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oMLX Review

oMLX is a native macOS LLM inference server built on Apple's MLX framework, offering continuous batching and a two-tier (unified-memory + SSD) KV cache with an OpenAI/Anthropic-compatible API.

shipped May 31, 2026freemium
Domain rating46Monthly visits523/moAI-readableblocked
oMLX — product screenshot

Why it matters

1Utilizes a two-tier KV cache (unified-memory + SSD) for enhanced performance on Apple Silicon Macs, extending usable memory for large models.
2Achieved 89% cache efficiency and 47 tokens/second generation speed on an M2 MacBook Pro with a Qwen 3.6 35-billion parameter 4-bit model.
3Reduces Time To First Token (TTFT) from 30-90 seconds to under 5 seconds for subsequent requests in long coding sessions via persistent SSD caching.
4Offers an OpenAI/Anthropic-compatible API, serving as a drop-in backend for AI programming assistants like Claude Code, Cursor, and Codex.

Stork’s verdict on oMLX

oMLX excels at running large models locally on Apple Silicon with its two-tier KV cache, but it's exclusively for macOS and MLX-native models.

oMLX reviewed by Stork AI · stork.ai/en/omlx

About oMLX

Platforms
macOS

Specs

API Docs

View Documentation →

API Available

Yes, public API

Screenshots

overview

What is oMLX?

oMLX is a local LLM inference server tool developed by oMLX that enables developers and programmers to run large language models locally on Apple Silicon Macs. It utilizes continuous batching and a two-tier KV cache (RAM + SSD) to enhance performance and efficiency. This open-source, specialized AI inference server is meticulously engineered for Apple Silicon (M1/M2/M3/M4) Macs, built directly atop Apple's native MLX framework. Its primary goal is to optimize the performance of large language models (LLMs) and other AI models locally on macOS, specifically addressing the 'Memory Tax' bottleneck often encountered with high-parameter models. The core innovation is a 'Two-Tier KV Cache' system, which intelligently manages memory by keeping active conversational context in fast RAM (hot cache) and offloading older, less critical context to the SSD (cold cache). This allows Macs to run significantly larger models than their physical RAM would typically permit, effectively extending usable memory for complex AI workloads.

features

Key Features of oMLX

oMLX integrates several technical features designed to optimize local AI inference on Apple Silicon hardware, enhancing both performance and user experience.

  • Native macOS LLM inference server built on Apple's MLX framework.
  • Implements continuous batching for optimized inference throughput and reduced latency.
  • Features a two-tier KV cache system (unified-memory + SSD) for extended memory capacity and efficient context management.
  • Provides an OpenAI/Anthropic-compatible API for broad compatibility with existing AI tools and frameworks.
  • Functions as a drop-in backend for AI programming assistants such as Claude Code, Cursor, and Codex.
  • Managed directly from the macOS menu bar, offering convenient control over server status and model configurations.
  • Supports simultaneous deployment of multiple model types, including LLMs, Vision-Language Models (VLMs), embedding models, and reranker models.
  • Includes built-in benchmark tools for MLX model research and experimentation.
  • Offers persistent SSD caching to drastically reduce Time To First Token (TTFT) for subsequent requests in long contexts.
  • Features a native Swift macOS app (v0.4.0+) with redesigned UI, onboarding, and a GitHub Releases-based updater.

use cases

Who Should Use oMLX?

oMLX is tailored for specific user groups seeking high-performance, local AI inference capabilities on Apple Silicon Macs, particularly those prioritizing privacy, efficiency, and integration with AI development workflows.

  • Developers and Programmers: For providing low-latency local model inference to AI programming assistants like Claude Code, OpenClaw, and Cursor, enhancing coding productivity.
  • AI Researchers and Experimenters: For facilitating model research, experimentation, and benchmarking of different MLX models on Apple Silicon hardware with built-in tools.
  • Mac users with Apple Silicon and limited RAM: For enabling local LLM capabilities by efficiently managing memory with its two-tier KV cache, allowing larger models to run.
  • Users with privacy-sensitive AI applications: For ensuring data never leaves the user's device by running LLMs locally, crucial for processing confidential information.
  • AI Agent developers and users: For accelerating development and reducing time-to-market for projects requiring high-speed, reliable, and local AI inference.

how to use

How to Use oMLX

oMLX is designed for straightforward deployment on Apple Silicon Macs, managed primarily through its native macOS menu bar application. Users can configure models and server settings directly from this interface, leveraging its OpenAI/Anthropic-compatible API.

  • 1Download and install the oMLX native macOS application (v0.4.0 or later) from omlx.ai.
  • 2Access the oMLX menu bar icon to manage server status, load MLX-compatible models, and configure settings.
  • 3Load desired MLX-compatible models through the application's interface, which supports various model types.
  • 4Configure the OpenAI/Anthropic-compatible API endpoint in AI programming assistants (e.g., Claude Code, Cursor) to point to the local oMLX server.
  • 5Monitor server performance, KV cache efficiency, and model activity via the web dashboard or menu bar status surfaces.
  • 6Utilize built-in benchmark tools for evaluating model performance on specific Apple Silicon hardware configurations.

pricing

oMLX Pricing & Plans

oMLX operates on a freemium model, providing its core local LLM inference server capabilities and optimizations for Apple Silicon Macs at no cost. This allows developers and researchers to leverage its advanced caching and batching features without an initial financial investment, making local AI development accessible.

  • Freemium: Free (Includes core LLM inference server, continuous batching, two-tier KV cache, OpenAI/Anthropic-compatible API, and macOS menu bar management for Apple Silicon Macs).

Pros

  • +Highly optimized for Apple Silicon (M1/M2/M3/M4) Macs, leveraging Apple's native MLX framework for efficient inference.
  • +Two-tier KV cache (unified-memory + SSD) significantly extends usable memory, enabling larger models to run on Macs with limited physical RAM.
  • +Persistent SSD caching drastically reduces Time To First Token (TTFT) from 30-90 seconds to under 5 seconds for subsequent requests in long coding sessions.
  • +Achieves high performance, with reported speeds of 47 tokens/second and 89% cache efficiency on a 35B parameter model on an M2 MacBook Pro.
  • +Provides an OpenAI/Anthropic-compatible API, making it a direct drop-in backend for popular AI coding assistants like Claude Code and Cursor.
  • +Native macOS application with menu bar management and a web dashboard enhances user experience and control over the local inference server.

Cons

  • −Exclusively designed for macOS and Apple Silicon, limiting compatibility with other operating systems or hardware platforms.
  • −Primarily focused on MLX-native models, potentially requiring model conversion for other popular formats (e.g., GGUF) not directly supported.
  • −Earlier versions experienced initial instability, including kernel panics and Out-Of-Memory (OOM) errors, though development is ongoing (e.g., v0.4.0+ improvements).
  • −The freemium model implies potential future paid tiers or advanced features that are not currently available, which could alter accessibility.
  • −Requires a foundational understanding of local LLM inference, API configuration, and model management for optimal setup and utilization.

Similar Tools

oMLX vs Competitors

oMLX is positioned as a highly optimized, Mac-native inference server, specifically designed to exploit the unified memory architecture and MLX framework of Apple Silicon. This specialization differentiates it from broader, cross-platform solutions.

1
MLX Studio↗

MLX Studio extends oMLX's core features with a 5-layer caching stack, image generation, and a suite of agentic tools, all free for Apple Silicon.

MLX Studio offers a more comprehensive suite of features than oMLX, including image generation and a deeper caching stack, while also being free for Apple Silicon Macs.

2
MaicOn Stork

Maic is a high-performance, MLX-optimized local LLM server for Apple Silicon with a modern web-based chat interface and real-time monitoring.

Like oMLX, Maic is built on Apple's MLX framework for Apple Silicon and provides an OpenAI-compatible API, but it additionally offers a built-in web-based chat interface and one-click model downloads.

3
OllamaOn StorkCompare

Ollama simplifies running large language models locally with a focus on ease of use and a broad model library, often utilizing the GGUF format and llama.cpp.

While oMLX focuses on MLX-native optimization and advanced caching for Apple Silicon, Ollama provides a more general, easy-to-use CLI and API for running a wide range of models (primarily GGUF via llama.cpp) across various operating systems, including macOS.

4
llama.cppOn Stork

llama.cpp is a foundational project that enables efficient LLM inference on consumer hardware, particularly with GGUF models and Metal acceleration on macOS, and provides a production-ready HTTP server with an OpenAI-compatible API.

llama.cpp serves as a highly flexible and performant backend for many local LLM applications, including those on macOS, offering a command-line interface and an OpenAI-compatible API. Unlike oMLX's specific MLX framework focus and menu bar app, llama.cpp is a lower-level library and server, providing maximum control and broader model format support (GGUF).

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