---
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!](https://unsloth.ai/docs/models/minimax-m3)
See Unsloth Dynamic 2.0 GGUFs for our quantization benchmarks.
- 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:
EXPERIMENTAL GGUF / support for MiniMax-M3 in llama.cpp:
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):
```bash
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:
```bash
./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](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:
```bash
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.
``` 
