Physical AI is redefining what robots can do in the real world – but delivering it at scale requires far more than onboard compute. From real-time perception to large-scale simulation and fleet coordination, modern robotics depends on robust cloud infrastructure and high-speed connectivity to operate reliably in dynamic environments.

In this use case, we explore how physical AI workloads run across cloud GPU clusters, near-edge compute, and advanced 5G and emerging 6G networks to support continuous learning and decision-making. Cloud infrastructure plays a critical role by enabling teams to train and iterate on complex models, simulate real-world conditions at scale, and centrally manage distributed robotic fleets – without the constraints of fixed, on-prem resources.

If you’re building or deploying robotics systems in manufacturing, logistics, agriculture, healthcare, construction, or industrial automation, this use case provides a clear view of how cloud and next-generation networks collaborate to unlock scalable, production-ready physical AI.

Read the full use case to see how these technologies come together to support faster innovation and real-world impact.