Practical guide to deploying LLMs on low-power devices. Learn to run Llama.cpp on Raspberry Pi 5 with GGUF quantization for IoT and home automation.

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A practical, step-by-step guide to deploying quantized LLMs on a Raspberry Pi 5 using Llama.cpp and GGUF-format models. Covers OS setup (Raspberry Pi OS Lite 64-bit, swap configuration), building Llama.cpp from source with ARM NEON/dotprod optimizations, selecting appropriate Q4_K_M quantized models (TinyLlama 1.1B through Phi-3 Mini 3.8B), running interactive inference, and exposing an OpenAI-compatible HTTP API for IoT and Home Assistant integration. Includes concrete performance benchmarks (12–18 tok/s for 1.1B, 4–7 tok/s for 3.8B), security guidance for API key management, thermal throttling monitoring, memory management tips, and a frank discussion of the quality ceiling of sub-4B models versus cloud LLMs.

Running LLMs on Raspberry Pi and Edge Devices

Setting Up the Raspberry Pi for AI Workloads

Limitations and When to Choose Cloud Instead