Ever wondered how llama.cpp runs locally, how GGUF works, or what makes LLM inference fast? Most AI developers only call high-level Python APIs — but never understand the low-level C engine that powers real-world local AI. This course teaches you to **build a complete Mini llama.cpp LLM inference engine entirely from scratch in pure standard C99**, with zero third-party dependencies.
You will hand-code every core component that professional LLM inference engines rely on: custom tensor system, GGUF file parsing, mmap zero-copy model loading, RMSNorm, SwiGLU, RoPE positional encoding, causal multi-head attention, KV Cache acceleration, INT4 quantization, autoregressive sampling, and finally build a fully working interactive chat demo.
All code is cross-platform and runs on macOS (Intel & Apple Silicon), Linux, and Windows (MinGW). By the end of this course, you will not only understand every key LLM inference algorithm at the code level — you will also own a fully functional lightweight LLM chat engine that you can compile, run, modify, and deploy on edge devices.
