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Data Center
Data center architecture for AI and HPC workloads: GPU cluster networking, high-bandwidth east-west fabrics, low-latency interconnects, and power and cooling at the design level. ianAsher LLC is the architecture design partner — vendor-neutral, not a product pitch.
Most enterprise data centers were built for north-south traffic: users to applications, applications to the internet. AI and HPC jobs are different. Training and tightly coupled inference move large volumes east-west, between GPU servers, and they stall when the fabric congests or latency spikes. A design that was fine for a virtualization cluster often cannot carry a GPU pod without a new fabric plan.
We design the topology around the job mix: leaf-spine for a general east-west fabric, or rail-optimized patterns when GPU-to-GPU traffic needs a dedicated rail. Non-blocking and oversubscription are planned on purpose, not left as a surprise in the first production run. Lossless Ethernet (RoCEv2 with PFC and ECN) and InfiniBand are treated as a trade-off against operations, skills, and what you already run — not as a brand decision.
400G and 800G planning includes the switches, the optics and the cabling plant, and a path from a pilot pod to a production row. We size for the first workload and show how the fabric grows a pod at a time, so you are not buying a forklift on day one.
Training data and checkpointing need a high-throughput storage network that does not share a congested path with the job itself. We map interconnects and storage paths so checkpoint traffic and east-west GPU traffic are designed together.
High-density racks change power distribution, cooling method and how many GPUs you can land in a row. We work this at the architecture level: rack density, liquid versus air cooling decisions, and capacity planning so the fabric and the facility story match. This is design guidance only. It is not a facilities engineering certification.
GPU clusters and data pipelines should be isolated from general enterprise traffic. We design segmentation and access paths so training data, model artifacts and management planes stay in the zones you intend. This sits alongside the AI Readiness practice and the AI Readiness Assessment.
Related: Data Center Assessment · AI Readiness Assessment · Network Assessment · AI Readiness · Data Center
