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%
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 30 matching records that have an operational measure. A further 81 matches are positional, with no capacity figure. Show all 111.
| Facility | Country | Type | Status | Power (MW) |
|---|---|---|---|---|
| Sakura's B200s Phase 2Epoch AI GPU Clusters | JP | AI training | Announced | 16 |
| AIST ABCI 3.0Epoch AI GPU Clusters | JP | AI training | Operational | 9 |
| SoftBank Planned B200 SuperpodEpoch AI GPU Clusters | JP | AI training | Announced | 8 |
| KDDI Sharp SakaiEpoch AI GPU Clusters | JP | AI training | Announced | 5 |
| FPT AI Factory JapanEpoch AI GPU Clusters | JP | AI training | Announced | 4 |
| SoftBank CHIE-2Epoch AI GPU Clusters | JP | AI training | Operational | 3 |
| SoftBank CHIE-3Epoch AI GPU Clusters | JP | AI training | Operational | 3 |
| AIST ABCI-QEpoch AI GPU Clusters | JP | AI training | Operational | 3 |
| GSIC TSUBAME 2.5Epoch AI GPU Clusters | JP | AI training | Operational | 3 |
| JAMSTEC ZettaScaler-2.2 GyoukouEpoch AI GPU Clusters | JP | AI training | Operational | 3 |
| Softbank SuperPODEpoch AI GPU Clusters | JP | AI training | Operational | 2 |
| JCAHPC MiyabiEpoch AI GPU Clusters | JP | AI training | Operational | 2 |
| GSIC TSUBAME 3.0Epoch AI GPU Clusters | JP | AI training | Operational | 2 |
| TSUBAME4.0Epoch AI GPU Clusters | JP | AI training | Operational | 1 |
| Preferred Networks MN-1bEpoch AI GPU Clusters | JP | AI training | Decommissioned | 1 |
| NEC Corp Japan SupercomputerEpoch AI GPU Clusters | JP | AI training | Operational | 1 |
| Japan Atomic Energy Agency and Quantum and Radiological Science and Technology HPE SGI8600Epoch AI GPU Clusters | JP | AI training | Operational | 1 |
| Preferred Networks MN-2Epoch AI GPU Clusters | JP | AI training | Operational | 1 |
| Nagoya University Flow Type II (Furo)Epoch AI GPU Clusters | JP | AI training | Operational | 1 |
| SAKURA Internet 2019 V100 SupercomputerEpoch AI GPU Clusters | JP | AI training | Operational | 1 |
| University of Tokyo Wisteria/BDEC-01 (Aquarius)Epoch AI GPU Clusters | JP | AI training | Operational | 0 |
| Osaka University SQUIDEpoch AI GPU Clusters | JP | AI training | Operational | 0 |
| University of Tsukuba CygnusEpoch AI GPU Clusters | JP | AI training | Operational | 0 |
| Center for Advanced Intelligence Project, RIKEN, RAIDENEpoch AI GPU Clusters | JP | AI training | Operational | 0 |
| AIST ABCI 1.0Epoch AI GPU Clusters | JP | AI training | Operational | — |
| AIST ABCI 2.0Epoch AI GPU Clusters | JP | AI training | Operational | — |
| JAMSTEC ZettaScaler-2.0 GyoukouEpoch AI GPU Clusters | JP | AI training | Operational | — |
| Sakura's H100s Phase 1Epoch AI GPU Clusters | JP | AI training | Operational | — |
| Equinix TY11 (Tokyo)Company disclosure (press / IR) | JP | Colocation | Operational | — |
| Equinix TY15 (Tokyo)Equinix SEC 10-K (2024) | JP | Colocation | Under construction | — |