AI is rewriting the rules of infrastructure.
GPU density, thermal load, and power volatility have pushed compute beyond what legacy facilities can support. We define the engineering response.
Why AI is reshaping infrastructure.
AI training and inference impose load patterns that classical data center design never anticipated — synchronized GPU draw, millisecond power transients, and continuous high-density heat dissipation.
The result is a structural reset: infrastructure must now be engineered as a workload-aware system, not a passive utility layer. Power, thermal, deployment, and intelligence become a single design surface.
The cluster is the computer.
Training a frontier model treats thousands of GPUs as one machine. Interconnect, telemetry, and power must behave with the determinism of a single substrate.
Every fiber, busway, and coolant loop is engineered against that constraint.
Four interlocking AI infrastructure problems.
GPU Density
Spatial, electrical, and thermal envelopes pushed past legacy limits.
Thermal Architecture
Liquid cooling, two-phase systems, and rear-door exchange engineered as one loop.
Power Density
Sub-millisecond response and harmonics control for AI load profiles.
Scalability
Linear capacity unlocks across three GPU generations.
Infrastructure densification timeline.
Air-cooled, 5–15kW per rack, generic data halls.
Rear-door cooling, 25–40kW per rack, dense clusters.
Direct-to-chip liquid, 80–120kW per rack.
Two-phase cooling, 250kW+ per rack, AI-aware MEP.
Engineering for what comes next.
