---
title: "Mastering Edge AI on Raspberry Pi with LiteRT and Gemma"
url: https://daily.dev/posts/mastering-edge-ai-on-raspberry-pi-with-litert-and-gemma-ueibnnbvo
source_url: https://developers.googleblog.com/mastering-edge-ai-on-raspberry-pi-with-litert-and-gemma
type: article
source: "Google Developers"
published: 2026-08-11T15:46:13.723Z
updated: 2026-08-14T07:05:05.846Z
tags: ["raspberry-pi", "local-ai", "gemma"]
reading_time: 7
upvotes: 0
comments: 0
language: en
---

> ## Documentation Index
> Fetch the complete documentation index at: https://daily.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# Mastering Edge AI on Raspberry Pi with LiteRT and Gemma

**[Google Developers](https://daily.dev/sources/googledevs)** · 7 min read · 0 upvotes · 0 comments

## Summary

Google AI Edge's LiteRT runtime enables deploying Gemma language models directly on Raspberry Pi 5 for fully offline, real-time edge AI applications. On a Raspberry Pi 5, LiteRT-LM achieves 99 tokens/sec prefill and 9 tokens/sec decode for Gemma 4 E2B with a 1432 MB peak memory footprint, translating to ~27.3 characters/sec (~300 wpm) end-to-end generation. The post demonstrates a dual CPU/GPU architecture where LLM inference runs on the ARM Cortex-A76 CPU while vision and audio models offload to the VideoCore VII GPU via LiteRT's WebGPU (Vulkan) backend. A Reachy Mini robot demo illustrates real-time perception and response entirely offline. Setup is straightforward via `pip install litert-cli`, with models pulled from the LiteRT Hugging Face Community. Hailo AI accelerator support is also announced as coming soon.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://developers.googleblog.com/mastering-edge-ai-on-raspberry-pi-with-litert-and-gemma>

## Questions this post answers

### What inference performance does Gemma 4 E2B achieve on a Raspberry Pi 5 with LiteRT-LM?

On a Raspberry Pi 5, LiteRT-LM running Gemma 4 E2B achieves 99 tokens/sec for prefill and 9 tokens/sec for decode, with a peak memory footprint of 1432 MB. Thanks to the model's efficient tokenizer averaging ~4.2 characters per token, end-to-end generation reaches ~27.3 characters/sec, roughly 300 words per minute — twice the speed of normal human speech.

_Developers targeting Raspberry Pi robotics or voice applications track LiteRT and Gemma performance updates on daily.dev._

### How do I install and run a Gemma model on Raspberry Pi 5 using LiteRT CLI?

Install LiteRT CLI with `pip install litert-cli`, then set your Hugging Face token via `export HUGGING_FACE_HUB_TOKEN=<token>` and run `litert lm run --from-huggingface-repo=litert-community/gemma-4-E2B-it-litert-lm gemma-4-E2B-it.litertlm`. Models are pulled directly from the LiteRT Hugging Face Community, and the CLI handles conversion, quantization, and inference in a single unified command set.

_Engineers shipping edge AI prototypes find the latest LiteRT tooling news on daily.dev before it hits the docs._

### How does LiteRT split workloads between CPU and GPU on Raspberry Pi 5 for real-time AI?

LiteRT uses a dual-processing architecture where LLM inference runs on the quad-core ARM Cortex-A76 CPU (~153.6 GFLOPS FP32, ~2.0 TOPS INT8), while continuous vision and audio models are offloaded to the Broadcom VideoCore VII GPU via LiteRT's WebGPU (Vulkan) backend through ML Drift. This preserves CPU cycles for high-priority tasks and improves thermal efficiency.

_Robotics developers balancing compute budgets on constrained hardware follow edge AI architecture discussions on daily.dev._

---

Tags: [#raspberry-pi](https://daily.dev/tags/raspberry-pi), [#local-ai](https://daily.dev/tags/local-ai), [#gemma](https://daily.dev/tags/gemma)

[View this post on daily.dev](https://daily.dev/posts/mastering-edge-ai-on-raspberry-pi-with-litert-and-gemma-ueibnnbvo)