AI Infrastructure

Overview

AI Infrastructure refers to the full hardware and facilities stack required to run large-scale AI training and inference workloads. At the system level, it consists of:

• Compute: GPU and ASIC accelerators (e.g., NVIDIA H100/B200, AMD MI-450) — the primary heat and power generators
• Memory: High-speed DDR (GDDR6X, HBM), directly attached to accelerators
• Storage: High-throughput NVMe SSDs for dataset and checkpoint I/O
• Networking: CPO switches, SFP optical modules, high-bandwidth interconnects (NVLink, UALink, UEC) for multi-GPU communication
• Power: High-voltage DC distribution (800V sidecar), power conversion hardware
• Thermal: Cold plates, manifolds, CDUs, facility cooling systems

The defining challenge of modern AI infrastructure is thermal density: server TDPs have risen from ~300 W for CPUs a decade ago to 700–2000 W+ for current AI accelerators, with next-generation chips (NVIDIA Feynman) projected to exceed 2500 W by 2027. This escalation makes conventional air cooling architecturally insufficient for high-density AI deployments.

Market scale: IDC's 2024 Worldwide AI Infrastructure Forecast projects global spending growing from approximately $42 billion in 2023 to nearly $90 billion by 2028, driven by GPU-centric compute models requiring increasingly dense, high-power server configurations.