Build a Mini llama.cpp in Pure C: LLM Inference Engine from

About this course

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.

You will master low-level LLM inference by coding llama.cpp core components in standard C. Implement model loading, acceleration, quantization and a working chatbot.

This is the most practical low-level LLM engineering course for developers who want to move beyond AI framework black boxes and master real inference engine programming.

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