AI Data Centers Near Me: What the Label Means
Key takeaways
- An “AI data center” is usually a facility built or equipped to train or run artificial-intelligence models, using specialized accelerator chips that draw much more power—and often different cooling—than older enterprise servers.
- You often cannot tell from the street whether a building is used for AI, cloud storage, enterprise computing, or a mix of those workloads.
- Lawrence Berkeley National Laboratory’s 2025 update finds that growth in accelerated servers is the main reason U.S. data-center electricity use is rising so quickly.
- The International Energy Agency has said a typical AI-focused data center uses as much electricity as 100,000 households, and that the largest sites now under construction would use about 20 times as much.
- This page does not publish an invented list of AI campuses. Use the map and local filings, and treat marketing language as a claim to verify.
Last reviewed: November 9, 2026. Workloads inside a building can change after it opens.
What is an AI data center?
An AI data center is a data center designed or fitted out to handle artificial-intelligence training or inference, typically with dense racks of graphics processors or other accelerator chips plus cooling that can remove that extra heat.
The buildings still look like large, windowless halls. What changes is the electrical and cooling design. Berkeley Lab’s 2025 update models AI servers as systems with dedicated accelerators (GPUs or custom chips) and notes that rack-scale designs share power and cooling across many nodes. The same report estimates a national average power-usage effectiveness of about 1.145 in 2024 for facilities serving AI equipment, falling slightly to about 1.136 in 2030 in its model—meaning those halls are assumed to use relatively little extra facility energy compared with older rooms, even as the IT equipment itself draws much more power.
Inference (answering user requests) and training (building a model) do not use energy the same way. The 2025 update assumes training servers run at high utilization and that inference servers often run lower average utilization because they are sized for peaks in user demand. Those modeling choices are one reason electricity forecasts are published as ranges.
Cryptocurrency mining is a different workload. LBNL excludes it from both the 2024 report and the 2025 update. EPRI’s 2026 scenarios include it. If a local filing mentions mining, do not assume the national AI figures apply unchanged.
Are there AI data centers near me?
There may be AI data centers near you if a local application, utility study, or operator disclosure says the site will host training or inference loads—but many maps cannot label every building that way with confidence.
Operators often describe a campus as “cloud,” “hyperscale,” or “high-performance computing” without using the words “artificial intelligence.” The same campus can host ordinary storage one year and accelerator racks the next. Security practices also keep some customers unnamed. For those reasons this site will mark a facility as AI-related only when a cited source supports that description. If the map near your ZIP code shows a data center without an AI tag, that is not evidence the site has no AI load.
A better local test than the label is the application file: requested electric capacity, cooling method (especially liquid cooling), generator counts, and whether the utility is treating the customer as a very large load. Then read proposed data centers if the site is not yet built.
How much electricity do AI data centers use?
AI-focused facilities use much more electricity per building than earlier enterprise rooms because each accelerated server draws more power and because operators are installing millions of those servers.
The International Energy Agency’s April 2025 Energy and AI report states that a typical AI-focused data center consumes as much electricity as 100,000 households, and that the largest sites under construction will consume 20 times as much. IEA also found that data centers used about 415 terawatt-hours worldwide in 2024 (about 1.5% of global electricity), that the United States accounted for about 45% of that total, and that data-center electricity use is set to more than double globally to about 945 terawatt-hours by 2030 in its Base Case.
Berkeley Lab’s U.S.-focused 2025 update adds more detail on what is driving the increase:
| Finding in the LBNL 2025 update | Figure | Why it matters to neighbors |
|---|---|---|
| U.S. data-center electricity use, 2024 | 192 TWh, about 4.7% of U.S. electricity | The starting point after a 14% rise from 2023 to 2024 |
| Near-term Reference Case growth | About 22% from 2024 to 2025 and 29% from 2025 to 2026 | Most of the near-term jump is attributed to accelerated servers |
| 2030 Reference Case | 649 TWh, about 11.8% of forecast U.S. electricity | AI servers account for about 84% of server energy and 55% of total data-center energy in that case |
| Accelerated servers in the installed base | 4.2 million of 30.9 million servers in 2024; nearly 19 million AI servers by 2030 in the Reference Case (about 40% of the base) | The mix inside the industry is shifting quickly |
| Modeled 2030 range | 521–843 TWh (9.5–15.3% of U.S. electricity) | Outcomes depend on shipments, chip lifetimes, and how heavily inference servers run |
EPRI’s 2026 executive summary estimates that AI workloads account for about 15–25% of data-center electricity today (citing IEA 2025 and JLL 2026) and that the share is rising even as non-AI data-center demand continues to grow. EPRI also compares a typical new 100-to-1,000-megawatt data center to the electrical load of 80,000 to 800,000 average homes. Those household comparisons describe electricity, not employment.
Because AI halls are so power-dense, they raise the same bill and water questions as other large data centers, only sooner and in larger increments. See electricity bills and water use.
Why can’t every facility be labeled AI or not?
Every facility cannot be labeled AI or not because the term is not a legal category in most zoning codes, workloads are not always disclosed, and a single building can host more than one kind of computing.
Zoning applications may say “data center” regardless of the chips inside. Utility large-load studies may record requested megawatts without recording the customer’s software. Even when an operator advertises an “AI factory” or a “training cluster,” later tenants can change. Berkeley Lab itself warns that several of its AI-server assumptions—especially idle power and inference utilization—are uncertain and are tested with sensitivity cases rather than treated as known constants.
For residents, the honest approach is to cite what the file actually says (requested load, cooling, generation) and to treat “AI” as extra context when a reliable source uses that word.
How should residents read an AI data center announcement?
Residents should read an AI data center announcement as a claim about intended use and size, then look for the same public documents they would seek for any other large data center.
Check, at minimum:
- Whether a land-use application has been filed, and under what name.
- The megawatts requested, and whether they are a first phase or a full campus vision.
- The cooling design, because dense AI racks are more likely to use liquid or hybrid cooling, which changes both electricity and water.
- Whether the utility or a state commission is reviewing a line upgrade or a special tariff.
- Whether backup generators or on-site power plants require air permits.
Bring those items to the hearing using the questions to ask. Do not assume that a larger advertised number will arrive on the date in a press release. EPRI repeatedly treats announced megawatts as a pipeline indicator, not a guaranteed near-term peak.
What are the most common questions about AI data centers near me?
Is every new data center an AI data center?
No. Operators continue to build halls for cloud storage, enterprise customers, and other workloads. AI is a growing share of energy use, not the only use.
Does “hyperscale” mean the same thing as “AI”?
No. Hyperscale usually refers to the size and operator type of a campus. Many hyperscale sites now host AI equipment, but the words are not interchangeable.
Will an AI data center near me use more water than an older data center?
It might, but only the cooling design and the local climate can tell you. Some high-density designs use more on-site water; others shift the burden toward electricity and therefore toward water used at power plants. Ask for both figures.
Why doesn’t this page list every announced AI campus?
Because public announcements are incomplete, sometimes speculative, and easy to get wrong if they are copied without a primary source. This site would rather omit a name than invent a project.
Is this page recommending for or against AI development?
No. It explains how researchers describe AI-related electricity use so that residents can read local claims more carefully. It is not legal or financial advice.
Sources
- Smith, S.J., et al., United States Data Center Energy Usage Report: 2025 Update, Lawrence Berkeley National Laboratory, June 2026.
- International Energy Agency, “Energy and AI, Executive summary,” April 2025.
- Electric Power Research Institute, “Powering Intelligence 2026, Executive Summary.”
- Shehabi, A., et al., 2024 United States Data Center Energy Usage Report, December 19, 2024.
AI labels on any map should be treated as incomplete. This site is not affiliated with AI developers or data-center operators. Related pages: the U.S. map and proposed projects.