SyncAI.news, a Varaisys broadcasting
NVIDIA brings agents to life with DGX Spark and Reachy Mini
HF

Hugging Face Blog

· 2 min read

AI LabsHugging Face Blog

NVIDIA brings agents to life with DGX Spark and Reachy Mini

Today at CES 2026, NVIDIA unveiled a world of new open models to enable the future of agents, online and in the real world. From the recently released NVIDIA Nemotron reasoning LLMs to the new NVIDIA Isaac GR00T N1.6 open reasoning VLA and NVIDIA Cosmos world foundation models, all the building blocks are here today for AI Builders to build their own agents.

But what if you could bring your own agent to life, right at your desk? An AI buddy that can be useful to you and process your data privately?

In the CES keynote today, Jensen Huang showed us how we can do exactly that, using the processing power of NVIDIA DGX Spark with Reachy Mini to create your own little office R2D2 you can talk to and collaborate with.

This blog post provides a step-by-step guide to replicate this amazing experience at home using a DGX Spark and Reachy Mini.

Let’s dive in!

Ingredients

If you want to start cooking right away, here’s the source code of the demo.

We’ll be using the following:

  1. A reasoning model: demo uses NVIDIA Nemotron 3 Nano
  2. A vision model: demo uses NVIDIA Nemotron Nano 2 VL
  3. A text-to-speech model: demo uses ElevenLabs
  4. Reachy Mini (or Reachy Mini Simulation)
  5. Python v3.10+ environment, with uv

Feel free to adapt the recipe and make it your own - you have many ways to integrate the models into your application:

  1. Local deployment – Run on your own hardware (DGX Spark or a GPU with sufficient VRAM). Our implementation requires ~65GB disk space for the reasoning model, and ~28GB for the vision model.
  2. Cloud deployment– Deploy the models on cloud GPUs e.g. through NVIDIA Brev or Hugging Face Inference Endpoints.
  3. Serverless model endpoints – Send requests to NVIDIA or Hugging Face Inference Providers.

Giving agentic powers to Reachy

Reachy Mini is designed to be customizable. With access to sensors, actuators, and APIs, you can easily wire it into your existing agent stack, by simulation or real hardware controlled directly from Python.

Original source

This story was published by Hugging Face Blog. SyncAI.news shows a preview; the complete article is on the publisher's site.

Read the full story on huggingface.co

Similar News