The Tool Desk
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Where should a data centre start?
Start with a measured baseline, not a cooling retrofit. A change that lowers facility energy but also reduces completed training work, slows inference or weakens availability is not an efficiency gain for the service. Track facility and IT energy alongside useful compute output and operational health.
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Build a representative baseline
Collect data over a representative operating period that includes the facility’s normal workload and environmental variation. Where practical, distinguish training from inference: their utilization, timing and performance requirements can differ. Record:
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- Workload output, such as completed training work or inference throughput, as well as utilization.
- Rack or equipment inlet conditions, cooling energy, water use, and availability or reliability measures.
- Relevant service measures, including inference latency and training completion time.
Power usage effectiveness (PUE) is annual total facility energy divided by annual IT equipment energy. Water usage effectiveness (WUE), as defined by the U.S. Department of Energy Federal Energy Management Program (DOE FEMP), is annual site water use in litres divided by annual IT equipment energy in kilowatt-hours. DOE FEMP published these definitions on January 9, 2019. Use consistent boundaries and reporting periods when comparing results.
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PUE can show changes in facility overhead, but it cannot establish whether the site delivered the same useful AI work. Pair it with workload output, utilization, water use and service reliability. The DOE FEMP’s January 9, 2019 page gives 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.
How can IT-side changes save energy?
Review IT efficiency before expanding mechanical capacity. DOE FEMP’s Best Practices Guide for Energy-Efficient Data Center Design (July 26, 2024) treats IT systems and their environmental conditions before air management and mechanical and electrical systems: an IT-side improvement can also reduce the cooling and power infrastructure needed to support a given amount of work.
Match capacity and configuration to actual work
- Look for idle or lightly used capacity and investigate whether workloads can be matched more effectively to the available hardware.
- Assess server configuration and power-management settings against workload performance, capacity and redundancy needs.
- Before consolidating systems or changing power policies, validate that peak demand, failover capacity and service commitments are still covered.
Use useful work—not just lower equipment power—as the test. Compare equivalent output, performance and reliability before and after a change.
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How can airflow and controls reduce cooling overhead?
Avoid cooling areas below the temperatures and humidity ranges recommended for the equipment, and prevent hot exhaust air from mixing with cool supply air. DOE FEMP identifies both overcooling and uncontrolled airflow mixing as opportunities to improve data-centre efficiency.
Separate supply and exhaust air
Use hot-aisle/cold-aisle separation or containment suited to the facility, and check that equipment receives the intended supply air rather than recirculated exhaust. Containment design should reflect the room layout and rack mix; a layout change that creates bypass airflow or obstructs maintenance can undermine its intended benefit.
Tune controls against measured conditions
Review fan and pump speeds, supply-air and water-temperature resets, and control sequences using conditions measured at the IT equipment. Recommission after changes and as AI workloads or rack layouts evolve. Do not pursue narrow humidity targets without a documented equipment requirement.
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DOE FEMP’s 2019 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 is a reported result for that context, not a guaranteed saving for an individual site.
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Can setpoints be raised safely?
Potentially, but only when equipment inlet conditions stay within the applicable environmental guidance and the equipment manufacturer’s requirements. A higher supply-air or IT inlet temperature can reduce cooling demand; exceeding an approved operating envelope can put hardware and service reliability at risk.
- Identify the environmental limits that apply to each equipment population, including any differences between rack types.
- Measure inlet conditions at the equipment, rather than relying only on room-level readings.
- Change setpoints in controlled increments and monitor temperatures, workload performance, alarms and availability under representative AI load.
- Keep the change only if the results remain inside requirements and useful workload output is maintained.
AI facilities may have heterogeneous rack densities and workloads, so one room-wide setpoint may not be appropriate for every area. The safe operating range and potential savings are site- and equipment-specific.
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Which cooling approach fits high-density AI racks?
Choose a cooling topology by evaluating energy use, heat rejection, water use, temperature capability, maintainability, reliability, scalability and retrofit complexity. Site climate, rack density and available heat sinks matter alongside the cooling equipment itself. ASHRAE’s AI data-centre guidance describes direct-to-chip liquid cooling, rear-door heat exchangers and integrated technology cooling systems for high-density environments; those options are not interchangeable prescriptions for every facility.
| Approach | What to assess | What the guidance establishes |
|---|---|---|
| Air cooling | Rack density, airflow management, climate and opportunities for economization. | DOE FEMP recommends effective air management and identifies free-cooling opportunities where climate and equipment conditions permit. A universal density limit or savings figure is not stated. |
| Direct-to-chip liquid cooling | Equipment compatibility, liquid loop design, heat rejection, maintenance and reliability. | ASHRAE describes it as an approach for high-density AI environments. A universal energy or water saving is not stated. |
| Rear-door heat exchangers | Rack integration, heat rejection, service access and suitability for the rack mix. | ASHRAE includes them among options for high-density AI environments. A universal performance figure is not stated. |
| Integrated technology cooling systems | Compatibility with IT equipment, facility systems, maintenance and expansion plans. | ASHRAE identifies these systems in its AI data-centre guidance. A universal performance figure is not stated. |
Where outdoor conditions and the installed architecture permit, evaluate airside, waterside or refrigerant-based economization and other free-cooling modes. These can reduce compressor use during suitable conditions, but available hours and savings depend on climate, equipment requirements and the size of any setpoint change.
Water constraints should influence the design. Where water is scarce, assess dry cooling and other low- or no-water options; where a suitable nearby heat sink exists, examine whether waste heat can be reused. These are facility-scale engineering decisions that require local analysis, not generic add-ons.
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Can AI workloads shift to save energy?
Some workloads may have scheduling or location flexibility, but it must be demonstrated workload by workload. DOE Secretary of Energy Advisory Board guidance from July 2024 supports exploring temporal and spatial flexibility for AI training and inference; it does not establish that every job can be delayed or moved without affecting latency, data locality, reliability or service commitments.
Set guardrails before shifting work
- Classify workloads by deadline, latency sensitivity, data locality and service criticality.
- For workloads with genuine slack, assess scheduling at cooler times, shifting among locations or participating in demand response.
- Validate compute output, model quality, completion time, data transfer, security and service-level effects as well as energy use.
Do not assume real-time inference can be shifted freely: a timing change that saves facility energy but breaches a latency commitment is not an acceptable trade.
How should operators verify that savings are real?
Compare results on a consistent basis, then revisit assumptions when GPU generations, rack density, the training-to-inference mix, cooling equipment, weather or workload patterns change. Monitoring, control-system data, modelling or digital-twin tools, and commissioning can help confirm performance under actual AI load profiles.
Track facility energy, IT energy, workload output and reliability together. Add water and carbon measures where material; a lower PUE alone does not show that equivalent useful compute was delivered or that water use and emissions improved.
What sequence does DOE recommend for sustainability?
DOE FEMP’s 2024 design guide recommends reducing energy use first, including maximising IT intake temperature within guidelines and using free cooling; reusing heat; rejecting remaining heat with dry coolers where feasible; and then maximising renewable energy. The sequence is a planning framework, not a substitute for equipment limits, reliability requirements, water availability or local engineering constraints.
The guide also cautions that there is no single most-efficient design for every site: “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.” The statement is from DOE FEMP’s Best Practices Guide for Energy-Efficient Data Center Design, dated July 26, 2024.
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