GPU-dense facilities engineered for the AI economy — training and inference at national scale, with the networking, cooling, and power that frontier compute demands.
Accelerated compute sized for frontier models and production inference.
Dense clusters of the latest accelerators, built for large-model training and high-throughput inference.
→High-performance CPU, memory, and parallel storage tiers feeding the accelerators.
→Bare-metal performance with managed Kubernetes and reference architectures.
→Validated cluster designs, sized and benchmarked before production.
→Non-blocking fabric so thousands of GPUs train as one.
Thermal design for racks that draw tens of kilowatts.
Dense, redundant, efficient power delivery.
Distribution engineered for tens of kilowatts per rack.
N+1 / 2N power paths for continuity.
Strong PUE targets across the facility.
The software layer that turns hardware into usable compute.
Sovereign AI, deployed where the mandate requires.
National AI compute under the owner's control, in-country — the machines built to train sovereign models.
On-premise, hybrid, or dedicated capacity.
Training data and models that stay inside the border.
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