GPU clusters sized from the workload, not the brochure
We design, procure, build and run the compute under AI workloads — from a pilot node to a multi-rack training cluster.
The method
We profile what you actually intend to run, then derive the accelerator count, fabric topology and storage bandwidth from that. Often the honest answer is that you should rent instead — we will say so.
Fabric
3.2 Tb/s
Typical east-west bandwidth we design for multi-node training
Utilisation
60%
Roughly where owning starts to beat renting on cost
Sovereignty
Fully disconnected environments with an offline mirror, internal registry and a bill of materials an auditor can follow.
Day two
Thermal and power telemetry, ECC and XID triage, firmware lifecycle, node draining — and the runbook that lets your team hold it.
Accelerators
Fabric
Storage
Orchestration
Platforms
Who we do this for
2–3 weeks
We profile the workload, model the capacity and tell you whether to build or rent.
Sizing model, indicative bill of materials, three-year cost case
8–16 weeks
Architecture through procurement, staging, commissioning and handover to your team.
As-built topology, benchmark results, operational runbook
Ongoing
We run the cluster under an SLA and you take it back whenever you want it.
Monitoring, patching, quarterly capacity review
Above roughly 60% utilisation with a two-year horizon, owning usually wins. Below that, or while the workload is still changing shape, renting is cheaper and far less risky. The assessment is scoped to be useful even when the answer is no.
Yes, and it is a common starting point. We benchmark what it can actually sustain and design around it, and we say plainly when something is mismatched to the workload.
You do. Architecture, bills of materials, benchmark data and runbooks are yours in editable form, whether or not you continue with us.
Send us the constraint you are actually up against — budget, latency, regulator, deadline.
