Capacity constraints drive GPU reservations

This points to capacity, not raw performance, as the real bottleneck in cloud GPU buying. For teams running robotics and scientific workloads, the practical question is often not whether an H100 is faster, but whether enough GPUs can be secured in one place, for long enough, to keep jobs running. That is why older chips and reservation contracts stay attractive, even when newer hardware exists.

• In this workflow, low switching costs make compute look like a commodity, but scarce inventory changes behavior. The team said moving providers would take only a day or two, yet still accepted older GPUs and annual reservation tradeoffs when that was the only way to guarantee supply.
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• Hyperscaler scarcity creates extra engineering work. The same interview describes testing multiple regions to find GPUs, which forced software rearchitecture. Google documents that GPU reservations are zone specific, and GPU availability varies by region and zone, so geography becomes part of application design.
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• This is exactly where specialist GPU clouds fit. The broader market has split between providers selling long term reserved clusters and providers selling easier, higher level model services. For a robotics team installing its own stack and caring about FP64 and batch throughput, reserved infrastructure is the better match.
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The next step in this market is more explicit capacity products, not just cheaper hourly GPUs. Providers that can offer guaranteed blocks of supply, clean upgrade paths from older to newer chips, and fewer region constraints will win workloads that cannot pause for lack of inventory.