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Infrastructure purpose-built for Physical AI

For your most demanding E2E robotics workloads.

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An architectural cutaway connecting cloud compute racks, simulation and a small target-hardware testing space

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.

Our fleet is balanced across DGX and RTX architectures. A shared foundation from setup to deployment.

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
Your stack, already in sync. Compatible software, illustrated in blue and ivory.
Simulator · Drivers · Robotics libraries

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
Generate the experience your models need. RTX rendering farms, illustrated in blue and ivory.
Simulated sensors · Synthetic data · Demonstrations

03 / 05

Change compute. Keep the dataset.

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.

Shared storage
Change compute. Keep the dataset. Shared storage, illustrated in blue and ivory.
Policy training · Shared data · Blackwell / Rubin

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
Put every checkpoint back into the world. Checkpoint evaluation, illustrated in blue and ivory.
RTX sensor rendering · Workload-matched policy compute

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.

Validate on the hardware you’ll deploy. Physical Jetson tests, illustrated in blue and ivory.
Target optimization · Container runtime · Hardware tests

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.

A continuous infrastructure loop connecting a shared dataset, cloud compute and target-hardware testing
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.

Physical AI workloads.

Enable robotics foundation model and world model training. Compute, data, and validation matched to each application.

Humanoid robot hands positioning a connector in a laboratory fixture

Humanoids

Train whole-body and manipulation policies from demonstrations and simulated experience. Evaluate checkpoints across tasks and robot configurations.

  • Parallel simulation
  • Policy training
  • Hardware validation
Industrial gripper positioning a machined component in a fixture

Industrial robotics

Generate sensor data for assembly and machine tending. Train manipulation policies and test precision across fixtures, parts, and contact conditions.

  • RTX data generation
  • Shared training data
  • Checkpoint evaluation
Autonomous mobile robot carrying a tote through a warehouse junction

Warehouse robotics

Train navigation and picking policies across warehouse layouts. Evaluate perception and multi-robot coordination under changing traffic and inventory.

  • Scene variation
  • Fleet simulation
  • Policy evaluation
Cameras and lidar on a test vehicle facing a road intersection

Autonomous vehicles

Train perception and planning models on recorded and synthetic sensor data. Evaluate rare scenarios and measure inference performance on target hardware.

  • Sensor pipelines
  • World model training
  • Target validation
Agricultural robot sensors inspecting plants beside a crop row

Field robotics

Train outdoor navigation and manipulation policies. Test against variations in terrain, lighting, vegetation, and weather.

  • Synthetic data
  • Policy training
  • Scenario testing
Drone camera inspecting the surface of a concrete bridge support

Inspection & aerial robotics

Train perception models for defect detection and policies for autonomous inspection. Evaluate sensor coverage, navigation, and inference constraints.

  • Dataset preparation
  • Model training
  • Edge validation

Explore the infrastructure behind these workloads.

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