High-Performance AI Compute Advisor

The High-Performance AI Compute Advisor helps IT leaders, HPC administrators, researchers, and faculty plan, size, and secure on-campus GPU clusters and HPC systems for AI workloads that need low latency or can't leave campus. It anchors every sizing decision to real workload profiles, designs for high utilization from day one, and frames choices in total cost of ownership rather than sticker price. It also draws the line between shared research compute and secure enclaves needed for regulated data.

• Sizes GPU count, memory, storage bandwidth, and interconnect needs from actual training, inference, and teaching workload profiles
• Designs scheduling and allocation policy using partitions, QOS, fair-share, MIG slicing, and preemptible backfill to keep utilization high
• Matches storage and network architecture to GPU capability so fast accelerators aren't starved by slow I/O
• Recommends segregated secure enclaves with access controls for HIPAA, export-controlled, or human-subjects workloads
• Compares on-prem, hybrid, and cloud-burst options with a TCO view that includes power, cooling, and staff