[GitHub Trending] jundot/omlx

5.7 relevance

LLM inference server optimized for Apple Silicon, relevant to AI infrastructure but niche hardware focus.

AI/ML github.com

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Why It Matters

For a Solutions Architext focused on developer experience and AI agent orchestration, oMLX makes local LLM inference practical for real coding workflows by eliminating context resets and providing menu-bar control, which is critical for running agents like Claude Code without cloud latency or cost.

Key Takeaways

Evaluate oMLX for your local LLM serving stack if you work on Apple Silicon and need persistent KV caching across sessions for agentic coding tools.

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

Summary

oMLX is an open-source LLM inference server for Apple Silicon that manages continuous batching and tiered KV caching across hot memory and cold SSD, persisting context even across conversation changes. It runs as a macOS menu bar app or Homebrew service, exposing an OpenAI-compatible API on localhost:8000, and supports native custom kernels for GLM-5.2 and MiniMax M3 that deliver up to 30x faster prefill (845 vs ~29 tok/s on M3 Ultra) but require full Xcode to build. The server auto-discovers LLMs, VLMs, embeddings, and rerankers from model directories, and integrates with tools like Claude Code via MCP.

Author

jundot