Reduce data-centre energy use by first making IT systems more efficient, then improving airflow, cooling controls and heat reuse. Match cooling to rack density and site conditions, and judge every change against useful AI output, thermal limits and service reliability—not facility efficiency alone.
Start with a baseline that includes useful AI work
Before changing equipment or controls, collect facility and IT energy data over a representative operating period. Record workload throughput or completed work, utilization, inlet conditions, cooling energy, water use, and relevant availability or reliability measures. Where operationally practical, distinguish training from inference: their timing, performance requirements and workload patterns can differ.
Calculate power usage effectiveness (PUE) consistently. The U.S. Department of Energy Federal Energy Management Program (DOE FEMP) defines it as total facility annual energy use divided by annual IT equipment energy use. Add water usage effectiveness (WUE) when water is material; DOE FEMP defines it as annual site water use in liters divided by IT equipment annual energy use in kWh. Pair these facility measures with IT utilization and useful-work measures, plus performance and reliability indicators. A lower PUE alone does not show that the facility delivered the same AI output.
DOE FEMP’s January 9, 2019 page cites PUE 2.0 as an illustrative average-efficiency figure and PUE 1.0 as the theoretical minimum. These are not a current universal average or an AI-specific target. The same page attributes 20% less chiller energy to its Best Practices Guide in the context of air-management practices that enable higher chilled-water temperatures and reduced airflow; that figure is not a guaranteed saving for an individual site.
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Reduce avoidable IT energy before expanding cooling
IT-side efficiency comes first because changes to servers and their operating conditions can reduce the cooling and electrical burden downstream. Review utilization, idle capacity, server configuration and workload-to-hardware matching before adding mechanical capacity. Consider consolidation or power management only after checking capacity, redundancy, performance and service constraints.
For AI, the useful question is not simply whether a server draws less power, but whether the system delivers the required training or inference output within its latency, completion-time and reliability requirements. Track workload throughput alongside energy when evaluating configuration or utilization changes.
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Improve airflow and tune controls
Cooling equipment works less effectively when hot exhaust mixes with cold supply air. Use hot-aisle/cold-aisle separation or containment suited to the facility, and address bypass airflow and recirculation so that supply air reaches equipment and exhaust is captured as intended. DOE notes that data-centre spaces are often controlled below recommended temperature and humidity ranges; avoid overcooling and unnecessarily narrow humidity targets unless equipment requirements call for them.
Tune fan and pump speeds, supply-air and water-temperature resets, and control sequences using measured conditions rather than assumptions. Recommission after changes and as workloads evolve, since changing rack loads can alter airflow and cooling needs.
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Raise temperatures and use free cooling only within limits
Higher supply-air or IT inlet temperatures can reduce cooling energy, but only when actual inlet conditions remain within the environmental envelope specified for the equipment and applicable thermal guidance. Evaluate the change against the installed hardware and its operating conditions rather than applying a single setpoint across mixed equipment.
Where climate, site architecture and equipment conditions permit, assess airside, waterside or refrigerant-based economization and other free-cooling modes. These can reduce compressor use during suitable outdoor conditions; available hours and savings depend on climate and the size of the setpoint change. There is no universal saving to assume in advance.
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Choose cooling to suit AI rack density and site resources
AI environments can combine heterogeneous rack densities and workloads, so cooling topology is a facility-scale engineering decision. Compare options using energy use, heat rejection, water consumption, temperature capability, maintainability, reliability, scalability and retrofit complexity. Also account for local water stress, climate, operating cost and how rack densities may change.
| Approach | Where it fits | What to assess |
|---|---|---|
| Air cooling | Existing or lower-density areas where air systems can meet equipment conditions. | Airflow separation, fan energy, achievable inlet conditions and whether the system can handle the actual rack load. |
| Direct-to-chip liquid cooling | High-density AI environments where removing heat at the chip is appropriate. | System energy and heat rejection, water use, temperature capability, maintainability and integration with facility cooling. |
| Rear-door heat exchangers | Racks where capturing heat at the rear door fits the design and operating conditions. | Heat rejection, water use, service access, reliability and compatibility with the existing room and rack layout. |
| Integrated technology cooling systems | High-density environments where an integrated cooling approach is appropriate. | Energy and water implications, maintainability, scalability and how the system performs across different rack densities. |
ASHRAE describes direct-to-chip and rear-door heat exchangers and integrated technology cooling systems for high-density environments. The appropriate choice still depends on the site: where water is scarce, examine dry cooling and other low- or no-water options; where a suitable nearby heat sink exists, assess heat reuse.
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Explore workload flexibility without breaking service requirements
Scheduling or relocating AI work can help align demand with cooler periods, different locations or demand-response opportunities, but flexibility is workload-specific. Separate work by deadline, latency, data locality and service criticality before considering a shift.
For workloads with genuine scheduling slack, validate the effects on energy and compute output, model quality, completion time, data transfer, security and service-level commitments. Do not assume that real-time inference can be freely delayed or moved; latency, locality and reliability requirements may rule out that option.
Validate savings under real workloads
Use monitoring, controls, modelling or digital-twin tools where appropriate, and commissioning to check whether a change works under real AI load profiles. Compare before-and-after results on a consistent basis, including useful work, energy, performance, inlet conditions and availability—not just facility overhead.
Revisit the operating assumptions when GPU generations, rack density, inference share, cooling equipment, weather or workload mix changes. DOE FEMP’s 2024 design guide says: “No design guide can offer ‘the most energy-efficient’ data center design, but these guidelines can provide efficiency benefits for a wide variety of data center scenarios.” Its recommended sustainability sequence is to reduce energy use first, including maximizing IT intake temperature within guidelines and using free cooling; reuse heat; reject remaining heat with dry coolers where feasible; and then maximize renewable energy. Apply that sequence alongside site-specific reliability, water and engineering constraints.
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