Rapid-MLX：在苹果芯片 Mac 运行编程 Agent

Rapid-MLX is an open-source (Apache 2.0) OpenAI- and Anthropic-compatible LLM inference server for Apple Silicon, built on MLX, focused o…

Rapid-MLX Run Codex, Claude Code, OpenCode, Pi, Hermes, Crush or Goose with a local model on Mac: rapid-mlx launch <agent> Install on a stock Apple Silicon Mac with curl -fsSL https://rapidmlx.com/install.sh | bash, restart your terminal (or run export PATH="$HOME/.local/bin:$PATH"), then install your chosen coding agent and run, for example, rapid-mlx launch codex. Run the same command tomorrow; rapid-mlx launch codex --remove removes Rapid's private profile without changing your existing Codex settings. One-command guides for each agent. A first reply can take longer while a model downloads, loads, or processes the agent's opening prompt. Rapid-MLX is an open-source (Apache 2.0) OpenAI- and Anthropic-compatible LLM inference server for Apple Silicon, built on MLX, focused on reliable tool calling for coding agents. Up to 4× faster than Apple's MLX (mlx-lm), 1.5× on a typical task — same model, same weights. Decode speed, Qwen3.5-9B 4-bit on a Mac mini M4 Pro, greedy decoding, both servers at their defaults — per-task results, method, and raw data. Faster decode than oMLX and LM Studio on Qwen3.5-9B, Qwen3.8-27B, and Qwen3.6-35B Measured speed on a Mac Studio M5 Max (36 GB) — Rapid-MLX 0.16.1 vs oMLX 0.7.0 and LM Studio, using the same base 4-bit weights for each model. Model Metric Rapid-MLX oMLX LM Studio Qwen3.5-9B Decode (tok/s) 115.5 77.9 81.0 Qwen3.5-9B 4 streams (tok/s) 248.2 224.6 230.0 Qwen3.8-27B Decode (tok/s) 37.9 25.3 26.0 Qwen3.8-27B 4 streams (tok/s) 82.8 75.3 69.7 Qwen3.6-35B Decode (tok/s) 173.4 145.8 124.5 Qwen3.6-35B 4 streams (tok/s) 293.9 281.9 265.6 Qwen3.6-35B 2K-prompt first token (s, lower is better) 0.55 1.14 1.49 Single run per engine; default serving modes with Rapid's MTP text mode, oMLX's SSD cache, and LM Studio's configured context and four parallel slots. Full results and raw data. rapidmlx.com · Docs · Model mirror · Desktop app · Discord How it compares Feature facts only, taken mostly from each project's own documentation as of 2026-09-26 (sources below). For speed, see the measured results above. Rapid-MLX oMLX Ollama LM Studio mlx-lm (mlx_lm.server) License Apache-2.0 Apache-2.0 MIT Closed-source app; its MLX engine and lms CLI are MIT MIT Inference engine on a Mac MLX MLX GGML/GGUF engine, plus an MLX engine for safetensors models llama.cpp (GGUF) and MLX engines MLX Local API OpenAI (/v1/chat/completions, /v1/responses) and Anthropic Messages (/v1/messages) OpenAI and Anthropic Messages Ollama API, OpenAI-compatible, Anthropic-compatible (subset) OpenAI-compatible, Anthropic-compatible, LM Studio REST API OpenAI-style chat API GUI Rapid-MLX Desktop (macOS app) macOS menu-bar app and web admin panel Desktop app (macOS, Windows) Desktop app None (CLI / Python) Concurrent requests Continuous batching Continuous batching (mlx-lm BatchGenerator) Parallel requests per model (OLLAMA_NUM_PARALLEL, default 1) Continuous batching (llama.cpp engine; MLX engine since 0.4.2) Continuous batching (BatchGenerator); one request at a time with a quantized KV cache Prompt / KV cache Radix prefix cache in memory (with state snapshots for hybrid models), saved to disk on shutdown and restored at startup; quantized KV cache Tiered KV cache: in-memory hot tier plus SSD cold tier, reused across restarts Reuses the previous request's prompt KV cache; KV cache quantization (OLLAMA_KV_CACHE_TYPE) — In-memory prompt cache; quantized KV cache (--kv-bits) Tool-call parsing 27 parser modules, including an auto-detect fallback (rapid_mlx/tool_parsers) mlx-lm formats plus the auto-detected families listed in its README Tool calling Tool use via the OpenAI-compatible API Tool calls for models whose tokenizer declares tool-calling support Sources: oMLX README · Ollama OpenAI and Anthropic compatibility, FAQ, MLX engine · LM Studio Anthropic compatibility, parallel requests, 0.4.2 changelog, app terms, mlx-engine · mlx-lm server docs and server.py. Ollama prompt-cache reuse is from our benchmark notes. — = not verified for this table. Spot an error? Please open an issue. vs Apple's mlx-lm: per-task results and raw data at https://rapidmlx.com/compare/mlx-lm. Works with your AI stack Use Rapid-MLX as a local backend for agents, apps, or your own code. If a client accepts an OpenAI- or Anthropic-compatible endpoint, it can usually use Rapid-MLX without an adapter. For Claude Desktop, set up its in-app Gateway. Five Tier-1 agents are exercised end-to-end on real weights before release. See the tested compatibility matrix for exact API coverage and setup status. Install Desktop — macOS (Apple Silicon) The easiest way to chat locally, manage models, and use vision, files, voice, and image generation from one app. Download Rapid-MLX Desktop Browse signed Desktop releases Requires an M-series Mac; Windows and Linux desktop builds are not available yet Share Compute setup and credit ledger CLI and server — macOS (Apple Silicon) # Homebrew — prebuilt bottle from homebrew-core brew install rapid-mlx # Or the guided installer — detects RAM and recommends a starter model curl -fsSL https://rapidmlx.com/install.sh | bash Both install the same rapid-mlx CLI. Prefer uv or pip, or want to verify the installer before running it? See alternative install methods and install security. The guided installer prefers a runnable model already cached on this Mac when it fits the RAM tier. Otherwise its quick first-chat download is lfm2.5-1b-4bit below 16 GB and qwen3.5-4b-4bit at 16 GB or above; larger quality picks remain available through rapid-mlx recipe and the model picker. Submit a Community Benchmark Help other Mac users choose a model by sharing a standardized result from your own Apple Silicon Mac. Runs stay local until you explicitly choose to publish; you do not need a GitHub account or a benchmark JSON pull request. Desktop: Enable Community Benchmark in Settings → Experimental, then choose a model in the Community Benchmark tab, run it, review the result in My Results, and select Publish if you