super-gpu — private AI compute clusters and AI services at scale, on premise or in any cloud
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AI compute and AI services at scale

Large-scale AI, run where your data lives.

super-gpu builds private, dedicated AI compute clusters and operates vision, speech and text pipelines on them — for municipalities, security operators, research programmes and product teams. The same services run on your premises or in any cloud: AWS, GCP, OCI, or ours.

Ingest → distributed inference → your systems

Service catalogue

Grouped by input type. Every service is available as an API, a batch job or a resident on-premise deployment.

The platform

We build private, dedicated AI compute clusters tailored to each customer's workload, with the right combination of GPUs, CPUs, memory, storage, and networking.

Our infrastructure can scale from a single GPU to clusters of up to 100 GPUs, allowing us to support everything from small workloads to large-scale distributed AI deployments.

This enables us to deliver cost efficient, low latency, highly resilient and high performance AI solutions across industries.

A large H200 cluster for video processing. Each rack contains 32xNVIDIA H200, 1.1TB VRAM - GPU memory, 1.1PB storage on NVMe SSDs

Runs where you need it to run

Deployment is a procurement decision, not a technical constraint. The same service catalogue, the same interfaces, on hardware in your building or in the cloud account you already hold.

Data residency, network isolation and retention rules are set per deployment.

  • On premise Your racks, your network, air-gapped if required.
  • AWS Deployed into your own account and region.
  • GCP Deployed into your own project and region.
  • OCI Deployed into your own tenancy and region.
  • Our cloud Private cluster, high scale up ability and fully automatic.
  • Hybrid Live feeds local, batch work burst to cloud.

How the platform is put together

Ingest

Files, archives and live streams arrive over the interfaces you already use. Jobs are queued, prioritised and traced end to end.

Scheduling

Work is distributed across GPU nodes by workload class, so high-load streaming and long batch jobs do not compete for the same capacity.

Models

Choose from the range of vision, speech and text models we operate, or bring your own model (BYOM) and we run it on your cluster.

Delivery

Results return as structured output to your systems, with a per-job audit record of what ran, on what, and how long it run.