Your team just hit VRAM OOM during a demo prep run. The A100 40GB you provisioned for a Llama-3-70B deployment looked fine on paper until the KV cache ballooned at 8K context. You could throw two H100s at it and move on, or you could run the 30 seconds of arithmetic you skipped before provisioning. Four decisions separate teams that run GPUs above 70% utilization from those idling at 35% while paying full price: workload classification, VRAM calculation, instance selection, and pricing model alignment. Get any of them wrong, and you’ll either hit a production ceiling or burn budget on capacity you can’t fill. Once all four are locked in, deployment is the execution step that wires them together. Start with your workload class, not the GPU spec sheet Workload classification comes first because training, fine-tuning, and inference each leave a different compute signature on the hardware, and that signature is what tells you which GPU to rent.…