"Physical AI can't be trained on internet text, like an LLM. It requires context-specific data - from images and video to LiDAR, sensor streams, and motion data - that maps directly to actions and outcomes. With variation across environments, tasks, and hardware configurations, this data is not easy to obtain. Collecting training data exclusively in the real world is slow and expensive. Virtual environments allow teams to generate synthetic data, test edge cases, and iterate faster than real-world deployment alone."
"Simulation has become a critical way to bootstrap training, but scaling it is a heavy lift. It requires orchestrating large GPU fleets, parallelizing simulations, preparing "sim-ready" 3D assets, and often using different classes of GPUs than training or inference. Inference inside simulation mirrors the forward pass on real robots, but must run at massive scale, optimized for throughput rather than latency, which creates a distinct infrastructure requirement of its own."
Physical AI and robotics require infrastructure that tightly couples large-scale simulation with real-world operations as robots move out of labs into factories, warehouses, and public spaces. Context-specific sensor data such as images, video, LiDAR, and motion streams are necessary for training and are scarce across varying environments, tasks, and hardware. Virtual environments and synthetic data accelerate data generation and edge-case testing, but scaling simulation demands orchestrating large GPU fleets, parallelizing runs, preparing sim-ready 3D assets, and using GPU classes optimized for throughput. Inference during simulation must prioritize throughput over latency, and hardware reliability, price-performance, and mean time to failure are critical cloud selection factors.
Read at Theregister
Unable to calculate read time
Collection
[
|
...
]