Making Deep Learning Go Brrrr from First Principles

# Summary The article explains how to improve deep learning performance by reasoning from first principles rather than applying ad-hoc tricks. It identifies three key components of deep learning efficiency—compute (actual floating-point operations), memory bandwidth (tensor transfers), and overhead (everything else)—and argues that understanding which bottleneck you face allows you to focus optimizations effectively. The piece emphasizes that compute utilization is increasingly challenging because GPU compute capacity grows faster than memory bandwidth, making it critical for engineers to diagnose whether a system is compute-bound, memory-bound, or overhead-bound before attempting optimizations.