Physical AI infrastructure that stays in your environment
A guide to the stack behind robots and autonomous machines, and to running it where your data, models and keys stay under your control.
Physical AI teams need a repeatable stack for simulation, synthetic data, training, evaluation and deployment, run on infrastructure they control because robot data, models and keys are sensitive. Hyperlake assembles, deploys and governs that stack on Kubernetes in a customer's environment or a client's, with one identity and policy path to the data.
What is Physical AI infrastructure?
Physical AI infrastructure is everything between a robot's sensors and a trained model running on the robot. Teams collect data from machines in the field, curate it, generate synthetic data, train and evaluate policies in simulation, then package and ship the result to a fleet. Each step needs compute, storage, orchestration and an audit trail.
Unlike a web application, the stack is rarely deployed once. A robotics company may need the same environment for its own lab, for each customer's site and for a partner's cloud, and each of those environments has its own rules about where data may go.
What does a Physical AI stack need?
Ten capabilities show up in almost every stack:
- Compute and Kubernetes, with GPU nodes for simulation and training and CPU nodes for everything else.
- Storage, usually object storage plus a shared filesystem for large datasets.
- A data layer that catalogs sensor data and lets teams query it.
- Simulation, for example NVIDIA Isaac Sim and Isaac Lab.
- Synthetic data generation, for example NVIDIA Cosmos.
- Workflow orchestration across data, training and evaluation, for example NVIDIA OSMO, which is open source.
- Model serving, for perception models and for the language models that increasingly sit beside them.
- Evaluation, in simulation and on hardware.
- Identity, policy and audit, so access is decided centrally and every action is logged.
- Delivery to sites and devices, repeatable across customers.
The guides listed at the end of this page show how these pieces fit together on Kubernetes.
Why does sovereignty matter for Physical AI?
Three things in a Physical AI project are sensitive at once. Sensor data can show the inside of a customer's facility. Trained models and policies are the product. Keys and credentials open the doors to both.
Customers often require that this material stays in a region, a data center or a network they control, and some sites are not connected to the internet at all. Sovereignty here is practical: where the stack runs, who can reach it and who holds the keys.
How does Hyperlake support a Physical AI stack?
Hyperlake is a sovereign AI operating platform. It does not replace the tools above; it assembles, deploys and governs them together.
- Deploys in your environment or a client's. It runs in AWS, Azure, Google Cloud, OVH, private cloud or on premises, on a Kubernetes foundation, and the same approach repeats across sites.
- Deploys applications from a catalog. Cloud-native Helm applications have typed configuration fields, can be previewed before they are deployed, and can be uninstalled again through the same API.
- One identity path to data. People and workloads sign in through an OAuth/OIDC proxy connected to your identity provider. A validated token carries a scoped identity, and policy decisions are made with OPA. Hyperlake's cluster tokens are signed with RS256, are valid for 24 hours and are limited to one cluster.
- Observability and diagnostics. Teams can run read-only diagnostics against a cluster or an application, and Hyperlake's agentic data cloud profile includes observability and fleet capabilities.
- One control surface. The same operations are available through an application, a CLI and MCP-compatible tools, with approvals where policy requires them.
- Integration with Physical AI tools. Hyperlake can bring simulation, orchestration, training, data and model services together in the customer's cloud, integrating tools such as NVIDIA OSMO, Omniverse, Isaac Sim and Isaac Lab, subject to licensing and validated integration.
What does Hyperlake not do?
It is not a robot simulator, a training framework or a GPU cloud, and it does not resell compute. If you want a managed Physical AI environment on one provider's cloud, a provider's own workbench may be simpler to start with. Hyperlake is for teams that must run the stack in their own or their clients' environments, and want to repeat it.
Who is this for?
- Robotics and AI product companies that deploy into many customer sites and need repeatable installs that the customer owns.
- Enterprise platform teams that run Physical AI for their own plants and warehouses and need governance they can audit.
- Service providers and integrators that build and operate stacks for several clients.
How do you get started?
Tell the Hyperlake team what you want to deploy, whose environment it must run in and which data and policies it needs. The platform is shaped around the real workload, not a fixed catalog. You can reach the team through the contact page.
See it live. A live demonstration: real deployed models analyze recorded robot feeds, with every result tied to evidence. Open the Physical AI reliability and safety operations demonstration.
More guides on this topic
- Open-source Physical AI stack map: 49 projects by layerA map of 49 open-source projects for Physical AI by layer, from simulation to governance. Each has its license and official links, checked on 5 October 2026.
Frequently asked questions
What is Physical AI?
Physical AI is AI that perceives and acts in the physical world, such as robots, autonomous vehicles and machines in factories and warehouses. It is usually developed with simulation, synthetic data and training on real sensor data, then deployed to devices at many sites.
Can a Physical AI stack run on premises?
Yes. The common orchestration and simulation tools run on Kubernetes, and NVIDIA says its open-source OSMO orchestrator runs on on-premises Kubernetes clusters as well as on the major clouds. Hyperlake runs in AWS, Azure, Google Cloud, OVH, private cloud or on premises.
Does Hyperlake include a robot simulator or training framework?
No. Hyperlake does not replace simulation or training software. It integrates tools such as NVIDIA OSMO, Omniverse, Isaac Sim and Isaac Lab, subject to licensing and validated integration, and deploys and governs them with the rest of the stack.
What does sovereign mean for Physical AI?
The customer controls where the stack runs and who can reach it. Data, models, applications and keys stay in infrastructure the customer or their client controls, and access is decided by identity and policy rather than shared passwords.
Is Hyperlake a GPU cloud?
No. Hyperlake coordinates the services that run in your infrastructure and adds no compute markup. You bring the compute: your own data center, or an account with a cloud provider.
Sources
Sources for the facts on this page, last checked October 1, 2026.
- Hyperlake FAQ checked October 1, 2026
- Hyperlake use cases checked October 1, 2026
- NVIDIA OSMO checked October 1, 2026
- Physical AI and Robotics on Nebius checked October 1, 2026
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