# MiniMax M3 GGUF

URL: https://interfaze.ai/models/unslothminimax-m3-gguf

MiniMax M3 GGUF by unsloth, a image-text-to-text model with multimodal capabilities. Understand and compare multimodal features, benchmarks, and capabilities.

## Comparison

| Feature | MiniMax M3 GGUF | Interfaze |
| --- | --- | --- |
| Input Modalities | text, image, video | image, text, audio, video, document |
| Native OCR | No | Yes |
| Long Document Processing | Yes | Yes |
| Language Support | 40 partial | 162+ |
| Native Speech-to-Text | No | Yes |
| Native Object Detection | No | Yes |
| Guardrail Controls | Yes | Yes |
| Context Input Size | 1M | 1M |
| Tool Calling | Yes | Tool calling supported + built in browser, code execution and web search |

### Scaling

| Feature | MiniMax M3 GGUF | Interfaze |
| --- | --- | --- |
| Scaling | Self-hosted/Provider-hosted with quantization | Unlimited |

[Try Interfaze](https://interfaze.ai/dashboard)[Read the Docs](https://interfaze.ai/docs)

View model card on [Hugging Face](https://huggingface.co/unsloth/MiniMax-M3-GGUF)

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](https://github.com/ggml-org/llama.cpp/pull/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.

* * *

**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](https://agent.minimax.io/)
-   [MiniMax API](https://platform.minimax.io/)

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](https://modelscope.cn/models/MiniMax/MiniMax-M3).

### 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.`

## Want more deterministic results?

[Try Interfaze](https://interfaze.ai/dashboard)[Read the Docs](https://interfaze.ai/docs)