GPU Infrastructure Building
We design and build the compute behind modern AI – H100, H200, and Blackwell B200 clusters wired with NVLink and InfiniBand, cooled with direct-to-chip liquid, and powered for the density these accelerators demand. From a single node to a full NVL72 rack.
AI compute is a systems problem.
A GPU cluster is not a rack of servers with cards in it. Training performance lives in the memory bandwidth of the accelerators, the NVLink domain that binds them, the scale-out fabric that connects nodes, and the cooling and power that let them run flat out without throttling. Get one of those wrong and expensive silicon sits idle. We engineer all of them together, so the cluster you pay for is the cluster you actually get.
- ✓Bandwidth first. HBM3e and NVLink so GPUs feed on data instead of stalling on it.
- ✓Fabric that scales. InfiniBand NDR or lossless 400/800 GbE Ethernet, sized to your jobs.
- ✓Cooling and power by design. Direct-to-chip liquid and density planning, not an afterthought.
Illustrative nominal HBM bandwidth per accelerator. B200 figure is approximate and configuration dependent.
The density of modern AI.
Every layer of the cluster.
Accelerators, fabric, cooling, power, and the orchestration that turns hardware into a platform – built as one system.
GPU accelerators & nodes
HGX 8-GPU baseboards and GB200 systems built around H100, H200, and Blackwell B200 silicon. HBM3 and HBM3e memory up to 192 GB per GPU with bandwidth to roughly 8 TB/s, and Blackwell FP4 and FP6 for low-precision inference and training. We spec, rack, cable, burn-in, and validate every node.
NVLink scale-up domain
NVLink gen4 at 900 GB/s scaling to gen5 at 1.8 TB/s per GPU, binding 8 GPUs on a baseboard or 72 in an NVL72 rack into one memory domain.
Scale-out fabric
Quantum-2 InfiniBand NDR at 400 Gb/s, or RoCEv2 over 400/800 GbE with Spectrum-X and Ultra Ethernet, engineered rail-optimised.
Direct-to-chip liquid cooling
Air cooling tops out around 20 to 40 kW per rack. Blackwell density needs direct-to-chip liquid, which also cuts PUE below 1.2 versus 1.4 to 1.6 for air. We design cold plates, manifolds, CDUs, and heat rejection for the density you are targeting.
Power & density planning
PDUs, feeds, and redundancy sized to GPU TDP – an NVL72 rack draws about 120 kW nominal – with headroom for growth.
Orchestration & scheduling
Slurm or Kubernetes with GPU-aware scheduling, MIG partitioning, drivers, and observability so the cluster runs as a platform.
Choosing the right silicon.
Reference specifications for current data center accelerators. Exact figures vary by SKU, board partner, and configuration.
| Accelerator | Memory | Bandwidth | TDP | Fabric |
|---|---|---|---|---|
| NVIDIA H100 | 80 GB HBM3 | 3.35 TB/s | ~700 W | NVLink gen4 900 GB/s |
| NVIDIA H200 | 141 GB HBM3e | 4.8 TB/s | ~700 W | NVLink gen4 900 GB/s |
| NVIDIA B200 | up to 192 GB HBM3e | ~8 TB/s | ~1000 W | NVLink gen5 1.8 TB/s |
| AMD MI300X | 192 GB HBM3 | 5.3 TB/s | 750 W | Infinity Fabric |
Nominal reference values for planning. B200 memory and bandwidth are approximate and configuration dependent.
From workload to running cluster.
Size the workload
We profile your models and jobs to size GPUs, memory, NVLink domain, and scale-out fabric to real performance targets.
Power & cooling
Assess the site or colo, plan power feeds, floor loading, and direct-to-chip liquid cooling for the target density.
Rack & cable
Rack nodes, wire NVLink and the InfiniBand or Ethernet fabric, plumb liquid loops, then burn-in and validate.
Orchestrate & scale
Stand up scheduling, drivers, and observability, then scale out on the same fabric as your demand grows.
GPU infrastructure, answered.
Air cooling or liquid cooling for GPU racks?
InfiniBand or Ethernet for the GPU fabric?
Do you build on-premises or in colocation?
How do you plan power and rack density?
Can we start with a single node and scale later?
Build the cluster.
Feed the GPUs.
Tell us the models you run and the scale you are aiming for. We will design the accelerators, fabric, cooling, and power, and build a cluster that runs your silicon flat out.