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GPU Cluster Architecture
Sizing and design of training and inference clusters on NVIDIA (HGX/DGX, H100/H200/Blackwell-class) and AMD Instinct platforms — matched to your workload profile, not the vendor's reference deck.
Capabilities
Everything Between the Concrete and the Model
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High-Performance Fabrics
InfiniBand and RoCEv2/Ethernet fabric design for east-west GPU traffic: topology, congestion control, rail-optimized designs and NCCL tuning. Where most AI clusters actually bottleneck.
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AI Storage & Data Pipelines
Parallel filesystems and object storage architectures that keep GPUs fed — checkpointing, dataset staging and throughput engineering for training and RAG workloads.
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Power & Cooling Design
Rack-level power planning for 40–130kW AI racks, liquid cooling (direct-to-chip) readiness assessments and facility retrofits — with partners for civil and mechanical execution.
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Cloud, Neocloud & Hybrid
GPU capacity strategy across hyperscalers, GPU neoclouds and your own metal — including burst architectures and workload placement that can cut compute spend dramatically.
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Sovereign & Private AI Builds
Air-gapped and on-premises AI environments for BFSI, healthcare, government and defense — aligned with India's DPDP Act and US regulatory regimes (HIPAA, FedRAMP-adjacent postures).
Delivery Model
Design–Validate–Deploy, with Benchmarks at Every Gate
Every build follows the same discipline: a reference architecture with a bill of materials and TCO model, a validation phase where we benchmark the design assumptions (or pilot on rented capacity), then staged deployment with acceptance testing — GPU burn-in, fabric stress tests and end-to-end training/inference benchmarks before sign-off.
We work with clients globally — directly and alongside your colocation provider, OEM or system integrator, remote or on-site.
- Vendor-neutral BoM. We specify what benchmarks justify — we don't resell hardware.
- Fabric-first designs. Network architecture from engineers who built enterprise and DC networks for years.
- Acceptance by measurement. Clusters are signed off on measured throughput, not delivery notes.
- Day-2 ready. Handover includes runbooks, monitoring and an operations model — or we run it as a managed service.
FAQ
Infrastructure Questions
Should we build on-prem GPU infrastructure or stay in the cloud?
It depends on utilization and data sensitivity. As a rule of thumb, sustained high-utilization workloads and regulated data favor owned or colocated infrastructure; spiky experimentation favors cloud. We model your actual workload and give you the crossover point in numbers.
Can you work with our existing data center or colocation provider?
Yes — that's the most common engagement. We design the GPU pods, fabrics and storage, then deploy into your facility or your colo partner's, handling power/cooling requirements with their facilities team.
Do you support both NVIDIA and AMD platforms?
Yes. We are vendor-neutral and benchmark both against your workloads. For many inference workloads, alternatives to the default choice can materially change the economics.
Planning a GPU Buildout?
Talk to us before you sign the purchase order. A two-week design review typically pays for itself many times over in avoided over-provisioning.
Get a Design Review