01 / 05
Your stack, already in sync.
Start from a validated combination of simulator, graphics drivers and robotics libraries. Keep versions consistent as you move from development to distributed jobs.

For your most demanding E2E robotics workloads.
Explore the platform
Infrastructure built around the workload.
Simulation / Data / Training / Evaluation / Deployment
Physical AI moves between simulation, data, training and real hardware. A general-purpose cloud gives you the parts. Your team still has to connect them.
Rendering a world and training a model ask different things of your infrastructure.
Every handoff can mean another copy, another pipeline and another wait.
A checkpoint still needs to be tested in simulation and on its target hardware.
Our fleet is balanced across DGX and RTX architectures. A shared foundation from setup to deployment.
01 / 05
Start from a validated combination of simulator, graphics drivers and robotics libraries. Keep versions consistent as you move from development to distributed jobs.

02 / 05
Scale sensor simulation and synthetic data generation on RTX capacity. Combine generated experience with demonstrations in a versioned dataset for training.

03 / 05
Train policies against the same versioned data used by your simulation workflows. Connect RTX-generated experience to Blackwell and Rubin training capacity through a shared storage layer.

04 / 05
Evaluate trained policies across controlled simulation scenarios. Pair RTX sensor rendering with the policy compute each workload needs, and preserve the results with the checkpoint.

05 / 05
Run containerized tests on physical Jetson devices. Compare model quality after quantization, measure runtime behavior, and exercise the target pipeline with simulated sensor inputs.
Build and test in the cloud.
Run inference onboard.

Scroll to follow the workflow
Keep the workload moving, from the first simulation to a build ready for the robot.

Enable robotics foundation model and world model training. Compute, data, and validation matched to each application.
Train whole-body and manipulation policies from demonstrations and simulated experience. Evaluate checkpoints across tasks and robot configurations.
Generate sensor data for assembly and machine tending. Train manipulation policies and test precision across fixtures, parts, and contact conditions.
Train navigation and picking policies across warehouse layouts. Evaluate perception and multi-robot coordination under changing traffic and inventory.
Train perception and planning models on recorded and synthetic sensor data. Evaluate rare scenarios and measure inference performance on target hardware.
Train outdoor navigation and manipulation policies. Test against variations in terrain, lighting, vegetation, and weather.
Train perception models for defect detection and policies for autonomous inspection. Evaluate sensor coverage, navigation, and inference constraints.
Explore the infrastructure behind these workloads.
Explore the platform