AI & SaaS
AI companies live and die by inference latency, training velocity and unit economics. Peregrine supplies Australian-hosted GPU capacity, operates the platform layer and helps you decide when reserved or dedicated infrastructure makes sense. For sustained workloads, dedicated or reserved infrastructure can provide more predictable economics than purely consumption-based compute.
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
Pre-training, continued training and fine-tuning of foundation and domain models need sustained multi-GPU throughput.
Reserved NVLink-coupled nodes keep training velocity predictable; on-demand burst absorbs experiments.
GPU considerations
Use case 02
Serving models to customers demands predictable latency and capacity that grows with usage, at a unit cost you can price against.
Dedicated or reserved inference fleets give more predictable economics than purely consumption-based compute for sustained traffic.
GPU considerations
Use case 03
Agentic products and retrieval pipelines generate sustained, bursty inference plus embedding and vector workloads.
Model serving environments can provide OpenAI-compatible APIs, so existing application code points at Peregrine endpoints.
GPU considerations
Use case 04
Batch evaluation, red-teaming and ablations are bursty and ideal for on-demand capacity.
Quotas and scheduling let research and product teams share GPUs without blocking releases.
GPU considerations
Part 2 · How to deploy with Peregrine
Size the workload before committing — GPU count, architecture and the point at which reserved or dedicated capacity becomes economical.
Learn moreWhere it makes sense, design dedicated inference infrastructure you own for steady-state traffic, with Peregrine handling specification and commissioning.
Learn morePeregrine runs the platform — scheduling, serving, monitoring, upgrades — so engineers ship product.
Learn moreReserved, dedicated or on-demand Australian-hosted GPUs, or a Private AI Cloud, matched to project requirements and subject to availability.
Learn moreRecommended platforms
Indicative only; the Compute Blueprint confirms platform, interconnect and scale per project.
Blackwell
Balanced Blackwell platform for training and production inference.
View platform
Hopper
Mature ecosystem with larger memory for inference of large models and training.
View platform
Blackwell · PCIe
Cost-effective per-GPU memory for inference fleets, vision, rendering and digital twins.
View platformInfrastructure models: capital vs operating
Early-stage teams usually prefer operating-expense capacity; as utilisation becomes predictable, an owned build (capital) for steady-state inference can lower unit cost. Peregrine scopes both paths per engagement; no prices are published on this site.
FAQ
Tell us about your ai & saas companies workloads and deployment preferences. A compute specialist will discuss the appropriate infrastructure model.
