AI training at cluster scale.

An AI training cluster is a group of GPU servers connected by high-bandwidth low-latency fabric (typically InfiniBand NDR at 400 or 800 Gbps) that work together to train large AI models at scale. Clusters range from 4 to 64+ nodes, with each node containing 4 to 8 NVIDIA GPUs. VRLA Tech designs and builds AI training clusters with matched hardware SKUs, per-node burn-in testing, and scheduler-ready base OS configuration, typically delivered as complete racks including networking, PDUs, and rack rails.

Cluster size depends on model size, training time target, and budget. Small research clusters start at 4 nodes with 32 GPUs. Production frontier-model training typically runs 16 to 32 nodes with 128 to 256 GPUs. Large-scale training for foundation models commonly uses 64 nodes or more. Tell us what you're training and we'll recommend a node count based on what we've seen work for similar workloads.

For frontier-scale training we typically recommend NVIDIA H200 (141 GB HBM3e) for largest model capacity, RTX PRO 6000 Blackwell Server (96 GB HBM3e) for cost-efficient training, and H100 NVL where H200 allocations are constrained. AMD Instinct MI300X is available for workloads that benefit from 192 GB HBM3 per GPU. Tell us what you're training and we'll recommend a GPU based on experience with similar workloads.

InfiniBand NDR at 400 Gbps per port offers approximately 1-microsecond latency and native GPU Direct RDMA support. Historically this was the default for tightly coupled GPU training. Well-tuned Ethernet with RoCE v2 has matured substantially — recent industry benchmarks (including WWT's MLPerf testing and Meta's 24,000-GPU LLAMA 3 training cluster on Ethernet) show the performance gap is often under 5 percent and sometimes at parity. We spec fabric based on scale, operational familiarity, and budget. Most clusters under 32 nodes still benefit from InfiniBand's simpler tuning; larger deployments frequently choose Ethernet for operational reasons.

Yes. Every cluster ships with base OS configuration, NVIDIA drivers, CUDA toolkit, and networking stack configured per node — everything needed to install Slurm, Kubernetes, or another scheduler on top. Full scheduler installation, multi-node benchmarking, user management, job queue configuration, and cgroup policies are customer-led or handled by your DevOps team. We can recommend Slurm or Kubernetes integration partners for customers who need turnkey scheduler deployment, and our engineers remain available for operational questions after handover.

Lead time is case by case — it depends on cluster size, GPU availability, fabric and storage spec, and whether you need custom engineering. Some configurations can ship in weeks; larger clusters with specialty GPUs can take longer. We give you a firm timeline at order confirmation and reserve GPU allocations through NVIDIA Partner Network where applicable.

Yes. We commonly ship clusters directly to Equinix, Digital Realty, Coresite, QTS, and regional colo facilities. You handle facility coordination, cage access, and physical install with your colo's remote-hands service or your own team. Our engineers are available by phone and email if your install team needs help.

Cluster training typically requires high-throughput parallel file systems — WEKA, VAST Data, and DDN EXAScaler are common choices for data loading. Local NVMe on each compute node handles training state and checkpointing. Parallel file system selection and sizing is typically handled by the customer or a storage vendor partner; we build compute and fabric, and can integrate with your chosen storage tier.

Every VRLA Tech cluster includes a 3-year parts warranty on all hardware and lifetime US-based engineer support at no extra cost. You speak directly with the engineers who built your cluster — no tiered support, no call centers. Support is remote via phone, email, and video; replacement parts ship under warranty and your team or the colo's remote-hands service handles physical replacement.

The big OEMs each have different strengths. Dell and HPE offer deep enterprise support infrastructure and procurement relationships. Supermicro competes on fast standard-SKU delivery. VRLA Tech sits differently — we build every cluster to your specific workload with no locked SKUs or AVL shortcuts, include lifetime US engineer support at no extra cost, and stay engaged directly with the engineers who built your system rather than through tiered support contracts. Since 2016 we've served Fortune 500, federal agencies, and research labs including General Dynamics, Los Alamos National Laboratory, and Johns Hopkins. The best fit depends on your workload: standard SKUs and enterprise contracts favor the large OEMs; custom workload sizing and direct engineer access favor us.