Wayve, Decart, NEURA Robotics, Nissan: All Running Physical AI on CoreWeave

CoreWeave's Stack for Physical AI | CoreWeave Blog

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A robot learns to fold laundry by training on thousands of simulated attempts before it ever touches real fabric. A self-driving car learns to react to a pedestrian stepping into the road, trained on millions of simulated miles before it ever drives on the road. A drone wing gets tested ten thousand times in simulation before a single prototype is built. A global car manufacturer turns historical test data into a faster vehicle development cycle. Companies like Wayve, Decart, NEURA Robotics, nTop, and Nissan don't share an industry, and they don't sit at the same point in the physical AI loop: some run the loop from simulation through deployment, others lean on a single step. What they share is the stack underneath. None of this work runs on a general-purpose cloud stitched together after the fact. This covers that stack: compute, orchestration, tooling, and engineering expertise, and how teams draw on different parts depending on where their workload sits in the loop. What makes physical AI different Physical AI closes the gap between perception and action: sensors gather real-world input, vision and 3D models interpret it, other models decide what to do, and actuators carry it out in real time. This dynamic introduces constraints conventional AI never has to deal with: millisecond latency requirements, safety-critical reliability standards, models that reason over three-dimensional space and visual input instead of flat text, and training data that often has to be generated through physics simulation rather than scraped from the Internet. Large language models were trained on the text data already sitting on the Internet. There is no equivalent library of footage for a robot learning to fold laundry or a vehicle learning to react to a pedestrian stepping into the road, so physical AI teams have to generate that training data themselves, through simulation or synthetic alterations of the limited real-world data they do have, rather than entirely relying on collection of real-world data. What physical AI infrastructure has to do Those real-world constraints translate into five requirements for the infrastructure physical AI actually needs: Simulation-generated training data. The compute burden starts before training even begins: infrastructure has to process massive volumes of real footage, curating it and generating synthetic versions, as well as rendering and simulating the environments and object interactions models learn from. Multi-modal sensor fusion. Vision, depth, force, and proprioceptive data all arrive simultaneously and need to be processed together, which demands more memory bandwidth than single-modality workloads. Millisecond inference. A two-second response is fine for a chatbot. It's not fine for a robot arm or a vehicle braking for an obstacle. Continuous retraining loops. Edge cases accumulate fast once a system is deployed, so checkpointing and fast data pipelines become operational requirements, not nice-to-haves. Fault tolerance with real consequences. A model failure in software produces a bad output. In a physical system, it can produce a dangerous one. That's why, for physical AI, most of that work runs in simulation and staged validation before a model earns the right to run in the real world, not in live production the way the AI loop describes for most software AI.

For a deeper walkthrough of the category, see CoreWeave's explainer: What Is physical AI?

The physical AI loop: observe, curate, improve, evaluate, run Meeting those requirements takes a flywheel: observing both real sensor data and simulated environments, curating and generating synthetic versions of it, improving models by training various forms with it, calling for predictions from those models in evaluation before deploying them to run in the real world. With CoreWeave's scale and performance, the flywheel runs until fidelity is reached, without infrastructure-based limitations. The flywheel isn't theoretical. CoreWeave has built and deployed reference pipelines across physical AI workloads, so teams can see the same generate, train, evaluate, repeat running on a real problem instead of starting from a blank cluster. The examples below aren't tuned for maximum throughput, they are simple demonstrations to show what's achievable: Robotic Manipulation in MuJoCo A simulated robot arm performs a number of pick-and-place tasks in a household kitchen. We ran 4,800 simulations in 85 minutes using MuJoCo on one node, executing simulation, rendering, and data generation for each task. Robotic Synthetic Data Generation in NVIDIA Isaac Sim We used Isaac Sim as a synthetic data factory to create 10,000 randomized warehouse scene samples in just 21 minutes.  Each sample was observed by three virtual cameras and saved with labels that a perception model can learn from. It was a large, parallel generation run for labeled visual training data.

Robotic Simulation in NVIDIA Isaac Sim With NVIDIA Isaac Sim’s Franka pick-and-place example, we ran 113,000 simulations in just under 8 hours. Every episode moves a simulated robot arm, closes the gripper on a cube, transfers the cube, and measures whether placement succeeded. Autonomous Vehicle Simulation in CARLA 1.25M autonomous vehicles simulations were run in approximately 12 hours in CARLA. These simulations varied weather, traffic density, map, intersection, and seed across independent one-GPU shards. Autonomous Vehicle Simulation-Based Policy Evaluation in NVIDIA AlpaSim AlpaSim renders camera observations, the model plans a trajectory and is fed back into the world environment, a closed loop that exposes compounding errors. We ran over 1,600 rollouts in under four hours. This speeds up model comparison over a variety of important scenarios to triage failure before road testing; it isn't a safety certification on its own. Reinforcement Learning in NVIDIA Isaac Lab The project trained an AI controller to reorient a cube with a simulated Shadow Hand using NVIDIA Isaac Lab and the RL-Games PPO...