--- title: "AI data centers and GPU clusters" description: "Capacity and power planning, liquid cooling readiness, compute and storage fabrics, rack integration, cluster validation and operations for AI training and inference." url: https://www.expandware.com/services/data-centers/ai-data-centers-and-gpu-clusters/ section: "Services" topics: ["Data Centers and Infrastructure", "NVIDIA HGX", "NVIDIA DGX", "NVIDIA Quantum InfiniBand", "RoCE v2"] publisher: "Expandware Private Limited" --- # AI data centers and GPU clusters Capacity and power planning, liquid cooling readiness, compute and storage fabrics, rack integration, cluster validation and operations for AI training and inference. GPU clusters that perform on the first training run. 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. Where it usually starts: - GPUs have been ordered, and the facility is not ready for their power or heat. - A cluster is built, but jobs run far slower than the hardware should allow. - Nobody has designed the network fabric or the storage that will feed the GPUs. - A decision is needed between owning GPU capacity and renting it. What is included: - Capacity planning: Accelerator count, power, cooling and space derived from the workload and the roadmap. - Cooling readiness: Air, rear door and direct to chip liquid cooling assessed with the facility. - Compute fabric: InfiniBand or Ethernet with RoCE, rail-optimized and non-blocking where training needs it. - Storage: Parallel file systems and object storage sized for checkpointing and data loading. - Integration and validation: Firmware, drivers, burn-in, fabric tests and benchmarks on every node. - Cluster operations: Scheduling, monitoring, health checks and failure handling after go-live. Where it sits: 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. Decisions to settle early: - Own or rent accelerators. Owned GPUs are cheaper at sustained use; rented capacity suits uncertain or bursty demand. Many organizations combine the two. - 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. - Where the cluster lives. Your own facility, a colocation suite or a specialist AI facility each trade control, power availability and time to ready. How the work runs: Plan, Workload, power and cooling. Design, Compute, fabric and storage. Integrate, Racks built, cabled and burned in. Validate, Fabric tests and benchmarks before handover. How it is bought: - 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. - A fixed-price build: Fabric and storage design, rack integration, burn-in and full cluster validation, priced for the cluster size in scope. - Running it afterwards: After go-live, cluster operations covering scheduling, health checks and failure handling can run monthly. 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. - 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. - Design: A rail-optimized InfiniBand fabric and a parallel file system for checkpoints are designed for the planned nodes, with room to add more. - Integrate: Nodes are baselined, cabled to the schedule and burned in, and faulty components are replaced before the cluster is assembled. - 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, not a client account.) What you have at the end: - 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. Standards and platforms worked with: NVIDIA HGX, NVIDIA DGX, NVIDIA Quantum InfiniBand, RoCE v2, NCCL tests, NVIDIA DCGM, Slurm, Kubernetes, Lustre, Direct to chip liquid cooling. Where it goes wrong, and what we design out: - Hardware before the room is ready. GPUs that arrive before power and cooling sit in boxes. Facility readiness is checked first. - A fabric nobody designed. An unbalanced network slows every job on the cluster. The fabric is designed for collective traffic. - Skipping validation. Slow links hide until the first long training run. Fabric tests and benchmarks run on the whole cluster before handover. What it changes: - The performance you paid for. Validated fabrics and balanced storage let jobs run at the speed the accelerators allow. - Facilities that keep up. Power and cooling are planned for current densities and for the generation after. - 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. Who you work with: - Cluster architect. Plans capacity and designs the fabric, storage and facility requirements. - Integration engineers. Build, cable and burn in the racks and nodes. - 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. Common questions: Q: InfiniBand or Ethernet? A: 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. Q: Can our facility support GPU racks? A: 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. Q: Are there export restrictions on GPUs? A: 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. Q: Should we build or rent GPU capacity? A: 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. Q: What is a rail-optimized fabric? A: 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. Q: Why do NCCL tests matter before handover? A: 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. Q: What storage does an AI cluster need? A: 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. Q: Can an existing data center host a GPU cluster? A: 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. Often paired with: Hardware and rack integration, Data center networking, MLOps and model operations. --- Canonical page: https://www.expandware.com/services/data-centers/ai-data-centers-and-gpu-clusters/ Site index for machines: https://www.expandware.com/llms.txt Full site text: https://www.expandware.com/llms-full.txt Expandware Private Limited. Inquiries: solutions@expandware.com, +92 (333) 32 11011.