AI Data Centers
Purpose-built computing facilities that house the GPUs, networking, cooling, and power infrastructure needed to train and serve large AI models at scale.
AI data centers are the industrial backbone of modern machine learning. Unlike traditional server rooms, they are designed around dense racks of accelerators, massive power delivery, and the cooling needed to keep thousands of GPUs running at full utilisation.
What distinguishes an AI facility:
- Accelerator density: racks holding many high-end GPUs, often liquid-cooled because air cannot remove the heat.
- Power: a single large training campus can draw hundreds of megawatts — comparable to a small city — making energy contracts and grid connection the limiting factor.
- Networking: high-bandwidth, low-latency interconnects so thousands of chips behave like one computer during training.
- Workload split: some sites focus on training, others on inference closer to users, and some on both.
The build-out has become a geopolitical and environmental story. Demand is straining electricity grids, driving interest in nuclear and renewable power deals, and raising land and water-use concerns. It has also created a new class of specialist operators and a race among cloud providers and model labs to secure capacity years in advance.