Entities
Registers of the facilities, companies and countries on record, with the ownership chains between them.
710 of 4,257 facility records carry an operational measure
4,257 facility records are individually browsable here, of which 710 carry an operational measure: power, GPU or FLOP capacity, or a substantive description. The other 3,547 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 Clusters437397 measured90.8%
- 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%
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. Any band’s key below opens the register scoped to that catalogue’s rows.
This is why the register opens on the measured cohort and says so; 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 all 4,257 records, including the 3,547 positional ones with no capacity figure attached. Show only the 710 measured.
| Facility | Country | Type | Status | Power (MW) |
|---|---|---|---|---|
| Max-Planck-Gesellschaft RavenEpoch AI GPU Clusters | DE | AI training | Operational | 1 |
| KT SuperPODEpoch AI GPU Clusters | KR | AI training | Operational | 1 |
| CSIRO VirgaEpoch AI GPU Clusters | AU | AI training | Operational | 1 |
| Karlsruher Institut für Technologie HoreKaEpoch AI GPU Clusters | DE | AI training | Operational | 1 |
| NSC Berzelius Phase 2Epoch AI GPU Clusters | SE | AI training | Operational | 1 |
| Saudi Aramco Tuwaiq-1Epoch AI GPU Clusters | SA | AI training | Operational | 1 |
| Nagoya University Flow Type II (Furo)Epoch AI GPU Clusters | JP | AI training | Operational | 1 |
| Simon Fraser University/Compute Canada CedarEpoch AI GPU Clusters | CA | AI training | Operational | 1 |
| NVIDIA Cambridge-1Epoch AI GPU Clusters | GB | AI training | Operational | 1 |
| Calcul Québec NarvalEpoch AI GPU Clusters | CA | AI training | Operational | 1 |
| EuroHPC KarolinaEpoch AI GPU Clusters | CZ | AI training | Operational | 1 |
| AIRAWAT-PSAI Phase 2Epoch AI GPU Clusters | IN | AI training | Operational | 1 |
| NSTDA Supercomputer Center (ThaiSC) LANTAEpoch AI GPU Clusters | TH | AI training | Operational | 1 |
| Microsoft Azure ND v2 Largest StatedEpoch AI GPU Clusters | US | AI training | Operational | 1 |
| AGH Cyfronet HeliosEpoch AI GPU Clusters | PL | AI training | Operational | 1 |
| hessian.AI fortytwoEpoch AI GPU Clusters | DE | AI training | Operational | 1 |
| Corvex B200sEpoch AI GPU Clusters | US | AI training | Announced | 1 |
| SAKURA Internet 2019 V100 SupercomputerEpoch AI GPU Clusters | JP | AI training | Operational | 1 |
| PCSS Poznan ProximaEpoch AI GPU Clusters | PL | AI training | Operational | 1 |
| TACC FronteraEpoch AI GPU Clusters | US | AI training | Operational | 1 |
| Oracle 2020 A100 ClusterEpoch AI GPU Clusters | US | AI training | Operational | 0 |
| Los Alamos NL ChicomaEpoch AI GPU Clusters | US | AI training | Operational | 0 |
| Calcul Québec BélugaEpoch AI GPU Clusters | CA | AI training | Operational | 0 |
| Aleph Alpha alpha ONEEpoch AI GPU Clusters | DE | AI training | Operational | 0 |
| Wroclaw Centre for Networking and Supercomputing LemEpoch AI GPU Clusters | PL | AI training | Operational | 0 |
| University of Edinburgh DiRAC TursaEpoch AI GPU Clusters | GB | AI training | Operational | 0 |
| NCI Australia GadiEpoch AI GPU Clusters | AU | AI training | Operational | 0 |
| RPI Supercomputer 2Epoch AI GPU Clusters | US | AI training | Operational | 0 |
| NVIDIA CirceEpoch AI GPU Clusters | US | AI training | Operational | 0 |
| Microsoft Research HyperclusterEpoch AI GPU Clusters | US | AI training | Operational | 0 |
| MPT-30B training clusterEpoch AI GPU Clusters | — | AI training | Operational | 0 |
| MosaicML MPT training clusterEpoch AI GPU Clusters | — | AI training | Operational | 0 |
| MITRE Federal AI SandboxEpoch AI GPU Clusters | US | AI training | Announced | 0 |
| Fastweb NeXXt AI FactoryEpoch AI GPU Clusters | IT | AI training | Operational | 0 |
| Opera Iceland KEF-1 SuperPODEpoch AI GPU Clusters | IS | AI training | Operational | 0 |
| NVIDIA In-house DGX A100 ClusterEpoch AI GPU Clusters | US | AI training | Operational | 0 |
| Universitaet Frankfurt Goethe-NHREpoch AI GPU Clusters | DE | AI training | Operational | 0 |
| AGH Cyfronet AthenaEpoch AI GPU Clusters | PL | AI training | Operational | 0 |
| University of Tokyo Wisteria/BDEC-01 (Aquarius)Epoch AI GPU Clusters | JP | AI training | Operational | 0 |
| MIT SupercloudEpoch AI GPU Clusters | US | AI training | Operational | 0 |
| Indiana University Bloomington Jetstream2Epoch AI GPU Clusters | US | AI training | Operational | 0 |
| NSCC ASPIRE 2A Phase 2Epoch AI GPU Clusters | SG | AI training | Operational | 0 |
| TACC LonghornEpoch AI GPU Clusters | US | AI training | Decommissioned | 0 |
| Continental DGX SupercomputerEpoch AI GPU Clusters | DE | AI training | Operational | 0 |
| Osaka University SQUIDEpoch AI GPU Clusters | JP | AI training | Operational | 0 |
| TU Dresden AlphaCentauriEpoch AI GPU Clusters | DE | AI training | Operational | 0 |
| Leonardo SpA davinci-1Epoch AI GPU Clusters | IT | AI training | Operational | 0 |
| Recursion BioHive-1Epoch AI GPU Clusters | US | AI training | Operational | 0 |
| University of Cambridge Wilkes-3Epoch AI GPU Clusters | GB | AI training | Operational | 0 |
| Laboratório Nacional de Computação Científica Santos DumontEpoch AI GPU Clusters | BR | AI training | Operational | 0 |