High-density compute, orchestrated end to end.
We architect GPU fabrics for the heaviest workloads — frontier model training, memory-bound research, and high-throughput inference — then run them with measurable reliability.
Platform Comparison
Pick the platform that fits the workload.
| Specification | NVIDIA H200 Training Cluster | AMD MI300X High-Memory Pod | NVIDIA L40S Enterprise Pod |
|---|---|---|---|
| GPU Platform | 8× NVIDIA H200 SXM | 8× AMD Instinct MI300X | 8× NVIDIA L40S PCIe |
| vRAM per GPU | 141 GB HBM3e | 192 GB HBM3 | 48 GB GDDR6 |
| vRAM per Node | 1,128 GB | 1,536 GB | 384 GB |
| Interconnect | 400Gb/s InfiniBand NDR | 400Gb/s InfiniBand NDR | 200Gb/s RoCE v2 |
| Intra-node Link | NVLink 4 · 900 GB/s | Infinity Fabric · 896 GB/s | PCIe Gen5 |
| Cooling | Direct-to-chip liquid | Direct-to-chip liquid | Optimized air / RDHx |
| Rack Density | 42 kW | 44 kW | 18 kW |
| Best For | Frontier LLM training | Memory-bound training | Inference & fine-tuning |
Configurations are representative per-node specs and can be tailored to your capacity and budget envelope.
Engineering Practice
What sets the deployment apart.
Liquid Cooling Loops
Closed-loop direct-to-chip cooling with CDU redundancy holds junction temps stable at full sustained load — no throttling on long training runs.
Custom Rack Assembly
Every rack is built, cabled, and burn-in tested in our integration lab before it ships, so commissioning on-site takes hours, not weeks.
Power Optimization
Engineered PDU topology, branch-circuit metering, and live PUE tracking keep energy predictable and auditable across the deployment.
Scope your cluster with our engineers
Bring your model size and timeline — we'll size compute, fabric, and cooling.
Start a Design Session