This article traces the path from RNNs to transformers, explaining why attention-based, non-recurrent architectures replaced older models. It then breaks down encoder vs decoder designs and shows how GPT’s decoder-only approach and tokenization power today’s large language models.

This article breaks down how an LLM turns your prompt into streamed tokens, covering tokenization, embeddings, transformer attention, and the two-phase pipeline of compute-bound prefill and memory-bound decode. It explains KV caching, quantization, and metrics like Time to First Token and Inter-Token Latency to show why inference speed depends on both compute and memory.

This gist provides a single-file, dependency-free implementation of a GPT-style transformer, complete with a custom autograd engine, training loop using Adam, and inference routine. It trains on a list of names, demonstrating both the core algorithm and a brief benchmark discussion for a GPU-based microgpt.cu variant.