Run LLM Locally on Raspberry Pi: No API, Free & Offline

Running a Large Language Model (LLM) locally on a Raspberry Pi transforms the single-board computer into an offline edge-computing AI node. The system relies on Raspberry Pi OS installed onto a high-speed MicroSD Card, which serves as the primary boot drive and storage medium for the operating system, software dependencies, and model weights. Power is supplied via a stable Power Supply to ensure the CPU and RAM maintain reliable performance under heavy computational loads. Software-wise, Ollama acts as the local model runner and management framework, handling the execution of quantized, lightweight LLMs such as TinyLlama and Phi-3 directly on the Pi's architecture. Because these models are heavily optimized and compressed through quantization, they can fit within the memory constraints of a Raspberry Pi 5 or Raspberry Pi 4. When a user prompts the system, Ollama loads the model parameters into the system memory, processes the text tokens sequentially using the board's processor, and generates responses entirely on-device without transmitting any data over the internet or interacting with external cloud APIs.