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AI data centers are integrated facilities, not rooms full of graphics cards. GPU-equipped servers perform the matrix calculations used for model training and inference, while CPUs, memory, storage and high-speed networking move data around them. Grid connections, power-distribution equipment, UPS batteries and generators deliver reliable electricity; cooling systems remove the heat that electricity creates. As AI racks become denser and their demand changes quickly, available grid capacity, transformers and heat-removal capacity can limit how fast a facility expands.
The figures below describe the data-center industry as a whole unless explicitly labeled otherwise. Estimates and forecasts come from different International Energy Agency (IEA) publications and years, so their measurement dates and forecast vintages are kept separate.
What an AI data center contains
An AI facility is a chain of systems that must work together continuously:
- Compute servers: CPUs handle operating-system, control and general-purpose tasks. GPUs or other accelerators execute many operations in parallel, which suits neural-network training and inference. Not every server has a GPU, and different AI jobs use different amounts of accelerator time.
- Memory and storage: High-bandwidth memory feeds the accelerator; local drives and shared storage hold training data, model checkpoints and application data.
- Networking: High-speed links connect accelerators within a server and across racks. Distributed training can require frequent movement of model parameters and data, making network equipment part of the workload and the power budget.
- Electrical infrastructure: A utility connection feeds switchgear, transformers, power-conversion equipment and rack-level distribution. UPS batteries bridge short interruptions, while backup generators support longer outages or maintenance.
- Environmental systems: Air handlers, pumps, heat exchangers and controls keep temperature and humidity within operating limits and reject heat to the outside environment.
This architecture is why a GPU specification alone cannot describe an AI data center. Service reliability depends on the complete path from the grid to the silicon and back out through the cooling plant.
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How GPUs are used for AI
Training a model
Training repeatedly adjusts a model’s parameters as it processes large datasets. The work consists largely of matrix and vector operations that can be split across thousands of arithmetic units. GPUs run these parallel operations, while CPUs prepare batches, coordinate software and handle tasks that do not map efficiently to the accelerator. The model may be distributed across many servers, so high-speed links and synchronized storage are essential.
Serving an AI model
Inference is the production phase: a service receives a prompt, image or other input and returns a result. A single request can use less compute than a training run, but a busy service handles many requests concurrently and must keep accelerators available with low latency. Energy per request therefore depends on model size, response length, batching, utilization and the surrounding infrastructure; there is no single universal watt-hour figure for “an AI query.”
Why CPUs, memory and networking still matter
Accelerators do not replace the rest of the server. CPUs schedule work and perform control logic, memory supplies data at high speed, and networking synchronizes distributed jobs. Storage systems and software orchestration add further electrical load. Treating all facility electricity as GPU electricity overstates what the evidence supports.
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The IEA’s 2025 analysis estimates that servers use about 60% of electricity in modern data centers on average, although the share varies by site. Cooling can represent about 7% of consumption in efficient hyperscale facilities and more than 30% in less-efficient enterprise facilities. The remainder covers networking, storage, power conversion, lighting, controls and other building systems. These are broad sector averages, not a promise for a particular building.
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| System | What it does | Why its load changes |
|---|---|---|
| GPU and CPU servers | Execute AI and general-purpose code | Accelerator count, utilization, clock settings, memory traffic and workload type |
| Storage and networking | Move and retain datasets, checkpoints and requests | Dataset size, distributed-training traffic, replication and service demand |
| Power delivery | Transforms and distributes utility power; UPS and generators provide continuity | Conversion efficiency, redundancy design and rapid changes in IT load |
| Cooling and environmental control | Removes heat and maintains temperature and humidity | IT heat output, outdoor conditions, set points, airflow and cooling technology |
| Buildings and controls | Provide monitoring, security, lighting and operational support | Facility size, occupancy and operating practices |
Nearly all electricity consumed by computing eventually becomes heat. Cooling does not eliminate that heat; it transfers it from chips and rooms to a heat-rejection system outside the building.
How much electricity data centers use
Global totals include every kind of data-center workload, not just AI. The IEA estimated about 415 terawatt-hours (TWh) of data-center electricity use in 2024, approximately 1.5% of global electricity consumption, in its 2025 Energy and AI analysis. That report’s Base Case projected roughly 945 TWh in 2030.
An updated IEA outlook published in 2026 estimated 485 TWh for 2025 and around 950 TWh in 2030. The apparent difference from the earlier 2030 number reflects a different publication vintage and outlook assumptions, not a measurement that can be treated as a simple year-to-year correction.
| IEA publication | Measured or estimated year | Data-center electricity | What the number means |
|---|---|---|---|
| Energy and AI (2025) | 2024 | About 415 TWh; about 1.5% of global electricity | Estimate for all data centers worldwide |
| Energy and AI (2025 Base Case) | 2030 | About 945 TWh | Projection for all data centers, not AI alone |
| Key Questions on Energy and AI (2026) | 2025 | About 485 TWh | Updated estimate for all data centers |
| Key Questions on Energy and AI (2026) | 2030 | About 950 TWh | Updated projection for all data centers |
The IEA’s 2026 reporting also said total data-center electricity demand grew 17% in 2025, while electricity use by AI-focused data centers grew 50% that year. Those percentages describe different scopes: the first covers the data-center sector, and the second covers the AI-focused portion.
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Why AI power demand is difficult for the grid
High rack density
The IEA’s 2026 summary reports that AI-server power density increased elevenfold between 2020 and 2025 and is expected to rise another fourfold by 2027. More power in each rack means more current through distribution equipment and more heat in a smaller space. A building with spare floor area may still lack the transformers, busways or cooling capacity needed for additional racks.
Rapid load swings
Training and inference can start, stop and shift across clusters. The IEA says AI workloads induce large, rapid power swings, creating reliability challenges. Electrical systems must handle those changes without voltage or frequency problems, and operators may need controls, storage or operating limits to smooth the load.
Local concentration
A global share of 1.5% does not describe every community’s grid. Data centers cluster where land, fiber and power are available, so a single region can face a much larger incremental demand than the worldwide percentage suggests. The IEA identifies grid-connection queues and shortages of transformers and other electrical equipment as potential constraints on development.
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How cooling removes AI-server heat
Air cooling
Traditional systems move room air through server fans, then use computer-room air handlers or chilled-water coils to carry heat outdoors. Hot and cold aisles, containment, airflow controls and higher supply temperatures can reduce mixing and fan work. Air systems are familiar and widely standardized, but their practical limit is the amount of heat that can be moved through a rack without excessive airflow, noise or fan power.
Liquid cooling
Liquid systems place a heat-transfer fluid close to the heat source. In direct-to-chip designs, cold plates remove heat from processors and carry it to a coolant-distribution unit and heat exchanger. Other designs immerse hardware or use rear-door heat exchangers. Liquid can transport more heat in a smaller volume than air, which helps support dense AI racks and can reduce the air-conditioning burden.
Liquid cooling is not a single product or guaranteed result. It adds pumps, manifolds, controls, fluid management and maintenance requirements. Compatibility with servers, standards, warranties, leak detection, technician training and the building’s existing pipework all affect whether a deployment is practical.
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|---|---|---|
| Heat transport | Uses room airflow and air handlers; capacity is constrained by airflow and temperature difference | Moves heat near the chip through coolant and heat exchangers; better suited to very high rack densities |
| Infrastructure | Uses established room and chilled-water designs | Requires distribution units, piping, controls and compatible server hardware |
| Retrofit | Often simpler where the building already has adequate air capacity | Can be difficult if floors, risers or racks cannot accommodate liquid loops |
| Risks and operations | More familiar servicing and fewer liquid components | Requires leak prevention and detection, fluid-quality management and new maintenance procedures |
| Best fit | Moderate densities or facilities optimized around air | High-density workloads where heat removal and space justify added complexity |
What efficiency gains liquid cooling can deliver
The IEA 4E EDNA report Liquid Cooling in Data Centres, published 22 June 2026, estimates potential savings of about 8% at the server level and 30–40% at the facility level in applicable scenarios. It places overall savings on the order of 10–21%. These are reported potentials, not guaranteed savings at every site: climate, workload, rack density, heat-rejection equipment and operating strategy determine the outcome.
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The report also cautions that power-usage effectiveness (PUE) alone can understate liquid cooling’s benefits. PUE compares total facility energy with IT energy, but it may not capture improvements in rack density, usable floor space or the way heat is delivered to a recovery system. Operators should examine component power, total cooling energy, water use, reliability and capital cost rather than treating one PUE value as a complete answer.
What happens during normal AI operation
- Work arrives: An application places a training job or inference request with the facility scheduler.
- Data is staged: Storage and CPUs prepare input batches; networking distributes data and model fragments to the selected servers.
- Accelerators compute: GPUs or other accelerators perform parallel operations while CPUs coordinate and monitor the job.
- Power is converted: Utility power passes through transformers, switchgear, UPS systems and rack power supplies before reaching the electronics.
- Heat is captured: Server fans and air handlers, or cold plates and coolant loops, collect heat from the IT equipment.
- Heat is rejected: Chillers, dry coolers, cooling towers or other heat exchangers transfer heat to the outdoor environment.
- Controls respond: Sensors watch electrical load, temperatures, flow, humidity and equipment status. Operators can move workloads, adjust set points or use backup systems when conditions change.
This chain explains why adding compute requires more than buying accelerators: each additional rack can require corresponding electrical distribution, network capacity, cooling flow and operational safeguards.
How operators choose a power and cooling design
There is no universal winner between air and liquid cooling, or between different power architectures. A useful evaluation starts with the workload and the building rather than a technology label.
- Measure the workload: Estimate sustained and peak accelerator utilization, memory traffic, training duration, inference concurrency and expected growth.
- Calculate rack density: Determine present and future kilowatts per rack, then check whether floors, busways, breakers and transformers can support it.
- Check the utility path: Confirm available grid capacity, connection milestones, voltage level, transformer lead times and rules for rapidly changing loads.
- Model heat rejection: Compare airflow, coolant temperatures, pump power, chiller performance, outdoor conditions and water availability.
- Design for continuity: Specify redundancy for power and cooling, UPS ride-through time, generator fuel or runtime, maintenance bypasses and failure testing.
- Assess deployment constraints: For liquid systems, verify server compatibility, piping routes, leak detection, fluid handling and technician procedures; for air systems, verify containment and airflow capacity.
- Price the whole life cycle: Include equipment, construction, energy, water, maintenance, replacement and the cost of delaying or limiting future expansion.
Because AI demand and hardware density are changing quickly, a design that meets today’s average load may still fail at tomorrow’s peak. Scenario planning should include higher rack density, sudden workload changes and delays in utility or electrical-equipment delivery.
What the numbers mean for communities and users
Data-center growth can bring computing capacity and infrastructure investment, but its electricity demand is geographically concentrated. A community evaluating a new facility should ask how much firm generation and transmission capacity is available, who pays for grid upgrades, how backup generation is managed and whether water or heat-rejection resources are constrained. A global percentage cannot answer those local questions.
For users, the practical lesson is that an AI response is supported by an industrial system: accelerator servers, networking, storage, power electronics and cooling all contribute to its resource use. The exact energy cost varies with the model, request, utilization and facility, so a single number presented without those conditions is misleading.
Sources and dates
- International Energy Agency, Energy demand from AI — Energy and AI, 2025.
- International Energy Agency, Executive summary — Key Questions on Energy and AI, 2026.
- IEA 4E EDNA, Liquid Cooling in Data Centres, 22 June 2026.
- International Energy Agency, Data centre electricity use surged in 2025, even with tightening bottlenecks driving a scramble for solutions, 16 April 2026.
“The IEA was early in recognising that there is no AI without energy – and that countries that provide secure, affordable and rapid access to electricity will be one step ahead,” said Fatih Birol, IEA Executive Director.
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