Enterprise AI
Enterprise AI programmes need infrastructure that satisfies data, governance and operating requirements while still moving at the pace of the business. Peregrine plans, builds and operates that infrastructure — in your facilities, Australian data centres or alongside your existing cloud.
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
Private assistants and copilots over enterprise documents, tickets and systems of record.
Running these on dedicated infrastructure keeps internal knowledge inside the perimeter while giving teams OpenAI-compatible endpoints.
GPU considerations
Use case 02
Models served inside controlled infrastructure for applications across the business.
Autoscaling endpoints with observability let platform teams meet SLAs without exposing data to shared services.
GPU considerations
Use case 03
Domain adaptation on proprietary data improves accuracy and reduces inference cost.
Reserved multi-GPU nodes keep fine-tuning cycles predictable.
GPU considerations
Use case 04
Extraction, classification, image analysis and GPU-accelerated forecasting across operations.
Cost-effective PCIe platforms serve these workloads well.
GPU considerations
Part 2 · How to deploy with Peregrine
Map workloads, residency and governance requirements to an architecture, and compare owned, Peregrine-hosted and cloud options with estimated economics.
Learn moreDesign and commission dedicated GPU infrastructure the enterprise owns — on premises, in an Australian data centre or colocation — integrated with your network and identity.
Learn moreOperate the platform layer with user and quota management, observability, patching and security operations, as a monthly managed service.
Learn moreReserved or dedicated Australian-hosted capacity, or a Private AI Cloud, to supplement owned infrastructure for projects and peaks.
Learn moreRecommended platforms
Indicative only; the Compute Blueprint confirms platform, interconnect and scale per project.
Hopper
Mature ecosystem with larger memory for inference of large models and training.
View platform
Blackwell
Balanced Blackwell platform for training and production inference.
View platform
Blackwell · PCIe
Cost-effective per-GPU memory for inference fleets, vision, rendering and digital twins.
View platformInfrastructure models: capital vs operating
Enterprises can treat steady production AI as a capital build operated by Peregrine, and use reserved or dedicated capacity as operating expense for programmes and peaks. Both are scoped per engagement; no prices are published on this site.
FAQ
Tell us about your enterprise ai workloads and deployment preferences. A compute specialist will discuss the appropriate infrastructure model.
