Optimizing AI performance is like peeling an onion — every time you remove one bottleneck, another layer appears underneath. What looks like a compute problem t

Optimizing AI performance is like peeling an onion — every time you remove one bottleneck, another layer appears underneath. What looks like a compute problem turns out to be a memory bottleneck, which then turns out to be a scheduling issue, which reveals a parallelism mismatch… and so on. It’s a process of continuous uncovering, and unless you have visibility across the whole stack — from kernel to cluster — you’ll spend all your time slicing through surface layers with lots of tears being shed. Fortunately, there are software automation solutions to this.