Power Champion
Open platform

Power Champion / GPU infrastructure

Compute, shaped around your workload.

Define the model, data, and performance requirements first. Explore dedicated GPUs, enterprise clusters, and custom data-centre deployment through a scoped infrastructure review.

Discuss infrastructureExplore your workload

Deployment paths

Bare-Metal Deployment

Isolated Enterprise Clusters

Custom IDC Deployment

Building with an API? Start here.

01 / GPU platforms

Choose a GPU platform for the job.

Use the platform families below as a starting point for a configuration discussion. Model size, memory needs, concurrency, and interconnect requirements determine the appropriate system.

CategoryPlatformBest for
Advanced AI workloadsNVIDIA HGX B300 / B200Large-model training and inference; configuration subject to project review
Enterprise AI TrainingNVIDIA H100 / H200Large-scale LLM training, multi-node inference
Memory-Intensive AI & HPCNVIDIA HGX H200Large context windows, memory-bound workloads
Cost-Efficient AI WorkloadsNVIDIA L40S / A100Inference, fine-tuning, cost-optimized pipelines

GPU allocation, memory, node count, network, storage, region, pricing, and delivery timing require a project-specific proposal. Platform listings are discussion options and do not represent immediately reservable inventory.

02 / Workload planning

What the infrastructure needs to support

Select a workload to see the requirements worth preparing.

Production inference

Discuss model loading, peak concurrency, context length, and response-time targets for application APIs and agent workloads.

Bring these details to the discussion

  • Which models and precision will you serve?
  • What are the typical and peak concurrent requests?
  • What context length and latency targets must the service meet?
Discuss this workload

03 / Deployment process

From workload brief to deployment scope

Each stage turns a requirement into something that can be reviewed. Capacity, service terms, and acceptance criteria are agreed for the specific project.

  1. Define the workload

    Share models, datasets, usage patterns, timing, and the constraints your team needs to meet.

  2. Review the configuration

    Discuss GPU family, node count, memory, storage, connectivity, and the intended deployment location.

  3. Agree on the scope

    Confirm availability, commercial terms, responsibilities, data handling, and measurable acceptance criteria in the proposal.

  4. Plan validation & handover

    Set out workload checks, access arrangements, operating responsibilities, and the steps required before production use.

04 / Your project brief

Deployment review inputs

A short technical brief helps turn an initial conversation into a useful configuration discussion. Include the following where available.

Workload
Use case, framework, and expected outputs; distinguish inference, training, and batch jobs.
Model requirements
Model names, parameter sizes, precision, context length, and any licensing constraints.
Usage profile
Expected concurrency, requests or jobs per day, traffic peaks, and target latency.
Deployment region
Preferred region or facility, networking needs, and data transfer constraints.
Data handling
Dataset volume, storage, retention, access controls, and any sensitive-data requirements.
Service-readiness gates
Target start date, budget range, validation criteria, and your team's operating responsibilities.
Deployment review

Capacity context

The public record below provides company and capacity context. Current API availability is reported separately from dedicated infrastructure proposals.

Counterparty-reported expected initial capacity and reservation context

Approximately 3.1 MW

Counterparty-reported expected hosting capacity; not live or completed deployment.

Counterparty SEC-filed disclosure

Counterparty-reported expectations and estimates; expansion is subject to future customer requirements, site availability, infrastructure readiness, and the agreement terms. No assurance can be given that expansion rights will be exercised or additional capacity deployed.

Review stages

  1. Counterparty context

    Capacity context

    Counterparty-reported expected hosting capacity; not live or completed deployment.

  2. Not ready

    Serving controls

    Model serving availability is measured live by the b300 gateway; this site reports it as-is and does not pre-claim readiness it cannot observe.

  3. Ready

    Delivery preview

    The unified API is deployed at b300.powerchampion.ai with pay-per-use billing from prepaid balance; current availability is shown on the status page.