AI Data Center Site Selection Is Splitting into Two Paths

Most site selection conversations still talk about "AI data centers" as one category. In practice, the industry is solving two distinct problems that happen to run on similar hardware. Training and inference put very different demands on a site, and treating them as the same exercise is how developers end up with a great training campus in the wrong place for inference, or an inference-optimized site that can't scale for training.

Understanding which problem you're actually solving for changes almost every input into the site selection process: power, land, location, cooling, and connectivity.

Training vs. Inference at a Glance

What Training and Inference Are Actually Doing

The site selection differences fall directly out of a technical difference, so it's worth being precise about it.

Training is how a model learns.

Training works like this: A massive dataset runs through a neural network over and over, and the model gets a little better with each pass. Thousands of GPUs handle this together, running nonstop for weeks or months. No one is using the model while it trains. It's only useful once the process is complete. So the facility's real job is simple: Keep a huge, tightly connected cluster powered and cooled without any interruption. It doesn't matter what city that cluster sits in. What matters is whether the power supply nearby can handle it.

Inference is how a trained model gets used.

A user submits a prompt, a fraud model evaluates a transaction, a car processes a camera feed, and the model answers in real time, often in a fraction of a second, for potentially millions of simultaneous users. The facility's job is not one enormous continuous job, but an enormous number of small, fast ones for people and systems scattered across a wide area.

Distance starts to matter here in a way it never did for training. Every extra hundred miles adds measurable latency.

Training is about running one huge job for a long time without stopping. Inference is about answering huge numbers of small requests instantly. That's why training rewards scale, and inference rewards proximity.

Training: Build for Scale, not Proximity

Training runs are enormous, sustained compute jobs. Thousands of GPUs work in tandem for weeks or months at a time, and the facility's job is to deliver massive, reliable power and let the cluster run without interruption. That changes what a training site needs to prioritize.

Power capacity and scalability come first.

Training campuses are chasing hundreds of megawatts today with clear line of sight to more. The site needs to sit near transmission infrastructure with real headroom for phased expansion, not just enough capacity for phase one.

Land needs room to grow.

A training campus is rarely a single building. It's a multibuilding campus with dedicated substations and backup generation planned years in advance. Raw acreage matters less than whether the parcel and the surrounding grid can support a second, third, and fourth phase without a redesign.

Location is flexible.

Training workloads aren't serving end users in real time, so a training cluster doesn't need to sit near a population center or a network hub. That's why emerging markets with available power and land, such as Kansas City and West Texas, have become strong training locations. What matters isn't proximity to Dallas or Chicago. It's how fast a site can get power.

Cooling intensity is extreme but manageable at scale.

Rack densities in training clusters are very high, which pushes many facilities toward liquid cooling. Because these are purpose-built campuses, developers can design cooling infrastructure into the site from day one rather than retrofitting it.

The bottom line for training: proximity doesn't matter, power does.

Inference: Build for Speed, not Headroom

Inference is a different job entirely. Once a model is trained, it has to respond to users, and speed is everything. An autonomous vehicle system, a fraud detection model, or a chatbot that takes a few hundred milliseconds too long isn't just slow; it's a failed product. That changes what matters most at the site.

Latency and proximity to users become the leading factor.

Inference workloads increasingly need to sit close to population centers and major network hubs, so requests don't travel further than necessary. This pulls inference deployments back toward primary markets and edge locations, even when those markets have tighter power availability than emerging ones.

Connectivity requirements are more demanding than for training.

Carrier diversity, proximity to internet exchanges, cloud on-ramp access, and regional network performance all matter more for inference than for training. The facility is in a constant, real-time conversation with end users rather than running an isolated batch job.

Power needs are real but more modular.

Inference facilities still need meaningful capacity, but the profile looks different. Demand tends to be distributed, often smaller per site, and more sensitive to fast energization timelines than to hundred-plus megawatt scale. A market that can deliver 10 to 50 megawatts quickly is a better inference fit than a market that is offering 100+ megawatts on a five+ year timeline.

Density and footprint are typically smaller.

Inference deployments don't always need the sprawling multi-building campus that training demands. Many can run in a smaller facility, or even a colocation footprint, provided the location and connectivity are right.

The bottom line for inference: Power helps, but proximity and speed win the deal.

Different Workload, Different Due Diligence

The due diligence process has to match the workload.

Training questions center on power and expansion runway: interconnection timeline for the full build-out, transmission headroom at the substation, and room for four buildings without redesign.

Inference questions center on proximity and network performance: latency to the target user base, carrier and internet exchange access, and whether the market can deliver power fast enough to hit a deployment deadline measured in months.

Run the wrong checklist, and you'll misjudge the site. A great training location can be a poor inference fit, and vice versa.

Some Markets Are Built for One, Some for Both

Primary hubs like Northern Virginia, Dallas-Fort Worth and Silicon Valley remain strong candidates for inference because of their existing network density and proximity to major population centers, even as power constraints make large-scale training builds harder to site there.

Emerging markets, including Kansas City, Memphis, and West Texas, are increasingly winning training campuses because they offer the power capacity and expansion room training demands, even though they aren't the first stop for latency-sensitive inference workloads.

A handful of markets, including Columbus and Reno, are positioning themselves to compete for both, combining available power with improving connectivity infrastructure. Those markets are worth watching closely over the next few years.

For the full list of secondary markets we're tracking across both training and inference fit, see our Secondary Markets blog.

The Takeaway

Training and inference are not the same real estate problem wearing different labels. Training site selection is a power and land scalability exercise. Inference site selection is a latency and connectivity exercise. The developers who get ahead in this cycle are the ones evaluating each workload against the criteria that actually matter for it, rather than running every AI deal through the same checklist.

Not sure whether your next deployment is a training play, an inference play, or both? Site Selection Group helps developers, investors, and enterprises match the right workload to the right market, from power and land diligence to network and latency analysis. Talk to our team about your next site.