Peregrine Compute
Caged dedicated GPU racks in a secure financial data centre suite

Financial Services

Dedicated compute for models that move money.

Banks, insurers, asset managers and fintechs need GPU infrastructure with data control, auditability and Australian residency built in. Peregrine plans, builds and operates dedicated environments — with logging and monitoring across the platform — designed with data residency and access control requirements in mind.

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Infrastructure can be designed to support customer security, governance and compliance requirements. Use one service or all four.

ASSESSBUILDOPERATECAPACITY

Part 1 · Use cases

GPU workloads in financial services.

GPU compute nodes and network appliances in a premium data centre aisle

Use case 01

Risk Modelling & Monte Carlo Simulation

Portfolio, credit and market risk simulations parallelise naturally across GPUs, turning overnight batch runs into intraday analysis.

Dedicated infrastructure gives risk teams predictable capacity for regulatory and internal reporting cycles.

GPU considerations

  • Memory: moderate; scales across many GPUs
  • Interconnect: PCIe platforms often sufficient
  • Storage: fast access to market and position data
  • Data residency: Australian hosting for regulated data
Close-up of network switch ports and a security module inside a locked rack

Use case 02

Fraud Detection & Anomaly Models

Graph and sequence models over transaction streams are trained continuously and served with low latency.

Private inference endpoints within controlled infrastructure keep transaction data inside the organisation's perimeter.

GPU considerations

  • Memory: moderate per GPU; high throughput inference
  • Interconnect: single-node inference, multi-GPU training
  • Storage: streaming and feature stores
  • Data residency: transaction data can remain onshore
DATAGPU COMPUTEMODEL / RESULT

Use case 03

LLMs for Research, Compliance & Customer Operations

Private retrieval-augmented generation over research, policy and customer records allows teams to use language models without exposing data to shared services.

Model serving environments can provide OpenAI-compatible APIs, so existing tooling connects to models running on dedicated infrastructure.

GPU considerations

  • Memory: model size drives GPU memory per endpoint
  • Interconnect: HGX for larger models, PCIe for replicas
  • Storage: vector and document stores
  • Data residency: records within dedicated Australian infrastructure
DATAGPU COMPUTEMODEL / RESULT

Use case 04

Quantitative Research & Backtesting

Strategy research, feature engineering and backtesting benefit from GPU acceleration and reproducible environments.

Reserved capacity with quotas lets research desks share infrastructure fairly while keeping utilisation high.

GPU considerations

  • Memory: dataset dependent
  • Interconnect: usually single node
  • Storage: large historical datasets on fast shared storage
  • Data residency: proprietary research stays in-house
Caged dedicated GPU racks in a secure financial data centre suite

Use case 05

Document Intelligence

Extraction, classification and summarisation of contracts, statements and onboarding documents at scale.

Batch and real-time inference can run on cost-effective PCIe platforms with isolation from other tenants.

GPU considerations

  • Memory: moderate
  • Interconnect: PCIe
  • Storage: document archives with controlled access
  • Data residency: customer documents can remain in Australia

Part 2 · How to deploy with Peregrine

Four ways to engage — use one or all.

  1. 01ASSESS

    Compute Blueprint

    Map risk, fraud and LLM workloads to an architecture, documenting data control, auditability and residency requirements for risk and security stakeholders.

    Learn more
  2. 02BUILD

    Build GPU Infrastructure

    Design and commission dedicated GPU infrastructure in your data centre or an Australian facility, integrated with identity, network segmentation and logging.

    Learn more
  3. 03OPERATE

    Managed AI Platform

    Operate the platform with audit logging, monitoring, change control and patching as a monthly managed service, under an agreed operating model.

    Learn more
  4. 04CAPACITY

    GPU Capacity + Private AI Cloud

    Dedicated or private-cluster Australian-hosted capacity, or a Private AI Cloud, for teams that need isolation without procuring hardware.

    Learn more

Recommended platforms

Platforms commonly considered for these workloads.

Indicative only; the Compute Blueprint confirms platform, interconnect and scale per project.

All GPU platforms
NVIDIA HGX H200 eight-GPU tray with gold-and-black heatsinks

Hopper

NVIDIA HGX H200

Mature ecosystem with larger memory for inference of large models and training.

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NVIDIA RTX PRO 6000 Blackwell Server Edition dual-slot PCIe card

Blackwell · PCIe

NVIDIA RTX PRO 6000 Blackwell Server Edition

Cost-effective per-GPU memory for inference fleets, vision, rendering and digital twins.

View platform
Top-down view of an NVIDIA HGX B200 eight-GPU baseboard

Blackwell

NVIDIA HGX B200

Balanced Blackwell platform for training and production inference.

View platform

Infrastructure models: capital vs operating

Institutions can own infrastructure for steady, regulated workloads (capital) and use dedicated or reserved capacity for research and project demand (operating). Peregrine scopes both models per engagement; no prices are published on this site.

FAQ

Common questions.

Talk to a Compute Specialist.

Tell us about your financial services workloads and deployment preferences. A compute specialist will discuss the appropriate infrastructure model.

Talk to a Compute SpecialistExplore Private AI Cloud