# MiniMaxAI/MiniMax-M2.1 — vLLM serving recipe

> MiniMax M2.1 MoE language model (230B total / 10B active) for coding, agent toolchains, and long-context reasoning — native FP8 checkpoint

Configuration: hardware `h200` (8× H200) · strategy `single_node_tp` (Tensor Parallel) · variant `default` (FP8) · features `tool_calling`, `reasoning`

- Interactive page: https://vllm-recipes-cimvhk5sf-inferact-inc.vercel.app/MiniMaxAI/MiniMax-M2.1
- This configuration as JSON: https://vllm-recipes-cimvhk5sf-inferact-inc.vercel.app/MiniMaxAI/MiniMax-M2.1.json?hardware=h200

The commands below were rendered for exactly this configuration and take precedence over any example in the guide.

## Other configurations

Change the query string (the same parameters as the page URL) and fetch this URL again. Unknown or incompatible values fall back to the recipe default, so check the Configuration line.

- `hardware`: `h100`, `h200`, `b200`, `gb200`, `b300`, `gb300`, `mi300x`, `mi325x`, `mi350x`, `mi355x`
- `variant`: `default`
- `strategy`: `single_node_tp`, `single_node_tep`, `single_node_dep`, `multi_node_tp`, `multi_node_tp_pp`, `multi_node_tep`, `multi_node_dep`, `pd_cluster` (multi-node strategies also need `nodes=<n>`)
- `features`: comma-separated list of `tool_calling`, `reasoning` (default: `tool_calling`, `reasoning`)

## Install

Requires vLLM >= 0.11.1.

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

Docker image: `vllm/vllm-openai:latest`

## Serve

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

Docker equivalent:

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

## Guide

> Background reference. Commands in it are examples and may target another configuration — use the Serve section above.

### 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/)