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Sovereign AI Index

Tracking the Global Push for AI Self-Reliance

Explore projects by category and status

The Rise of Sovereign AI

The United States and China control 90 percent of the computing power needed to develop and deploy frontier AI.1 They own all 50 of the top-ranked AI foundation models.2 Concerned about this concentration of AI power, governments worldwide have responded with initiatives to strengthen their AI capabilities under the banner of “sovereign AI.”

Although a consensus definition of sovereign AI remains elusive, this index defines a sovereign AI project as a government-backed AI initiative tied explicitly to national strategic interests and backed by material public investment in domestic compute, models, or data ecosystems.3

The drivers of sovereign AI vary widely. For some countries, the imperative is security: protecting sensitive data and ensuring access to advanced capabilities for defense and intelligence. For others, it is the economy: leveraging AI to spur AI-related local investment, jobs, productivity, and long-term value. Culture is another driver, with nations seeking AI systems that better reflect local languages and norms. Autonomy also motivates countries that see danger in growing AI dependence on the United States or China. These drivers often overlap.

The result is a surge in sovereign AI activity across the globe.

Sovereign AI Projects Have Accelerated4

Breakdown of Sovereign AI Projects Across the Stack

This index tracks sovereign AI projects by three broad categories that mirror key layers of the AI stack: infrastructure, models, and data.

  • Infrastructure projects, such as AI data centers, supercomputers, graphics processing unit (GPU) clusters, and compute access programs, which make up 59 percent of all projects tracked.
  • Model projects, which include government-backed efforts to develop or adapt foundation models, make up 34 percent. Few of these projects are built from scratch—most adapt open-weight or commercially licensed models for local languages and contexts.
  • Data projects, or initiatives to build national training datasets or data-sharing platforms, are the rarest at just 7 percent.
Sovereign AI Projects Concentrate On Compute and Models5

Sovereign AI infrastructure projects are growing. Although infrastructure and model projects grew at roughly similar rates through mid-2024, infrastructure projects have accelerated sharply since then. There were 23 new infrastructure projects worldwide in the last quarter of 2025 alone.

Sovereign AI Infrastructure Projects Lead the Way6

Sovereign AI Projects by Region

Governments on every continent have declared AI sovereignty a priority, but rhetoric has not translated evenly into reality. The index reveals a substantial gap between intent and action. Investment remains heavily concentrated in two regions—the Middle East and East Asia—which together account for more than 80 percent of all tracked and publicly disclosed sovereign AI investment worldwide.

Governments on every continent have declared AI sovereignty a priority, but rhetoric has not translated evenly into reality.

The Middle East and East Asia Lead Global Sovereign AI Investment7

Top Sovereign AI Investors

Zooming in from regions to countries sharpens the picture. Within regions, sovereign AI investments are dominated by a few outsized national bets. The United Arab Emirates (UAE) and Japan alone account for over two thirds of total disclosed sovereign AI investments. Beyond these two outliers, disclosed investment rarely exceeds a few hundred million dollars.

The UAE and Japan Lead in Disclosed Sovereign AI Investment8

Who Builds “Sovereign” AI?

In practice, most “sovereign” AI projects rely heavily on foreign—overwhelmingly American—technology providers. These foreign companies remain deeply embedded across more than 130 national infrastructure and model projects tracked by the index. Roughly 70 percent of tracked projects involve at least one foreign partner, and four-fifths of these involve a U.S. company. The remaining 31 percent of projects have no disclosed foreign partner.

Countries Seeking AI Sovereignty Depend Heavily on American Technology9

U.S. Vendors Are Embedded Across the Sovereign AI Stack

A range of mostly U.S. technology providers underpins the supply chains vital to other countries’ AI ambitions. NVIDIA alone supplies GPUs for 52 percent of all tracked infrastructure projects. U.S.-headquartered companies also appear in the majority of sovereign infrastructure projects across nearly every layer of the stack—from accelerator chips to server systems to cloud platforms.

Foreign technology companies play different roles in sovereign AI infrastructure: accelerator chip designers (NVIDIA, AMD, Intel, Cerebras), server manufacturers (HPE, Dell, Supermicro, Lenovo), cloud and compute platforms (AWS, Oracle, Nebius), and networking providers (Cisco).

This concentration is itself a driver of sovereign AI initiatives. Governments wary of dependence on foreign models and cloud providers are pouring resources into domestic compute. But building national data centers to escape reliance on U.S. cloud platforms does not eliminate dependence on American technology. It only shifts exposure from one layer of the U.S. tech stack to another.

In the near term, it is hard to envision any sovereign compute project outside of China wholly independent of the U.S. technology stack.

Building national data centers to escape reliance on U.S. cloud platforms does not eliminate dependence on American technology. It only shifts exposure from one layer of the U.S. tech stack to another.

Sovereign Compute Relies Heavily on U.S. Tech10

Open-Weight Models and Sovereign AI

Only a handful of countries have the compute, data, talent, and capital to develop competitive frontier models. Most countries instead opt to fine-tune open-weight models on local data to embed national languages, cultural context, and domain-specific knowledge at a fraction of the cost.

At the model layer, most countries conceptualize sovereignty as less about exclusive development than about exercising the ability to run, modify, and govern a model domestically without dependence on a foreign firm’s application programming interface (API) or licensing terms.

Among countries building on open-weight models, Meta’s Llama family is the clear favorite, appearing in 36 percent of such projects, followed by Mistral (France), Gemma (U.S.), and Qwen (China). Even at the model layer, realizing sovereign AI ambitions still often relies on U.S. foundations.

Even at the model layer, implementing countries’ sovereign AI ambitions still often relies on U.S. foundations.

Most Sovereign AI​​ Models are Built on U.S. Foundations11

The Sovereign AI Paradox

The path to sovereign AI is full of tensions. Countries across the globe are pursuing sovereign AI, but actual sovereign AI spending remains concentrated in just a few. For all the rhetoric of technology independence, most sovereign AI projects still depend heavily on foreign—mostly U.S.—technology across the stack. For most of the world, the gap between sovereign aspiration and capacity remains vast.

No country—not even the United States—can achieve full control over the complex and varied inputs that power frontier AI systems. Even the most advanced economies, for instance, rely on chips and chipmaking equipment produced overseas. For emerging economies, sovereign AI will inevitably mean managing rather than eliminating dependencies—choosing which layers of the stack matter most and accepting reliance on foreign providers for the rest. Non-U.S. alternatives exist at specific layers—France’s Mistral, China’s open-weight models—but none offers a comprehensive alternative across the stack.

The question facing policymakers worldwide is not whether full sovereign AI is achievable (it is not), but whether they can craft partnerships, deploy technologies, and implement governance frameworks to give countries meaningful agency over how AI is deployed within their borders.

Data is current as of January 2026.

For emerging economies, sovereign AI will inevitably mean managing rather than eliminating dependencies—choosing which layers of the stack matter most and accepting reliance on foreign providers for the rest.

References
  1. Konstantin F. Pilz et al., “The US Hosts the Majority of GPU Cluster Performance, Followed by China,” Epoch AI, June 5, 2025, https://epoch.ai/data-insights/ai-supercomputers-performance-share-by-country ↩︎
  2. “Leaderboard,” LMArena, https://lmarena.ai/leaderboard/text. ↩︎
  3. Additional information on definitions and inclusion criteria can be found in the index’s methodology memo. ↩︎
  4. Includes all project categories (infrastructure, model, data, access programs). Quarter reflects announcement date. Pablo Chavez, Vivek Chilukuri, and Ruby Scanlon, “Sovereign AI Index,” Center for a New American Security, April 20, 2026, https://interactives.cnas.org/reports/sovereign-ai-index/. ↩︎
  5. Projects spanning multiple categories are counted in each. Percentages reflect share of 139 total projects. Illustrative examples: Infrastructure: Germany’s JUPITER Exascale Supercomputer, Japan’s METI Cloud Program; Models: Switzerland’s Apertus, Chile’s Latam-GPT; Data: EU Common European Language Data Space, India’s AIKosha. Chavez, Chilukuri, and Scanlon, “Sovereign AI Index.” ↩︎
  6. Based on announcement quarter. Projects spanning multiple categories are counted in each. Chavez, Chilukuri, and Scanlon, “Sovereign AI Index.” ↩︎
  7. Figures reflect publicly disclosed sovereign AI investment aggregated at the country and regional level. Disclosed totals encompass a range of funding stages—from announced roadmaps and signaled government intent to signed contracts to appropriated program budgets to operational spending already deployed. Source documents do not consistently distinguish between these stages and totals should be read as an aggregate of publicly disclosed investment across the pipeline rather than as a direct measure of deployed capital. Chavez, Chilukuri, and Scanlon, “Sovereign AI Index.” ↩︎
  8. Figures reflect publicly disclosed sovereign AI investment aggregated at the country and regional level. Disclosed totals encompass a range of funding stages – from announced roadmaps and signaled government intent, to signed contracts, to appropriated program budgets, to operational spending already deployed. Source documents do not consistently distinguish between these stages, and totals should be read as an aggregate of publicly disclosed investment across the pipeline rather than as a direct measure of deployed capital. Chavez, Chilukuri, and Scanlon, “Sovereign AI Index.” ↩︎
  9. Covers national infrastructure and model projects only. “U.S. Partner” means at least one American company is involved in the project. Chavez, Chilukuri, and Scanlon, “Sovereign AI Index.” ↩︎
  10. Companies shown play different roles in sovereign AI infrastructure: accelerator chip designers (NVIDIA, AMD, Intel, Cerebras), server and systems original equipment manufacturers (HPE, Dell, Supermicro, Lenovo), cloud platforms (AWS, Oracle, Nebius), networking (Cisco), systems integrators (Eviden), and national high-performance computing coordinators (GENCI). Counts reflect project-level appearances and are comparable within a role but not across roles. A single project may involve multiple partners. Only partners appearing in two-plus projects are shown. Chavez, Chilukuri, and Scanlon, “Sovereign AI Index.” ↩︎
  11. Projects may use multiple base models and are counted under each. Only projects with identified base models are included. Chavez, Chilukuri, and Scanlon, “Sovereign AI Index.” ↩︎

By Pablo Chavez, Vivek Chilukuri, and Ruby Scanlon

Published April 2026

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  • Infrastructure

    1HealthAI

    Spain (EU)

    Europe Buildout

    Infrastructure | System type: European High Performance Computing (EuroHPC) AI Factory (1HealthAI; planned; CESGA AI-specific supercomputer + experimental AI-optimized platform) | Accelerators: Not specified | Interconnect: Not specified | Performance: Not specified | Quantified power/cooling: Not specified

    1HealthAI

    Spain (EU)

    Spain (EU)

    Infrastructure

    Buildout (2025)

    Budget

    $97.1 million total

    Access & Licensing

    AI Factory services + access pathways to EuroHPC resources; sector scope includes climate and health interactions, genomics, personalized medicine, sustainable agrifood systems, blue biotechnology, pharmaceuticals, and environmental health

    Purpose

    An EU-selected AI Factory delivering health-focused AI capability across human, animal, and environmental health domains, including a dedicated AI supercomputer, experimental AI platform, and free support services for companies and research centers

    Lead Sponsor

    Ministry of Science, Innovation and Universities | EuroHPC Joint Undertaking (JU) | Xunta de Galicia

    Location

    Centro de Supercomputación de Galicia (CESGA) | Santiago de Compostela, Galicia, Spain

    Technical Specifications

    Infrastructure | System type: European High Performance Computing (EuroHPC) AI Factory (1HealthAI; planned; CESGA AI-specific supercomputer + experimental AI-optimized platform) | Accelerators: Not specified | Interconnect: Not specified | Performance: Not specified | Quantified power/cooling: Not specified

    Partners

    CESGA (host/operator; project lead) (ES) Consejo Superior de Investigaciones Científicas (CESGA coowner) (ES) | EuroHPC JU (lead sponsor; funder) (EU) | European Digital Innovation Hub DATAlife (ecosystem partner) (ES) | Gradiant Technology Center (ecosystem partner) (ES) | Ministry of Science, Innovation and Universities (lead sponsor; funder) (ES) | University of A Coruña (ecosystem partner; Galician University System Network of Research Centres [CIGUS] Network) (ES) | University of Santiago de Compostela (ecosystem partner; CIGUS Network) (ES) | University of Vigo (ecosystem partner; CIGUS Network) (ES) | Xunta de Galicia (lead sponsor; funder; CESGA coowner) (ES)

    Sovereign Profile

    “Spain is leading Europe in advanced technological capabilities, democratising access to AI innovation, thanks to the intense work that this government is carrying out to boost the technological independence of Spain and Europe.” —Óscar López, minister for digital transformation and public function, quoted by La Moncloa.

    More Information

  • Infrastructure

    Abu Dhabi Sovereign AI Cloud

    UAE

    Middle East Operational

    Infrastructure | System type: OCI Supercluster deployment in the Oracle Cloud Abu Dhabi Region | Accelerators: More than 4,000 × NVIDIA Blackwell GPUs | Interconnect: Not specified | Performance: Not specified | Quantified power/cooling: Not specified

    Abu Dhabi Sovereign AI Cloud

    UAE

    UAE

    Infrastructure

    Operational (2025)

    Budget

    $3.54 billion

    Access & Licensing

    Cloud service access (OCI); specific public sector/regulatory use cases referenced; detailed access model not specified

    Purpose

    Sovereign AI training and inference capacity in Abu Dhabi via an Oracle Cloud supercluster, positioned to support government and regulated-industry AI workloads while meeting data sovereignty requirements

    Lead Sponsor

    Department of Government Enablement (DGE)

    Location

    Oracle Cloud Infrastructure (OCI) Dedicated Region/Oracle Cloud Abu Dhabi Region | Abu Dhabi, United Arab Emirates (in-country sovereign boundaries; Core42/G42 infrastructure referenced)

    Technical Specifications

    Infrastructure | System type: OCI Supercluster deployment in the Oracle Cloud Abu Dhabi Region | Accelerators: More than 4,000 × NVIDIA Blackwell GPUs | Interconnect: Not specified | Performance: Not specified | Quantified power/cooling: Not specified

    Partners

    DGE (lead sponsor; government owner) (AE) OCI (cloud provider/operator) (U.S.) | Core42/G42 (infrastructure foundation) (AE) | Deloitte (implementation/orchestration) (U.S.) | NVIDIA (accelerator platform) (U.S.)

    Sovereign Profile

    “OCI’s sovereign AI infrastructure will directly support Abu Dhabi’s goals of becoming the world’s first fully AI-native government by 2027.” —Oracle.

    “. . . ensuring their sensitive government data does not leave the emirate . . .” —NVIDIA blog, quoting Deloitte.

    More Information

  • Infrastructure

    Adastra2 (MI300A partition at CINES)

    France (EU)

    Europe Operational

    Infrastructure | Hewlett Packard Enterprise (HPE) Cray EX255a | Accelerators: 112 × AMD Instinct MI300A APUs | Interconnect: Slingshot-11 | Performance: HPL (FP64) Rmax: 2.53 PFLOP/s; Rpeak: 4.06 PFLOP/s | Quantified power/cooling: Power: 36.60 kW; Cooling: 100% fanless direct liquid cooling (97% of heat cooled via warm water); PUE: Not specified

    Adastra2 (MI300A partition at CINES)

    France (EU)

    France (EU)

    Infrastructure

    Operational (2024)

    Budget

    EUR 25 million ($26.45 million)

    Access & Licensing

    Open to academic and industry researchers via France’s national computing allocation framework

    Purpose

    A new AMD-based computing partition integrated into France’s national Adastra supercomputer, combining traditional high-performance computing with AI capability

    Lead Sponsor

    Grand Equipement National de Calcul Intensif (GENCI) | CINES

    Location

    Centre Informatique National de l’Enseignement Supérieur (CINES) | Montpellier, Hérault, France

    Technical Specifications

    Infrastructure | Hewlett Packard Enterprise (HPE) Cray EX255a | Accelerators: 112 × AMD Instinct MI300A APUs | Interconnect: Slingshot-11 | Performance: HPL (FP64) Rmax: 2.53 PFLOP/s; Rpeak: 4.06 PFLOP/s | Quantified power/cooling: Power: 36.60 kW; Cooling: 100% fanless direct liquid cooling (97% of heat cooled via warm water); PUE: Not specified

    Partners

    GENCI (owner/coordinator) (FR) CINES (host/operator) (FR) | AMD (hardware provider) (U.S.) | HPE (system vendor) (U.S.)

    Sovereign Profile

    “France continues to strengthen its strategic autonomy with three major supercomputers: Jean Zay, Adastra, and Alice Recoque.” —Élysée document (Make France an AI Powerhouse).

    More Information

  • Infrastructure

    AGH Cyfronet Helios

    Poland (EU)

    Europe Operational

    Infrastructure | System type: Hewlett Packard Enterprise (HPE) Cray EX254n (Helios GPU partition; Cyfronet) | Accelerators: 440 × NVIDIA Grace Hopper GH200 Superchips + 24 × NVIDIA H100 GPUs | Interconnect: Slingshot-11 | Performance: HPL (FP64) Rmax: 19.14 PFLOP/s; Rpeak: 30.44 PFLOP/s | Quantified power/cooling: Power: 316.88 kW; Cooling: Not specified; PUE: Not specified

    AGH Cyfronet Helios

    Poland (EU)

    Poland (EU)

    Infrastructure

    Operational (2024)

    Budget

    Not specified

    Access & Licensing

    Research access via ACC Cyfronet AGH allocation processes (details not specified)

    Purpose

    A national high-performance computing system supporting large-scale scientific simulation and AI workloads for Poland’s research community

    Lead Sponsor

    Ministry of Development Funds and Regional Policy | European Union

    Location

    Academic Computer Centre (ACC) Cyfronet, AGH University of Krakow | Kraków, Lesser Poland Voivodeship, Poland

    Technical Specifications

    Infrastructure | System type: Hewlett Packard Enterprise (HPE) Cray EX254n (Helios GPU partition; Cyfronet) | Accelerators: 440 × NVIDIA Grace Hopper GH200 Superchips + 24 × NVIDIA H100 GPUs | Interconnect: Slingshot-11 | Performance: HPL (FP64) Rmax: 19.14 PFLOP/s; Rpeak: 30.44 PFLOP/s | Quantified power/cooling: Power: 316.88 kW; Cooling: Not specified; PUE: Not specified

    Partners

    Ministry of Development Funds and Regional Policy (lead sponsor) (PL) European Union (lead sponsor) (EU) | ACC Cyfronet AGH (operator) (PL) | AGH University of Krakow (host institution) (PL) | HPE (system vendor) (U.S.) | NVIDIA (GPU platform supplier) (U.S.) | AMD (CPU platform supplier) (U.S.)

    Sovereign Profile

    “Wspierając rozwój AI na rodzimych zasobach, dbamy o cyfrową suwerenność oraz budujemy fundament pod inteligentne usługi publiczne i biznesowe [By supporting AI development on domestic resources, we safeguard digital sovereignty and build a foundation for intelligent public and business services].” —EuroCC (European High Performance Computing National Competence Center) Poland, post about Polish Large Language Model training moving to Helios at Cyfronet AGH).

    More Information

  • Infrastructure

    AI Bridging Cloud Infrastructure (ABCI) 3.0 (upgrade)

    Japan

    Asia-Pacific Operational

    Infrastructure | System type: Hewlett Packard Enterprise (HPE) Cray XD670 (ABCI 3.0) | Accelerators: 6,128 × NVIDIA H200 SXM5 141GB | Interconnect: InfiniBand NDR200 | Performance: HPL (FP64) Rmax: 145.10 PFLOP/s; Rpeak: 181.49 PFLOP/s | Quantified power/cooling: Power: 3,596.10 kW; Cooling: Not specified; PUE: Not specified

    AI Bridging Cloud Infrastructure (ABCI) 3.0 (upgrade)

    Japan

    Japan

    Infrastructure

    Operational (2025)

    Budget

    Not specified

    Access & Licensing

    Open access via usage fees, with standard and discounted tiers for qualifying projects

    Purpose

    Japan’s national open AI cloud supercomputing infrastructure providing large-scale computing resources for generative AI research and development across industry, government, and academia

    Lead Sponsor

    AIST | Ministry of Economy, Trade and Industry (METI)

    Location

    National Institute of Advanced Industrial Science and Technology (AIST) Kashiwa Center | Kashiwa, Chiba Prefecture, Japan

    Technical Specifications

    Infrastructure | System type: Hewlett Packard Enterprise (HPE) Cray XD670 (ABCI 3.0) | Accelerators: 6,128 × NVIDIA H200 SXM5 141GB | Interconnect: InfiniBand NDR200 | Performance: HPL (FP64) Rmax: 145.10 PFLOP/s; Rpeak: 181.49 PFLOP/s | Quantified power/cooling: Power: 3,596.10 kW; Cooling: Not specified; PUE: Not specified

    Partners

    AIST Solutions Co. (operator) (JP) HPE (system supplier) (U.S.) | METI (lead sponsor) (JP) | AIST (lead sponsor) (JP) | NVIDIA (accelerator platform) (U.S.)

    Sovereign Profile

    “日本のAI主権を強化し、研究開発能力を向上させるための戦略的な施策として . . . ABCI 3.0 [As a strategic measure to strengthen Japan’s AI sovereignty and enhance R&D capability . . . [AIST is] boosting ABCI 3.0].” —NVIDIA.

    More Information

  • Infrastructure (access program)

    AI Compute Access Fund

    Canada

    Americas Operational

    Infrastructure (access program) | Allocated resource: Subsidy for cloud-based AI compute costs for eligible projects | Mechanism: Program application and approval; cost-share subsidy for approved compute spend | Quantitative limits (caps; quotas; duration): Eligible project compute costs CAD 100,000 to CAD 5 million; cost share up to two-thirds for Canadian cloud-based compute and half for non-Canadian cloud-based compute (reported) | In-scope resources/providers/systems: Canadian and non-Canadian cloud compute services (provider list not specified in cited releases)

    AI Compute Access Fund

    Canada

    Canada

    Infrastructure (access program)

    Operational (2025)

    Budget

    CAD 300 million ($220.4 million)

    Access & Licensing

    SME subsidy (application via online portal/statement of interest; due diligence assessment described by ISED)

    Purpose

    A federal subsidy program to reduce the cost of cloud-based AI computing for eligible Canadian small- and medium-sized enterprises developing made-in-Canada AI products and solutions

    Lead Sponsor

    ISED

    Location

    Not specified (virtual subsidy program) | Ottawa, Ontario, Canada (administered by Innovation, Science and Economic Development Canada [ISED]; compute is cloud based)

    Technical Specifications

    Infrastructure (access program) | Allocated resource: Subsidy for cloud-based AI compute costs for eligible projects | Mechanism: Program application and approval; cost-share subsidy for approved compute spend | Quantitative limits (caps; quotas; duration): Eligible project compute costs CAD 100,000 to CAD 5 million; cost share up to two-thirds for Canadian cloud-based compute and half for non-Canadian cloud-based compute (reported) | In-scope resources/providers/systems: Canadian and non-Canadian cloud compute services (provider list not specified in cited releases)

    Partners

    ISED (lead sponsor; program owner) (CA)

    Sovereign Profile

    “The AI Compute Access Fund will help break down barriers and empower businesses and entrepreneurs to develop made-in-Canada solutions . . . and ensure that Canada’s digital future is secure and innovative.” —Evan Solomon, minister of artificial intelligence and digital innovation (June 25, 2025).

    More Information

Authors

  • Pablo Chavez

    Adjunct Senior Fellow, Technology and National Security

    Pablo Chavez is an Adjunct Senior Fellow with CNAS's Technology and National Security Program. His research focuses on the national security, economic, and geopolitical implications of cloud and AI policy.

  • Vivek Chilukuri

    Senior Fellow and Director, Technology and National Security Program

    Vivek Chilukuri is senior fellow and program director of the Technology and National Security Program at the Center for a New American Security (CNAS), where he studies how artificial intelligence (AI) and emerging technologies are reshaping national security and great power competition.

  • Ruby Scanlon

    Research Associate, Technology and National Security Program

    Ruby Scanlon is a research associate for the Technology and National Security Program at the Center for a New American Security (CNAS), supporting the Center’s research on US-China technology competition, China's innovation ecosystem, and artificial intelligence (AI) policy.

More On This Report

This project was made possible with general support for the CNAS Technology and National Security Program, along with corporate support for the index. A list of CNAS supporters can be found here.

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