Global Compute Infrastructure Map
Facilities drawn where a source gives coordinates; the rest are on record but unplaced.
Facilities drawn where a source gives coordinates; the rest are on record but unplaced.
Plate I The map, live interactive · screen only
Plate I is interactive and does not print. Plate II below prints the same corpus in an equal-area projection, with the records Plate I cannot show. The live plate is at scrutica.com/map.
3,639 sites, one mark each. 2,961 name an operator, 72 state a capacity and 20 state an accelerator count.
What each mark attests
Each row narrows the map to the records that state that attribute.
Every record that has coordinates.
The record names an organisation for the site, or where it names none, the organisation the source records as owning the AI hardware inside it. Those are often two different companies, and the record keeps them in separate fields.
The record has a megawatt figure, which every energy, cost and FLOP estimate needs.
The record has a chip count, from a filing or an operator statement.
The record declares a building or campus address, so the mark sits on the site itself.
The record has no operator, no capacity and no chip count. It attests that something is here.
The map draws every placed record: 3,639. The class, status and capacity selections beside this apply on top.
The publishable corpus holds 4,226 facility records. 586 of them have no coordinates, so there is nothing to place, though the country is usually known and the operator often named. 1 more has coordinates and is withheld from the plate by two drawing rules: a record sharing a campus with another collapses into that campus’s single pin, so one site does not read as several, and an OpenStreetMap record above 70° north carrying no operator, no power figure, no chip count and no description is dropped as noise. 587 records in total are held and not drawn.
A further 316 records never enter that corpus. Each comes from a licensed vendor feed whose terms permit analysis and forbid republication, so every public page here reads a filtered set that leaves those rows out. Plate II draws them as an outline: counted, with everything else withheld.
Three populations run through this page: 3,639 placed marks, 4,226 records in the publishable corpus, and a raw facility table larger than either, which carries the licence-withheld rows. A national compute share computed off the pin count divides by the first. Plate II puts all three in one frame, at one scale.
Plate II Map coverage
The first three categories account for 4,226 public records. Licence-withheld records sit outside that total.
One mark, one facility, in the Equal Earth projection. Marks overlap where sites cluster and nothing is binned or weighted, so tone reads as density, and the projection is equal-area so that density stays comparable between latitudes. There is no basemap; the graticule is the projection’s own geometry, at 30°. The three rows beneath the world drawing count what it leaves out, at the same mark and the same size. Sparse marks mean sparse records, and sparse records may mean little compute or little disclosure.
A sparse region on Plate II is sparse for one of two reasons: there is little compute there, or there is little record of it. An external proxy would settle it, since a country importing accelerators plausibly runs them, and this platform holds bilateral semiconductor trade. Those rows are HS 8542 lines, which is every integrated circuit including the ones in phones and cars, with no data-processing-machine line to isolate. Joining on them would convert “imports chips” into “runs data centres”, so no join was built.
A source’s coverage is the geography of whatever it was compiled for. 90 per cent of every placed mark comes from 4 general registries: participation-based feeds, where a site appears because someone listed it or mapped it. None of them was compiled to find compute. The compute-scoped catalogues, where a site is on the map because somebody went looking for compute and found it, place 80 of 3,639.
So a sparse region is a region this portfolio does not reach, and the claim is falsifiable: add a source with reach there and the region fills. Plate III draws the portfolio.
Plate III The reach of the sources volume against reach
| Source | Records placed0–1,492 | Countries reached0–93 |
|---|---|---|
| PeeringDBGeneral registry | Records placed:1,492 | Countries reached:76 |
| OpenStreetMap — data-centre extractGeneral registry | Records placed:1,170 | Countries reached:93 |
| IM3 (PNNL) — US data-centre bulk corpusGeneral registry | Records placed:389 | Countries reached:1 |
| OpenStreetMapGeneral registry | Records placed:231 | Countries reached:33 |
| Epoch AI — frontier data centresCompute catalogue | Records placed:63 | Countries reached:4 |
| Microsoft Azure region docsOperator disclosure | Records placed:56 | Countries reached:32 |
| Oracle Cloud region docsOperator disclosure | Records placed:45 | Countries reached:27 |
| Google Cloud region docsOperator disclosure | Records placed:43 | Countries reached:29 |
| AWS region docsOperator disclosure | Records placed:34 | Countries reached:27 |
| Company press releasesOperator disclosure | Records placed:25 | Countries reached:11 |
| OVHcloud — Universal Registration Document (2024)Operator disclosure | Records placed:20 | Countries reached:8 |
| Source | Records placed0–1,492 | Countries reached0–93 |
|---|---|---|
| Equinix — SEC 10-K (2024)Operator disclosure | Records placed:15 | Countries reached:8 |
| EuroHPC Joint UndertakingCompute catalogue | Records placed:12 | Countries reached:12 |
| GDS Holdings — SEC 20-F (2024)Operator disclosure | Records placed:10 | Countries reached:1 |
| DayOne — corporate siteOperator disclosure | Records placed:9 | Countries reached:3 |
| Iron Mountain — SEC 10-K (2024)Operator disclosure | Records placed:8 | Countries reached:3 |
| Epoch AI — GPU clustersCompute catalogue | Records placed:5 | Countries reached:5 |
| GDS Holdings — Q4 2024 resultsOperator disclosure | Records placed:4 | Countries reached:2 |
| Scaleway — corporate siteOperator disclosure | Records placed:3 | Countries reached:1 |
| Press reportingOperator disclosure | Records placed:2 | Countries reached:1 |
| Scrutica curated reference recordsUnclassified | Records placed:2 | Countries reached:2 |
| European Commission decisionOperator disclosure | Records placed:1 | Countries reached:1 |
| Mistral Compute — announcementOperator disclosure | Records placed:1 | Countries reached:1 |
General registry Open to whoever chooses to appear in it: a network listing the facility it peers in, a volunteer mapping a building. Its coverage is the geography of who took part.
Compute catalogue Curated and compute-scoped: someone decided what counts as a frontier site and went looking.
Operator disclosure First-party: the operator states where its own sites are.
The countries holding the most records the map cannot draw, ordered by how many, and every country that places nothing, regardless of volume. The rest are named in full beneath the table.
| Country | Placed | Held | Unplaced |
|---|---|---|---|
| United States | 1,051 | 1,277 | 226 |
| Japan | 67 | 111 | 44 |
| No country on the record | 85 | 112 | 27 |
| India | 106 | 132 | 26 |
| France | 280 | 301 | 21 |
| China | 35 | 56 | 21 |
| South Korea | 19 | 36 | 17 |
| United Kingdom | 181 | 197 | 16 |
| Brazil | 93 | 109 | 16 |
| Germany | 198 | 211 | 13 |
| Saudi Arabia | 6 | 19 | 13 |
| Russia | 50 | 61 | 11 |
| Italy | 68 | 78 | 10 |
| Thailand | 15 | 24 | 9 |
| Poland | 39 | 47 | 8 |
| Sweden | 30 | 38 | 8 |
| United Arab Emirates | 10 | 18 | 8 |
| Singapore | 53 | 60 | 7 |
| Netherlands | 132 | 138 | 6 |
| Canada | 98 | 104 | 6 |
| Malaysia | 16 | 22 | 6 |
| Taiwan | 7 | 13 | 6 |
| Djibouti | 0 | 1 | 1 |
| Guatemala | 0 | 1 | 1 |
| Oman | 0 | 1 | 1 |
24 further countries hold 59 unplaced records between them, none more than 5 each: Australia, Finland, Indonesia, Switzerland, New Zealand, Norway, Ukraine, Argentina, Turkey, Israel, Mexico, Hong Kong, Iceland, Philippines, Vietnam, Tanzania, Spain, Ireland, Denmark, Greece, Kenya, Ghana, Qatar, Slovenia. 3 countries hold records and place none: Djibouti, Guatemala, Oman. Each is in the table above whatever it holds.
| Facility | Country | Operator | Type | Status | Power (MW) | Accelerators |
|---|---|---|---|---|---|---|
| Colossus 2 | United States | — | AI Training | Operational | 946 | 530,000 |
| Meta | United States | Meta Platforms, Inc. | Hyperscale DC | Operational | — | 202,224 |
| Microsoft Goodyear | United States | — | AI Training | Operational | 202 | 100,000 |
| ORNL Frontier (Exascale HPC) | United States | — | HPC Center | Operational | 22.7 | 37,888 |
| JUPITER (EuroHPC) | Germany | EuroHPC JU | HPC Center | Operational | — | 23,752 |
| Jupiter, Jülich | Germany | — | AI Training | Operational | 18 | 23,536 |
| Leonardo (EuroHPC) | Italy | EuroHPC JU | HPC Center | Operational | — | 13,824 |
| Eclairion Bruyères-le-Châtel (Mistral Compute) | France | Eclairion | AI Training | Operational | 44 | 13,800 |
| LUMI (EuroHPC) | Finland | EuroHPC JU | HPC Center | Operational | — | 11,912 |
| LUMI Supercomputer (CSC Finland) | Finland | — | HPC Center | Operational | 7.1 | 10,240 |
| MareNostrum 5 (EuroHPC) | Spain | EuroHPC JU | HPC Center | Operational | — | 4,480 |
| Microsoft | United States | Microsoft Corporation | Hyperscale DC | Operational | — | 4,000 |
| Cineca Marconi-100 | Italy | — | AI Training | Operational | 2.644 | 3,952 |
| GENCI Adastra | France | — | AI Training | Operational | 1.609 | 1,352 |
| Hut 8 | Canada | Hut 8 | Hyperscale DC | Operational | — | 1,000 |
| EuroHPC MeluXina | Luxembourg | — | AI Training | Operational | 0.687 | 800 |
| MeluXina (EuroHPC) | Luxembourg | EuroHPC JU | HPC Center | Operational | — | 800 |
| EuroHPC Karolina | Czech Republic | — | AI Training | Operational | 0.548 | 576 |
| Karolina (EuroHPC) | Czech Republic | EuroHPC JU | HPC Center | Operational | — | 576 |
| Vega (EuroHPC) | Slovenia | EuroHPC JU | HPC Center | Operational | — | 240 |
| Stargate UAE (OpenAI/G42/Oracle) | United Arab Emirates | Oracle Corporation | AI Training | Under construction | 5,000 | — |
| AWS Project Rainier (New Carlisle, IN) | United States | Amazon.com, Inc. | AI Training | Operational | 2,200 | — |
| Meta Richland Parish (Hyperion) | United States | Meta Platforms, Inc. | AI Training | Under construction | 2,000 | — |
| Stargate Abilene (OpenAI/Oracle/SoftBank) | United States | Oracle Corporation | AI Training | Operational | 1,200 | — |
| Microsoft Fairwater Atlanta | United States | — | AI Training | Operational | 636 | — |
| Meta Prometheus | United States | — | AI Training | Operational | 631 | — |
| TeraWulf Lake Mariner Campus | United States | — | AI Training | Operational | 510 | — |
| Khazna–Eni Ferrera Erbognone AI Campus | Italy | — | Hyperscale DC | Announced | 500 | — |
| Microsoft Fairwater Wisconsin | United States | — | AI Training | Operational | 369 | — |
| Colossus 1 | United States | — | AI Training | Operational | 340 | — |
| Google New Albany | United States | — | AI Training | Operational | 339 | — |
| Google Columbus | United States | — | AI Training | Operational | 303 | — |
| CoreWeave Lancaster PA | United States | CoreWeave | AI Training | Announced | 300 | — |
| Amazon Madison Mega Site | United States | — | AI Training | Operational | 284 | — |
| CoreWeave Denton TX | United States | — | AI Training | Operational | 262 | — |
| CoreWeave Kenilworth NJ (NEST Campus) | United States | CoreWeave | AI Training | Under construction | 250 | — |
| Huawei Horinger | China | — | AI Training | Operational | 241.8 | — |
| DayOne Nusajaya Tech Park | Malaysia | DayOne Data Centers Limited | Colocation | Operational | 240 | — |
| QTS Richmond 1 | United States | — | AI Training | Operational | 238 | — |
| Google Council Bluffs (East) | United States | — | AI Training | Operational | 237 | — |
| Google Omaha | United States | — | AI Training | Operational | 237 | — |
| Google Papillion | United States | — | AI Training | Operational | 237 | — |
| Amazon Ridgeland | United States | — | AI Training | Operational | 228 | — |
| VNET Bayin Ulanqab | China | — | AI Training | Operational | 221 | — |
| Microsoft Project Osmium | United States | — | AI Training | Operational | 190 | — |
| QTS Richmond 2 | United States | — | AI Training | Operational | 180 | — |
| Meta Jeffersonville | United States | — | AI Training | Operational | 178 | — |
| Meta Rosemount | United States | — | AI Training | Operational | 178 | — |
| Alibaba Zhangbei | China | — | AI Training | Operational | 169 | — |
| Google Storey County | United States | — | AI Training | Operational | 161 | — |
| Google The Dalles | United States | — | AI Training | Operational | 154 | — |
| Meta Montgomery | United States | — | AI Training | Operational | 153 | — |
| Meta Kuna | United States | — | AI Training | Operational | 152 | — |
| Meta Temple | United States | — | AI Training | Operational | 152 | — |
| QTS Richmond 3 | United States | — | AI Training | Operational | 144 | — |
| Google Lincoln | United States | — | AI Training | Operational | 141 | — |
| Google Lancaster | United States | — | AI Training | Operational | 137 | — |
| Coreweave Helios | United States | — | AI Training | Operational | 132 | — |
| Google Midlothian | United States | — | AI Training | Operational | 103 | — |
| Google Mesa | United States | — | AI Training | Operational | 90 | — |
| Google Waltham Cross | United Kingdom | — | AI Training | Operational | 88 | — |
| Meta Gallatin | United States | — | AI Training | Operational | 87 | — |
| Meta Los Lunas | United States | — | AI Training | Operational | 87 | — |
| STACK Infrastructure NVA02 | United States | — | AI Training | Operational | 85 | — |
| CoreWeave Chester VA | United States | — | AI Training | Operational | 82 | — |
| Google Arcola | United States | — | AI Training | Operational | 77 | — |
| Google Red Oak | United States | — | AI Training | Operational | 77 | — |
| Microsoft SAT40 | United States | — | AI Training | Operational | 75 | — |
| CoreWeave Ellendale ND | United States | — | AI Training | Operational | 68 | — |
| CoreWeave Marble NC | United States | — | AI Training | Operational | 65 | — |
| Stream Phoenix | United States | — | AI Training | Operational | 64 | — |
| Google Pryor (North) | United States | — | AI Training | Operational | 53 | — |
| Microsoft SAT14 | United States | — | AI Training | Operational | 47 | — |
| Start Campus Sines Data Campus | Portugal | — | AI Training | Operational | 33 | — |
| Vantage TX1 | United States | — | AI Training | Operational | 32 | — |
| CoreWeave Dalton 1 & 2 | United States | — | AI Training | Operational | 28 | — |
| DayOne SG1 Singapore | Singapore | DayOne Data Centers Limited | Colocation | Under construction | 20 | — |
| Equinix SG3 | Singapore | Equinix, Inc. | Colocation | Operational | 20 | — |
| Scaleway DC5 (PAR2) | France | Iliad Sa | Hyperscale DC | Operational | 20 | — |
| Equinix LD5 (London) | United Kingdom | Equinix, Inc. | Colocation | Operational | 12 | — |
| Scaleway DC3 (PAR1) | France | Iliad Sa | Colocation | Operational | 6.9 | — |
| Scaleway DC2 (PAR1) | France | Iliad Sa | Colocation | Operational | 3.8 | — |
| ASML Veldhoven (EUV Manufacturing HQ) | Netherlands | ASML | Packaging | Operational | — | — |
| AWS US-East-1 (Northern Virginia) | United States | Amazon.com, Inc. | Hyperscale DC | Operational | — | — |
| AWS Wavelength Zone (Chicago) | United States | Amazon.com, Inc. | Edge | Operational | — | — |
| AWS Africa (Cape Town) | South Africa | Amazon.com, Inc. | Hyperscale DC | Operational | — | — |
| AWS Asia Pacific (Hong Kong) | Hong Kong | Amazon.com, Inc. | Hyperscale DC | Operational | — | — |
| AWS Asia Pacific (Taipei) | Taiwan | Amazon.com, Inc. | Hyperscale DC | Operational | — | — |
| AWS Asia Pacific (Tokyo) | Japan | Amazon.com, Inc. | Hyperscale DC | Operational | — | — |
| AWS Asia Pacific (Seoul) | South Korea | Amazon.com, Inc. | Hyperscale DC | Operational | — | — |
| AWS Asia Pacific (Osaka) | Japan | Amazon.com, Inc. | Hyperscale DC | Operational | — | — |
| AWS Asia Pacific (Mumbai) | India | Amazon.com, Inc. | Hyperscale DC | Operational | — | — |
| AWS Asia Pacific (Hyderabad) | India | Amazon.com, Inc. | Hyperscale DC | Operational | — | — |
| AWS Asia Pacific (Singapore) | Singapore | Amazon.com, Inc. | Hyperscale DC | Operational | — | — |
| AWS Asia Pacific (Sydney) | Australia | Amazon.com, Inc. | Hyperscale DC | Operational | — | — |
| AWS Asia Pacific (Jakarta) | Indonesia | Amazon.com, Inc. | Hyperscale DC | Operational | — | — |
| AWS Asia Pacific (Melbourne) | Australia | Amazon.com, Inc. | Hyperscale DC | Operational | — | — |
| AWS Asia Pacific (Malaysia) | Malaysia | Amazon.com, Inc. | Hyperscale DC | Operational | — | — |
| AWS Asia Pacific (New Zealand) | New Zealand | Amazon.com, Inc. | Hyperscale DC | Operational | — | — |
| AWS Asia Pacific (Thailand) | Thailand | Amazon.com, Inc. | Hyperscale DC | Operational | — | — |
Showing 100 of 3,639. View the full keyboard-accessible table →