We mostly prioritize AI data centers by computing capacity. However, we have a lower bar for capacity internationally compared to the US, to achieve more globally balanced coverage. We also limit coverage to data centers that become operational in 2024 or later, with some exceptions. This includes planned data centers, but only if those data centers are under construction and have a significant chance of being completed. In practice, this limits most of the future coverage to 2–3 years from now.

Based on our prior work we believe most, but not all, of the largest data centers are in the United States. Additionally, focusing on the US first allowed us to become familiar with permitting standards, providing an additional source for us to validate our methodology. We continue to expand coverage in other countries.

We collect general information about each data center, such as the location, owner and users. We also collect satellite, aerial and drone images, and estimate key metrics over time as each data center evolves. Every data center we cover has a timeline of total IT power capacity, compute capacity, and capital cost.

IT power is estimated for each data center based on the available evidence, which may include cooling equipment observed in satellite imagery, permitting documents, and statements from companies. Compute is usually calculated from IT power, based on the energy efficiency of chips that we believe are most likely to be used (which is mostly informed by the chip owners dataset). In the uncommon case where the specific type and quantity of chips is reported, then compute estimates are based on that. Capital costs are entirely calculated from IT power, based on a general cost-per-watt model. We estimate construction start dates, operation dates and expansion dates primarily from commercial satellite imagery, but also publicly available imagery and commercial drone imagery. For details, see the methodology.

The database is the result of a continuous effort since mid-2025 to cover the largest AI data centers globally, most of which are in the US. Based on the total amount of AI compute reported in the chip sales database, we estimate this database’s coverage of global deployed AI computing capacity to be 44% as of October 23, 2026 (90% CI: 30% to 79%).

These percentages are based on a model that accounts for our uncertainty in each data center’s capacity and operational dates (see FAQ: “How confident are you in the data?”), and the lag between chip sales and deployment. More recent dates have higher coverage, improving steadily since Q1 2024 where coverage is 22% (90% CI: 11% to 49%). We continue to expand coverage globally, including Asia, Europe and the Middle East.

The coverage is strongest for the largest AI data centers between 2024 and 2028, because we have prioritized finding those. We believe the database captures the majority of record-holding facilities (in terms of compute capacity) during that period.

The deprecated GPU clusters database covers computing clusters used for AI and other applications. These clusters may make up just part of the total compute present in one data center building or campus. GPU clusters has broad coverage across time and space, but only covers an estimated 10–20% of global compute as of March 2025. In contrast, this database looks at AI data centers at a project level, and focuses on the largest current and upcoming data centers to achieve higher compute coverage. It also uses primary sources much more, including permitting and satellite imagery, to get greater detail and accuracy on individual data centers.

We model our uncertainty both in terms of quantities and construction timelines. We expect that 80% of the time, a given IT power capacity estimate is accurate within a factor of 1.4x. For compute capacity, this factor increases to 1.5x, and for cost, the factor is 1.6x. As for the timelines, we expect that 80% of the time, our estimated date is within 6 months of the actual date.

These confidence levels are informed by data on how our estimates differ from the most credible reference values, and modeling on top of that data. We expect our estimates to become more accurate as we find more data centers and do more research on how they work.

By default, if we list a user, owner, or other affiliate of a data center, we have strong evidence for it. However, sometimes we are uncertain about these affiliations, especially the data center users. We indicate this uncertainty with “Speculative” and “Likely” tags.

“Speculative” means that we have no record of an affiliation, but we have some reason to believe it. For example, we speculate that Anthropic will use two Amazon data centers in Mississippi because the New York Times reported that at least one of the Anthropic-Amazon Project Rainier data centers is located there, but they didn’t specify which ones.

“Likely” means we have some record of an affiliation, but we aren’t confident. For example, OpenAI is a “Likely” user for Microsoft Fairwater because a Microsoft spokesperson stated it “initially will be used to train OpenAI models”, but Microsoft’s partnership with OpenAI has been weakening since 2023.

This is possible, but does not happen often in practice. The total capacity of a data center is more like a cap on the size of a training run in that data center. Data centers often run multiple jobs in parallel and/or get partitioned for multiple users. Even if the entire data center is deployed on a single job, hardware failures will slightly reduce the capacity. Relatedly, when we report the total compute capacity of a data center in 8-bit OP/s, this is the theoretical peak capacity for number formats of 8 bits or above. In practice, the computational performance is typically 20–50% of the peak value, due to inefficiencies.

A previous analysis concluded that distributed training runs across distant data centers is technically feasible. AI companies have also proposed distributed training runs. Our database will document which data centers are networked together as we learn more.