What it takes to build AI-ready infrastructure — and where most organizations fall short.
AI workloads are not just "standard compute but bigger." They fundamentally break traditional datacenter design assumptions regarding power, cooling, and networking.
Power Density Shock
A standard enterprise rack draws 5-8kW. A rack of NVIDIA H100s can draw 40kW+. Most colocation cages cannot cool that density without supplemental liquid cooling or rear-door heat exchangers.
The InfiniBand vs. Ethernet Debate
For training large models, latency is the enemy. InfiniBand has long been the king of low-latency, lossless fabric. However, Spectrum-X Ethernet is catching up. For valid inference workloads, standard 400G Ethernet is often sufficient, but for training clusters, the fabric design is as critical as the GPU choice.