The Tool Desk
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What infrastructure does an AI data center need?
At a minimum, planning must account for the IT equipment and the facility systems that supply and support it. Compute and storage determine electrical loads; rack configuration concentrates those loads and shapes airflow; networking adds equipment, power and thermal needs; and cooling must capture equipment heat and reject it outside. Controls, monitoring, maintenance and commissioning connect the design to reliable day-to-day operation.
The U.S. Department of Energy’s Best Practices Guide for Energy-Efficient Data Center Design (July 26, 2024) and ASHRAE’s AI Data Center Energy Performance Framework both treat facility design as an integrated problem. The DOE’s December 11, 2024 article on data-center design also stresses that there is no one-size-fits-all energy-efficient design. A plan should therefore start with the intended workload and site, then coordinate IT, electrical and mechanical decisions.
Start with workload, equipment and capacity
Training, inference and other high-performance workloads can differ in equipment mix, utilization, storage needs and how much traffic moves among servers. Those characteristics affect the compute configuration and network, and in turn the rack’s power and heat loads. Establish the expected workload and hardware plan before selecting facility distribution and cooling systems; do not treat a generic rack-density number as a design requirement.
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Coordinate racks, power and airflow
Plan rack placement alongside electrical distribution and thermal management. The ASHRAE framework identifies rack-layout and airflow coordination, intelligent power distribution units (PDUs) and thermal management as engineering and design considerations. Hot- and cold-aisle separation can limit mixing between server exhaust and supply air in air-cooled layouts, but the room arrangement must match the equipment and cooling architecture.
If creating an equipment checklist, a rack PDU is a relevant category—not a model recommendation. Establish the installation’s electrical ratings, voltage, plug and outlet configuration, monitoring requirements, redundancy and compatibility with the facility design before choosing equipment.
Specify the network for the workload
Network and storage equipment share rack space, electrical capacity and thermal constraints with compute. The ASHRAE framework discusses InfiniBand and AI-optimized Ethernet, as well as the move toward faster fabrics, but neither option is universally correct. Define communication patterns, scale, software, interoperability and operational needs, then verify fabric and equipment requirements against current vendor documentation.
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Choose cooling as a heat-transfer and heat-rejection chain
Cooling is not just the device that removes heat from a server. The facility needs a complete path to capture IT heat, transfer it through the cooling system and reject it outdoors—or make productive use of it where practical. The U.S. Department of Energy’s Cooling Water Efficiency Opportunities for Federal Data Centers (January 9, 2019) describes common air- and liquid-cooling arrangements. The International Telecommunication Union’s ITU-T L.1327, approved August 29, 2024, frames cooling selection as matching components and technologies to the project’s application scenario.
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Air cooling
In a traditional air-cooled arrangement, equipment transfers heat to room air. Air handlers or computer-room cooling equipment move that heat into facility cooling systems, which carry it to heat-rejection equipment. A common evaporative arrangement described by DOE uses computer-room air-conditioning equipment, a chilled-water loop, a chiller, a condenser-water loop and a cooling tower. Actual configurations vary.
Direct liquid cooling
Direct liquid cooling transfers equipment heat into a recirculating liquid loop rather than relying only on room air. A coolant distribution unit (CDU) can transfer heat from the IT loop to another loop or heat-rejection stage. The facility still may need room-air cooling for residual heat or equipment that is not liquid-cooled.
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Liquid cooling therefore entails compatible IT hardware, coolant distribution, piping, controls, maintenance and a heat-rejection plan. It is an architecture, not simply a component swap. DOE’s guidance covers both traditional air-cooled sites and high-density liquid-cooled facilities; ITU-T L.1327 likewise emphasizes that project scenarios should guide component selection. Neither source supports treating one approach as invariably more efficient or the other as obsolete.
Compare architectures against site conditions
Use the same workload and operating assumptions when comparing options. The design may need to balance equipment density, ambient conditions, water availability, energy use, reliability and staff capabilities. A hybrid arrangement may also be relevant where some heat is captured in liquid and remaining loads are served by air; its suitability depends on the actual equipment and facility configuration.
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Efficiency has several dimensions. A result that improves one metric may affect another, so comparisons should define their boundaries and disclose facility context.
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- PUE (Power Usage Effectiveness): total annual facility energy use divided by annual energy use by IT equipment. A value closer to 1 indicates less facility energy outside the IT load. PUE alone does not describe water consumption, carbon intensity, compute efficiency or useful heat recovery.
- WUE (Water Usage Effectiveness): in DOE’s guidance, site water use divided by annual IT equipment energy, expressed in liters per kilowatt-hour. State the definition and accounting boundary when comparing WUE figures.
- Energy and carbon context: identify the energy supply and carbon-accounting boundary rather than treating a facility metric as a complete measure of environmental impact.
- Useful heat recovery: account for whether recovered heat can serve a real local use, rather than assuming all captured heat will be useful.
DOE’s Federal Energy Management Program describes a hierarchy of design priorities: improve component-level energy efficiency, reuse as much waste heat as feasible, reject unusable heat through dry coolers when possible to save water, and maximize renewable energy supplied on site or in the grid region. These are decision directions, not guarantees that every site can implement each measure at the same cost or with the same result.
The Open Compute Project’s March 2026 DCF Water-Heat-Energy Overview v4 notes that evaporative cooling can increase water consumption, while higher-temperature liquid cooling can reduce reliance on water-intensive cooling. It also discusses heat reuse, renewable electricity, siting and workload scheduling as carbon-mitigation considerations. The effects of these measures depend on the facility and its energy supply.
A DOE article dated December 11, 2024 attributes a comparison to NREL: equipment cooling accounted for 6% of data-center energy in the cited NREL case, compared with 70% for a typical data center. This is that article’s specific comparison, not a universal or current benchmark for AI data centers.
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Use a site-and-workload comparison before selecting a design
There is no single winning architecture to select in isolation. For each candidate, document the same set of assumptions so that trade-offs are visible and meaningful.
| Decision area | What to establish | Why it matters |
|---|---|---|
| Workload and IT configuration | Training, inference or other workload; equipment mix; expected utilization; network and storage needs | These shape the compute load, communication demands and operating profile. |
| Rack and facility capacity | Rack layout, electrical service and distribution, redundancy, and room for future changes | IT deployment must fit available power and facility capacity while supporting the planned arrangement. |
| Thermal architecture | Air, direct liquid or hybrid approach; CDU and loop configuration; outdoor heat rejection | The full heat-transfer path must suit both the equipment and the site. |
| Site conditions | Ambient climate, water availability, grid access and electricity characteristics, land and potential heat users | These conditions constrain which cooling, energy and heat-reuse measures are practical. |
| Operations | Availability, maintainability, monitoring, staff capabilities, commissioning and change management | A technically sound design also needs to be operable and maintainable. |
| Measured outcomes | PUE, WUE, energy source and carbon-accounting boundaries, useful heat recovery and workload performance | Metrics need consistent definitions and context to support a fair comparison. |
This approach aligns with DOE guidance and ITU-T L.1327: compare technologies against a defined project scenario, not against an assumed universal design.
Turn the design into an operating facility
Infrastructure choices only deliver their intended results when commissioning and operations verify that systems work together under the facility’s actual requirements. Treat commissioning, monitoring and maintenance as part of the engineering plan, not as a separate task after equipment selection.
- Define the workload and equipment plan. Record expected compute, storage and networking needs, utilization assumptions and the intended deployment scale.
- Set capacity and reliability requirements. Coordinate rack layout, electrical distribution, redundancy and provision for planned changes.
- Compare complete cooling paths. Specify how heat moves from equipment through room or liquid loops to outdoor rejection or useful recovery, including any CDU and residual-air needs.
- Check site constraints. Evaluate climate, water, electricity supply, land and potential heat-reuse opportunities against each candidate.
- Define performance boundaries. State how PUE, WUE, energy sourcing, carbon and recovered heat will be measured before comparing results.
- Plan commissioning and ongoing operations. Establish monitoring, maintenance responsibilities, staff needs and change-management processes for the chosen configuration.
What the evidence does not establish
The cited guidance does not establish universal rack-density thresholds, vendor-neutral performance benchmarks for particular AI server generations, or a complete grid-interconnection picture for every geography. Those values and requirements must be established for the specific equipment, installation and location. The 2019 DOE cooling-water publication remains a source for basic cooling diagrams and metric definitions, but it should not be treated alone as evidence about current AI hardware.
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