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Where Are AI Data Centers Located?

A cautious public overview of major AI and hyperscale data center regions, known AI supercomputer sites and public sources for tracking where AI infrastructure is being built.

Stylized world map showing public AI-capable data center regions
Public maps can show regions and announced sites, but they cannot reveal every AI workload or private facility. The useful view is regional and sourced, not exhaustive.

Key takeaway

There is no complete public map of every AI data center. The best public view combines cloud regions, operator data center pages, announced AI campuses and open infrastructure datasets, while clearly separating confirmed AI sites from broader AI-capable hyperscale regions.

AI data centers are not distributed randomly. They tend to cluster near power capacity, fiber networks, available land, cloud regions, cooling options, skilled labor and permitting environments that can support large infrastructure projects.

Some locations are publicly announced as AI facilities, such as large supercomputer sites or Stargate campuses. Many others are hyperscale cloud regions that can run AI workloads but do not disclose which models or products run inside each building.

This page is therefore a public, partial map of major regions and examples. It avoids exact street-level mapping and does not claim to identify every facility.

How to read this map

The entries below are selected from official operator pages, cloud infrastructure documentation, public announcements and open data center mapping resources. A region can appear because it is an explicit AI site, a public hyperscale data center campus or a major cloud region that supports AI-capable infrastructure.

The list is intentionally conservative. It should be read as a practical guide to public signals, not a complete inventory or a security-sensitive facility directory.

Major public AI and hyperscale data center regions

Selected examples based on public sources. Categories describe why the region is relevant; they do not mean every facility in the region is dedicated to AI.

Hyperscale region

Northern Virginia, United States

Operating

One of the largest cloud and data center regions in the world, represented publicly through AWS, Azure and public U.S. data center mapping resources.

AWS Global Infrastructure

Cloud and hyperscale region

Oregon and Pacific Northwest, United States

Operating

A major western U.S. infrastructure region, including public cloud regions and large campuses such as Google The Dalles and Meta Prineville.

Google Data Center Locations

AI and hyperscale buildout

Texas, United States

Operating / announced

Texas includes public hyperscale campuses and several announced AI infrastructure sites, including Stargate locations in Abilene, Milam County and Shackelford County.

OpenAI Stargate sites

Explicit AI supercomputer site

Memphis, Tennessee, United States

Operating

Memphis is publicly identified by xAI as the home of Colossus, a large AI supercomputer facility used for Grok.

SpaceXAI Memphis

European cloud and data center regions

Ireland and the Netherlands

Operating

Ireland and the Netherlands host public hyperscale data center locations and cloud regions used for European workloads.

Meta global data centers

Cool-climate infrastructure region

Nordics: Denmark, Sweden and Finland

Operating

Nordic locations appear in public data center fleets because cool climates, power access and network connectivity can support large-scale infrastructure.

Google Data Center Locations

Asia-Pacific cloud region

Singapore and Southeast Asia

Operating

Singapore is a publicly listed data center location and a dense regional cloud hub for Asia-Pacific workloads.

Meta global data centers

Asia-Pacific AI-capable infrastructure

Japan and Taiwan

Operating

Japan and Taiwan host public cloud and data center infrastructure, with strong network, semiconductor and enterprise demand around AI workloads.

Google Data Center Locations

Why the full list is not public

Operators often publish cloud regions, data center campuses or investment announcements, but they do not usually publish the exact workloads running in every building. One facility may support search, storage, enterprise cloud, AI inference and internal systems at the same time.

Some sites are leased from colocation providers, some are under construction, and some are announced before final capacity, power agreements or permitting details are complete. Public records can also lag behind construction or operational changes.

For that reason, a responsible AI data center map should use broad regions, clear source labels and uncertainty language. It should not imply that every visible cloud location is an exclusive AI facility.

Further reading and references

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