Robotics GPU Ownership Threshold

The real threshold is not a magic dollar number, it is the point where compute has become a fixed industrial input instead of a flexible developer tool. At that stage, buying GPUs can beat renting because the company can spread a large upfront hardware purchase across constant training and inference demand, but only if it is ready to run racks, power, cooling, networking, and uptime like a small data center.

• This robotics team already uses both owned GPUs and Voltage Park, runs several A100s for training and inference, and says switching cloud providers takes only a day or two. That means cloud remains attractive until spend is high enough that lower unit cost matters more than flexibility and speed.
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• The cost math becomes concrete fast. Lambda lists A100 instances at about $1.48 per GPU hour and H100 instances around $2.49 to $3.78 per GPU hour, while CoreWeave advertises committed discounts and lists 8x H100 InfiniBand instances at $49.24 per hour, or about $6.16 per GPU hour. Sustained usage at that level can push monthly bills into the range where owning hardware deserves a serious model.
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• The market is splitting by workload. Large training buyers want reserved clusters and predictable pricing, while teams with unusual robotics and scientific workloads often just want raw infrastructure they can configure themselves. Voltage Park has started adding managed Kubernetes, but this customer still values cheaper reserved capacity and reliability over higher level software.
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As GPU demand becomes steadier and older NVIDIA generations stay useful for batch workloads, more customers will make a blended choice, owning a base layer of compute and renting cloud capacity for bursts. That favors providers that can keep prices low, guarantee reservations, and act as overflow capacity rather than trying to force customers into heavier platform lock in.