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| title: README |
| emoji: 🐆 |
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| |
| ## Run it on your Mac |
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| Rapid-MLX is a local OpenAI- and Anthropic-compatible inference server for Apple Silicon. |
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|
| ```bash |
| curl -fsSL https://rapidmlx.com/install.sh | bash |
| rapid-mlx serve ling-3.0-tiny-4bit |
| curl -s http://localhost:8000/v1/chat/completions -H 'Content-Type: application/json' -d '{"model":"default","messages":[{"role":"user","content":"Say hello"}]}' |
| ``` |
|
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| Use it from OpenCode / Claude Code / Codex: `rapid-mlx agents <name> --setup` · [Agent setup](https://rapidmlx.com/docs/agents) |
|
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| [Download the Desktop app](https://rapidmlx.com/download) · The example needs at least 8 GB unified memory; leave extra headroom for long contexts. |
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| [See the measured comparison](https://rapidmlx.com/compare) |
|
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| # Rapid-MLX |
|
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| **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. |
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| **Up to 4× faster than Apple's MLX (mlx-lm), 1.5× on a typical task — same model, same weights.** |
| Qwen3.5-9B 4-bit on a Mac mini M4 Pro, greedy decoding, Rapid-MLX 0.15.6 vs mlx-lm 0.32.0 at defaults, measured 2026-10-04. Per-task results, method and raw data: [rapidmlx.com/compare/mlx-lm](https://rapidmlx.com/compare/mlx-lm) |
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|
| ```bash |
| curl -fsSL https://rapidmlx.com/install.sh | bash |
| rapid-mlx serve qwen3.5-9b-4bit # OpenAI-compatible API on http://127.0.0.1:8000/v1 |
| ``` |
|
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| Any MLX model on the Hub runs the same way: `rapid-mlx serve <org/repo>` ([bring your own model](https://rapidmlx.com/docs/byom)). |
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| - Website and docs: [rapidmlx.com](https://rapidmlx.com) |
| - Source: [github.com/raullenchai/Rapid-MLX](https://github.com/raullenchai/Rapid-MLX) |
| - Which model fits your Mac: [community leaderboard](https://rapidmlx.com/leaderboard) |
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