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Physical AI is moving from research into production. Robots are increasingly trained in high-fidelity simulation before being deployed to factories, warehouses, and logistics centers, because training in the real world is slow, expensive, and often unsafe, while GPU-accelerated simulation can compress months of learning into hours.

This shifts the challenge to compute. Reinforcement learning (RL) for complex behaviors like humanoid locomotion on rough terrain is compute-intensive, with single-node training runs stretching from hours to days. Robotics teams need to iterate quickly during research and also run production-grade, long-horizon training jobs without the operational burden of maintaining compute clusters.…

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