1
ZenMux
Shares tags: ai
AI ToolDead Man Walking
oMLX Review
oMLX is a native macOS LLM inference server built on Apple's MLX framework, offering continuous batching and a two-tier KV cache with an OpenAI/Anthropic-compatible API.
shipped May 31, 2026aifreemium
1oMLX processes a Qwen 3.6 35-billion parameter 4-bit model with 89% cache efficiency, achieving an average generation speed of 47 tokens per second on an M2 MacBook Pro.
2The server's continuous batching and SSD caching can accelerate AI agent prefill speeds by 5.1x to 5.7x compared to raw MLX.
3Version 0.3.9.dev2, released May 13, 2026, integrated Gemma4's MTP visual path and DFlash engine, enhancing multi-modal decoding speed.
4Persistent SSD caching reduces Time To First Token (TTFT) from 30-90 seconds to under 5 seconds for subsequent requests in long coding sessions.
Stork Quadrant
Dead Man Walking· 0/100
An LLM can do most of what this tool's UI promises. No moat, no agent presence.
Confidencemedium(3 runs · ±18)
“This is a local inference runner with Apple Silicon optimizations. The MLX-specific performance gains are real but temporary — Apple will improve MLX, Ollama already targets Apple Silicon, and LM Studio ships a polished UI. There is no moat here: no proprietary data, no network effects, no regulatory gate, nothing that compounds. This will get absorbed by a better-funded competitor or by Apple itself.”
Defensibility · 0/100
- Physical-world coupling
- Regulatory moat
- Network liquidity
- Proprietary refreshing data
- High-trust catastrophic workflows
- Multi-party coordination
- Brand / community / taste
An LLM alone could replace
- Run an LLM locally and answer coding questions — any local inference runtime does this
- Provide an OpenAI-compatible API endpoint — Ollama, LM Studio, llama.cpp all do this today
- Manage model downloads and switching — standard feature of every local inference tool
- Serve as a backend for Cursor or Claude Code — any OpenAI-compatible server already works
Agent-Readiness · 0/100
- Verified MCP
- Listed on agent surfaces
- Usage-based pricing
- Headless agent auth
- Public OpenAPI
- Active changelog
- llms.txt
How to defend
Stop being a generic inference server and own a specific workflow — enterprise air-gapped Mac fleets where IT needs centralized model management and audit logs, or become the inference layer that agent frameworks call via a stable SDK with SLAs. Generic local inference is a race to zero.
- Ship an MCP server and list it on Stork — biggest single point gain (+25).
- Get listed in the Anthropic MCP registry, Cursor, or Claude Desktop (+20).
- Add a usage-based or per-call tier; per-seat-only pricing dies when agents replace seats (+15).
- Expose API-key auth with a self-serve sandbox tier; remove sales-call gates (+15).
- Publish an OpenAPI spec at /openapi.json or /.well-known/openapi (+10).
oMLX at a Glance
About oMLX
Similar Tools
Compare Alternatives
Other tools you might consider
View on Stork→
2
theORQL
Shares tags: ai
3
General Compute
Shares tags: ai
4
Edgee Fallback Models
Shares tags: ai
</>Embed "Featured on Stork" Badge▼
<a href="https://www.stork.ai/en/omlx" target="_blank" rel="noopener noreferrer"><img src="https://www.stork.ai/api/badge/omlx?style=dark" alt="oMLX - Featured on Stork.ai" height="36" /></a>
[](https://www.stork.ai/en/omlx)
overview
What is oMLX?
oMLX is a specialized AI inference server developed as an open-source project leveraging Apple's MLX framework that enables developers, AI researchers, and Mac users with Apple Silicon to optimize the performance of large language models (LLMs) and other AI models locally. It features a two-tier (unified-memory + SSD) KV cache and continuous batching to enhance local execution efficiency on macOS 15+.
Functioning as a local LLM inference server, oMLX significantly improves the speed and efficiency of running AI models directly on Apple Silicon hardware. Its 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 approach effectively extends a Mac's usable memory for AI tasks, supporting models that might otherwise exceed physical RAM limits. The server provides an OpenAI/Anthropic-compatible API, allowing it to serve as a drop-in backend for various AI programming assistants and applications.
quick facts
Quick Facts
| Attribute | Value |
|---|---|
| Developer | Open-source project leveraging Apple's MLX framework |
| Business Model | Freemium |
| Pricing | Freemium |
| Platforms | macOS |
| API Available | Yes |
| Integrations | Claude Code, Cursor, Codex, OpenClaw, Hermes Agent |
features
Key Features of oMLX
oMLX is engineered with specific features to maximize local AI inference performance on Apple Silicon, focusing on efficient memory management and API compatibility. These capabilities enable developers and researchers to deploy and experiment with large language models directly on their macOS devices.
- 1Native macOS inference server optimized for Apple Silicon (M1, M2, M3, M4 chips).
- 2Continuous batching for improved throughput and reduced latency in sequential requests.
- 3Two-tier (unified-memory + SSD) KV cache, intelligently managing active context in RAM and offloading older context to SSD.
- 4OpenAI/Anthropic-compatible API for broad integration with existing AI tools and frameworks.
- 5Managed directly from the macOS menu bar for simplified control and monitoring.
- 6Paged SSD KV caching, enhancing memory efficiency for long contexts and large models.
- 7Drop-in API compatibility for AI programming assistants such as Claude Code, OpenClaw, and Cursor.
- 8Support for deploying and serving multiple model types simultaneously, including LLM, VLM, embedding, and reranker models.
- 9Integrated Gemma4's MTP visual path, DFlash engine, and ParoQuant quantization technology (Version 0.3.9.dev2).
- 10Rewritten memory guard for enhanced stability on low-memory Macs (Version 0.3.11).
use cases
Who Should Use oMLX?
oMLX is designed for specific user groups who require high-performance, privacy-preserving, and efficient local AI inference capabilities on Apple Silicon Macs. Its architecture caters to both development and research needs, particularly for those working with large language models and AI agents.
- 1**Developers and Programmers:** Especially those utilizing AI coding tools like Claude Code, Cursor, and Codex, requiring low-latency local model inference for enhanced productivity.
- 2**AI Researchers and Experimenters:** For facilitating model research, including benchmarking MLX models, and testing various AI architectures directly on Apple Silicon hardware.
- 3**Mac Users with Apple Silicon and Limited RAM:** Seeking to run large language models locally more efficiently than alternatives, leveraging the two-tier KV cache to extend usable memory.
- 4**Users with Privacy-Sensitive AI Applications:** Enabling local execution of LLMs to ensure data remains on-device, suitable for processing confidential information.
- 5**AI Agent Developers and Users:** Benefiting from continuous batching and advanced caching mechanisms that significantly accelerate multi-turn interactions and complex agentic workflows.
pricing
oMLX Pricing & Plans
oMLX operates on a freemium model, providing its core inference server functionality and optimizations for Apple Silicon Macs at no cost. This allows developers and researchers to leverage its advanced features, such as continuous batching and two-tier KV caching, without an initial financial investment. Specific premium tiers or subscription plans for advanced features, enterprise support, or managed services are not publicly detailed as of current information, but the foundational tool remains accessible.
- 1Freemium: Core functionality available at no cost.
competitors
oMLX vs Competitors
oMLX is positioned as a highly optimized, Mac-native inference server built directly on Apple's MLX framework, specifically designed to exploit the unified memory architecture of Apple Silicon. This specialization differentiates it from broader, cross-platform solutions by focusing on performance and efficiency within the Apple ecosystem.
1
Ollama simplifies running large language models locally with a focus on ease of use and a broad model library, utilizing the GGUF format and llama.cpp.
While Ollama is generally easier to set up and offers a wider range of models, oMLX, built on Apple's MLX framework, often demonstrates superior performance on Apple Silicon, particularly for long-context coding agent workflows due to its advanced caching and continuous batching.
2
LM Studio provides a user-friendly graphical interface for downloading and running a diverse selection of GGUF models locally, complete with an OpenAI-compatible API.
LM Studio is a popular choice for local AI on Mac due to its straightforward installation and intuitive UI. However, oMLX's native MLX optimizations and two-tier KV cache can offer significantly faster generation speeds and more efficient memory management for extended conversations on Apple Silicon, where LM Studio may consume more RAM and experience slowdowns.
3
MLX Studio↗
MLX Studio is positioned as a comprehensive local AI application for Mac, extending oMLX's core features with a 5-layer caching stack, image generation, and a suite of agentic tools.
MLX Studio claims to encompass all of oMLX's functionalities, including continuous batching and SSD KV caching, while adding advanced capabilities like Flux image generation, over 20 agentic tools, and JANG adaptive quantization, making it a more feature-rich offering.
4
Jan.ai↗
Jan.ai is an open-source, offline AI platform that supports local LLMs and integrates cloud services, offering an OpenAI-compatible API on localhost across various hardware.
Jan.ai provides a robust open-source solution for running local LLMs with an OpenAI-compatible API, similar to oMLX's offering. While oMLX focuses specifically on Apple Silicon's MLX framework for optimized performance and advanced caching, Jan.ai emphasizes broader hardware compatibility and custom assistant creation.
❓
Frequently Asked Questions
+What is oMLX?
oMLX is a specialized AI inference server developed as an open-source project leveraging Apple's MLX framework that enables developers, AI researchers, and Mac users with Apple Silicon to optimize the performance of large language models (LLMs) and other AI models locally. It features a two-tier (unified-memory + SSD) KV cache and continuous batching to enhance local execution efficiency on macOS 15+.
+Is oMLX free?
Yes, oMLX operates on a freemium model. Its core inference server functionality and performance optimizations for Apple Silicon Macs are available at no cost. Specific premium tiers or subscription plans for advanced features or enterprise support are not publicly detailed.
+What are the main features of oMLX?
Key features of oMLX include its native macOS inference server optimized for Apple Silicon, continuous batching, a two-tier (unified-memory + SSD) KV cache, and an OpenAI/Anthropic-compatible API. It is managed from the macOS menu bar and supports various model types, including LLM, VLM, embedding, and reranker models.
+Who should use oMLX?
oMLX is primarily intended for developers and programmers using AI coding assistants, AI researchers and experimenters, Mac users with Apple Silicon and limited RAM seeking local LLM capabilities, and users requiring privacy-sensitive AI applications to run locally. It is also beneficial for AI agent developers and users.
+How does oMLX compare to alternatives?
oMLX differentiates itself from alternatives like Ollama and LM Studio by its deep optimization for Apple Silicon using Apple's MLX framework, offering superior performance for long-context workflows and more efficient memory management via its two-tier KV cache. While competitors may offer broader model support or user-friendly GUIs, oMLX focuses on maximizing speed and efficiency specifically on macOS.
For builders
This page is doing a job for someone else’s tool.
AI agents read it. Buyers find it. Backlinks accrue. Your tool can have one too — live in 24 hours, indexed by Claude, ChatGPT, and Perplexity, queryable via MCP.