Google DeepMind has shown that its Gemma AI model can run directly on small robots and edge devices like Raspberry Pi computers, enabling machines to see, hear, and take action entirely offline without sending data to the cloud. The demonstration featured Reachy Mini, an open-source robot from Hugging Face and Pollen Robotics, which uses Gemma to have conversations, recognize its surroundings through cameras, move its head expressively, and even reason about chess moves.

Why Does Running AI Locally on Robots Matter?

The shift toward local, on-device AI represents a significant change in how robotics and smart devices operate. When AI models run directly on hardware rather than sending data to cloud servers, two critical benefits emerge: privacy and speed. Data never leaves the device, meaning conversations, video feeds, and sensor readings stay completely private. Additionally, the robot responds instantly without waiting for information to travel across the internet.

This approach opens doors for robotics in sensitive environments like homes, classrooms, and research facilities where privacy concerns would otherwise limit deployment. Ian Ballantyne, Developer Relations Engineer at Google DeepMind, demonstrated how Gemma enables this capability on consumer-grade hardware.

What Can a Robot Do With Gemma Running Locally?

The Reachy Mini demonstration showcased several practical applications that hint at the broader potential of local AI on robots. The robot can control smart home devices and APIs, including lights, thermostats, and calendars. It can also fetch live data and manage information without relying on cloud connectivity. Beyond home automation, the robot showed early reasoning abilities, such as analyzing a physical chessboard through its cameras and explaining how chess pieces move.

The system combines multiple AI components working together seamlessly. Gemma serves as the core language model, while other specialized models handle specific tasks like detecting when someone is speaking, converting speech to text, and converting the robot's responses back into natural-sounding speech.

How to Run Gemma on Your Own Robot or IoT Device

• Single-Board Computer Setup: Deploy highly compressed versions of Gemma directly on edge devices like the NVIDIA Jetson Orin Nano with 8GB of memory or a Raspberry Pi 5, enabling fully offline operation without external hardware.
• Local Companion PC Method: Run the heavier AI model backend on a Mac or PC, such as an M3 Pro MacBook or dedicated AI workstation, while the robot's interface connects to your local network.
• Browser-Based Execution: Use a web browser with WebGPU and WebSerial protocols to run Gemma entirely offline through Transformers.js, requiring only a USB-C connection to your device.

For those implementing a fully offline setup, the recommended approach involves several layers working in concert. First, deploy a local speech backend using Hugging Face tools that handle voice activity detection, speech-to-text conversion, and text-to-speech synthesis. The system then connects the robot's microphones and cameras to this local processing pipeline through a WebSocket connection, allowing real-time audio and video streaming without internet access.

Once connected, the robot can execute actions based on what Gemma decides to do. When the model determines that the robot should take a picture, move its head, or interact with a smart device, it outputs structured commands that trigger physical movements and device control through the robot's Python SDK.

What Real-World Applications Are Emerging?

The practical use cases demonstrated by Reachy Mini point toward several emerging applications. Offline chess play allows the robot to visually inspect a physical chessboard and explain moves or rules without any internet connection. Low-latency conversations enable the robot to chat naturally and responsively, giving it a fluid personality that doesn't depend on cloud response times. Total privacy isolation means the robot can operate securely in private homes, classrooms, or confidential research spaces where data protection is paramount.

Google DeepMind's focus on making Gemma work on small, affordable hardware reflects a broader industry trend toward democratizing AI. Rather than requiring expensive cloud subscriptions or powerful GPUs, developers and hobbyists can now experiment with capable AI models on devices they already own or can afford to purchase. This accessibility could accelerate innovation in robotics, smart home automation, and edge AI applications across industries.

For those interested in exploring this technology, Google DeepMind has published comprehensive documentation and code examples through its Gemma Cookbook and official documentation, making it possible for developers to start building their own AI-powered robots and IoT devices today.