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Reduce data center energy use by cutting wasted computation and cooling overhead—not by throttling useful AI work. Start with a workload-and-energy baseline, consolidate safely, tune cooling to equipment limits, and shift only the AI jobs whose deadlines and latency requirements allow it. Measure energy per unit of useful work alongside facility energy so efficiency gains do not come at the cost of service quality or reliability.
What to measure before making changes
Use the same accounting boundary and time interval for facility energy, IT energy, workload output, cooling, and environmental conditions. Compare results under similar workload volume and service-level conditions; otherwise, a change in demand or weather can look like an efficiency improvement.
- Facility energy: the total energy entering the data center over the chosen interval.
- IT energy: energy used by the computing equipment, including servers and other IT equipment covered by your accounting boundary.
- Useful output: a workload-appropriate measure such as transactions served, completed jobs, or a suitable compute-throughput metric.
- Operating conditions: utilization, inlet temperatures, cooling energy, peak demand, and relevant humidity or weather conditions.
Power usage effectiveness (PUE) is facility energy divided by IT-equipment energy. The U.S. Department of Energy’s Federal Energy Management Program (DOE/FEMP) describes PUE in its 2024 guide as an infrastructure-efficiency metric; it does not show how efficiently the facility turns electricity into useful computation. Track it alongside output per unit of energy. Where they matter to your site, also track water usage effectiveness (WUE), carbon usage effectiveness (CUE), and energy reuse effectiveness (ERE) for recovered heat.
Reduce wasted computation before changing cooling capacity
Find idle servers and consolidate compatible work
DOE/FEMP’s 2024 guide reports typical enterprise server utilization of 20% to 40%. Virtualization and workload consolidation can run compatible work on fewer physical servers, allowing redundant capacity to be idled or retired and reducing the heat that cooling systems must remove.
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Do not pursue utilization as a goal in itself. First validate performance, availability, licensing, security, and redundancy requirements. Preserve enough headroom for demand spikes, maintenance, and failures; consolidation that breaches latency or availability targets is not an efficiency win.
Choose efficient equipment and software for the work
Evaluate processors, power supplies, fans, storage, and networking by useful work per watt, not just peak specifications. Review whether algorithms or software configurations can complete the same work with fewer resources, and assess AI hardware against the workload it will actually serve.
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The DOE/FEMP guide, citing Rahkonen and Dietrich (2023), reports about 50% higher server efficiency when processor utilization is doubled from low levels of 20% to 30%. This is a finding attributed to that guide and its cited study, not a guarantee for every server, AI accelerator, or data center.
Improve cooling without exceeding thermal limits
Fix airflow before adding more cooling
Keep cool supply air from mixing with hot exhaust. Review rack layout, blanking panels where appropriate, aisle separation or containment, and airflow paths through equipment. Then tune fan speeds and supply airflow to actual demand rather than leaving them at unnecessarily high settings. Monitor rack inlets and hotspots as you make changes, and retain required cooling redundancy.
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Review setpoints and controls at the equipment inlet
DOE/FEMP’s cooling guidance notes that data-center spaces may be cooled below recommended conditions and humidity held within narrower ranges than necessary. Such practices can increase chiller demand or make adjacent systems work against one another. Check sensor placement and actual IT-inlet readings, then compare proposed temperature and humidity settings with the applicable equipment and facility guidance before changing them. A warmer permissible operating range can create more opportunities for economizer operation, but the result depends on climate and control strategy.
Choose the cooling approach for the site and rack density
Air-side economizing, water-side economizing, conventional mechanical cooling, containment, and liquid cooling are design choices, not universal upgrades. Air- or water-side economizing depends on climate, air quality, humidity, plant configuration, and equipment tolerances. Liquid cooling may suit higher rack densities, but its total energy and water performance depends on the complete cooling system and site. Compare options against thermal limits, water availability, reliability and redundancy, retrofit complexity, maintenance skills, operating and capital costs, and whether recovered heat has a nearby useful demand. DOE/FEMP cautions that no single design guide identifies the most efficient design for every scenario.
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A DOE page describing a Vigilent cooling-control demonstration reports annual savings of more than 2.3 million kWh at California data-center sites. The visible page text does not clearly date that figure, and it describes a particular demonstration—not a current benchmark or a saving to expect at another facility.
Schedule AI workloads flexibly where service requirements allow
The DOE Secretary of Energy Advisory Board’s July 2024 report recommends exploring temporal and spatial flexibility for AI workloads. That means considering when and where eligible jobs run, rather than slowing every workload or moving every request.
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Use time flexibility for jobs with slack
Training or other batch work with flexible deadlines may be candidates for rescheduling to a lower-load or lower-carbon period. Before shifting a job, check its deadline, data availability, grid conditions, carbon intensity, and the reliability of the destination capacity. A shifted job is not automatically lower-energy; measure its total energy and outcome against the original schedule.
Use geographic flexibility only when latency and constraints permit
Inference requests may sometimes be routed among locations based on local grid load or renewable availability when response latency is not critical. Account for latency targets, service-level agreements, data residency, network energy, and capacity at each destination. The advisory report also stresses that reliability is essential for customer-facing inference, so routing decisions must preserve service quality.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Verify that the change improved efficiency, not just one ratio
For each change, compare before-and-after periods with comparable workload volume and service levels. Review facility energy, IT energy, useful output, cooling energy, peak demand, water use, and carbon intensity where available. A lower PUE can coexist with worse useful compute per watt, and the reverse can also occur. Efficiency is less energy for a unit of useful work; total energy can still rise if the facility performs more work.
- Set a baseline interval and define the workload output and accounting boundary.
- Change one operational factor at a time where practical, while monitoring inlet temperatures, hotspots, latency, and availability.
- Keep sufficient capacity for redundancy and demand variation.
- Recalculate the same metrics after the change and investigate any service or resource tradeoff.
For assessments, tools, and operator training, DOE identifies Lawrence Berkeley National Laboratory’s Center of Expertise for Energy Efficiency in Data Centers and its Data Center Energy Practitioner training as technical-assistance resources.
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