MiniMax-H3 Ported to MLX, Runs on Apple Silicon to Generate Videos with Audio
Score
72提供了具体模型移植项目、实测步骤和性能数据，信息增量较高。
内容来自资深开发者的亲测体验，细节丰富，但缺乏深入解析。
给出了完整命令和注意事项，可直接复现。
模型发布仅两天即出现移植和实测，时效性极强。
Simon Willison 是知名开发者博客作者，来源可信度高。
Base 79.5 × T2 Source weight ×0.9 = 72
Curation line 70Selected
Why selected
Provides a reproducible local deployment path and real-world performance data, highly valuable to the open-source AI community.
AI commentary
「This is worth watching because it shows the feasibility of open-source multimodal models on consumer hardware, good news for developers without high-end GPUs. If you build local AI tools or video generation and own an Apple Silicon device, this project is worth trying immediately.」
Quick overview
Simon Willison blogged about PipeNetwork/minimax-h3-mlx, a port of MiniMax-H3, released two days ago as a general-purpose omni-modal generative system, to the MLX framework for Apple Silicon. He tested it on an M5 Max MacBook Pro, downloading ~115GB of model files and generating a 15-second video with audio in just under 45 minutes. The video is impressive, but the audio sounds like weird speech-like garbage because no prompt guidance was provided for audio. He notes that the prompting guide offers tips for better results.
- MiniMax-H3 accepts text, images, audio, and video inputs, and can generate up to 15-second video clips with audio.
- PipeNetwork/minimax-h3-mlx ports it to MLX for running on Apple Silicon.
- Real-world test: ~115GB download, ~45 min generation; video is impressive but audio needs guidance from the prompting guide.
Full text
Showing the source text; the full translation is being generated.
This Python package ports it to MLX for running on Apple Silicon.
I got it running on my M5 Max MacBook Pro. I cloned the repo and ran the model like this:
# First download the models
uvx --from huggingface_hub hf download MiniMaxAI/MiniMax-H3 \
--include 'FL2VA/*' --exclude 'FL2VA/transformer/*'
uvx --from huggingface_hub hf download pipenetwork/MiniMax-H3-MLX-8bit
# Now run the prompt
uv run --with mlx-vlm \
--with-requirements requirements.txt python scripts/generate.py \
"a rainbow colored skunk leaps over a mossy log in a supermarket" \
-o skunk.mp4 \
-c ~/.cache/huggingface/hub/models--MiniMaxAI--MiniMax-H3/snapshots/fa9c8ab1eaa21c8ae25e7e40b83b2e6002f340af/FL2VA \
-t ~/.cache/huggingface/hub/models--pipenetwork--MiniMax-H3-MLX-8bit/snapshots/3ac52081470b0488921c3ec3ba84a39097bf2361
Here's the video I got for the prompt:
a rainbow colored skunk leaps over a mossy log in a supermarket
Your browser does not support HTML5 video.
It downloaded ~115 GB of model files, and the video generation took just under 45 minutes.
The video is impressive, but the audio is weird speech-like garbage, because I didn't provide any prompt guidance as to what the audio should be. The prompting guide (which I didn't read prior to this experiment) has a whole bunch of information on how to get this to work.
Tags: ai, generative-ai, mlx, text-to-video, minimax
Related items
FeaturedDeepSeek V4 Doubles Token Share on OpenRouter, Driven by Agentic Workloads
DeepSeek V4 在 OpenRouter 上的代币份额翻倍，智能体工作负载成主要驱动力
The rapid adoption of DeepSeek V4 signals a strong market preference for high-performance, low-cost inference models, especially as agentic use cases reshape AI deployment.
91
FeaturedEnabling Two Settings Triples ARC-AGI-3 Benchmark Scores
启用两项设置使ARC-AGI-3基准测试分数提升三倍
This finding is crucial! It shows that model performance depends not only on training but also on inference-time configuration. For AI engineers and researchers, this is a must-know optimization direction. Teams deploying models on resource-constrained devices in particular could find a low-cost way to boost capabilities.
91
FeaturedGPT-5.6: Fusing Frontier Intelligence with Frontier Efficiency
GPT-5.6：前沿智能与前沿效率的融合
This news is worth watching as it marks a shift from pure parameter scaling to efficiency competition. For AI developers and business decision-makers, GPT-5.6 means higher performance at lower cost, especially for long-context or agentic tasks previously hindered by inference expenses. OpenAI's move may redefine what 'frontier' means.
91
FeaturedLFM2.5-Encoders: New Models for Fast Long-Context Inference on CPU
LFM2.5-Encoder：面向CPU快速长上下文推理的新模型
This blog post catches my eye because it directly solves the pain point of running long-context LLMs on CPU. For teams or individual developers without high-end GPUs, the LFM2.5-Encoder series offers a ready-to-use open-source solution. Highly recommended for those working on RAG, document search, or code analysis—now a standard server can handle million-token documents.
91
FeaturedGPT-Red: OpenAI Automates Red Teaming via Self-Play to Boost Model Robustness
GPT-Red：OpenAI通过自我对弈实现AI红队自动化，提升模型鲁棒性
GPT-Red marks a milestone from human to automated red-teaming—essential for safety engineers and alignment researchers. While reducing cost, it also raises ethical questions about automated attacks, worth pondering for everyone in AI governance.
91
FeaturedOpenAI Launches GPT-5.5 Bio Bug Bounty Program
OpenAI推出GPT-5.5生物漏洞赏金计划
This program deserves attention as it pioneers the extension of AI bug bounty to biosecurity, offering a new research direction for the security community and a practical training ground for AI risk governance. It is particularly important for model safety researchers, biosecurity experts, and practitioners concerned about dual-use AI risks.
91
FeaturedvLLM Introduces Native-Speed Transformers Modeling Backend for Enhanced Inference Performance
vLLM推出原生速度Transformers建模后端，大幅提升推理性能
This update is critical for AI engineering teams: eliminating format conversion speeds up model iteration, and 2-5x speedup directly reduces GPU costs. If you're building LLM online services or batch inference, this is practically a must-read technical update.
90
FeaturedGoogle DeepMind Introduces SL2T, a Sign Language-to-Text Model for Deaf Users
Google DeepMind 发布手语转文本模型 SL2T，助力聋人用户
This news matters not only as a technical breakthrough but also as a sign that AI is beginning to address the needs of marginalized groups. For developers, the architecture and technical approach of SL2T merit deep study; for accessibility advocates, it's a key case for advancing industry standards.