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4,573 tracked

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 on this page

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.

  1. Epoch AI Frontier Data Centers7064 measured91.4%
  2. Epoch AI GPU Clusters437397 measured90.8%
  3. PeeringDB facility registry1,56963 measured4.0%
  4. IM3 / PNNL data-center corpus3912 measured0.5%
  5. OpenStreetMap (tagged data centers)1,1731 measured0.1%
  6. OpenStreetMap2310 measured0.0%
  7. Microsoft Azure region documentation560 measured0.0%
  8. Oracle Cloud region documentation450 measured0.0%
  9. 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 carrying at least one operational measure (power, GPU or FLOP capacity, or a substantive description). 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 25 August 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. 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.

351400 of 4,257Page 8 of 86
FacilityCountryTypeStatusPower (MW)
Max-Planck-Gesellschaft RavenEpoch AI GPU ClustersDEAI trainingOperational1
KT SuperPODEpoch AI GPU ClustersKRAI trainingOperational1
CSIRO VirgaEpoch AI GPU ClustersAUAI trainingOperational1
Karlsruher Institut für Technologie HoreKaEpoch AI GPU ClustersDEAI trainingOperational1
NSC Berzelius Phase 2Epoch AI GPU ClustersSEAI trainingOperational1
Saudi Aramco Tuwaiq-1Epoch AI GPU ClustersSAAI trainingOperational1
Nagoya University Flow Type II (Furo)Epoch AI GPU ClustersJPAI trainingOperational1
Simon Fraser University/Compute Canada CedarEpoch AI GPU ClustersCAAI trainingOperational1
NVIDIA Cambridge-1Epoch AI GPU ClustersGBAI trainingOperational1
Calcul Québec NarvalEpoch AI GPU ClustersCAAI trainingOperational1
EuroHPC KarolinaEpoch AI GPU ClustersCZAI trainingOperational1
AIRAWAT-PSAI Phase 2Epoch AI GPU ClustersINAI trainingOperational1
NSTDA Supercomputer Center (ThaiSC) LANTAEpoch AI GPU ClustersTHAI trainingOperational1
Microsoft Azure ND v2 Largest StatedEpoch AI GPU ClustersUSAI trainingOperational1
AGH Cyfronet HeliosEpoch AI GPU ClustersPLAI trainingOperational1
hessian.AI fortytwoEpoch AI GPU ClustersDEAI trainingOperational1
Corvex B200sEpoch AI GPU ClustersUSAI trainingAnnounced1
SAKURA Internet 2019 V100 SupercomputerEpoch AI GPU ClustersJPAI trainingOperational1
PCSS Poznan ProximaEpoch AI GPU ClustersPLAI trainingOperational1
TACC FronteraEpoch AI GPU ClustersUSAI trainingOperational1
Oracle 2020 A100 ClusterEpoch AI GPU ClustersUSAI trainingOperational0
Los Alamos NL ChicomaEpoch AI GPU ClustersUSAI trainingOperational0
Calcul Québec BélugaEpoch AI GPU ClustersCAAI trainingOperational0
Aleph Alpha alpha ONEEpoch AI GPU ClustersDEAI trainingOperational0
Wroclaw Centre for Networking and Supercomputing LemEpoch AI GPU ClustersPLAI trainingOperational0
University of Edinburgh DiRAC TursaEpoch AI GPU ClustersGBAI trainingOperational0
NCI Australia GadiEpoch AI GPU ClustersAUAI trainingOperational0
RPI Supercomputer 2Epoch AI GPU ClustersUSAI trainingOperational0
NVIDIA CirceEpoch AI GPU ClustersUSAI trainingOperational0
Microsoft Research HyperclusterEpoch AI GPU ClustersUSAI trainingOperational0
MPT-30B training clusterEpoch AI GPU ClustersAI trainingOperational0
MosaicML MPT training clusterEpoch AI GPU ClustersAI trainingOperational0
MITRE Federal AI SandboxEpoch AI GPU ClustersUSAI trainingAnnounced0
Fastweb NeXXt AI FactoryEpoch AI GPU ClustersITAI trainingOperational0
Opera Iceland KEF-1 SuperPODEpoch AI GPU ClustersISAI trainingOperational0
NVIDIA In-house DGX A100 ClusterEpoch AI GPU ClustersUSAI trainingOperational0
Universitaet Frankfurt Goethe-NHREpoch AI GPU ClustersDEAI trainingOperational0
AGH Cyfronet AthenaEpoch AI GPU ClustersPLAI trainingOperational0
University of Tokyo Wisteria/BDEC-01 (Aquarius)Epoch AI GPU ClustersJPAI trainingOperational0
MIT SupercloudEpoch AI GPU ClustersUSAI trainingOperational0
Indiana University Bloomington Jetstream2Epoch AI GPU ClustersUSAI trainingOperational0
NSCC ASPIRE 2A Phase 2Epoch AI GPU ClustersSGAI trainingOperational0
TACC LonghornEpoch AI GPU ClustersUSAI trainingDecommissioned0
Continental DGX SupercomputerEpoch AI GPU ClustersDEAI trainingOperational0
Osaka University SQUIDEpoch AI GPU ClustersJPAI trainingOperational0
TU Dresden AlphaCentauriEpoch AI GPU ClustersDEAI trainingOperational0
Leonardo SpA davinci-1Epoch AI GPU ClustersITAI trainingOperational0
Recursion BioHive-1Epoch AI GPU ClustersUSAI trainingOperational0
University of Cambridge Wilkes-3Epoch AI GPU ClustersGBAI trainingOperational0
Laboratório Nacional de Computação Científica Santos DumontEpoch AI GPU ClustersBRAI trainingOperational0