Key takeaways
- Power scale is the defining requirement; AI and gigawatt campuses run from 500 MW to 1 GW and beyond.
- Large campuses usually need transmission at 230 kV and above, often 345 kV or 500 kV.
- Training sites can be remote if power is available; inference sites need network proximity.
- Liquid cooling raises density and changes water and mechanical planning.
- Land for substations, generation and future phases matters as much as land for buildings.
01How AI data centers differ from traditional ones
AI workloads run on dense clusters of accelerators that draw far more power per rack than traditional servers. That density changes the building, the cooling and, above all, the scale of power a site must deliver. A traditional colocation building might need tens of megawatts. A large AI training campus can need hundreds of megawatts at first and plan for a gigawatt or more over its life.
There are two broad types of AI facility. Training campuses build and refine models and can run anywhere the power and network backbone reach. Inference facilities serve models to users and need to be closer to population centers and network hubs, more like cloud or colocation sites.
| Factor | Cloud and colocation | AI training campus | AI inference |
|---|---|---|---|
| Typical scale | 10–60 MW per building; 100–500 MW for hyperscale campuses | 500 MW – 1 GW+ | Wide range; often colocation to hyperscale scale |
| Land | 10–75 acres; 150–600 for hyperscale | 500–2,000+ acres | Similar to colocation or hyperscale |
| Location driver | Users, networks, power | Power first | Users and networks, then power |
| Latency sensitivity | Moderate to high | Low | Moderate to high |
| Rack density | Lower to moderate | Very high | High |
| Cooling | Mostly air, some liquid | Mostly liquid | Mix, trending to liquid |
02Power: scale, voltage and timing
Power is the defining requirement. Loads of this size usually need service from transmission at 230 kV, 345 kV, 500 kV or, in some regions, 765 kV, with one or more on-site substations. Lower voltages such as 69 kV or 115 kV can serve smaller AI deployments but rarely a full campus. Our guide to transmission voltage for data centers explains why.
Few utilities have hundreds of megawatts sitting idle at a single location. Serving an AI campus often requires new substations, new or upgraded transmission lines and sometimes new generation in the region. That makes timing the core question: when can the first block of capacity arrive, and how fast can it grow?
Some developers bridge the gap with on-site generation, often fueled by natural gas, while waiting for grid service, or plan long-term on-site generation alongside the grid. That shifts site needs toward gas pipeline access, air permitting and extra land. See on-site generation and bridge power.
Load behavior matters too. AI training loads can change quickly as jobs start and stop, and utilities and grid operators pay close attention to how very large loads behave during grid disturbances. Expect detailed technical questions about load profile and ride-through during interconnection.
Redundancy assumptions can differ too. Some AI training facilities accept less electrical redundancy than a mission-critical colocation building, because workloads can checkpoint and restart. Others are built to similar standards. The design choice affects how many utility feeds, substations and backup generators the site must accommodate.
03Land and campus layout
AI campuses need large, contiguous and regular sites. Planning ranges of 500–2,000+ acres reflect more than buildings. The site must also hold one or more high-voltage substations, possible on-site generation, cooling plants, water storage, setbacks and buffers, and room for future phases.
- Buildable acreage after floodplain, wetlands and easements, in a shape that fits long rectangular buildings.
- Space for substations at the edge closest to transmission, with room for new line entries.
- Separation from homes for noise from cooling equipment and any generation.
- Heavy-haul access for large transformers and equipment.
- Structural capacity: very dense racks and liquid cooling equipment are heavy, which favors slab-on-grade buildings on good soils.
04Cooling and water
High rack densities push AI facilities toward liquid cooling, such as direct-to-chip cold plates or immersion, usually alongside some air cooling. Liquid cooling moves heat more efficiently and allows warmer coolant temperatures, which can widen the range of climates where dry or hybrid heat rejection works.
Water needs depend on the heat rejection design. Evaporative systems use more water and can lower energy use; closed-loop dry coolers use far less water but more power in hot weather. That trade-off shows up in WUE and PUE. Sites should be evaluated against the specific design, not a generic figure. Our guide to data center water requirements covers the choices.
Climate still matters. Cooler, drier climates give more hours of efficient heat rejection, though power availability usually outweighs climate in AI site selection.
05Fiber and network
Training campuses need high-capacity, diverse fiber to move large datasets and connect to other facilities, but they are less sensitive to latency to end users. That is why they can sit in rural areas with strong power. Inference facilities need low latency to users and so favor sites near metro areas and established network hubs.
In both cases, the site needs at least two physically diverse fiber paths. Building new long-haul fiber to a remote site is possible but adds time and cost.
06Entitlement and community at gigawatt scale
The scale of AI campuses draws attention. Large buildings, new transmission lines, on-site generation and water use all raise local questions. Zoning that suits a single data center building may not contemplate a multi-building campus with power generation, so approvals can be more involved.
- Confirm whether on-site generation is allowed under zoning and what air permits it would need.
- Check whether new transmission lines to the site would need state siting approval.
- Plan buffers and setbacks for noise from cooling and generation equipment.
- Engage early with local officials on water, traffic and tax effects.
07Screening sites for AI campuses
When we screen for AI campuses, we start with the grid: where high-voltage transmission and substations could plausibly support hundreds of megawatts, and where the utility or grid operator signals room to grow. Land comes next, filtered for large contiguous buildable acreage near those corridors. Water, gas, fiber and zoning then separate the strong candidates from the rest.
For a broader comparison of how requirements change by facility type, see site needs by data center type.
Common questions
How much power does an AI data center need?
It varies widely by size. Smaller AI deployments inside colocation or cloud facilities may need a few to tens of megawatts. Large AI training campuses are planned at 500 MW to 1 GW or more, usually built in phases. The scale depends on the number and type of accelerators, cooling design and the campus plan. Power availability at that scale is the main constraint in site selection.
How much land does an AI data center campus need?
Large AI and gigawatt campuses are typically planned on roughly 500 to 2,000 or more acres. That land holds multiple buildings plus high-voltage substations, cooling plants, possible on-site generation, buffers and future phases. What matters is buildable, contiguous acreage after floodplain, wetlands and easements are removed, in a shape that fits large rectangular buildings.
Do AI data centers need to be near cities?
Training campuses generally do not. They are less sensitive to latency for end users, so they can locate where large amounts of power and land are available, as long as high-capacity fiber can reach them. Inference facilities, which respond to user requests, benefit from being closer to population centers and network hubs, much like cloud and colocation facilities.
Do AI data centers use more water?
Not necessarily. Water use depends on the cooling and heat rejection design, not on whether the workload is AI. Liquid cooling at the chip can pair with closed-loop dry coolers that use little water, or with evaporative systems that use more water and less energy. The right choice depends on climate, water availability, power cost and local rules.
Have a site in mind?
Get a straight answer on your land.
Send a parcel number, an address, a map pin or a target load. We’ll tell you what it can support and what it would take.
This guide is general information about data center site selection. It is not engineering, legal, tax or investment advice. Requirements vary by state, utility and county, so confirm the specifics for any site with the relevant authorities and advisors.