Description
oMLX is a cutting-edge inference server designed specifically for macOS, leveraging the power of Apple Silicon. Built on the MLX framework, it offers a native solution for running large language models (LLMs) locally on Mac computers. One of its standout features is the paged SSD KV caching, which drastically reduces the time to first token (TTFT) from a typical 30-90 seconds to under 5 seconds. This improvement is particularly beneficial for coding agents that frequently invalidate the cache, as oMLX ensures that every cache block is persisted to SSD, allowing for rapid restoration from disk.
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The server is compatible with popular APIs such as OpenAI and Anthropic, making it a versatile tool for developers working with these platforms. It supports continuous batching and provides a drop-in API for models like Claude Code, OpenClaw, and Cursor, ensuring that responses are delivered in mere seconds. The performance benchmarks conducted on an M3 Ultra with 512GB RAM demonstrate impressive throughput and memory efficiency across various contexts and batch sizes.
oMLX is designed for developers and AI enthusiasts who require a reliable and fast local AI solution on their Macs. It eliminates the need for cloud-based inference, reducing latency and dependency on external servers. The tool is open-source under the Apache 2.0 license, with a strong community presence, as evidenced by its 23,000+ GitHub stars. Users can easily get started by downloading the DMG or installing from source, with compatibility for standard Hugging Face caches and LM Studio folders.
Overall, oMLX offers a robust and efficient solution for running AI models locally on macOS, providing significant speed improvements and seamless integration with existing AI workflows.
- CategoryMLOps & Model Deployment
- Pricing ModelOpen source
- DeploymentLocal
- PlatformsmacOS 15+
oMLX's Core Features
Native macOS inference server
Paged SSD KV caching
OpenAI & Anthropic API compatibility
Continuous batching support
Drop-in API for Claude Code, OpenClaw, and Cursor
Rapid cache restoration from SSD
Open-source under Apache 2.0
23,000+ GitHub stars
How to use oMLX?
Download: Get the DMG or source
Install: Follow installation instructions
Configure: Set up with Hugging Face cache
Use: Run models with reduced TTFT
Optimize: Leverage continuous batching
oMLX's Use Cases
- Local AI Development
- API Integration
- Coding Agents
- Batch Processing
- Open-Source Contribution
