Running LLMs Locally: Privacy, Cost, and Experimentation

This article explores the advantages and methods of running large language models (LLMs) locally. While acknowledging that local LLMs won't match cloud services in performance, the author highlights their benefits for privacy, cost control, and experimental development. Three tools are presented: Ollama (user-friendly, extensive model library), Llama.cpp (cross-platform, powerful), and Llamafiles (single executable, easy sharing). The article also covers crucial aspects like model selection, parameters, quantization, and model capabilities, while cautioning about model file sizes and security. Ultimately, running LLMs locally offers developers a flexible and controllable approach to AI development.