Entities
Registers of the facilities, companies and countries on record, with the ownership chains between them.
709 of 4,257 facility records have an operational measure
4,257 facility records are individually browsable here, of which 709 have an operational measure: power, GPU or FLOP capacity, or a substantive description. The other 3,548 establish that a site exists and stop there. Which of the two a record is depends almost entirely on the catalogue it arrived in.
Two counts appear on this page and they are not the same quantity. The masthead stamps 4,573 tracked records: the full corpus across 110 countries, the figure used site-wide. The register and the plate count the 4,257 that are individually browsable; the 316 in between are rows a licensing term keeps from being served as their own record. They inform aggregates and they are not hidden. They cannot be opened, so a figure about what a reader can inspect must not count them. The same universe is plotted in space on the map, rolled up by jurisdiction on Countries, and the organisations that own and operate these sites are in the company register.
- Epoch AI Frontier Data Centers7064 measured91.4%
- Epoch AI GPU Clusters437396 measured90.6%
- PeeringDB facility registry1,56963 measured4.0%
- IM3 / PNNL data-center corpus3912 measured0.5%
- OpenStreetMap (tagged data centers)1,1731 measured0.1%
- OpenStreetMap2310 measured0.0%
- Microsoft Azure region documentation560 measured0.0%
- Oracle Cloud region documentation450 measured0.0%
- 43 smaller catalogues285183 measured64.2%
Coverage by catalogue. Each band is one catalogue: its length is that catalogue’s share of the largest one’s row count, and the inked run inside it is the rows with at least one operational measure.
Nothing here is schematic; both lengths are measured proportions of the counts printed beside them.
No share-of-megawatts is drawn: every megawatt-bearing row satisfies the measured predicate by construction, so a capacity share would be a tautology.
Counts cover the full individually-browsable set, 4,257 rows, read 4 September 2026.
The smallest catalogues are folded into one band whose run is hatched, because it averages catalogues of both kinds. Each named catalogue’s key opens the register below scoped to its rows.
Epoch AI Frontier Data Centers runs 91.4% measured across 70 records; OpenStreetMap runs 0.0% across 231. No single catalogue falls in between; the hatched band does, but it is the fold of 43 smaller catalogues and reads mid-scale only because it averages the two kinds. The separation exists because the two kinds of catalogue do different jobs: one is a research dataset built by measuring sites, the other an infrastructure registry built by recording that they exist.
Shown whole, most of the 4,257 rows could not answer the question a reader arrived with. The positional records are not filler: a site that exists is a real fact, and for a governance reader tracing where capacity might appear it is often the fact that matters. But it is a different fact from a megawatt figure, and a register that prints them in one undifferentiated count makes a uniformity claim its records do not support.
The register
Showing the 293 matching records that have an operational measure. A further 1,010 matches are positional, with no capacity figure. Show all 1,303.
| Facility | Country | Type | Status | Power (MW) |
|---|---|---|---|---|
| NVIDIA TethysEpoch AI GPU Clusters | US | AI training | Operational | 0 |
| TACC Lonestar6Epoch AI GPU Clusters | US | AI training | Operational | 0 |
| Dell RattlerEpoch AI GPU Clusters | US | AI training | Operational | 0 |
| Argonne NL Sophia (Formerly Theta)Epoch AI GPU Clusters | US | AI training | Operational | 0 |
| Texas A&M GraceEpoch AI GPU Clusters | US | AI training | Operational | 0 |
| NVIDIA SATURN V VoltaEpoch AI GPU Clusters | US | AI training | Operational | 0 |
| IBM DYEUSEpoch AI GPU Clusters | US | AI training | Operational | 0 |
| Immunity Bio ClusterEpoch AI GPU Clusters | US | AI training | Operational | 0 |
| Indiana University Big Red 200Epoch AI GPU Clusters | US | AI training | Operational | 0 |
| University of Minnesota AgateEpoch AI GPU Clusters | US | AI training | Operational | 0 |
| U.S. Army JeanEpoch AI GPU Clusters | US | AI training | Operational | 0 |
| Recursion BioHive-1Epoch AI GPU Clusters | US | AI training | Operational | 0 |
| TACC LonghornEpoch AI GPU Clusters | US | AI training | Decommissioned | 0 |
| Indiana University Bloomington Jetstream2Epoch AI GPU Clusters | US | AI training | Operational | 0 |
| MIT SupercloudEpoch AI GPU Clusters | US | AI training | Operational | 0 |
| NVIDIA In-house DGX A100 ClusterEpoch AI GPU Clusters | US | AI training | Operational | 0 |
| MITRE Federal AI SandboxEpoch AI GPU Clusters | US | AI training | Announced | 0 |
| Microsoft Research HyperclusterEpoch AI GPU Clusters | US | AI training | Operational | 0 |
| NVIDIA CirceEpoch AI GPU Clusters | US | AI training | Operational | 0 |
| RPI Supercomputer 2Epoch AI GPU Clusters | US | AI training | Operational | 0 |
| Los Alamos NL ChicomaEpoch AI GPU Clusters | US | AI training | Operational | 0 |
| Oracle 2020 A100 ClusterEpoch AI GPU Clusters | US | AI training | Operational | 0 |
| TACC FronteraEpoch AI GPU Clusters | US | AI training | Operational | 1 |
| Corvex B200sEpoch AI GPU Clusters | US | AI training | Announced | 1 |
| Microsoft Azure ND v2 Largest StatedEpoch AI GPU Clusters | US | AI training | Operational | 1 |
| University of Illinois NCSA DeltaEpoch AI GPU Clusters | US | AI training | Operational | 1 |
| Meta 2017 P100 ClusterEpoch AI GPU Clusters | US | AI training | Operational | 1 |
| Princeton Della Phase 2Epoch AI GPU Clusters | US | AI training | Operational | 1 |
| Ahrefs Yep1Epoch AI GPU Clusters | US | AI training | Operational | 1 |
| Recursion BioHive-2Epoch AI GPU Clusters | US | AI training | Operational | 1 |
| Lawrence Livermore NL RZAdamsEpoch AI GPU Clusters | US | AI training | Operational | 1 |
| Microsoft Azure Immunity BioEpoch AI GPU Clusters | US | AI training | Operational | 1 |
| Paper on AlphaZeroEpoch AI GPU Clusters | US | AI training | Operational | 1 |
| RPI AiMOSEpoch AI GPU Clusters | US | AI training | Operational | 1 |
| University of Florida HiPerGator 3.0 SuperpodEpoch AI GPU Clusters | US | AI training | Operational | 1 |
| Microsoft Azure Pioneer-EUSEpoch AI GPU Clusters | US | AI training | Operational | 1 |
| Microsoft Azure Pioneer-SCUSEpoch AI GPU Clusters | US | AI training | Operational | 1 |
| Microsoft Azure Pioneer-WUS2Epoch AI GPU Clusters | US | AI training | Operational | 1 |
| AWS Fast BERT TrainingEpoch AI GPU Clusters | US | AI training | Operational | 1 |
| Hut 8 H100 ClusterEpoch AI GPU Clusters | US | AI training | Operational | 1 |
| Denvr Dataworks H100Epoch AI GPU Clusters | US | AI training | Operational | 2 |
| IBM Blue VelaEpoch AI GPU Clusters | US | AI training | Operational | 2 |
| LeptonAI H100 ClusterEpoch AI GPU Clusters | US | AI training | Operational | 2 |
| Tesla Auto-Labeling ClusterEpoch AI GPU Clusters | US | AI training | Operational | 2 |
| ExxonMobil Discovery 5Epoch AI GPU Clusters | US | AI training | Operational | 2 |
| ND A100 v4Epoch AI GPU Clusters | US | AI training | Operational | 2 |
| Microsoft Azure Voyager-EUS2Epoch AI GPU Clusters | US | AI training | Operational | 2 |
| Sandia NL El DoradoEpoch AI GPU Clusters | US | AI training | Operational | 2 |
| Argonne NL PolarisEpoch AI GPU Clusters | US | AI training | Operational | 2 |
| Microsoft Explorer-WUS3Epoch AI GPU Clusters | US | AI training | Operational | 2 |