Audience
Developers and AI power users needing to run fast local LLM inference and agentic coding workflows on Apple Silicon
About oMLX
oMLX is a macOS-native MLX server designed to make local AI faster and more practical on Apple Silicon. Built for the way coding agents actually work, it uses paged SSD KV caching to persist cache blocks to disk, allowing previously seen prefixes to be restored across requests and server restarts instead of being recomputed from scratch. This can reduce time to first token on long contexts from 30–90 seconds to under five seconds after the first turn. Continuous batching handles concurrent requests through mlx-lm’s BatchGenerator, improving generation throughput without forcing requests to wait behind a single job. oMLX can serve LLMs, vision-language models, embedding models, and rerankers simultaneously, using LRU eviction when memory runs low. It supports any MLX-format model from Hugging Face, including Qwen, LLaMA, Mistral, Gemma, DeepSeek, MiniMax, and GLM, and can reuse models already stored in the standard Hugging Face cache, LM Studio folders, or custom directories.
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Company Information
oMLX
United States
omlx.ai/
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Product Details
Platforms Supported
Mac
Training
Documentation
Support
Online
oMLX Frequently Asked Questions
Q: What kinds of users and organization types does oMLX work with?
oMLX works with these users and organization types: Mid Size Business, Small Business, Enterprise, Freelance, Nonprofit, Government, and Startup.
Q: What languages does oMLX support in their product?
oMLX supports these languages: English.
Q: What kind of support options does oMLX offer?
oMLX offers support via online.
Q: What other applications or services does oMLX integrate with?
oMLX integrates with: Anthropic, Claude Code, Cursor, DeepSeek, Gemma, Gemma, GitHub, GLM-4.1V, Hugging Face, JSON, Llama, LM Studio, MiniMax, Mistral AI, Model Context Protocol (MCP), OpenAI, OpenClaw, Python, and Qwen.
Q: Does oMLX have an API?
Yes, oMLX has an API.
Q: What type of training does oMLX provide?
oMLX provides training in the form of documentation.
