Robotics Favor Raw GPU IaaS

The real split in GPU cloud is not cloud versus startup provider, it is raw compute versus opinionated serving. For robotics and scientific workloads, teams often need to pick the GPU type, stand up their own cluster, install custom software, and tune around FP64 precision, batch tests, and mixed training and inference jobs. In that setup, a higher level platform adds cost and removes the control that actually matters.

• This robotics customer used Voltage Park like rented bare metal. The team provisioned its own clusters, ran density functional theory workloads tied to quantum mechanics, cared heavily about floating point precision, and said switching providers would take only a day or two. That makes flexibility and price more important than managed features.
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• Fireworks solves a different job. Hebbia used it for plug and play inference of new open models through OpenAI style APIs, with built in concurrency targets, autoscaling, token logging, and fast model availability. That is valuable when the goal is serving many user requests, not custom cluster control.
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• This also explains why GPU infrastructure feels commoditized at the bottom of the stack. The robotics customer chose mainly on price and reliability, saw little differentiation across providers, and viewed newer GPUs, reservations, and guaranteed pricing as the main retention levers. Managed software only helps when it matches the workload shape.
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The market is moving toward overlap, with raw GPU clouds adding managed layers and inference platforms adding more control. Even so, the stack will keep separating by workload. Standard LLM serving will favor packaged platforms, while robotics, scientific computing, and other unusual GPU jobs will keep pulling demand toward flexible infrastructure first.