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# AI on the Raspberry Pi 5: LLM-Powered Arcades and Agentic Assistants

Published 21 Jun 2026 · Category: Knowledge · Web: https://level-floor.bnr.la/blog/ai-on-raspberry-pi-5/

*AI is moving to the edge — and the Raspberry Pi 5 is right at the front of it. Inspired by two projects in Raspberry Pi Official Magazine #166, here's how makers are running large language models and autonomous AI agents on a Pi, and why the new 16GB Raspberry Pi 5 we now stock is the ideal board to do it on.*

Artificial intelligence used to mean racks of GPUs in a distant data centre. Not any more. A new wave of projects is running large language models (LLMs) and even autonomous AI agents on a board that fits in your hand — the Raspberry Pi 5. The June 2026 issue of Raspberry Pi Official Magazine (#166) featured two standout examples, and both point to the same conclusion: with enough RAM, a Pi 5 makes a genuinely capable little AI machine.

![The Raspberry Pi 5 — the board behind both AI projects. Image: Raspberry Pi Official Magazine #166](https://cdn.tech2have.com.au/blog/ai-on-raspberry-pi-5/pi5-board.png)


## Why the Raspberry Pi 5 — and why 16GB — for AI


When you run AI workloads, memory is the headroom . Bigger models, longer context, more background tools and more of the operating system kept in RAM all compete for the same space. Both magazine projects call out RAM as the thing that matters most: the OpenClaw agent tutorial recommends a Pi 5 with 8GB RAM or higher , and the AI Arcade console was built on an 8GB Pi 4.

That's exactly why the model we stock is the 16GB Raspberry Pi 5 (BCM2712) — the top of the Pi 5 range. The extra memory is the difference between just fitting a model and having room to run it comfortably alongside a browser, a local web app, an agent's tool stack, or a local model on an AI accelerator HAT.

> **Note:** Rule of thumb for AI on a Pi: get as much RAM as you can. 4GB will boot and experiment, 8GB is the recommended floor for agents and local models, and 16GB gives you real headroom for richer generation and multitasking.


## Project 1: AI Arcade — a console that invents its own games


London developer Grigor Todorov built AI Arcade : a retro games console that never runs out of games, because it generates them on the fly with an LLM . Power it on and the Pi boots straight into a local web app in full-screen Chromium; from there it calls a cloud LLM (via the OpenAI API) to invent a brand-new mini-game each time you play.

![AI Arcade: a wooden box with a joystick that generates endless retro games. Image: Raspberry Pi Official Magazine #166](https://cdn.tech2have.com.au/blog/ai-on-raspberry-pi-5/ai-arcade-console.png)

The clever part is how lightweight it is. “First, the app asks the model to generate four questions, each with two possible answers,” Grigor explains. Your choices feed a second prompt that returns a complete HTML game, which is displayed and played with the arcade buttons. Two LLM calls, and you have a playable game in seconds. The whole app was ‘vibe-coded’ — described in plain language to an AI rather than written by hand.

![A game generated entirely by the LLM, played on the console's joystick. Image: Raspberry Pi Official Magazine #166](https://cdn.tech2have.com.au/blog/ai-on-raspberry-pi-5/ai-arcade-game.png)

“The system is able to produce simple, addictive games that evoke the nostalgia of a bygone era.”

![Inside the build: a Raspberry Pi wired to a joystick and two arcade buttons. Image: Raspberry Pi Official Magazine #166](https://cdn.tech2have.com.au/blog/ai-on-raspberry-pi-5/ai-arcade-inside.png)

Grigor built it on an 8GB Pi 4 and notes the weakest part is the visuals — exactly the kind of richer, higher-quality generation that benefits from the extra compute and memory headroom of a 16GB Pi 5 .


## Project 2: OpenClaw — an agentic AI assistant on your Pi


The second project goes further than generation — into autonomy . Magazine editor Lucy Hattersley 's tutorial sets up OpenClaw , an agentic AI assistant, on a Raspberry Pi 5. Unlike a chatbot that just answers questions, an agent takes actions : with your permission it can read your email and calendar, create and edit files, browse the web, send messages, and — thanks to the Pi's GPIO pins — control real hardware and home automation.

![Add an AI accelerator HAT and the Pi 5 can run a local LLM — no cloud, no per-token cost. Image: Raspberry Pi Official Magazine #166](https://cdn.tech2have.com.au/blog/ai-on-raspberry-pi-5/pi5-ai-hat.png)

The tutorial's hardware shopping list is a good blueprint for any serious AI Pi build: a Raspberry Pi 5 with 8GB RAM or higher , a 128GB NVMe drive (agents write to storage constantly, so skip the microSD card), a power supply, and an OpenAI API key. The Pi 5 is described as the ideal platform precisely because it gives the agent an isolated, contained environment to work in — a sandbox you control rather than turning it loose on your main computer.

> **Note:** Agency and cost: an AI agent has real power, so treat it with care. Run it on a dedicated Pi, keep an eye on what it does, set spend limits on your API key, and never share that key. Powerful tools deserve a sandbox — which is exactly what a separate Pi 5 gives you.

“We have an agentic coder running on our Raspberry Pi, ready to do all kinds of weird and wonderful things.”


## Two ways to run AI on a Raspberry Pi 5


Both projects start with a cloud LLM (the OpenAI API) because it's the quickest way to get going. But the Pi 5 can also run a model locally on an AI accelerator HAT — no internet, no per-token bill, and your data never leaves the board. Here's how the two approaches compare:

The more RAM your Pi has, the more capable the local option becomes — another reason the 16GB board is the one to build on.


## What you'll need to get started


If either project has you reaching for a soldering iron (or just an SSH session), here's the core kit:
Raspberry Pi 5 — 16GB recommended for AI headroom
NVMe SSD (e.g. 128GB) on an M.2 HAT or a USB 3.0 enclosure — agents and local models write to storage constantly, so avoid microSD
Official Raspberry Pi 5 power supply — stable power matters under load
An LLM — an OpenAI API key to start, or a local model on an AI accelerator HAT for offline, no-cost inference
A fresh install of Raspberry Pi OS (Raspberry Pi Imager), plus an internet connection for the initial setup

**Q: Can a Raspberry Pi really run AI?**

A: Yes. A Raspberry Pi 5 can run AI workloads in two ways: by calling a cloud LLM over the internet (as both the AI Arcade and OpenClaw projects do to get started), or by running a smaller model locally on an AI accelerator HAT. It won't replace a GPU server for training huge models, but for running models and AI agents at the edge it's genuinely capable.

**Q: Do I need the 16GB model specifically?**

A: You don't strictly need it, but it's the best choice for AI. The projects we cover recommend at least 8GB of RAM; AI is memory-hungry, so the 16GB Raspberry Pi 5 — the top of the range — gives you the most headroom to run bigger models and keep an agent's tools, a browser and the OS comfortable at the same time.

**Q: Do these projects need an internet connection?**

A: To start, yes — both use a cloud LLM via an API key, which needs internet. But the Pi 5 can also run a model locally on an AI accelerator HAT, which works fully offline with no per-token cost and keeps your data on the device.

**Q: Is it safe to give an AI agent control of my Pi?**

A: Treat an autonomous agent with respect. The safest approach — and the reason the OpenClaw tutorial uses a dedicated Pi 5 — is to give the agent its own isolated, contained machine rather than your main computer. Keep an eye on what it does, set spend limits on your API key, and never share that key.

Projects and images featured in Raspberry Pi Official Magazine #166 (June 2026): ‘AI Arcade’ by David Crookes (maker: Grigor Todorov) and ‘Set up OpenClaw on Raspberry Pi’ by Lucy Hattersley.

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