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Yes. AI—especially GPU-intensive training and inference—is changing server infrastructure requirements, but it has not made conventional servers obsolete. The biggest shift is that compute choices now affect power delivery, cooling, networking, facility layout and hardware-refresh plans together. The right response depends on the workload and the site, not simply on whether a server is labeled “AI.”
What has changed in server infrastructure?
AI workloads can require high-performance accelerators and move data among processors at high rates. That combination makes power density, heat removal and network design more prominent in infrastructure planning than they may be for general-purpose workloads. It also means that a server decision can have consequences beyond the IT rack: the facility must deliver the required power, remove the heat and provide room for cabling, cooling equipment and maintenance.
A 2026 paper by Jovan Stojkovic, Chaojie Zhang, Íñigo Goiri and Ricardo Bianchini of Microsoft Research and The University of Texas at Austin gives a sense of the power difference in two specific configurations: an eight-H100 NVIDIA DGX server at 10.2 kW and a 64-core Intel Emerald Rapids server at 385 W. These are the configurations cited in the paper, not a universal comparison between all GPU and CPU servers. The authors characterize the high-density GPU racks they discuss as producing 4–8 times more heat per rack than CPU systems. Read the paper, “Rearchitecting the Datacenter Lifecycle for AI”.
These figures illustrate why planning around a workload matters more than treating “AI server” as a single specification. Inference and training can differ in compute and communication demands; even among inference systems, model size, throughput, latency targets, utilization, memory and interconnect requirements affect the fit.
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How does AI affect data-center power and cooling?
Power demand and capacity planning
More power-hungry servers can raise demand at both the rack and facility level. A site may have enough total electrical capacity on paper yet still struggle to deliver it where and when particular racks need it. The Microsoft and University of Texas paper discusses power fragmentation: the shape of power domains can leave usable capacity stranded. Power-distribution topology therefore affects expansion, fault isolation, redundancy and how effectively the available supply is used.
Site conditions matter as much as the server specification. Eaton’s guidance on AI data-center power architecture identifies grid access, brownfield-site limits, power quality and integration with older electrical systems as planning concerns. Eaton’s overview of evolving AI data-center architecture provides vendor guidance on these considerations.
Cooling is a design choice, not a blanket requirement
Higher rack power means more heat to remove. The 2026 paper describes air cooling as being pushed toward its limits in the high-density GPU deployments it considers, and discusses liquid cooling and hybrid air/liquid designs. That does not make liquid cooling mandatory for every AI workload: the choice depends on rack density, facility design, cost, serviceability and plans for future deployment.
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For context, the International Energy Agency says servers account for around 60% of electricity demand in modern data centers on average, while cooling ranges from about 7% in efficient hyperscale centers to more than 30% in less-efficient enterprise centers. Those shares vary by facility type and efficiency; they should not be combined as though they describe one universal data center. See the IEA’s 2025 report, “Energy demand from AI — Energy and AI”.
The Microsoft and University of Texas authors model a 75/25 hybrid cooling design and report a 9% total-cost-of-ownership improvement in their modeled comparison. That is a result under the study’s assumptions, not a promised saving for other facilities.
Do AI servers need different networking and storage?
Often, they need infrastructure planned around the workload’s data movement as well as its compute. AI systems can communicate more intensively than general-purpose workloads; a network that is adequate for one job or scale may be a bottleneck for another. Bandwidth, latency, topology, cost, power use and operational complexity are all relevant. The Microsoft and University of Texas paper discusses matching network design to workload needs and uses hierarchical networking as a design reference, not a universal prescription.
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Storage and network architecture also belong in the same planning conversation as power and cooling. NVIDIA’s GPU-ready data-center guide treats these as related design concerns; it is vendor guidance, not evidence that one particular architecture suits every deployment. See NVIDIA’s guide to running deep-learning workloads in a modern AI data center.
How large is the wider energy shift?
The IEA estimates that data centers used about 415 TWh of electricity worldwide in 2024, around 1.5% of global electricity consumption. In its 2025 base case, it projects data-center electricity demand at around 945 TWh in 2030 and accelerated-server electricity consumption growing 30% annually, mainly driven by AI adoption. These are global estimates and scenario projections, not observed outcomes or forecasts for every country or operator. The agency emphasizes uncertainty in current estimates and future demand and considers multiple cases.
The scale of AI’s infrastructure impact will therefore differ widely. A global projection does not tell an individual organization how much power its site will need: that depends on the work it runs, how intensively systems are used, the hardware selected and the facility’s starting point.
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How should an organization assess its infrastructure needs?
Compare viable designs against the intended job and the constraints of the site. Do not assume that adding accelerators alone will deliver the desired performance or that a data-center redesign is necessary before measuring the actual need.
- Define the workload. Specify whether the system is for training, inference or a mix, then identify model size, throughput, latency targets, utilization and communication patterns.
- Match compute to the job. Compare CPU-only, accelerator-equipped and heterogeneous configurations using performance and power for the intended workload. Account for memory and interconnect needs; an accelerator label by itself does not establish a good fit.
- Check site and rack power. Establish available capacity, distribution topology, redundancy, fault isolation, expansion constraints and the possibility of stranded capacity. Include grid access, power quality and any limits imposed by existing electrical infrastructure.
- Choose a cooling approach for the density. Compare air, liquid and hybrid options based on rack heat load, facility design, capital and energy costs, maintenance and service access.
- Plan network and storage together. Match bandwidth, latency and topology to the workload’s data movement, scale and operational requirements rather than assuming a single interconnect is best.
- Calculate lifecycle cost. Consider server refresh, facility and network capital costs, electricity, cooling, maintenance and utilization over a stated planning horizon. Include physical layout, structural needs, cooling loops and technician access.
These checks are coupled. For example, a higher-density server choice may alter power delivery, cooling equipment and maintenance access, while a network design may affect both performance and energy use. Reviewing them as one infrastructure decision reduces the risk of solving a compute problem only to expose a facility bottleneck.
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How to interpret proposed savings and forecasts
Infrastructure studies can help frame design choices, but modeled results should stay attached to their assumptions and comparisons. In addition to the hybrid-cooling result, the Microsoft and University of Texas paper reports a 4.2% lifecycle TCO reduction for its modeled data-center-wide flat power-distribution design. Its abstract reports a 40% TCO reduction for the authors’ holistic lifecycle framework compared with their traditional methods. These are study-specific model outcomes, not guaranteed savings at other sites.
The paper focuses its model on inference as the dominant AI data-center workload and notes that training differs in compute and communication demands. Its results are useful reference points for lifecycle planning, not proof that one design or saving applies universally. For related design context, Schneider Electric publishes vendor material on AI’s effects on data-center design: “How 6 AI Attributes Change Data Center Design,” version 3.1, dated May 21, 2026.
What has not changed?
Organizations do not all need an AI data center, and conventional servers remain appropriate for many workloads. The practical change is in how teams evaluate systems that use accelerators or otherwise impose higher demands: compute, power, cooling, networking, storage, site limits and refresh cycles have to be considered together. AI has widened the infrastructure decision, not replaced workload-specific engineering with a universal server recipe.
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