This product was not featured by Product Hunt yet. It will not be visible on their landing page and won't be ranked (cannot win product of the day regardless of upvotes).
MiniMLX is an open-source Mac app for running popular MLX models on Apple Silicon. Discover and manage models, then connect your apps through a local OpenAI-compatible API.
I built MiniMLX to make running local AI models on a Mac feel simpler and more approachable.
I wanted one place to discover models, download them, start an inference service, and see how everything is performing—without switching between terminal commands, model repositories, and monitoring tools.
MiniMLX is a native macOS app built for Apple Silicon. You can:
• Discover, download, and manage a wide range of popular MLX models from Hugging Face and ModelScope
• Run compatible models locally on your Mac
• Connect your apps through an OpenAI-compatible API
• Measure generation speed and monitor memory and GPU usage
• Control the service from the macOS menu bar
MiniMLX also supports MTP acceleration for compatible models and provides options for running certain larger models on Macs with less available memory.
Projects like oMLX inspired me to explore what's possible with local inference on Apple Silicon. With MiniMLX, my focus is a simple desktop experience that makes finding, running, and managing models feel natural on a Mac.
MiniMLX is open source and free to use. This is our first release, and I'd love to hear about your experience—especially which models you run and what would make the workflow better for you.
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About MiniMLX on Product Hunt
“Run AI models on your Mac. Fast and simple.”
MiniMLX was submitted on Product Hunt and earned 2 upvotes and 1 comments, placing #89 on the daily leaderboard. MiniMLX is an open-source Mac app for running popular MLX models on Apple Silicon. Discover and manage models, then connect your apps through a local OpenAI-compatible API.
MiniMLX was featured in Open Source (69k followers), Developer Tools (522.7k followers), Artificial Intelligence (483.2k followers) and GitHub (41.5k followers) on Product Hunt. Together, these topics include over 280.8k products, making this a competitive space to launch in.
Who hunted MiniMLX?
MiniMLX was hunted by shi jizhi. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how MiniMLX stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
shi jizhi
Hi Product Hunt! 👋
I built MiniMLX to make running local AI models on a Mac feel simpler and more approachable.
I wanted one place to discover models, download them, start an inference service, and see how everything is performing—without switching between terminal commands, model repositories, and monitoring tools.
MiniMLX is a native macOS app built for Apple Silicon. You can:
• Discover, download, and manage a wide range of popular MLX models from Hugging Face and ModelScope
• Run compatible models locally on your Mac
• Connect your apps through an OpenAI-compatible API
• Measure generation speed and monitor memory and GPU usage
• Control the service from the macOS menu bar
MiniMLX also supports MTP acceleration for compatible models and provides options for running certain larger models on Macs with less available memory.
Projects like oMLX inspired me to explore what's possible with local inference on Apple Silicon. With MiniMLX, my focus is a simple desktop experience that makes finding, running, and managing models feel natural on a Mac.
MiniMLX is open source and free to use. This is our first release, and I'd love to hear about your experience—especially which models you run and what would make the workflow better for you.
GitHub: https://github.com/shijizhi/MiniMLX