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jundot /

omlx

I built an open-source LLM inference server optimized for Apple Silicon. The main motivation was coding agents - tools like Claude Code send requests where the context prefix keeps shifting, invalidating KV cache. A few …

DT桌面效率DT软件开发DTAgent 能力扩展DTAgent 评测与安全DT桌面应用DTmacOSDT终端DTWebDTClaude Code

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I built an open-source LLM inference server optimized for Apple Silicon. The main motivation was coding agents - tools like Claude Code send requests where the context prefix keeps shifting, invalidating KV cache. A few turns later the agent circles back, and your Mac has to re-prefill the entire context from scratch. oMLX solves this with paged SSD caching. Every KV cache block is persisted to disk. When a previous prefix returns, it's restored instantly instead of being recomputed. This makes long coding sessions significantly faster. It also supports continuous batching for concurrent requests, multi-model serving (LLM + embedding + reranker) with LRU eviction, block-level KV cache with prefix sharing and copy-on-write, OpenAI and Anthropic compatible APIs, and tool calling. Ships as a signed macOS menubar app with a web dashboard. GitHub: https://github.com/jundot/omlx

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    2026年2月20日 – 2026年3月4日 · 3 次收录

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