🧭 AI Lineup
MLX
by Apple
Apple's own open-source framework for running AI on Mac hardware. The foundation, more for builders than for browsers.
FreeRuns LocallyOpen Source
The details
- What it is
- An open-source framework from Apple for running AI efficiently on Apple Silicon. It's a foundation that other Mac AI tools are built on, aimed at developers more than everyday users.
- Cost
- Free — open source.
- Open Source?
- Yes — made by Apple
- What you need
- An Apple Silicon Mac. Built for developers.
Best for
- Developers building Mac AI tools
- Apple Silicon performance
- The foundation under other Mac tools
Kevin’s take
“MLX is Apple's home-grown toolkit for running AI efficiently on their chips. It's a building block, really — the thing developers and tools like oMLX build on top of, rather than something you'd open and use directly. Worth knowing the name if you're on a Mac and going deeper. Otherwise, this one's more for the tinkerers.”
— Kevin, formerly of IT Support
Related reading
From the AI Glossary
- Open SourceSoftware whose underlying code is made freely available for anyone to inspect, use, and modify. The idea predates AI by decades and powers much of the internet. In AI the label is used loosely, and often a bit generously: some models share everything, others share only parts and still claim it. Genuinely open AI lets people see how a tool works and run it themselves, rather than taking a company's word for it.
- Open WeightsA model whose trained internals, the weights, have been released publicly, so anyone can download and run it themselves. Meta's Llama models are a well-known example. It's not quite the same as fully open source, because the recipe and data used to build the model aren't always shared. But open weights let people run capable AI on their own machines, without sending anything to a company's servers.
- QuantizationA technique for shrinking an AI model by storing its numbers less precisely, which makes it smaller and faster, usually with only a small loss in quality. Think of it like compressing a photo: the file gets much smaller, and most people can't see the difference. Quantization is part of how large models get squeezed onto laptops and phones.
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