AI GPU Infrastructure Advisory
GPU Architecture & Cluster Optimization
Engineer performance. Avoid overspend.
GPU architecture and cluster optimization is about choosing the GPU platform, the size of its NVLink domain and the fabric between servers so the cluster performs as needed — without paying for architecture the workload does not use.
What we advise
- B300, GB300 NVL72 and Rubin architecture choices
- 8-GPU servers versus 72-GPU NVLink domains
- NVLink, NVSwitch, InfiniBand and Ethernet
- Cluster scaling: 104 to 1,024+ GPUs
- GPU, rack and procurement BOM optimization
Buy the right GPU architecture—not the most expensive one.
Workload-first / Vendor-neutral / Commercially optimized
Looking at GPU compute options yourself? GPUClouds (opens gpuclouds.com in a new tab) sets out what it offers, and how it relates to this work.
Related solutions
AI Workload, Model & Inference Advisory
Workload discovery, model evaluation, precision and memory planning, and benchmark-driven GPU sizing.
AI Data Center Infrastructure Advisory
Own DC, colocation and hybrid options, high-density racks, liquid cooling, power and facility expansion.
Infrastructure Economics, Capacity & Operations
Commercial models, CapEx, OpEx, TCO and ROI, utilization forecasting and capacity expansion.
Next step
Start with the workload.
Tell us what you need to run, where it has to run and what it can cost, and we will set out the options.