Largest AI Data Center: Doubling every 7 months | Epoch AI

Data Insight<br>Jun. 11, 2026

The record for computing capacity in a single data center has doubled every 7 months

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By Ben Cottier

Since the launch of SpaceXAI’s Colossus 1 in August 2024, the record for the largest AI data center by computing capacity has doubled every seven months. Facilities like Anthropic-Amazon New Carlisle, Microsoft Fairwater Atlanta, and Meta Prometheus have each claimed the top spot at different times. Increased single-site capacity facilitates the training of more capable AI models.

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Looking ahead, our research suggests that no data center will have meaningfully greater capacity than Colossus 2 until the second half of 2027. However, we expect a reversion to trend in late-2027/early-2028 when QTS Cedar Rapids and Meta Hyperion are projected to be completed. We believe our data captures most record-holding facilities from 2024 through 2028, though estimates of individual capacities and operational dates carry uncertainty.

Epoch's work is free to use, distribute, and reproduce provided the source and authors are credited under the Creative Commons BY<br>license.

Learn more about this graph<br>Data comes from our Frontier Data Centers dataset, which includes computing capacity estimates for each data center over time. Computing capacity is the total peak floating point operations per second (FLOP/s) of AI chips in the data center. We express this as the equivalent number of NVIDIA H100 chips required.

We focus on computing capacity rather than power capacity because it is more relevant to AI capabilities. The frontier trend in computing capacity indicates the limits of what the AI industry is willing and able to build in a single facility. The data shows that this frontier is dominated by hyperscalers (Google, Microsoft, Amazon, Meta) along with SpaceXAI.

Code for the analysis is available here.

Data

The full dataset extends from 2019 to 2030, but it’s only likely to cover the true frontier between 2024 and 2028. We start at the launch of Colossus 1 in August 2024 — a milestone almost two years after ChatGPT, when the AI investment boom began to deliver completed data centers. We end in April 2028, giving the future extrapolation an equal timespan to the historical trend.

We estimate that the dataset only covers 26% of global AI computing capacity as of March 31st, 2026 (5.2 million operational H100-equivalents against 20.2 million sold through 2025, assuming a 3-month deployment lag). However, we’ve prioritized finding the largest AI data centers, so there’s unlikely to be many that are larger than the ones we’ve identified.

Computing capacity is measured using the maximum FLOP/s specification available for each chip at 8-bit numerical precision, then converted to H100-equivalents using the FP8 Tensor Core specification for the NVIDIA H100 (dividing the specification by 2 to account for sparsity, i.e., 1,979 teraFLOPS).

Most estimates are derived from each data center’s peak power capacity and the energy efficiency of the most-sold chips (which varies by company, e.g., Google mostly runs TPUs). Power capacity itself is estimated from satellite imagery, public disclosures, and permitting documents. A few sites, like Colossus 1 and 2, disclosed chip types and counts directly, giving more confident estimates. Full methods are in the dataset documentation and each data center’s “Calculations sheet” field.

Analysis

To analyze the frontier trend, we filtered to the single largest data center by computing capacity over time. Note that the largest data center by compute capacity is not necessarily the largest by power capacity, as some data centers have more energy-efficient chips than others. The filtering resulted in 10 past observations and 5 future projections at the frontier.

Since the frontier trend appeared to grow exponentially, we did a simple log-linear regression to the past observations, resulting in the 3.3x per year growth trend. This is equivalent to a doubling time of 7 months. The chart shows the 90% prediction interval from this regression as a shaded area.

The growth rate is uncertain both due to the small sample size and the noise in each data point. For the frontier points, we expect that 80% of the time, the true data point is within a factor of 1.3x of our estimated compute capacity, and within ±3 months of our estimated operational date. For other points we use a factor of 1.5x for compute capacity and ±6 months for the operational date, because there is often less public information and we have done less research on them.

To measure the impact of this noise, we ran 1,000 simulations that randomly perturb the data before filtering to the frontier and fitting the trendline. The resulting fits ranged from 2.3x/year at the 5th percentile to 5.1x/year at the 95th percentile, compared to our point estimate of 3.3x/year. This is an illustrative...