Design. Build. Operate. GPU Clusters.

A cluster is a revenue asset, not a capex line. Before power is contracted or a GPU is ordered, we check that the business case closes and the site can deliver it.

• Capex and opex by phase and year, from accelerators to people
• Utilization-to-revenue curve: break-even at 40%, 60%, and 80% booked
• Sensitivity on power tariff, hardware lead time, and depreciation
• Financing readiness: vendor financing, GPU-backed structures, AI-compute programs

You getAn investment memo with a go / no-go, a phased capex plan, and a site scorecard.

We map the site, power, and cooling envelope before a single rack ships, so the build fits the environment instead of fighting it.

• Power and cooling envelope: kW per rack, CDU and chiller sizing, PUE target
• Hall layout: rack count, containment, and expansion path
• Structured cabling and an MEP integration plan
• Edge, colocation, modular, and greenfield variants

You getA site design the racks will actually fit, with headroom for the next phase.

The right accelerators for the workload, not the biggest line item. We size against your inference, training, and agentic profiles, so you buy capacity you will use.

• Workload profiling: model sizes, context lengths, batch shapes
• Accelerator choice across NVIDIA, AMD, and custom silicon
• Node count, storage, and memory tiering sized to the profile
• What you buy now vs. what you add in 12 months

You getA bill of materials tied to a revenue model, not to a vendor quota.

Racks, nodes, and topology assembled to a plan that survives contact with production. Power distribution, cabling, and layout done once, done right.

• Rack elevation, PDU and busway design, liquid-cooling manifolds
• Node configuration: BIOS, firmware baseline, OS image, drivers
• Cable plant labelled and documented, every port traceable
• Scheduler bring-up: Slurm, PBS, or Kubernetes with health checks

You getA cluster built to a documented baseline the platform verifies against for its whole life.

Real-workload baselines, not synthetic hero numbers. You see tokens per second, utilization, and failure behavior under load before the cluster goes live.

• Your models and batch shapes: tokens/s, TTFT, MFU, and $/token
• NCCL or RCCL collectives at every gang size; HPL and storage as the floor
• Failure injection: pulled cables, throttled GPUs, drained nodes
• Baselines stored as the production reference

You getA performance contract you can hold vendors, and us, to.

Burn-in, validation, and handoff. We stress every node, confirm the numbers match the plan, and hand over a cluster the platform is already watching.

• Multi-day burn-in per node: thermal, ECC, PCIe, link-margin drift
• Readiness checks, with an evidence pack for your buyers
• Runbooks, as-built documentation, and operator training
• Agents enrolled and playbooks active on handoff day

You getA cluster that enters the revenue pool already under watch.