CoreWeave's Stack for Physical AI | CoreWeave Blog

This post details CoreWeave's infrastructure stack for 'Physical AI' workloads, which involve real-world perception, decision-making, and action. It highlights how companies use CoreWeave for simulation-heavy training (MuJoCo, NVIDIA Isaac Sim, CARLA), multi-modal sensor fusion, and millisecond-latency inference. Specific examples include robotic manipulation, synthetic data generation, autonomous vehicle simulation, and reinforcement learning. The post emphasizes the importance of a robust infrastructure to support the 'observe, curate, improve, evaluate, run' flywheel for physical AI development, showcasing reference pipelines and blueprints available on CoreWeave.