Physical AI, from prototype to production
Train world models and deploy fleet-scale inference with Crusoe Cloud.




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The support has been first class. We could actually talk to human beings at Crusoe. Crusoe gives us dedicated GPU capacity with the economics of a reservation and the responsiveness of a startup.
Why Crusoe for physical AI?
Get the capacity, flexibility, and support you need to build intelligent machines for the physical world.
Scale on demand
Deploy near your fleet or train at gigawatt scale. Get the compute capacity you need, when you need it, without waiting years for it.
Move at your
own pace
Bring your own stack or partner with our solutions team to optimize your physical AI workload pipeline.
Build with experts
Partner with our team and bring the next big idea to life. Gain early access to NVIDIA accelerated computing through our collaboration.
Where Crusoe supports physical AI
Model labs
Train a VLA or world foundational model on massive multimodal data at scale, then serve it as a stream or a policy.
Run on Crusoe for:
Reserved GPU clusters with Managed Kubernetes and Managed Slurm for long-horizon pretraining runs
Simulation GPUs for data processing and synthetic data generation
Multimodal data storage through Crusoe Object Storage (VAST Data), with no egress fees
Tailored Deployments enable one foundation model across many robots

Humanoid robotics
Turn a world model into a data factory for your onboard policy, then serve the reasoning and task-planning layer with SLA-backed guarantees as your fleet scales.
Run on Crusoe for:
Multi-node VLA training via Crusoe Managed Kubernetes and Managed Slurm
Simulation-capable GPU and storage capacity that generate data for policy evaluation
Tailored Deployments with SLA-backed guarantees as fleets go live
Crusoe Serverless Fine-Tuning for the language and task-planning layer

Autonomous vehicles
Run billions of simulated miles and retrain driving models on fleet logs, using high-utilization clusters with petabyte-scale sensor storage.
Run on Crusoe for:
Petabyte-scale drive-log storage via Crusoe Object Storage (VAST Data)
No-egress storage for petabyte camera, LiDAR, and radar logs
Low-latency, multi-node training via RDMA networking
Crusoe Managed Kubernetes and Slurm with simulation-capable GPUs for multi-node simulation runs

Industrial
Develop the vision and robotics models behind factory automation and quality control, then serve real-time predictions onsite.
Run on Crusoe for:
Crusoe Edge Zones for low-latency inference on the plant floor
Crusoe Managed Kubernetes and Crusoe Managed Slurm for vision model training
No-egress storage for high-volume plant-floor sensor data
Tailored Deployments with SLA-backed guarantees on the plant floor

Logistics
Build and serve the models behind picking, sorting, and fulfillment, matching inference speed to a growing warehouse fleet.
Run on Crusoe for:
Real-time predictions for picking, sorting, and fulfillment
Crusoe Managed Kubernetes and Crusoe Managed Slurm scale training with fleet growth
No egress fees on continuous fulfillment data pipelines
Crusoe Managed Inference to scale models on demand

Internet of Robotic Things (IoRT)
Train and run always-on vision AI and ambient sensing models across distributed devices, serving low-latency inference wherever it's needed.
Run on Crusoe for:
Crusoe Edge Zones bring low-latency inference near distributed devices
Crusoe Managed Inference tuned for always-on response times
Crusoe Managed Kubernetes orchestrates fleets of distributed devices
Tailored Deployments serving for vision models trained off-platform
No egress fees on continuous ambient sensor data

Medical
Train and validate perception and control models for surgical and care robots, on infrastructure built for data isolation.
Run on Crusoe for:
Physical isolation and VPC-isolated networking by default
Tailored Deployments with SLA-backed guarantees for clinical-grade reliability
Crusoe Serverless Fine-Tuning for clinical documentation or dialogue layers
HIPAA, SOC 2 Type 2 compliant cloud environment

Crusoe Cloud for physical AI
Frequently
asked questions
Physical AI is artificial intelligence that perceives, plans, and acts in the real world through robots, autonomous vehicles, humanoids, and other embodied systems. These systems are built on world models and vision-language-action (VLA) models trained on multimodal sensor data, then served as low-latency inference to machines in the field.
Standard AI infrastructure trains a model in the data center and serves it as text through an API. Physical AI infrastructure carries that stack into the real world, adding continuous multimodal sensor pipelines, large-scale simulation, low-latency inference near operating fleets, and capacity that grows with the machines in the field.
Yes. Crusoe runs NVIDIA’s latest hardware and full software stack, and supports the open frameworks physical AI teams build on, including PyTorch for training, Ray for scaling across GPUs, and vLLM and SGLang for serving, with Crusoe Managed Kubernetes and Managed Slurm for orchestration.
No. Train world models on Crusoe and serve policies onboard your robots, bring a model trained elsewhere and serve it here, or run the full loop from pretraining to fleet-scale inference. Crusoe meets you at whichever stage you need.
Pricing is workload-based, with reserved capacity that ramps as your AI and physical fleet needs grow, and flexible terms from three months to five years. There are no egress fees, which matters for the continuous sensor data physical AI generates. Contact our team or check the pricing page.
Yes. Crusoe Edge Zones bring low-latency inference next to your robots. And with our Crusoe SparkTM modular data centers, we can stand up a scalable data facility in about three months, backed by Crusoe's own energy pipeline.
Ready to start building?
