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AI infrastructure costs come from a whole data-center system, not just its GPUs. Accelerator-heavy servers can dominate an AI facility’s modeled cost, but the site also needs buildings, grid connections, substations, backup power, networking and cooling. Electricity and other operating expenses then recur over time. A modeled 1 GW U.S. hyperscale facility from Epoch AI illustrates the distinction: $38 billion in upfront capital expenditure, $0.9 billion in annual operating expenses and $8.5 billion in annualized total cost of ownership. Those figures describe one model, not a universal price.
Which parts of an AI data center cost the most?
The largest line item depends on what is being measured. Servers may lead annualized total cost of ownership (TCO), while the upfront investment also has to cover the facility and the equipment that supplies power, moves data and removes heat. Operating costs continue after construction, with electricity among the recurring expenses.
What the 1 GW model includes
Epoch AI’s 2026 illustrative model assumes a U.S. hyperscale AI data center using NVIDIA GB200 NVL72 systems. It estimates the following amounts on different time bases:
| Measure | Modeled amount | What it means |
|---|---|---|
| Upfront capital expenditure (CapEx) | $38 billion | Initial investment for the modeled facility and its infrastructure. |
| Annual operating expenditure (OpEx) | $0.9 billion per year | Recurring costs; the model considers energy, maintenance, labor, taxes and water. |
| Annualized TCO | $8.5 billion per year | The model’s annualized cost measure, with servers accounting for $5 billion annually, or 60%. |
These numbers should not be added together: CapEx is an upfront amount, OpEx is a yearly amount, and annualized TCO expresses cost on an annual basis. They are also not a quote for a real project. Epoch AI says actual costs can vary with location, design and procurement. Its construction input includes a 7–10% premium for liquid cooling; that is an assumption in this model, not a general rule for liquid-cooled facilities.
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Servers are more than accelerator chips
AI compute costs include accelerator-equipped server systems and their associated components, including memory. The number and type of servers, their power density, useful life and utilization affect the purchase bill and how much useful work the investment produces. A costly server that is poorly utilized spreads its cost across less work; a longer useful life can change the annualized cost calculation. The Epoch example’s specific server assumption makes it a useful illustration, not a price benchmark for every AI workload.
The facility and power chain
A server fleet needs a site and a building as well as mechanical and electrical infrastructure. Depending on the project, the cost scope can include land, utility works, substations and external cabling. Within the facility, transformers, backup generation, uninterruptible power supplies (UPS) and power distribution help deliver electricity to equipment. A headline that counts servers but excludes these elements is not a full infrastructure cost.
Why power is both a bill and a bottleneck
Electricity is a recurring operating expense, and its cost depends on location and contract terms; there is no single universal price per kilowatt-hour for an AI data center. But power also affects project timing and feasibility. A site needs enough available grid capacity and delivery equipment, and those connections and supporting systems have their own costs. A facility can therefore face a power constraint even when its operator is willing to pay for electricity.
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Global data-center electricity figures help show the scale, but they cover data centers broadly rather than measuring AI alone. The International Energy Agency (IEA), in 2025, estimated that data centers used 415 terawatt-hours (TWh) in 2024, about 1.5% of global electricity consumption. Its base case projects about 945 TWh in 2030. In that same base case, electricity use by accelerated servers grows 30% annually, compared with 9% for conventional servers. These are scenario estimates, not a count of electricity used exclusively by AI.
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What networking and cooling contribute
Networking connects the workload
Data-center networks do different jobs. Front-end networking handles traffic to and from the facility; back-end networking links servers so they can communicate during distributed workloads. Network equipment is therefore part of the system needed to make server capacity useful, even though its cost may appear smaller than the server bill in some estimates. TrendForce’s 2025 discussion of a typical 125 MW hyperscale data center attributes roughly 60% of CapEx to servers and notes rising network CapEx. Its public landing page does not provide the detailed cost tables, so that share should be treated as the report’s estimate rather than a fully inspectable breakdown.
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Cooling uses power and capital
Cooling has two distinct cost effects: the cooling system requires equipment and construction investment, and it consumes electricity during operation. The IEA’s 2025 estimates put cooling at about 7% of electricity demand in efficient hyperscale facilities, versus more than 30% in less-efficient enterprise facilities. The difference illustrates why cooling-energy shares should not be read as cooling’s share of capital cost. Facility type and efficiency matter, and the IEA figures describe electricity consumption, not equipment purchase prices.
The same accounting distinction applies to networking: the IEA estimates network equipment can account for up to 5% of data-center electricity demand, not up to 5% of CapEx. Energy-use percentages and construction budgets answer different questions.
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A cost estimate is only comparable with another if both cover similar systems and use similar accounting. Check the following before comparing a project, forecast or vendor claim:
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- Scale and scope: facility size or IT load, number and type of accelerators, and whether the estimate includes the building, land, substations, utility works and external fiber.
- Location and power: local electricity rates and contract terms, grid capacity, connection equipment and backup-power requirements.
- Design: server configuration, network topology, cooling method and the facility’s efficiency.
- Use and time horizon: accelerator utilization, equipment useful life, financing assumptions and discount rate.
- Accounting basis: upfront CapEx, annual OpEx or annualized TCO—and whether maintenance, labor, taxes, water and energy are included.
- Evidence type: a modeled scenario, a forecast or an observed project. Scenario forecasts also depend on adoption, efficiency gains and infrastructure bottlenecks.
These distinctions are especially important when a headline number combines assumptions that cannot be seen in a short summary. For instance, Epoch AI’s 1 GW example is explicitly a model, while electricity forecasts from the IEA and LBNL are scenario-based. They should not be mistaken for measured costs of a standard AI data center.
What the wider investment forecasts do—and do not—say
McKinsey’s 2025 forecast of $6.7 trillion in cumulative worldwide data-center capital outlays by 2030 gives a sense of the projected investment scale. It is a broad forecast for data centers, not realized spending and not an AI-only tally. Like electricity-demand projections, it depends on assumptions about future build-out and should not be treated as a settled cost total.
In a 2024 Department of Energy announcement about the LBNL data-center energy report, U.S. Energy Secretary Jennifer M. Granholm said, “We can meet this growth with clean energy.” That is an official policy statement, not a measured finding that clean power supply or grid capacity will automatically keep pace with demand.
How to read an AI infrastructure cost claim
Start by identifying the unit and time period: dollars upfront, dollars per year, cost per unit of computing work or annualized TCO. Then ask what infrastructure is inside the boundary and whether the figure describes a model, forecast or completed project. Without those details, two apparently conflicting cost estimates may simply be measuring different systems.
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