Inside llama.cpp: How Local LLMs Run on Your Hardware

This article explains how llama.cpp enables large language models to run efficiently on consumer hardware by converting models into the GGUF format and applying quantization to reduce memory requirements. It details the underlying pipeline of tokenization, matrix math, and hardware backends, demonstrating that local models can be executed effectively on standard CPUs and GPUs without requiring enterprise-grade infrastructure. Open article →