Data Centers and Infrastructure
GPU clusters that perform on the first training run.
Capacity and power planning, liquid cooling readiness, compute and storage fabrics, rack integration, cluster validation and operations for AI training and inference.
Sound familiar
Where this usually starts.
AI clusters fail in ways general infrastructure does not: one mis-cabled link or an unbalanced fabric slows every GPU in a job, and racks draw power and shed heat at densities most rooms were never designed for. We plan clusters from the workload down, size power and cooling, design the InfiniBand or Ethernet fabric and the storage that feeds it, integrate and validate every node, and prove the cluster with collective communication tests and benchmarks before the first real job runs.
Scope
What is included.
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Capacity planning
Accelerator count, power, cooling and space derived from the workload and the roadmap.
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Cooling readiness
Air, rear door and direct to chip liquid cooling assessed with the facility.
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Compute fabric
InfiniBand or Ethernet with RoCE, rail-optimized and non-blocking where training needs it.
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Storage
Parallel file systems and object storage sized for checkpointing and data loading.
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Integration and validation
Firmware, drivers, burn-in, fabric tests and benchmarks on every node.
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Cluster operations
Scheduling, monitoring, health checks and failure handling after go-live.
Reference architecture
From the hall to the rack to the GPU.
GPU clusters occupy the AI compute layer of the data center architecture: accelerated nodes on a compute fabric, depending on cooling from the facility layer below them.
What this service delivers here
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Cooling
Air or liquid, designed for the heat the hardware will actually produce.
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GPU nodes
Accelerators in dense nodes, the most expensive thing in the room.
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Compute fabric
A lossless network that keeps every GPU fed during training.
How it fits together
How a training pod is wired.
Each GPU has its own path to the leaf for its rail, so the traffic that dominates training stays one hop away and the spine carries only what crosses rails.
Spine switches. Carry traffic between rails.
Leaf switches, one per rail. GPU 3 of every server meets the others on leaf 3.
Servers, eight GPUs each. Joined inside the server by NVLink.
Before you start
Decisions to settle early.
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Own or rent accelerators
Owned GPUs are cheaper at sustained use; rented capacity suits uncertain or bursty demand. Many organizations combine the two.
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InfiniBand or Ethernet
InfiniBand is the established choice for large training; Ethernet with RoCE has closed much of the gap and fits existing skills. The workload and the team decide.
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Where the cluster lives
Your own facility, a colocation suite or a specialist AI facility each trade control, power availability and time to ready.
Approach
How the work runs.
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Plan
Workload, power and cooling
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Design
Compute, fabric and storage
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Integrate
Racks built, cabled and burned in
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Validate
Fabric tests and benchmarks before handover
Commercials
How it is bought.
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A fixed-fee assessment
We review the workload, the hardware ordered or planned and the facility, and test whether power and cooling can carry it. You receive the gaps and a fixed price for design and integration.
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A fixed-price build
Fabric and storage design, rack integration, burn-in and full cluster validation, priced for the cluster size in scope.
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Running it afterwards
After go-live, cluster operations covering scheduling, health checks and failure handling can run monthly.
Start with the assessment.
Worked example
GPUs ordered before the room was ready.
Say a research lab has ordered GPU servers for model training, but its data hall was built for general servers, and nobody has designed the fabric or the storage the cluster will need.
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Plan
We derive power, cooling and space from the training workloads, and confirm with the facility which racks can take the load and which need liquid cooling.
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Design
A rail-optimized InfiniBand fabric and a parallel file system for checkpoints are designed for the planned nodes, with room to add more.
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Integrate
Nodes are baselined, cabled to the schedule and burned in, and faulty components are replaced before the cluster is assembled.
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Validate
Collective communication tests and benchmarks run across the whole cluster, and slow links are traced and fixed before the first real job.
At the end, the lab runs training jobs at the speed the hardware should deliver, and has the plans and runbooks to grow and operate the cluster.
An illustration
Where it goes wrong
What we design out.
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Hardware before the room is ready
GPUs that arrive before power and cooling sit in boxes. Facility readiness is checked first.
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A fabric nobody designed
An unbalanced network slows every job on the cluster. The fabric is designed for collective traffic.
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Skipping validation
Slow links hide until the first long training run. Fabric tests and benchmarks run on the whole cluster before handover.
Outcomes
What it changes.
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The performance you paid for
Validated fabrics and balanced storage let jobs run at the speed the accelerators allow.
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Facilities that keep up
Power and cooling are planned for current densities and for the generation after.
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Problems found before production
Burn-in and fabric tests catch failing parts and bad links before a long job does.
How you will know it is working
- Benchmarks after go-live match the validation results.
- Every node passed burn-in and fabric tests.
- Failed nodes are detected and drained automatically.
- Utilization is reported and planned against.
You keep
What you have at the end.
Handover pack
five items
- Cluster capacity and power plan The accelerators, power, cooling and space the workload needs, now and as the roadmap grows.
- Fabric and storage architecture The compute fabric topology and the storage that feeds it, sized for training and inference.
- Rack elevations and cable schedules Every node, switch and cable in position, with labels that match the installed fabric.
- Validation report with benchmark results Fabric, collective communication and benchmark results for the cluster as delivered.
- Operations runbooks and monitoring How the cluster is scheduled, monitored, health checked and repaired after go-live.
The team
Who you work with.
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Cluster architect
Plans capacity and designs the fabric, storage and facility requirements.
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Integration engineers
Build, cable and burn in the racks and nodes.
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Performance engineer
Runs fabric tests and benchmarks and tunes the cluster before handover.
What we need from you
- The workloads you plan to run and how they will grow.
- Hardware orders or quotes already in progress.
- Details of the facility that will host the cluster.
- The team that will run jobs, for acceptance testing.
Planned for the load it will carry in five years, not the one it carries today.
Questions
Questions we get asked.
InfiniBand or Ethernet?
Both can deliver lossless, low latency fabrics for training. InfiniBand remains common in the largest training clusters; Ethernet with RoCE has closed much of the gap and suits estates that want one networking technology. We choose from the job sizes, the scale and your operations team.
Can our facility support GPU racks?
It depends on the power available per rack, cooling capacity and floor loading. Current accelerator racks can need several times the power of a standard rack, and the densest need liquid cooling. We assess the facility before hardware is ordered.
Are there export restrictions on GPUs?
Yes. The most advanced accelerators are subject to United States export controls, with licensing requirements that depend on the destination and the end user. Supply and licensing are confirmed before a design is committed, and we do not move controlled hardware without the required authorization.
Should we build or rent GPU capacity?
Renting suits uncertain or short-lived demand; owning pays back when utilization is high and sustained, or when data and models must stay on your own infrastructure. We model both against your workload before anything is bought.
What is a rail-optimized fabric?
A network design in which each GPU in a server connects to its own leaf switch, so matching GPUs across servers share a rail. It shortens the path for the collective operations training depends on and reduces congestion, which is why it is the usual design for large training clusters.
Why do NCCL tests matter before handover?
NCCL is the library GPUs use to exchange data during training. Its tests measure real bandwidth across the cluster, and a single misconfigured link or cable shows up as a slow result. Finding it before handover means slow training is never blamed on the model.
What storage does an AI cluster need?
Fast shared storage for loading training data and writing checkpoints, usually a parallel file system, and object storage for datasets and models. Checkpoint writes are large and bursty, so storage is sized for them rather than for average throughput.
Can an existing data center host a GPU cluster?
Sometimes, with changes. The questions are power per rack, cooling capacity and method, floor loading and cable pathways. We assess the facility against the cluster design, and the answer may be a few high density rows with liquid cooling rather than a new building.
Ask about your situation
Answered from this site, with its sources.
Where it is bought
AI data centers and GPU clusters, by sector and region.
Sectors that buy Data Centers and Infrastructure
Often paired with
What usually comes next.
- Integration
Hardware and rack integration
Racks built, cabled, firmware-baselined and burned in before they ship. - Networking
Data center networking
Spine-leaf, EVPN-VXLAN and AI fabrics built for east-west traffic. - AI
MLOps and model operations
Training, serving and monitoring models as a reliable service.
CONTACT
Plan the cluster before the hardware arrives.
Share the workload, the accelerator plan and the facility. We will return a capacity, fabric and cooling plan you can build to.
- Senior-led
- Accountable
- Direct
- Fixed-price options
- Compliance-ready
- 24/7 capable
Prefer to talk it through? Call +92 (333) 32 11011
What happens next
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You pick the time
Tell us what you plan to train or serve and on what hardware; the call is at a time that suits you.
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Thirty minutes, with an engineer
A cluster engineer discusses the facility, the fabric and the date jobs must run.
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A written assessment
It sets out the gaps and a fixed design and integration price.
