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oMLX
by the oMLX project (independent)
A native Apple Silicon inference server with continuous batching, SSD-backed caching, and OpenAI- and Anthropic-compatible APIs.
FreeRuns LocallyOpen Source
Last checked: 21 September 2026
The details
- What it is
- A native macOS inference server built on Apple's MLX framework, with continuous batching and paged SSD KV caching for Apple Silicon.
- Cost
- Free — open source.
- Interfaces
- OpenAI- and Anthropic-compatible APIs, managed from a macOS menu-bar app.
- What you need
- An Apple Silicon Mac. oMLX is macOS-only and is aimed more at local serving and agent workloads than first-time consumer chat.
Best for
- Apple Silicon Mac owners
- Serving local models through compatible APIs
- Agent workloads that benefit from SSD KV caching
- Privacy-first local inference
Kevin’s take
“oMLX is for Mac people — specifically the newer Apple Silicon ones (M1 and up). It's built on Apple's own MLX framework and squeezes more speed out of your machine than some older tools. It's promising, but new, so I'd treat it as one to watch rather than the first place to start. LM Studio is the gentler entry point.”
— 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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