vLLM/recipes/models/MiniMaxAI/MiniMax-M2.1.yaml

meta:
  title: "MiniMax-M2.1"
  slug: "minimax-m2.1"
  provider: "MiniMax"
  description: "MiniMax M2.1 MoE language model (230B total / 10B active) for coding, agent toolchains, and long-context reasoning — native FP8 checkpoint"
  date_added: 2025-12-20
  date_updated: 2026-04-17
  tasks:
    - text
  performance_headline: "Updated M2 series MoE with strong SWE-Bench and Terminal-Bench performance, 196K context"
  related_recipes: []
  hardware:
    h100: verified
    mi300x: verified
    mi325x: verified
    mi355x: verified

model:
  model_id: "MiniMaxAI/MiniMax-M2.1"
  min_vllm_version: "0.11.1"
  architecture: moe
  parameter_count: "230B"
  active_parameters: "10B"
  context_length: 196608
  base_args:
    - "--trust-remote-code"
  base_env: {}

features:
  tool_calling:
    description: "MiniMax M2 tool call parser with automatic tool choice"
    args:
      - "--tool-call-parser"
      - "minimax_m2"
      - "--enable-auto-tool-choice"
  reasoning:
    description: "MiniMax M2 reasoning parser for chain-of-thought extraction"
    args:
      - "--reasoning-parser"
      - "minimax_m2"

opt_in_features: []

variants:
  default:
    precision: fp8
    vram_minimum_gb: 276
    description: "Native FP8 checkpoint — 4x H200/H20/H100 or 4x A100/A800 for weights, plus KV cache headroom"

compatible_strategies:
  - single_node_tp
  - single_node_tep
  - single_node_dep
  - multi_node_tp
  - multi_node_tp_pp
  - multi_node_tep
  - multi_node_dep
  - pd_cluster

kv_offload_support:
  offloading_cpu: verified
  offloading_fs: verified

hardware_overrides:
  amd:
    extra_args:
      - "--tensor-parallel-size"
      - "2"
      - "--attention-backend"
      - "ROCM_AITER_FA"
    extra_env:
      VLLM_ROCM_USE_AITER: "1"
      VLLM_ROCM_SHUFFLE_KV_CACHE_LAYOUT: "1"

strategy_overrides:
  single_node_tp:
    # Pure TP=8 fails on the FP8 block-quantized gate_up weights:
    # intermediate_size / 8 = 192 is not divisible by block_n = 128.
    # Pin TP=4 on 8-GPU nodes; for full-node utilization use
    # single_node_tep (TP+EP) or single_node_dep (DP+EP).
    tp: 4

guide: |
  ## Overview

  [MiniMax-M2.1](https://huggingface.co/MiniMaxAI/MiniMax-M2.1) is part of the MiniMax
  M2 series of advanced MoE language models. It retains the M2 architecture (10B active,
  230B total) with improvements over the original M2 release. Supports 196K context per sequence.

  ## Prerequisites

  - **OS:** Linux
  - **Python:** 3.10 - 3.13
  - **NVIDIA:** compute capability >= 7.0; ~220 GB for weights + 240 GB per 1M context tokens
  - **AMD:** MI300X / MI325X / MI350X / MI355X with ROCm 7.0+

  ### Install vLLM (NVIDIA)

  ```bash
  uv venv
  source .venv/bin/activate
  uv pip install -U vllm --torch-backend auto
  ```

  ### Docker (dedicated M2-series image)

  ```bash
  docker run --gpus all \
    -p 8000:8000 \
    --ipc=host \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    vllm/vllm-openai:minimax27 MiniMaxAI/MiniMax-M2.1 \
        --tensor-parallel-size 4 \
        --tool-call-parser minimax_m2 \
        --reasoning-parser minimax_m2 \
        --enable-auto-tool-choice \
        --trust-remote-code
  ```

  ## Launching the Server

  ### NVIDIA — TP4

  ```bash
  vllm serve MiniMaxAI/MiniMax-M2.1 \
    --tensor-parallel-size 4 \
    --tool-call-parser minimax_m2 \
    --reasoning-parser minimax_m2 \
    --enable-auto-tool-choice \
    --trust-remote-code
  ```

  Pure TP8 is not supported. For >4 GPUs use DP+EP or TP+EP.

  ### TP4+EP (recommended for H100)

  ```bash
  vllm serve MiniMaxAI/MiniMax-M2.1 \
    --tensor-parallel-size 4 \
    --enable-expert-parallel \
    --tool-call-parser minimax_m2 \
    --reasoning-parser minimax_m2 \
    --enable-auto-tool-choice
  ```

  ### AMD ROCm — TP2

  ```bash
  VLLM_ROCM_USE_AITER=1 vllm serve MiniMaxAI/MiniMax-M2.1 \
    --tensor-parallel-size 2 \
    --tool-call-parser minimax_m2 \
    --reasoning-parser minimax_m2 \
    --enable-auto-tool-choice \
    --trust-remote-code
  ```

  ## Benchmarking

  ```bash
  vllm bench serve \
    --backend vllm \
    --model MiniMaxAI/MiniMax-M2.1 \
    --endpoint /v1/completions \
    --dataset-name random \
    --random-input 2048 \
    --random-output 1024 \
    --max-concurrency 10 \
    --num-prompt 100
  ```

  ## Troubleshooting

  - See [MiniMax-M2](./MiniMax-M2.yaml) for shared troubleshooting notes
    (latest vLLM fusion behavior, nightly vs stable, DeepGEMM, AITER).

  ## References

  - [Model card](https://huggingface.co/MiniMaxAI/MiniMax-M2.1)
  - [MiniMax](https://www.minimax.io/)