Run Ollama in Raspberry PI: Self-Hosted Generative AI
Originally published on hackaday.com by peppe8o on 30 February 2099
This tutorial by peppe8o offers a straightforward approach to installing and running Ollama, a self-hosted, open-source generative AI platform, on a Raspberry Pi. Tailored specifically for higher-end models like the Raspberry Pi 4 or 5 with 8GB of RAM, this guide emphasizes the importance of hardware requirements due to the substantial computing demands of generative AI. Ollama’s primary benefit is its ability to run large language models locally, which keeps data private and reduces dependency on external service providers, making AI more accessible for personal setups.
The tutorial first details the essential hardware and software requirements, including the recommended Raspberry Pi OS Lite for a more efficient, resource-light environment. It walks users through installing Ollama with a simple one-line command, followed by a quick system verification step to ensure the service is running correctly. The guide stresses the value of high-speed and large-capacity micro SD cards, which are crucial for storing models locally, given their considerable size.
Once installed, Ollama lets users download and interact with models from its library using straightforward commands like ollama pull
and ollama run
. The tutorial provides real-life examples of three models tested on the Raspberry Pi 5—tinyllama, llama3.1, and llama3.2—each with different performance and accuracy trade-offs. These observations help users gauge which model balances speed and quality for their needs, as lighter models tend to be faster but less accurate on specific queries.

The tutorial also introduces advanced Ollama commands for managing models, such as listing and removing models to conserve storage. Through commands like ollama list
and ollama rm
, users can see model details and free up space by deleting unnecessary files. Overall, this guide is comprehensive yet user-friendly, making it a valuable resource for those interested in exploring AI on Raspberry Pi.
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