[GitHub Trending] jundot/omlx

6.4 relevance

LLM inference server for Apple Silicon with SSD caching, relevant to AI/ML deployment and cloud infrastructure alternatives.

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Why It Matters

For a solutions architect building AI-assisted coding workflows on Apple Silicon, oMLX eliminates the convenience-vs-control tradeoff by keeping everyday models pinned in memory while auto-swapping heavier ones, making local LLM inference practical for real-time tool integration like Claude Code.

Key Takeaways

Evaluate oMLX for your Mac-based dev environment if you need persistent KV caching across context changes and 30x faster inference on GLM-5.2 via precompiled Metal kernels.

LLM inference server with continuous batching & SSD caching for Apple Silicon — managed from the macOS menu bar - jundot/omlx

Summary

oMLX is a macOS-native LLM inference server that manages continuous batching and tiered KV caching across RAM and SSD, persisting context even when conversation topics shift. It runs as a menu bar app or Homebrew service, supports OpenAI-compatible endpoints, and includes custom Metal kernels that accelerate GLM-5.2 inference to 845 tok/s on M3 Ultra versus 29 tok/s on generic fallback paths.

Author

jundot