How LLMs Work: A 3-Level Abstraction Breakdown

This article provides a comprehensive overview of the software stack for modern Large Language Models (LLMs). The author argues that to effectively optimize performance, one must understand the interplay between three critical factors: computational power, memory hierarchy, and communication latency. The material explores the 'Roofline' model, which helps balance system throughput and response time. Rather than providing an exhaustive manual, the author shares a conceptual vision of the LLM system landscape. The focus is on how hardware constraints dictate software design and the goals developers pursue when scaling models. This overview is useful for professionals seeking a deeper understanding of the relationship between hardware and neural network software implementation, as well as the principles of building high-performance AI systems under modern constraints.