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Measure useful work as well as energy
Before changing equipment or operating conditions, establish a baseline over a consistent interval. Record facility energy, IT-equipment energy, workload volume or compute throughput, utilization, cooling energy, peak demand, and relevant environmental conditions. Keep the measurement boundary and time interval consistent when comparing results.
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Power usage effectiveness (PUE) is facility energy divided by IT-equipment energy. The U.S. Department of Energy’s 2024 Best Practices Guide for Energy-Efficient Data Center Design defines it as an annual ratio and explains that it measures infrastructure efficiency—not the efficiency of the compute work itself. Pair PUE with a workload measure that suits the service, such as transactions per watt or an appropriate compute-throughput-per-watt metric. Where relevant, also track water use, carbon intensity, and energy recovered as useful heat.
This distinction matters for AI: a site can improve PUE while delivering less useful compute, or deliver more compute without improving PUE. Compare before-and-after results at comparable workload volumes and service-level conditions rather than treating one ratio as the verdict.
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Remove idle and inefficient computation first
Find servers that are lightly used, workloads that can be consolidated, and software or hardware choices that consume energy without producing useful output. DOE/FEMP’s 2024 guide reports enterprise server utilization commonly in the 20%–40% range. It describes virtualization as a way to run workloads on fewer physical servers at higher utilization, which can reduce server power and the cooling capacity those servers require.
Consolidate only where service requirements allow
Check whether workloads can share fewer physical machines without compromising performance, availability, licensing, security, or redundancy. Validate the change under representative load before idling or retiring capacity. Maximizing utilization is not the goal if it creates latency problems or removes the spare capacity needed for resilience.
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Evaluate the whole compute path
Assess algorithms and hardware in terms of useful work per watt. Processors, fans, power supplies, storage, and networking all contribute to IT energy use; a more efficient component can help, but the relevant outcome is the energy required to deliver the required AI service. DOE/FEMP’s guide cites a study reporting about 50% higher server efficiency when processor utilization rises from low levels of 20% to 30%. That figure is attributed to the guide and its cited study; it is not a general prediction for every server, AI accelerator, or data center.
Cut cooling overhead without crossing thermal limits
Improve IT efficiency before sizing a cooling response: fewer powered servers or better-used equipment can reduce both IT power and the heat that cooling systems must remove. Then tune cooling to the actual conditions at equipment inlets, within manufacturer requirements and applicable ASHRAE guidance.
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- Separate cool supply air from hot exhaust with appropriate rack layout, aisle separation, or containment.
- Inspect for bypass airflow and recirculation; use blanking panels where rack openings allow hot and cold air to mix.
- Set supply airflow and fan speeds to match demand, while monitoring inlet temperatures and hotspots.
- Retain adequate cooling redundancy and commission major airflow or control changes for the specific facility.
Review temperature, humidity, and control settings
DOE/FEMP’s 2019 cooling-water guidance says data-center spaces are often cooled below recommended conditions and humidity is sometimes controlled within unnecessarily narrow ranges. Both practices can increase cooling demand; adjacent systems can also end up working against one another. Audit sensor placement and readings at IT inlets before changing setpoints, and follow equipment and facility limits. A warmer permissible operating range may make economizer operation possible for more hours, but the result depends on climate and controls.
Choose cooling methods for the site and rack
Air-side economizing, water-side economizing, conventional mechanical cooling, and liquid cooling are options with different constraints—not a universal ranking. Consider climate, air quality and humidity, rack density, equipment thermal limits, water availability, energy draw, capital and operating cost, retrofit complexity, reliability and redundancy, and maintenance skills. Liquid cooling can be an option for higher rack densities, but it does not automatically reduce total facility energy or water use; the full system and site determine the result. If recovered heat is part of the case for a design, consider whether there is a nearby, dependable use for it.
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DOE’s 2024 guide gives a specific example rather than a general benchmark: it says NREL’s data center was designed for equipment cooling to consume 6% of energy, contrasting that with a typical data-center figure of 70%. These attributed figures should not be treated as a prediction for another facility. Likewise, DOE’s page on a Vigilent cooling-control demonstration reports more than 2.3 million kWh in annual savings at California data-center sites. The page does not clearly date the figure in its visible text, and it is a case result, not an expected saving for other operators.
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Shift AI work only when the workload can tolerate it
DOE’s Secretary of Energy Advisory Board identified temporal and spatial flexibility as opportunities to explore for AI workloads in its July 2024 recommendations. These are orchestration options for eligible jobs, not requirements for every deployment or guarantees of lower energy use.
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| Scheduling option | When it may fit | Checks before using it |
|---|---|---|
| Temporal flexibility | Training or other jobs with deadlines that allow execution to move to a different time. | Job deadlines, service-level agreements, grid conditions, carbon intensity, and whether delaying work affects dependent services. |
| Spatial flexibility | Training that can run at another suitable site, or inference that can be routed geographically when response latency is not critical. | Latency tolerance, data-residency and geographic constraints, site capacity, grid conditions, and reliability. |
| Fixed-time, fixed-location execution | Customer-facing inference or other workloads with strict response, location, or availability requirements. | Preserve the required service level; do not shift work where doing so would breach latency, data, or reliability requirements. |
The advisory report describes geographically distributed training and routing some inference requests according to local grid load and renewable availability when latency is not critical. It also stresses that reliability is essential for customer-facing inference. For each candidate workload, identify its deadline, latency tolerance, data constraints, and reliability needs before deciding whether to move it.
Verify that efficiency gains preserve the service
After a change, compare results under comparable workload volume and service-level conditions. Track facility and IT energy alongside useful compute, cooling energy, peak demand, and—where available—water and carbon. Check latency, availability, and thermal conditions as operational guardrails, not just energy ratios.
Keep efficiency separate from total demand: using less energy per unit of compute does not ensure lower total energy if more computation is performed. DOE’s July 2024 advisory report notes uncertainty around future data-center demand and efficiency. Revisit the baseline as workloads and operating conditions change so that improvements are judged against the work the facility actually delivered.
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Use public technical assistance where useful
DOE identifies Lawrence Berkeley National Laboratory’s Center of Expertise for Energy Efficiency in Data Centers and its Data Center Energy Practitioner training as resources for assessments, tools, training, and efficiency or decarbonization opportunities. These may help operators evaluate facility-specific measures rather than assume one design or cooling approach fits every site.
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