Version	26.05.01
Calibration	STEM and Agentic
Languages	`EN` `ZH` `HI` `AR` `RU` `JA` `KO` `NL` `FR` `ES`
Model Size	258.02 GB
Contact	Email

Serving with vLLM

This checkpoint needs a patched vLLM (MiniMax-M3 compressed-tensors support). The patch is Python-only, so it installs on top of upstream's precompiled binaries — no CUDA compilation.

Install

bash

# uv (skip if already installed)
curl -LsSf https://astral.sh/uv/install.sh | sh

# clone the fork + fetch the upstream base commit
git clone https://github.com/toncao/vllm.git
cd vllm
git remote add upstream https://github.com/vllm-project/vllm.git
git fetch upstream a7fdfeef72323eb3db6f0620e4ea200290d0ca5a
git checkout minimax-m3-compressed-tensors

# Python 3.12 env + install with upstream precompiled kernels
uv venv --python 3.12
source .venv/bin/activate
VLLM_USE_PRECOMPILED=1 uv pip install -e . --torch-backend=auto

Serve

vllm serve cyankiwi/MiniMax-M3-AWQ-INT4 --block-size 128

MiniMax

Hugging Face

MiniMax-M3 is a native multimodal model with 1M context. It has ~428B parameters and ~23B activated parameters.

Highlights:

Native Multimodality: M3 undergoes mixed-modality training from the very first step, enabling deeper semantic fusion across text, image, and video.
Context Scaling via Sparse Attention: M3 introduces MiniMax Sparse Attention (MSA) to improve long context efficiency. M3 delivers 9× prefill and 15× decode speedups compared to M2 at 1M context, reducing per-token compute to 1/20.
Coding & Cowork Capability: M3 achieves frontier-level performance across long-horizon agentic benchmarks, excelling in both coding and cowork.

MiniMax Sparse Attention (MSA)

M3 is powered by MiniMax Sparse Attention (MSA), a high-performance sparse attention operator designed for million-token contexts. Compared with GQA, MSA dramatically reduces the attention compute and memory footprint while preserving model quality.

📄 Read the technical report: arXiv:2606.13392 · Hugging Face Papers

How to Use

M3 supports two reasoning modes:

thinking — for complex reasoning, agentic tasks, and long-horizon collaboration.
non-thinking — for latency-sensitive scenarios such as chat and code completion.

Local Deployment

Download the model:

hf download MiniMaxAI/MiniMax-M3 --local-dir MiniMax-M3

We recommend the following inference frameworks (listed alphabetically) to serve the model:

SGLang - see SGLang cookbook.
vLLM - see vLLM recipes.
Transformers - see Transformers docs.

Inference Parameters

We recommend the following parameters for best performance: temperature=1.0, top_p=0.95, top_k=40.

cyankiwi/MiniMax-M3-AWQ-INT4