Raw GPU Compute vs Managed Inference

This reveals a hard split in the GPU cloud market between raw compute and managed inference. For robotics and HPC teams, the valuable product is rented GPU capacity they can shape themselves, not an API layer that bundles model serving, scheduling, and abstractions built for mainstream LLM use cases. That is why a provider like Voltage Park can win on price and reliability, while Lambda and Fireworks fit teams that want the platform to do more of the work.

• Fireworks sits at the managed inference end of the stack. Hebbia used it only for inference, because it exposed open models through OpenAI style endpoints, handled autoscaling and GPU scheduling, and gave token throughput and latency tooling. In that workflow, raw GPU providers were not even part of the evaluation set.
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• Lambda is closer to infrastructure, but still adds enough workflow and cluster software that some buyers see it as more opinionated than they want. Iambic used Lambda for reserved multi GPU training clusters with custom Kubernetes, storage, and InfiniBand setup, while still keeping AWS for inference and production reliability.
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• The robotics buyer is not optimizing for model catalog, fine tuning, or agent features. They are running density functional theory and other custom workloads where floating point precision matters, they install their own software, switching costs are low, and provider choice comes down mostly to price, uptime, and available inventory.
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Over time, GPU clouds will keep separating into two money pools, cheap configurable compute for bespoke workloads, and higher margin managed platforms for standard model serving. The providers that win both ends will be the ones that let customers start with bare metal economics, then add only the minimum software needed as workloads become more repeatable.