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
- 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 5 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.
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 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 709 records that have an operational measure. A further 3,548 positional records establish that a site exists and have no capacity figure. Show all 4,257.
| Facility | Country | Type | Status | Power (MW) |
|---|---|---|---|---|
| DayOne HK3 Hong Kong (Tsuen Wan)DayOne corporate disclosures (May 2026) | HK | Colocation | Under construction | — |
| DayOne HK4 Hong KongDayOne corporate disclosures (May 2026) | HK | Colocation | Announced | — |
| DayOne HK5 Hong KongDayOne corporate disclosures (May 2026) | HK | Colocation | Under construction | — |
| DayOne Kempas Tech ParkGDS Holdings Q4 2024 results | MY | Colocation | Under construction | — |
| Anthropic-Amazon New CarlisleEpoch AI Frontier Data Centers | US | AI training | Operational | — |
| Crusoe Abilene ExpansionEpoch AI Frontier Data Centers | US | AI training | Under construction | — |
| AIST ABCI 1.0Epoch AI GPU Clusters | JP | AI training | Operational | — |
| AIST ABCI 2.0Epoch AI GPU Clusters | JP | AI training | Operational | — |
| Alps Supercomputer Phase 1Epoch AI GPU Clusters | CH | AI training | Operational | — |
| Applied Digital CoreWeave Ellendale Phase 1Epoch AI GPU Clusters | US | AI training | Announced | — |
| Applied Digital CoreWeave Ellendale Phase 2Epoch AI GPU Clusters | US | AI training | Announced | — |
| AWS EC2 Trn1Epoch AI GPU Clusters | US | AI training | Operational | — |
| Condor Galaxy 1 (CG-1)Epoch AI GPU Clusters | US | AI training | Operational | — |
| Condor Galaxy 3 (CG-3)Epoch AI GPU Clusters | US | AI training | Announced | — |
| Condor Galaxy 4 (CG-4)Epoch AI GPU Clusters | — | AI training | Announced | — |
| Condor Galaxy 5 (CG-5)Epoch AI GPU Clusters | — | AI training | Announced | — |
| Condor Galaxy 6 (CG-6)Epoch AI GPU Clusters | — | AI training | Announced | — |
| Condor Galaxy 7 (CG-7)Epoch AI GPU Clusters | — | AI training | Announced | — |
| Condor Galaxy 8 (CG-8)Epoch AI GPU Clusters | — | AI training | Announced | — |
| Condor Galaxy 9 (CG-9)Epoch AI GPU Clusters | — | AI training | Announced | — |
| Core42 AI-03Epoch AI GPU Clusters | AE | AI training | Operational | — |
| CoreWeave Denton GB200s OpenAI/MicrosoftEpoch AI GPU Clusters | US | AI training | Announced | — |
| CSCS Piz Daint Phase 1Epoch AI GPU Clusters | CH | AI training | Operational | — |
| DataVolt Neom 1.5 GW Phase 1Epoch AI GPU Clusters | SA | AI training | Announced | — |
| Eni HPC4 Phase 1Epoch AI GPU Clusters | IT | AI training | Operational | — |
| Gcore data center Phase 1Epoch AI GPU Clusters | KR | AI training | Operational | — |
| Horizon Compute Baobab Phase 1Epoch AI GPU Clusters | US | AI training | Operational | — |
| HPE GreenLake for LLMs Q01Epoch AI GPU Clusters | CA | AI training | Operational | — |
| HUMAIN Saudi Arabia/NVIDIA Phase 1Epoch AI GPU Clusters | SA | AI training | Announced | — |
| IndiaAI Mission SupercomputerEpoch AI GPU Clusters | IN | AI training | Announced | — |
| JAMSTEC ZettaScaler-2.0 GyoukouEpoch AI GPU Clusters | JP | AI training | Operational | — |
| Jean Zay Supercomputer Phase 1Epoch AI GPU Clusters | FR | AI training | Operational | — |
| Jean Zay Supercomputer Phase 2Epoch AI GPU Clusters | FR | AI training | Operational | — |
| Jean Zay Supercomputer Phase 3Epoch AI GPU Clusters | FR | AI training | Operational | — |
| Lawrence Livermore NL El Capitan Phase 1Epoch AI GPU Clusters | US | AI training | Operational | — |
| Lawrence Livermore NL Lassen Phase 1Epoch AI GPU Clusters | US | AI training | Operational | — |
| Meta Prometheus New AlbanyEpoch AI GPU Clusters | US | AI training | Announced | — |
| Meta Research SuperCluster (RSC-1) Phase 1Epoch AI GPU Clusters | US | AI training | Operational | — |
| Microsoft Sweden GävleEpoch AI GPU Clusters | SE | AI training | Announced | — |
| Microsoft Sweden SandvikenEpoch AI GPU Clusters | SE | AI training | Announced | — |
| Microsoft Sweden StaffanstorpEpoch AI GPU Clusters | SE | AI training | Announced | — |
| MSU-270Epoch AI GPU Clusters | RU | AI training | Operational | — |
| Nebius 8k Finland Phase 1Epoch AI GPU Clusters | FI | AI training | Operational | — |
| Nebius Kansas City Phase 1Epoch AI GPU Clusters | US | AI training | Operational | — |
| Neevcloud planned GPUsEpoch AI GPU Clusters | IN | AI training | Announced | — |
| NFDG Andromeda Phase 1Epoch AI GPU Clusters | US | AI training | Operational | — |
| NSC Berzelius Phase 1Epoch AI GPU Clusters | SE | AI training | Operational | — |
| NSCC ASPIRE 2A Phase 1Epoch AI GPU Clusters | SG | AI training | Operational | — |
| NVIDIA CoreWeave Eos-DFW Phase 1Epoch AI GPU Clusters | US | AI training | Operational | — |
| NVIDIA Eos Phase 1Epoch AI GPU Clusters | US | AI training | Operational | — |