Complete Guide to Building llama.cpp from GitHub for Local AI Models
TL;DR Building llama.cpp from source gives you a high-performance C/C++ inference engine for running GGUF-format language models locally without cloud dependencies. The process involves cloning the GitHub repository, installing build dependencies like cmake and a C++ compiler, then compiling with hardware acceleration flags for your CPU or GPU. The main advantage of building from source rather than using pre-built binaries is control over optimization flags and hardware support. You can enable CUDA for NVIDIA GPUs, ROCm for AMD cards, or Metal for Apple Silicon. CPU-only builds work everywhere but run slower on large models. ...