Demystifying Decoder-Only Transformers: From Theory to Efficient Inference

This article demystifies the internal workings of decoder-only transformers powering modern LLMs, enabling software engineers without deep ML expertise to understand critical optimization concepts. By clearly distinguishing between training and inference phases—particularly the role of KV caching—it provides essential insights for developers building efficient, production-scale LLM applications. The practical explanation of Masked Multi-Head Attention bridges the gap between theoretical ML knowledge and real-world implementation.