A tech enthusiast successfully configured a Raspberry Pi 5 to host small-scale large language models using llama.cpp, Open WebUI, and Tailscale for remote access to local AI tools.

Key Points

• The project utilized a Raspberry Pi 5 (8GB) running Raspberry Pi OS Lite to minimize system resource overhead.
• Llama.cpp was selected over Ollama for its superior performance and efficiency on single-board computer hardware.
• The setup successfully ran the Qwen3.5-0.8B model and achieved 5.6 tokens per second with the Llama-3.2-3B model.
• Open WebUI was deployed via Docker to provide a user-friendly interface for interacting with the hosted models.
• Tailscale was implemented to enable secure, remote access to the LLM workstation without exposing router ports to the internet.

Why it Matters

This experiment demonstrates the feasibility of running lightweight AI models on edge hardware for niche, low-power computing tasks. While not a replacement for high-end GPU-based systems, it provides a practical blueprint for developers looking to build portable or standalone AI-powered appliances.