Small, inexpensive single-board computers like the Raspberry Pi 5 are becoming viable platforms for running local large language models (LLMs). By utilizing quantization techniques to reduce model size and memory requirements, users can run quantized versions of popular models such as Llama 3, Mistral, and Qwen. While processing speeds remain limited compared to high-end GPUs, these devices offer a private and low-cost way to implement AI for specific tasks.
- Quantization allows large models to fit into the Pi's limited RAM by reducing numerical precision.
- Tiny models (1B-3B parameters) run comfortably, while 7B parameter models are usable on 8GB versions with managed expectations.
- Performance is measured in low single-digit tokens per second, making it suitable for non-real-time tasks.
- Hardware upgrades like the Raspberry Pi AI HAT+ or external eGPUs can significantly boost neural processing capabilities.
- Quantization allows large models to fit into the Pi's limited RAM by reducing numerical precision.
- Tiny models (1B-3B parameters) run comfortably, while 7B parameter models are usable on 8GB versions with managed expectations.
- Performance is measured in low single-digit tokens per second, making it suitable for non-real-time tasks.
- Hardware upgrades like the Raspberry Pi AI HAT+ or external eGPUs can significantly boost neural processing capabilities.
This article guides you through the process of building a local RAG (Retrieval-Augmented Generation) system using Llama 3, Ollama for model management, and LlamaIndex as the RAG framework. The tutorial demonstrates how to get a basic local RAG system up and running with just a few lines of code.
