If you’ve worked with AI workloads long enough, you already know this: The hardest part isn’t building the model. It’s running it reliably. You pick a GPU → it OOMs. You switch providers → capacity disappears. You fix configs → CUDA breaks. You retry → stuck in queue. At some point, you’re not doing ML anymore. You’re debugging infrastructure. The Problem: GPU Roulette Today’s workflow looks like