Frustrated by slow cloud inference and privacy concerns when using AI on a Mac? You need a local, private AI backend that keeps data on device.
oMLX is a native macOS inference server for Apple Silicon that runs LLMs, vision language, embeddings, and rerankers with OpenAI- and Anthropic-compatible APIs, all locally. This means lower latency and data control without cloud round trips.
Stop Wasting Time on Cloud Inference
Thanks to paged SSD KV caching and continuous batching, oMLX delivers faster responses and higher throughput. Manage models from a web dashboard or a native menu bar app and serve multiple models in parallel.
How It Delivers the Aha Moment
Install is simple: download the macOS DMG, drag to Applications, configure the model directory, start the server, and connect via OpenAI- or Anthropic-compatible endpoints for private, local AI workloads.
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Customer Reviews for oMLX
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I run a private chat app on my Mac and needed speed with zero cloud dependency. The continuous batching is the aha moment: I can handle multiple chats with the same hardware without extra servers. Paged SSD KV caching slashes the time to first token even on long prompts. The native macOS menu bar app is perfect for quick server toggles during tests. Documentation on batch tuning could be clearer, but setup overall was smooth.
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Showing 1 - 2 of 2 reviews .
Ava Rodriguez
Trusted ReviewerContinuous batching and fast KV caching rescue my local chat app
What I liked
What could be better
I run a private chat app on my Mac and needed speed with zero cloud dependency. The continuous batching is the aha moment: I can handle multiple chats with the same hardware without extra servers. Paged SSD KV caching slashes the time to first token even on long prompts. The native macOS menu bar app is perfect for quick server toggles during tests. Documentation on batch tuning could be clearer, but setup overall was smooth.
Maria Garcia
Trusted ReviewerOpenAI-compatible local testing finally feels cloud-synced
What I liked
What could be better
For a university project I needed to test private AI workloads offline across several models. The OpenAI-compatible APIs let me port cloud experiments to local tests without rewriting prompts. The web dashboard gives real-time metrics as I tweak MCP workflows, and Anthropic-compatible APIs broaden the model set for comparisons. The integration is solid, though the compatibility layer sometimes needs small tweaks for very large models.
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How it works
How oMLX Works In 3 Steps?
1. Install oMLX
Download the signed macOS DMG and move oMLX to Applications.
2. Configure model dir
Point oMLX to your MLX model directory to load local models.
3. Start the server
Launch the server and connect via OpenAI compatible APIs for local inference.
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