Reachy Mini goes fully local

After building your Reachy Mini, you'll install the conversation app and start talking to it. Until now, you had to send your audio to a server. But not anymore. Today we'll walk you through running the whole stack locally.

This stack is powered by speech-to-speech , our cascaded VAD → STT → LLM → TTS pipeline that exposes a Realtime API-compatible /v1/realtime WebSocket. Once you launch the backend, point the robot at it from the UI.

Cascades are the most flexible option in the open-source landscape today, and with the right pieces they're also the fastest. We'll recommend the components we like best, but the whole point of a cascade is that you can swap them. New models drop every week.

Same idea, but using vanilla transformers . Use this if you are on a CUDA box, on Linux, or if you want to swap models freely without re-converting weights for MLX.

The LLM is the layer with the most impact on latency and overall performance of the system. We support two options: run a model locally (llama.cpp, MLX, Transformers, vLLM), or use a server with a Responses API (OpenAI, Gemini, HF Inference Endpoints, llama.cpp, vLLM, etc).

The main bottleneck in the system is LLM inference latency. To address that, we support external inference engines exposed through the Responses API protocol.

Requires vLLM ≥ 0.21.0. Full support for the Responses API protocol, including tool-call streaming used by the speech-to-speech backend, landed in vLLM 0.21.0. Older versions will boot but trip up as soon as the assistant tries to call a tool.

This blog walks you through running conversations with Reachy Mini fully locally. No cloud, no API keys, no data leaving your machine. Here's a video showing this live:

To serve the LLM, we'll use Hugging Face's llama.cpp . If you need to install it, the simplest way is brew install llama.cpp or winget install llama.cpp , for more help, check the docs . First, we'll run:

And done! The first time it will download the model, subsequent launches are fast.