Solutions · AI Data Center Infrastructure

Reference architectures for accelerated compute.

Site selection, electrical topology, mechanical systems and network fabric — engineered as one coherent infrastructure for frontier AI workloads.

Density target
120kW+/rack
Cooling
Liquid-to-chip
Fabric
Optical, 800G
PUE
< 1.15
S/01 · Thesis

Designed for training. Tuned for inference. Hardened for both.

An AI data center is not a hyperscale data center with denser racks. It is a different physical instrument — one where the entire facility behaves as a single, tightly-coupled distributed computer. Every layer of the stack reflects that.

Our reference architectures start from the workload: model size, training regime, inference latency budget, jurisdictional posture. Every downstream choice — site, power, mechanical, network — is governed by those constraints.

Liquid-to-chip cooling at the rack
Fig. — In situ
Capabilities
  • 01 · Site & power strategy
    Grid-adjacent terrain, behind-the-meter generation, multi-source electrical topology.
  • 02 · Mechanical systems
    Liquid-to-chip cold plates, rear-door HX, two-phase immersion options where density demands.
  • 03 · Network fabric
    Non-blocking fat-tree, rail-optimized GPU placement, optical interconnect throughout.
  • 04 · Resilience design
    Concurrent maintainability and fault-tolerant topology without overbuilding redundancy.
  • 05 · Sustainability
    Heat reclaim, water-positive design where possible, grid-aware load shifting.
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