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Efficient AI infrastructure starts with the workload, not a particular accelerator or cooling system. Design the IT environment and facility together: determine what computing the workload needs, then plan air management, cooling, electrical capacity, reliability, heat recovery and measurement around it. A low facility overhead is valuable, but it is not a complete measure of efficiency; useful work per unit of energy, water and cooling demands, cost and grid constraints matter too.
Why does AI infrastructure efficiency matter?
AI infrastructure is more than the processors doing the calculations. A data center also relies on servers, storage, networking, cooling, uninterruptible power supplies (UPS), backup generation and grid connections. Those systems all affect energy use, and their shares vary by facility.
The scale makes the design choices consequential. The International Energy Agency (IEA), in its 2025 Energy and AI report, estimated that data centers used 415 terawatt-hours (TWh) of electricity worldwide in 2024, about 1.5% of global electricity consumption. Its Base Case projects global data-center electricity use at about 945 TWh in 2030. That is a scenario projection, not a guaranteed outcome.
The IEA also reports that energy use per AI task has improved rapidly while expanding adoption and new uses have helped lift overall data-center demand. Greater efficiency per task therefore does not, by itself, mean lower total electricity consumption.
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What should designers measure besides PUE?
Power usage effectiveness (PUE) is the ratio of a data center’s total facility energy to the energy used by its IT equipment. It is useful for understanding facility overhead: a lower ratio means less energy is used outside the IT load under that measure. But PUE does not say how much useful AI work the equipment completed, and it does not capture every resource or operational constraint.
Compare design options using a set of measures that reflects the actual workload and site:
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- Useful work per unit of energy: assess how much of the required workload is completed for the energy consumed, using measures appropriate to the workload and service needs.
- Facility overhead: use PUE as one indicator, with a consistent system boundary and measurement method.
- Cooling and environmental fit: account for thermal requirements, cooling approach, water limits and local environmental conditions.
- Electrical capacity and resilience: check available power, grid connection, reliability needs and backup strategy.
- Cost and operating context: compare options for the facility’s scale, age, climate and operating profile.
These measures can point in different directions. A design with lower overhead is not automatically the best choice if it cannot meet the workload’s performance, reliability, water or cost requirements.
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Recent figures describe different things: worldwide consumption, scenario projections, facility-level cooling shares, reported demand growth and one company’s fleet-level PUE. They should not be treated as directly interchangeable targets or forecasts.
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| Measure | Reported figure | How to interpret it |
|---|---|---|
| Global data-center electricity consumption | 415 TWh in 2024, about 1.5% of global electricity consumption | IEA estimate published in 2025. |
| Global data-center electricity consumption | About 945 TWh by 2030 | IEA 2025 Base Case scenario projection. |
| Efficiency scenario | More than 15% less energy by 2035 than the Base Case | IEA 2025 High Efficiency Case scenario, assuming stronger efficiency gains in software, hardware and infrastructure. |
| Cooling share of data-center consumption | About 7% in efficient hyperscale facilities; over 30% in less-efficient enterprise facilities | IEA 2025 figures illustrate variation by facility type and boundary; they are not a universal cooling allowance. |
| Data-center electricity demand growth | 17% in 2025; 50% for AI-focused data centers | IEA figures reported in 2026 for growth that occurred in 2025, not projections. |
| Google global data-center fleet average annual PUE | 1.09 in 2025 | Company-reported result published by Google in 2026; it is fleet-level and depends on the company’s measurement method, not a general design target. |
The range in cooling shares is particularly important: a figure from one type of facility should not be applied to another without matching the facility type and accounting boundary. Likewise, Google’s reported fleet PUE is an example of a company’s measured result, not proof that a different site can achieve the same value.
How should an efficient AI facility be designed?
The U.S. Department of Energy’s Federal Energy Management Program (FEMP) says data centers offer “a tremendous opportunity for energy and cost savings.” Its data-center design guidance, dated July 26, 2024, treats efficiency as a sequence of connected decisions and cautions that no single guide can identify the most efficient design for every scenario.
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- Define the workload and IT requirements. Establish what the AI workload must do and the requirements its computing, storage and networking systems must satisfy. The workload determines the IT demand the facility has to support.
- Set environmental conditions and plan air management. Design operating conditions and airflow around the IT systems. FEMP places IT systems and environmental conditions early in its guidance because decisions there can create secondary savings in mechanical and electrical systems.
- Select cooling for the site and thermal needs. Compare cooling approaches against equipment requirements, local environmental conditions, water constraints, energy use and cost. The available evidence does not establish one universally best cooling method.
- Design electrical systems and reliability together. Account for IT demand, facility equipment, power availability, grid connection, backup generation and the reliability the operation requires. Grid access and electricity supply can limit expansion, so capacity planning is part of the efficiency problem, not a separate afterthought.
- Consider heat recovery where it fits. Evaluate whether recovered heat has a useful destination in the local context. FEMP includes heat recovery in its design framework; whether it makes sense depends on the facility and its surroundings.
- Measure and refine the whole system. Track facility overhead alongside workload output, energy use and relevant cooling, water, reliability and cost measures. Use consistent boundaries when comparing performance over time or between options.
How can AI workload choices reduce facility demand?
IT decisions influence the infrastructure built around them. Google Cloud’s vendor guidance for AI and machine-learning workloads recommends choosing efficient models and processors, optimizing workloads and considering deployment location. These are Google’s recommendations, not guarantees that every workload will achieve a particular efficiency improvement.
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How do you choose between real design options?
Start with the constraints that cannot be negotiated, then compare the options against a common set of requirements. A site with limited power or water, for example, has different viable choices from a site without those constraints. Facility scale, age, climate and operating profile also change the result.
- Document workload performance and reliability requirements.
- Establish the site’s electrical capacity, grid access and backup needs.
- Identify cooling, thermal and water constraints.
- Compare useful workload output per energy unit as well as facility overhead.
- Include cost and the operating conditions expected over the facility’s use.
The cited guidance and global figures do not specify the workload, location, water limits, cost assumptions or scale of a particular project. They support a design method, not a location-specific engineering recommendation. A project comparison needs those inputs before it can identify a suitable configuration.
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