---

pipeline_tag: image-text-to-text

license: other

license_name: minimax-community

license_link: LICENSE

library_name: transformers

tags:

• multimodal
• moe
• agent
• coding
• video
• minimax_m3_vl

base_model:

• MiniMaxAI/MiniMax-M3

---

Read our How to Run MiniMax M3 Guide!

<div>

<p style="margin: 0 0 0px 0; margin-top: 0px;">

<em>See <a href="https://unsloth.ai/docs/basics/unsloth-dynamic-v2.0-gguf">Unsloth Dynamic 2.0 GGUFs</a> for our quantization benchmarks.</em>

</p>

<div style="display: flex; gap: 5px; align-items: center; margin-bottom: 0px;">

<a href="https://github.com/unslothai/unsloth/">

<img src="https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png" width="133">

</a>

<a href="https://discord.gg/unsloth">

<img src="https://github.com/unslothai/unsloth/raw/main/images/Discord%20button.png" width="173">

</a>

<a href="https://unsloth.ai/docs/models/minimax-m3">

<img src="https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/documentation%20green%20button.png" width="143">

</a>

</div>

<ul style="margin: 0;">

<li>EXPERIMENTAL GGUF / support for MiniMax-M3</li>

<li><b>Jun 12 Update:</b> You can now run MiniMax M3 in Unsloth Studio. See our <a href="https://unsloth.ai/docs/models/minimax-m3#unsloth-studio-guide">Guide</a>.</li>

<li>Example of MiniMax M3 (5-bit GGUF) running in Unsloth Studio:</li>

</ul>

</div>

<img width="600" alt="minimax m3 in unsloth studio" src="https://cdn-uploads.huggingface.co/production/uploads/62ecdc18b72a69615d6bd857/4ZRsRdcz9YsSkNDisoxfO.gif" />

<div style="margin: 0;">

<b>EXPERIMENTAL GGUF / support for MiniMax-M3 in llama.cpp:</b>

</div>

</div>

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

| | |

| --- | --- |

| Architecture | MoE + MSA (MiniMax Sparse Attention) |

| Total Parameters | ~428B |

| Activated Parameters | ~23B |

| Experts | 128 (4 active per token) |

| Layers | 60 |

| Context Length | 1M tokens |

| Modalities | Text, Image, Video |

| Precision | bfloat16 |

| Transformers | ≥ 4.52.4 (trust_remote_code=True) |

| License | MiniMax Community License |

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.