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GPU Infrastructure

Build. Orchestrate. Meter. Monetise.

From physical GPU infrastructure to a managed compute service. We combine AI servers, orchestration, metering, and token or credit-based access into one model, so compute is allocated fairly, tracked precisely, and paid for by use.

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Private or shared clusters Metered by usage Credit or token access

Cluster · Node A
Metering
  • GPU-01 · Fine-tune · Finance LLM 92%
  • GPU-02 · Inference · Support bot 71%
  • GPU-03 · Training · Vision model 88%
  • GPU-04 · Idle · available 6%

GPU hours today

61.4 h

Team credits left

8,420 / 12,000

Capabilities

Everything between the GPU and the invoice.

Hardware alone is not a service. Orchestration, metering, and access control turn a GPU cluster into capacity people can use, share, and pay for.

GPU infrastructure

Deploy high-performance AI servers and scalable GPU clusters on a sound storage and network foundation.

Compute orchestration

Run container workloads, schedule jobs, allocate resources, and monitor utilisation across the cluster.

Usage metering

Track compute consumption by workload, user, duration, or GPU resource: GPU hours, jobs, and memory.

Tokenized access

Turn metered usage into credits or token-based access rules, so every user and application draws from a balance.

Tokenized AI compute

How we package GPU infrastructure into a metered service.

GPU infrastructure, orchestration, metering, and token or credit access, combined into one managed compute model.
  1. 01

    GPU clusters

    AI servers, storage, and network foundation.

  2. 02

    Orchestration

    Container workloads, scheduling, and resource allocation.

  3. 03

    Usage metering

    GPU hours, jobs, memory, and utilisation.

  4. 04

    Token / credit engine

    Usage converted into credits or token-based access rules.

  5. 05

    Consumption & settlement

    Users run workloads, consume balances, and receive reports.

GPU cluster → Workload orchestration → Usage metering → Token / credit access → Users & applications

What we deliver

Compute capacity, delivered your way.

Run it for your own teams, or offer it to customers as a metered service.

GPU-as-a-Service

Managed access to AI compute capacity, without your team running the hardware.

Private or shared clusters

Dedicated clusters for one organisation, or multi-tenant deployments shared across many.

Metered commercial model

Pay per usage, by credit, or on a token model, matched to how your users consume compute.

Operational visibility

Dashboards, usage reports, and governance over who uses which resources and how much.

Workloads

Built for the full AI lifecycle.

One cluster serves training, inference, and fine-tuning, and earns from capacity that would otherwise sit idle.

Training

Train models on dedicated or shared GPU capacity.

Inference

Serve models to applications with metered access.

Fine-tuning

Adapt models to your data and business processes.

Monetised idle capacity

Sell spare GPU time instead of leaving it unused.
Pairs with AI Gateway

Serve private models through one governed API.

Put AI Gateway in front of models running on your own GPUs, and applications reach them through the same OpenAI-compatible API and policies as any cloud provider.

Explore AI Gateway

Get started

Turn GPU infrastructure into a managed, metered service.

Tell us about your workloads, users, and how you want compute billed. We'll design the cluster, orchestration, and metering model around them.

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