Every programmer needs to know a few things about hardware, like processors, memory, and disks. Due to AI systems' extreme demand for mathematical processing power, AI engineers need to know a few things about GPUs -- the world's most popular high-throughput mathematical co-processor.

In this talk, I will explain the fundamental engineering constraints and design decisions that shape GPUs and trace those up to some counter-intuitive facts about the performance characteristics of AI systems, with actionable insights for their deployers and consumers.