GPU Infrastructure: The Five Calculations That Actually Matter New
I was building a GPU recommendation engine — one that maps workload descriptions to specific configurations, primary recommendations, and cost ranges — and kept hitting the same wall: getting the recommendations right meant going deep on every constraint that determines whether a deployment actually works. Not whether it’s affordable. Whether it works at all. VRAM has to fit the full training state, not just the model weights. Training data has to be where the GPUs are. The interconnect has to support the parallelism strategy. None of that shows up in a $/hr comparison. Here are the five calculations that come before it. ...