Running a Local AI Model on Raspberry Pi (Lessons Learned)

Running AI models locally has become a popular idea, especially for privacy-focused and edge-computing use cases. But most examples assume powerful hardware. This project explores a more constrained question: can a Raspberry Pi realistically run a local AI language model, and stay stable while doing it?

Rather than aiming for performance, the goal was to understand limits—memory, storage, thermals—and see how far a small single-board computer can be pushed when running modern AI workloads.

Why Try This on a Raspberry Pi?

The Raspberry Pi is not designed for AI inference. It has limited RAM, no dedicated GPU, and relies on SD cards for storage. On paper, this makes it a poor candidate for running language models.

At the same time, that’s exactly what makes it interesting. If AI can run here—even slowly—it opens doors to offline assistants, edge automation, and educational experimentation. The value isn’t speed, but insight.

Early Attempts and Reality Checks

Initial attempts were unstable. Models would load but fail mid-inference, containers would exit without clear errors, and the system would occasionally freeze entirely. At first, it looked like the hardware simply couldn’t handle the workload.

The turning point came from stepping back and treating this as a systems problem, not an AI problem. The failures weren’t random—they were symptoms of resource exhaustion happening silently in the background.

Choosing a Model That Fits the Hardware

One of the most important lessons was that model selection matters more than software choice. Larger, popular models consistently failed on the Raspberry Pi due to memory pressure. Through testing, smaller models like TinyLlama proved to be far more realistic for Pi 4 and Pi 5 boards.

The responses are slower and simpler, but stable—and stability is the real goal on this platform.

Deployment Using Docker, Ollama, and Open WebUI

Docker was used to keep the setup clean and repeatable. Ollama handled model management, while Open WebUI provided a usable interface. This combination worked well, but Docker introduced its own overhead, which required careful tuning on low-RAM systems.

Without memory and storage planning, containerization can actually make things worse on small devices.

The Problems That Actually Matter

Three issues kept resurfacing throughout the build.

Storage limitations caused repeated failures when using small SD cards. AI models and containers consume more space than expected, and insufficient storage leads to unpredictable behavior.

Memory crashes were the most common failure. Processes were repeatedly killed by the system with no clear message until swap was configured correctly.

Thermal throttling became apparent during longer inference runs. Passive cooling wasn’t enough. Without proper cooling, performance dropped sharply and system stability suffered.

Solving these didn’t require exotic tools—but it did require understanding how the Pi behaves under sustained load.

Final Outcome

Once these constraints were addressed, the Raspberry Pi was able to run a local language model reliably. Response times are slow, and this setup is not meant to replace cloud-based AI. However, it is stable, educational, and surprisingly capable for experimentation.

More importantly, it provides a clear view into how AI workloads interact with real hardware limits.

Full Build Guide and Troubleshooting

This article focuses on the reasoning and lessons learned rather than listing every command and configuration. The complete step-by-step setup, including exact Docker commands, swap configuration, storage recommendations, and thermal fixes, is documented separately.

The full guide can be found here: Running AI Locally in Raspberry Pi

If you’re planning to replicate this project, that guide covers the details that make the difference between a system that boots once and one that actually works.