Robotics & Computer Vision
Physical AI combines large-scale perception training, simulation and synthetic data with inference that increasingly runs close to fleets and cameras. Peregrine supplies the training capacity, operates the platform and is developing distributed options for inference closer to the edge.
Infrastructure can be designed to support customer security, governance and compliance requirements. Use one service or all four.
Part 1 · Use cases
Use case 01
Detection, segmentation and tracking models are retrained continuously as new video and sensor data arrives.
Sustained GPU throughput and fast storage keep training cycles short.
GPU considerations
Use case 02
GPU-rendered environments and scenario generation are intensive and bursty.
Reserved capacity with on-demand burst matches campaign-driven demand.
GPU considerations
Use case 03
Policy learning couples simulation and training loops across many GPUs.
Scheduling and quotas let robotics teams share infrastructure efficiently.
GPU considerations
Use case 04
Centralised inference for connected fleets and real-time video analytics near sites.
ComputeGrid micro and edge nodes are being developed as a potential future fit for inference closer to sites, subject to deployment.
GPU considerations
Part 2 · How to deploy with Peregrine
Size training, simulation and inference separately — they need different architectures — and plan storage and networking for video-scale data.
Learn moreWhere owned infrastructure makes sense, design GPU systems for training and simulation with the storage throughput perception pipelines need.
Learn moreOperate training clusters and inference endpoints with container workflows, scheduling and monitoring, so robotics engineers focus on models and behaviours.
Learn moreReserved or on-demand Australian-hosted GPUs for training and simulation bursts; edge-adjacent options are deployment dependent.
Learn moreRecommended platforms
Indicative only; the Compute Blueprint confirms platform, interconnect and scale per project.
Blackwell
Balanced Blackwell platform for training and production inference.
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Blackwell · PCIe
Cost-effective per-GPU memory for inference fleets, vision, rendering and digital twins.
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Hopper
Proven production platform with the broadest software maturity.
View platformInfrastructure models: capital vs operating
Robotics companies often own a training cluster (capital) and use reserved or on-demand capacity (operating) for simulation campaigns. Peregrine scopes both per engagement; no prices are published on this site.
FAQ
Tell us about your robotics & computer vision workloads and deployment preferences. A compute specialist will discuss the appropriate infrastructure model.
