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.
Compatible software
The cloud forphysical AI.
From simulation to training. From compute to the real world.
Explore the platform
Infrastructure built around the workload.
Simulation / Data / Training / Evaluation / Deployment
More GPUs won’t
close the loop.
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.
Different jobs.
Different compute.
Rendering a world and training a model ask different things of your infrastructure.
Data that has
to keep moving.
Every handoff can mean another copy, another pipeline and another wait.
A model is only
part of the story.
A checkpoint still needs to be tested in simulation and on its target hardware.
Different jobs.
One continuous workflow.
The right infrastructure at every stage.
A shared foundation from setup to deployment.
02 / 05
Generate the experience your models need.
Scale sensor simulation and synthetic data generation on RTX capacity. Combine generated experience with demonstrations in a versioned dataset for training.
RTX rendering farms
03 / 05
Change compute. Keep the dataset.
Train 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.
Shared storage
04 / 05
Put every checkpoint back into the world.
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.
Checkpoint evaluation
05 / 05
Validate on the hardware you’ll deploy.
Run containerized tests on physical Jetson devices. Compare model quality after quantization, measure runtime behavior, and exercise the target pipeline with simulated sensor inputs.
Physical Jetson tests
Build and test in the cloud.
Run inference onboard.
Scroll to follow the workflow
Spend more time
improving the model.
Keep the workload moving, from the first simulation to a build ready for the robot.
- Faster iteration
- Reuse data and checkpoints as you move through the loop.
- Less infrastructure work
- Start with compatible environments for specialized workloads.
- Fewer data transfers
- Work from a shared, versioned data foundation across compute pools.
- Confidence on target
- Measure model quality and runtime behavior on physical hardware.
One cloud. Many ways
to move the world.
Infrastructure for the teams building physical intelligence.
Humanoids
Whole-body control, dexterous manipulation and learning from demonstrations.
Industrial robotics
Assembly, machine tending and precise manipulation in changing work cells.
Warehouse robotics
Navigation, picking and coordination across moving fleets.
Autonomous vehicles
Perception and planning across varied roads, conditions and scenarios.
Field robotics
Robust behavior across farms, construction sites and outdoor terrain.
Inspection & aerial robotics
Autonomous sensing and navigation around complex physical infrastructure.