I saved a client $95/day on inference costs.

Their first reaction: "How?" My answer: "I learned CUDA."

Most ML engineers treat PyTorch as a black box. Tensors go in, predictions come out, and whatever happens on the GPU is someone else's problem.

That's fine until you're paying $100/day for inference that should cost $5.

Understanding GPU optimization — CUDA basics, Triton for custom kernels, Flash Attention, mixed precision training, quantization — is the closest thing to a salary cheat code in AI. The supply of people who understand this stuff is tiny. The demand is enormous.

I'm not saying everyone needs to write raw CUDA. But understanding WHY quantizing from fp16 to int4 works, or HOW continuous batching improves throughput, or WHAT Flash Attention actually does differently — that knowledge compounds in every project you touch.

The engineers who can make models run faster and cheaper will always have job security. Because compute costs are the #1 line item killing AI startups.