By By Tom Fenton03/30/2026
Publication Date: 2026-03-30 00:00:00
How-To
Running AI on a Raspberry Pi, Part 2: Running AI on a Pi in Under 5 minutes
In a recent
article, I discussed running AI locally on a relatively
low-powered system, particularly a Raspberry Pi 500+. In that
article, I discussed the major software components of a local AI,
including LLMs and RAGs, what they are, and how they are used. I also
discussed why I think the Pi, with its ARM processor, 16 GB RAM, and
NVMe drive, could handle the load that AI would require. Since the Pi
500+ is based on the more popular Pi 5, I will use that model’s name
interchangeably in this article, as I did in my past article.
In this article,
I will run a Local Large Language Model (LLM) system on a
Raspberry Pi. This was a great way for me to dip my toe into AI,
and the best part was that I could do it in less than 5 minutes!
Which LLM on a Pi
Recent advances
in model architecture and aggressive quantization have enabled the
running of AI models on extremely small devices, such as the
Raspberry Pi. People have reported that LLMs in the 1–4 billion
parameter range can now deliver impressive performance for tasks such
as text generation, reasoning, coding, tool calling, and even
vision understanding, all without requiring GPUs, cloud resources, or
heavy…
