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rMLX is a zero-Python-dependency, single-binary MLX inference backend that supports a wide range of quantization formats and KV cache optimizations, delivering an exceptional local large-model inference experience for Apple Silicon users.
Section 01
rMLX is a zero-Python-dependency, single-binary MLX inference backend that supports a wide range of quantization formats and KV cache optimizations, delivering an exceptional local large-model inference experience for Apple Silicon users.
Section 02
Section 03
rMLX is a native MLX inference and model conversion backend written in Rust, designed specifically for Apple Silicon. Its most notable feature is zero Python runtime dependency—you only need a binary file generated by cargo build --release to run it. This solves the problems of complex Python virtual environment management and slow startup in traditional MLX inference.
The project supports the most extensive weight quantization and KV cache quantization matrix available today, including some rotational KV quantization families (TurboQuant, IsoQuant, PlanarQuant, RotorQuant, ParoQuant) that other MLX servers do not yet support.
Section 04
rMLX provides OpenAI-compatible /v1/chat/completions and /v1/completions endpoints, as well as Anthropic-compatible interfaces. It supports temperature adjustment, top-k/p sampling, repetition penalty, thinking-budget, and schema-guided decoding. This means you can directly migrate your existing OpenAI client code to run on a locally hosted rMLX server without any modifications.
Section 05
image_url content section (supports data-URI, HTTP links, file paths, or base64)/v1/embeddings endpoint, including jina-v4's multimodal (text + image) embeddingsSection 06
Supports OpenAI's tool_calls and Anthropic's tool_use, including multi-turn conversations and multiple output formats (Qwen XML, Hermes-JSON, Gemma). This makes it possible to build complex Agent systems.
Section 07
rMLX leads the industry in quantization support:
Weight Quantization: Affine 2-8 bits, mxfp4/mxfp8, nvfp4, ParoQuant
KV Cache Quantization: fp8, TurboQuant, RotorQuant, PlanarQuant, IsoQuant, paged KV, mixed/asymmetric K/V, and SSD KV hierarchy
This comprehensive quantization support allows users to significantly reduce memory usage and improve inference speed while maintaining inference quality.
Section 08
Supports speculative decoding drafters such as MTP (Multi-Token Prediction), DFlash, and Eagle3, which can significantly reduce latency in long text generation. It also supports automatic prefix caching (prompt caching) to avoid redundant computations via block hashing technology.
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