project/vllm#57933).

Launch command

Single-node TP8 (H200). Selecting the RL (original) variant in the builder swaps only the checkpoint id (XiaomiMiMo/MiMo-V2.6-Pro-RL); everything else is identical:

vllm serve XiaomiMiMo/MiMo-V2.6-Pro-MOPD \
  --tensor-parallel-size 8 \
  --trust-remote-code \
  --gpu-memory-utilization 0.95 \
  --max-model-len auto \
  --reasoning-parser mimo \
  --tool-call-parser mimo \
  --enable-auto-tool-choice \
  --generation-config vllm

AMD (MI355X, TP4)

VLLM_ROCM_USE_AITER=1 \
VLLM_ROCM_QUICK_REDUCE_QUANTIZATION=INT4 \
vllm serve XiaomiMiMo/MiMo-V2.6-Pro-MOPD \
  --tensor-parallel-size 4 \
  --trust-remote-code \
  --gpu-memory-utilization 0.93 \
  --kv-cache-dtype fp8 \
  --max-model-len auto \
  --reasoning-parser mimo \
  --tool-call-parser mimo \
  --enable-auto-tool-choice \
  --generation-config vllm

MXFP4 MoE weights run natively on CDNA 4, so no load-time requantization is needed. VLLM_ROCM_USE_AITER=1 is off by default and is required to reach the AITER MXFP4 MoE and FP8 block-scaled GEMM kernels. Attention resolves to TRITON_ATTN_DIFFKV, the only backend covering this model's asymmetric head dims (QK 192 / V 128) together with attention sinks and sliding-window attention.

On MI355X, Tensor Parallel defaults to TP4: 4x288 GiB clears the 680 GiB requirement, so TP4 halves the GPUs per server and doubles the servers per node. For TP8, pass --tensor-parallel-size 8.

The checkpoint pre-shards its fused QKV into num_key_value_heads (8) chunks, each carrying its own FP8 scales. TP8 consumes them directly; TP4 merges two chunks per rank, which vLLM handles by dequantizing, reordering and re-quantizing at load time. TP4 costs no accuracy.

With DFlash speculative decoding (drafter ships inside the checkpoint). vLLM does not resolve a <repo>/dflash Hub subfolder as the draft model — model must be a concrete local path to the dflash/ directory inside the downloaded snapshot. Resolve it per checkpoint (run hf download <checkpoint> first if it is not cached yet):

# MOPD variant
DFLASH_DIR=$(ls -d ~/.cache/huggingface/hub/models--XiaomiMiMo--MiMo-V2.6-Pro-MOPD/snapshots/*/dflash)
# RL (original) variant
# DFLASH_DIR=$(ls -d ~/.cache/huggingface/hub/models--XiaomiMiMo--MiMo-V2.6-Pro-RL/snapshots/*/dflash)
echo "$DFLASH_DIR"

vllm serve XiaomiMiMo/MiMo-V2.6-Pro-MOPD \
  --tensor-parallel-size 8 \
  --trust-remote-code \
  --gpu-memory-utilization 0.95 \
  --max-model-len auto \
  --speculative-config "{\"method\":\"dflash\",\"model\":\"$DFLASH_DIR\",\"num_speculative_tokens\":7}" \
  --compilation-config '{"cudagraph_mode":"FULL_DECODE_ONLY"}' \
  --reasoning-parser mimo \
  --tool-call-parser mimo \
  --enable-auto-tool-choice \
  --generation-config vllm

The drafter inherits the target's tensor-parallel size by default. To pin it explicitly, add "draft_tensor_parallel_size": 8 to the speculative config (useful if you want the draft on fewer GPUs than the target, or to silence mis-detection on asymmetric topologies).

Client Usage

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "XiaomiMiMo/MiMo-V2.6-Pro-MOPD",
    "messages": [{"role": "user", "content": "Hello MiMo!"}],
    "temperature": 1.0,
    "top_p": 0.95,
    "chat_template_kwargs": {"enable_thinking": true}
  }'

Recommended sampling: temperature=1.0, top_p=0.95. Set "enable_thinking": false (or omit the kwargs) to disable thinking mode.