MiniMax-M3-GGUF

• EXPERIMENTAL GGUF / support for MiniMax-M3
• Jun 12 Update: You can now run MiniMax M3 in Unsloth Studio. See our Guide.
• Example of MiniMax M3 (5-bit GGUF) running in Unsloth Studio:

MiniMax-M3 support in llama.cpp is preliminary and not yet in a released build. To run these GGUFs, build llama.cpp from PR #24523:

git clone https://github.com/ggml-org/llama.cpp
cd llama.cpp
git fetch origin pull/24523/head:minimax-m3
git checkout minimax-m3
cmake -B build -DGGML_CUDA=ON
cmake --build build --config Release -j --target llama-cli llama-server

Then run a quant. The model is large (~428B params), so offload across GPUs with -ngl 99 or keep the weights in CPU RAM:

./build/bin/llama-cli -hf unsloth/MiniMax-M3-GGUF:UD-IQ1_M

Note: MiniMax Sparse Attention is not supported yet, so inference falls back to dense attention.

MiniMax-M3

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.

Model Details

How to Use

• MiniMax Agent
• MiniMax API

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

You can also get model weights from ModelScope.

Inference Parameters

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

You are a helpful assistant. Your name is MiniMax-M3 and was built by MiniMax.