Instructions to use FastVideo/FastH3-Omni-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use FastVideo/FastH3-Omni-MLX with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download FastVideo/FastH3-Omni-MLX --local-dir FastH3-Omni-MLX
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
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license: other
license_name: minimax-h3-community-license-agreement
license_link: https://huggingface.co/MiniMaxAI/MiniMax-H3/blob/main/LICENSE
base_model: FastVideo/FastVideo-FastH3-Omni-8-Step-V1
tags: [mlx, apple-silicon, fastvideo, minimax-h3, fasth3-omni, benchmark]
---
# FastH3 Omni on Apple Silicon (MLX) — build + benchmark kit
FastH3 Omni (reference-conditioned video + audio) runs natively on Apple Silicon through FastVideo's MLX runtime
([hao-ai-lab/FastVideo#2000](https://github.com/hao-ai-lab/FastVideo/pull/2000)).
This repository holds the kit, not the weights:
- `bench/prepare_model_dir.sh` builds the MLX model directory from the public releases
(`FastVideo/FastVideo-FastH3-Omni-8-Step-V1`, `FastVideo/FastH3-Omni-NVFP4-QAD`, `FastVideo/FastH3-NVFP4-VAE-Decoder`,
`lightx2v/LightVAE`) at pinned revisions: INT6 (default), INT8 or BF16 transformer, NVFP4 text-encoder cache,
FastH3 36L/8L decoders and the LightVAE Lite H3 reference encoder. It verifies every file against SHA-256.
- `bench/run_matrix.py` runs the FastH3 Omni reference workloads and writes a latency table. See `bench/README.md`.
Licenses: MiniMax-H3 Community License for the model weights; Qwen3-VL and LightVAE are Apache-2.0 (see `bench/licenses/`).
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