What is AI infrastructure?
AI infrastructure is the integrated stack of GPU compute, high-density data centres, energy capacity, network fabric, and capital that makes modern artificial intelligence workloads possible. It differs from traditional cloud infrastructure in density, cost structure, and how it must be financed and delivered.
This guide explains the components, why AI workloads demand a different approach, and how to evaluate an AI infrastructure provider.
The five layers of AI infrastructure
Compute. Dense GPU clusters built on NVIDIA H100, H200, GB200, or GB300 silicon. Frontier training runs at rack scale (NVL72), inference at single-node scale. Compute choice cascades into every other layer.
Data centre shell. Liquid-cooled, high-density facilities designed for 70–132 kW per rack with PUE targets under 1.25. Traditional 8–15 kW air-cooled rooms cannot host modern AI hardware.
Power. Long-duration PPAs and secured grid capacity, increasingly renewable-led. Power, not silicon, is the binding constraint on the global AI build-out.
Network. Non-blocking InfiniBand or RoCE fabrics inside the cluster; high-capacity transit to customer environments. Network latency and bandwidth shape training throughput as much as the GPUs themselves.
Capital. Institutional-grade structures matched to the asset's 15–20 year duration. Project finance, green-aligned debt, sovereign co-investment, and structured offtake replace generic infrastructure assumptions.
Why AI workloads need a new infrastructure model
AI training is concentrated, predictable, and capital-intensive. A single frontier training run can consume tens of megawatts continuously for months. That changes everything downstream: cooling shifts from air to liquid, capacity is contracted years in advance, customers anchor sites before construction, and capital structures must reflect the actual workload rather than generic data centre underwriting.
The market mostly separates these decisions — developers build sites, financiers fund them, customers commit late, and energy is sourced last. The result is missed timelines and stranded capacity. Integrated platforms originate around all four together.
Cooling AI infrastructure
At GB200 and GB300 densities, air cooling is no longer viable. The serious options are direct-to-chip liquid cooling (the dominant choice for new builds), rear-door heat exchangers for retrofit scenarios, and full immersion for the highest densities. Cooling is fixed at site-selection stage because it dictates power, water, mechanical layout, and ultimately PUE.
How to choose an AI infrastructure provider
Evaluate four things together, not in sequence:
- Secured power. PPA signed, grid connection confirmed — not letters of intent.
- Customer anchoring. Real offtake commitments before construction begins.
- Capital structure. Institutional-grade, matched to asset duration.
- Operational integration. Developer and operator under one roof, not handed off.
Providers who originate around all four together deliver on schedule. Those who assemble them sequentially typically slip and re-price.
Frequently asked questions
- What infrastructure is needed for AI?
- AI workloads require dense GPU compute (typically NVIDIA H100, H200, GB200, or GB300 class), high-bandwidth interconnect (NVLink and InfiniBand or RoCE), liquid-cooled racks delivering 70 kW to 130 kW or more per rack, low-PUE power infrastructure backed by long-term PPAs, and a data centre site with secured grid capacity. Capital structure, customer offtake, and energy strategy must be designed together rather than sequentially.
- How is AI infrastructure different from traditional data centres?
- AI infrastructure is built around rack-scale training clusters rather than distributed enterprise workloads. Rack densities are 5–10x higher, cooling shifts from air to liquid, network fabrics are non-blocking, and capacity is contracted years in advance against committed end-customer demand rather than built speculatively.
- How do you cool AI infrastructure?
- Modern AI data centres use direct-to-chip liquid cooling, rear-door heat exchangers, or full immersion to handle rack densities above 50 kW. Air cooling is no longer viable at GB200 or GB300 densities. Cooling design is fixed at the site-selection stage because it dictates power, water, and mechanical layout.
- How do you choose an AI infrastructure provider?
- Evaluate four things in parallel: secured power (PPA in place, not just letters of intent), customer anchoring (real offtake, not speculative capacity), capital structure (institutional-grade, matched to asset duration), and operational control (developer–operator integration rather than handoffs). Providers who originate around all four together deliver on schedule; those who assemble them sequentially typically slip.
- How do you build AI infrastructure?
- Start with energy: secure power and grid connection before site design. Anchor end-customer commitment before construction. Structure capital against the actual project, not generic infrastructure assumptions. Design liquid cooling, network fabric, and rack topology around the workload. Commission with institutional handover standards. This is the integrated approach Claymont operates.
An integrated AI infrastructure platform
Claymont develops AI-native data centres and the compute layer that runs on them. Compute, energy, and capital are engineered as one system.