Fast local LLM inference on Apple Silicon with persistent SSD cache
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Tracked since2026
0 reviews trackedThe Bottom Line
Entry price
Free, no paid tier
Biggest pro
Dramatically reduces TTFT on long contexts for coding agents by persisting KV cache to SSD
Biggest con
Requires macOS 15+ and Apple Silicon, limiting compatibility to recent Mac hardware
TL;DR - oMLX
- Paged SSD KV caching eliminates recomputation by persisting cache blocks to disk, enabling sub-5-second TTFT on long contexts for coding agents.
- Continuous batching delivers up to 4x generation speedup at high concurrency, outperforming in-memory-only solutions.
- Native macOS app with OpenAI and Anthropic drop-in API compatibility, supporting Claude Code, OpenClaw, and Cursor.
Pricing: Free forever
Best for: Individuals & startups
What is oMLX?
oMLX is a macOS-native MLX server designed for high-performance local LLM inference on Apple Silicon. It features a unique paged SSD KV caching system that persists cache blocks to disk, enabling sub-5-second time-to-first-token on long contexts even after cache invalidation, a common issue with coding agents. The server supports continuous batching for up to 4x generation speedup at high concurrency, multi-model serving (LLM, VLM, embedding, reranker), and drop-in API compatibility with OpenAI and Anthropic endpoints.
The application includes a native macOS menu bar app for server control, a web dashboard for model management and real-time metrics, and supports tool calling in multiple formats (JSON, Qwen, Gemma, GLM, MiniMax) along with MCP tool integration. oMLX reads the standard Hugging Face cache, so previously downloaded models are automatically available without re-downloading.
Pros & Cons
Pros
- Dramatically reduces TTFT on long contexts for coding agents by persisting KV cache to SSD
- Significant throughput improvements with continuous batching at high concurrency
- Seamless integration with popular coding tools via OpenAI/Anthropic compatible APIs
Cons
- Requires macOS 15+ and Apple Silicon, limiting compatibility to recent Mac hardware
- Large models demand substantial RAM (64GB+ recommended), making it less accessible on lower-end Macs
Key Features
Paged SSD KV caching with two-tier RAM/SSD architecture and LRU eviction policyContinuous batching via mlx-lm's BatchGenerator for concurrent request handlingNative macOS menu bar app with web dashboard for model management and real-time metricsMulti-model serving supporting LLM, VLM, embedding, and reranker models simultaneouslyOpenAI and Anthropic drop-in API endpoints with one-click config generator for tools like Claude Code and CursorTool calling support for JSON, Qwen, Gemma, GLM, and MiniMax formats with MCP integrationAutomatic model discovery from standard Hugging Face cache (~/.cache/huggingface/hub)Signed and notarized app with in-app auto-update (not Electron-based)
Pricing
Free
oMLX is completely free to use with no hidden costs.
Reviews
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oMLX FAQ
How does oMLX improve performance for coding agents that work with long contexts?
oMLX uses a paged SSD KV caching system that persists cache blocks to disk, enabling sub-5-second time-to-first-token on long contexts even after cache invalidation, a common issue with coding agents. This dramatically reduces latency when revisiting or continuing long conversations, making it well-suited for iterative coding workflows.
How does oMLX compare to Ollama for local LLM inference on Apple Silicon?
Unlike Ollama, oMLX is specifically optimized for Apple Silicon with a native macOS MLX server and features a unique paged SSD KV cache that persists to disk, reducing time-to-first-token on long contexts. oMLX also supports continuous batching for up to 4x generation speedup at high concurrency and offers drop-in API compatibility with OpenAI and Anthropic endpoints.
What are the main hardware requirements for running oMLX?
oMLX requires macOS 15+ and Apple Silicon, limiting compatibility to recent Mac hardware. Large models demand substantial RAM, with 64GB or more recommended, making it less accessible on lower-end Macs.
Which teams benefit most from using oMLX?
Development teams building AI-powered coding agents or applications that rely on long context windows will benefit most from oMLX's persistent SSD KV cache. It is also ideal for teams needing high-throughput local inference with continuous batching and multi-model serving on Apple Silicon hardware.
How is oMLX priced?
oMLX is free to use with no paid plan required, making it accessible for individual developers and teams without licensing costs.
Can oMLX integrate with existing coding tools and APIs?
Yes, oMLX provides drop-in API compatibility with OpenAI and Anthropic endpoints, allowing seamless integration with popular coding tools. It also supports tool calling in multiple formats including JSON, Qwen, Gemma, GLM, and MiniMax, along with MCP tool integration.
Does oMLX support multiple model types simultaneously?
Yes, oMLX supports multi-model serving including LLM, VLM, embedding, and reranker models. It reads the standard Hugging Face cache, so previously downloaded models are automatically available without re-downloading.
How does oMLX manage models and monitor performance?
oMLX includes a native macOS menu bar app for server control and a web dashboard for model management and real-time metrics. This allows users to easily start and stop the server, manage loaded models, and monitor throughput and latency.
Source: omlx.ai