How To Build Your Own LLM Runtime From Scratch

You can build your own large language model (LLM) inference runtime from the ground up using C++ and CUDA to gain deep insight into how models operate on bare-metal hardware. A critical technique involves wrapping the decode step in a CUDA graph, which dramatically improves speed by reducing driver overhead and slashing per-token latency by over 85%. This from-scratch approach requires designing a custom binary weight format, implementing INT4 quantization, and carefully managing the grouped-query attention KV cache. While not as fast as optimized libraries like `llama.cpp`, this hands-on journey demystifies the entire stack and reveals crucial lessons learned from debugging low-level synchronization errors.