AI 팩토리 인프라 : 시장 점유율 분석, 업계 동향 및 통계, 성장 예측(2026-2031년)

According to Mordor Intelligence, the AI factory infrastructure market size is expected to grow from USD 266.8 billion in 2025 to USD 381.5 billion in 2026 and is forecast to reach USD 782.4 billion by 2031 at 15.45% CAGR over 2026-2031.

This report is Segmented by Component (Compute Infrastructure, and More), Deployment (Cloud-Based AI Factories, On-Premise AI Factories, and More), Infrastructure Type (AI Server Clusters, and More), Application (Generative AI and Large Language Model Training, and More), End User (Hyperscalers and Cloud Providers, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Global AI Factory Infrastructure Market Trends and Insights

Rapid Expansion of Hyperscale AI Buildouts

Hyperscaler capital spending has moved to a level that is reshaping the AI factory infrastructure market across procurement, construction, and supply planning. Amazon reported USD 128.3 billion in capital expenditure for FY2025, while Alphabet reported USD 91.4 billion, pushing their combined 2025 spending above USD 219 billion and setting a high baseline for 2026 planning. Meta also committed USD 115 billion to USD 135 billion in 2026 infrastructure capex, which showed that large operators are treating AI capacity as a core investment line rather than a discretionary program. Microsoft's Q1 FY2026 filing showed Azure revenue grew 40%, while Intelligent Cloud cost of revenue grew 43%, indicating how quickly AI infrastructure scale is pushing operating requirements higher. NVIDIA stated in its FY2026 10-K that data center availability, energy, and capital remain key factors for AI infrastructure buildout, which underlines how hyperscaler deployment pace is shaping the wider supply chain. This pattern is driving demand for system integration, networking fabrics, cooling systems, and power equipment across the AI factory infrastructure market, as each new build locks in spending far beyond the GPU layer.

Rising Generative AI And Large Language Model Training Demand

Generative AI and LLM training remain a major driver of the AI factory infrastructure market, as large-scale training still requires the highest-density compute environments. The AI factory infrastructure market is also expanding because inference capacity is being built alongside training capacity, rather than replacing it. Deployed AI services require low-latency inference systems that differ from frontier training clusters, widening the infrastructure surface area operators must provision. Google Cloud's Virgo network design showed the scale of this requirement by linking 134,000 chips with up to 47 petabits per second of non-blocking bisectional bandwidth in a single fabric. The application mix inside the AI factory infrastructure market reflects the same shift, with generative AI and LLM training leading in 2025 and AI inference and deployment posting the fastest forecast growth through 2031. This combination supports parallel demand for compute nodes, high-speed switching, storage throughput, and thermal control, rather than concentrating spending in a single layer of the stack.

High Capex for Compute, Power, and Cooling Systems

The AI factory infrastructure market remains constrained by the cost of building production-ready environments around modern GPU systems. Buyers are not only paying for compute, but also funding liquid-cooling loops, higher-capacity power systems, UPS upgrades, networking layers, and control software that scale with rack density. Schneider Electric's engineering guidance made clear that an integrated power and cooling architecture is now a core requirement for high-density AI facilities, pushing project budgets upward even before full hardware deployment begins. Microsoft's Q1 FY2026 filing showed a 300-basis-point decline in Intelligent Cloud gross margin, tied directly to AI infrastructure scaling costs, indicating that even large operators are under financial pressure as capacity expands. This cost profile is steering some organizations toward managed AI cloud capacity or smaller rack-scale deployments instead of greenfield builds across the AI factory infrastructure market. It is also increasing the value of vendors that can shorten deployment time or reduce integration complexity because time-to-production now has a direct budget effect.

Other drivers and restraints analyzed in the detailed report include:

1. Shift Toward AI-Native Data Center Architectures
2. Enterprise Preference for Hybrid AI Deployment Models
3. Grid Interconnection Delays and Power Availability Constraints

For complete list of drivers and restraints, kindly check the Table Of Contents.

Segment Analysis

Compute infrastructure held 71.53% of the AI factory infrastructure market share in 2025, which reflected the central role of GPU procurement in every large deployment cycle. That lead was tied to successive NVIDIA platform generations, including H100, Blackwell, and Vera Rubin, which kept compute at the front of capital allocation for both hyperscalers and specialist AI cloud operators. NVIDIA confirmed in April 2026 that Vera Rubin moved into full production, with Dell Technologies, HPE, Lenovo, and Super Micro Computer serving as system builders. Storage infrastructure and management software still matter because buyers need sustained throughput, workload scheduling, and tighter GPU utilization to make dense clusters economically viable.

Networking infrastructure is projected to grow at a 16.18% CAGR through 2031, making it the fastest-growing component of the AI factory infrastructure market. The rise of networking spend reflects the need to move data across larger GPU fabrics without creating bottlenecks at the switch layer. NVIDIA's Vera Rubin platform includes Spectrum-X Ethernet options, which shows that open Ethernet architectures are becoming a more visible part of rack-scale AI system design. Co-packaged optics are also entering the discussion because lower latency and better power efficiency become more valuable as cluster sizes expand. This is creating a wider vendor field in the AI factory infrastructure market because performance is now being judged across compute, switching, fabric design, and system integration rather than at the accelerator layer alone.

Cloud-based AI factories held a 65.36% share of the AI factory infrastructure market in 2025, reflecting the build pace and scale of AWS, Microsoft Azure, and Google Cloud. Public cloud remains central because hyperscalers control much of the commissioned capacity that can support large training and inference workloads at short notice. At the same time, on-premises AI factories remain relevant for organizations that need classified compute, national data control, or low-latency inference that the public cloud cannot fully provide. The European Commission, the United Kingdom, and Canada each moved policy attention toward sovereign compute in 2026, which supported the case for domestically controlled infrastructure in regulated environments.

Hybrid AI factories are projected to grow at a 16.53% CAGR through 2031, which makes them the fastest-growing deployment model in the AI factory infrastructure market. Organizations that began with cloud-only strategies are now balancing burst capacity against cost volatility, data movement limits, and latency requirements. That shift is widening demand for orchestration software that can place workloads across cloud, on-premise, regional, and edge environments without wasting GPU capacity. HPE AI Grid, launched in April 2026, directly addressed this requirement by linking AI factories and distributed inference clusters across multiple sites. Hybrid adoption also strengthens adjacent spending in the AI factory infrastructure market because network fabrics, storage coordination, and policy controls become more important when compute is distributed instead of centralized.

Complete Report Scope:

• By Component
  • Compute Infrastructure
  • Networking Infrastructure
  • Storage Infrastructure
  • Infrastructure Management Software
  • Other Components
• By Deployment Model
  • Cloud-Based AI Factories
  • On-Premise AI Factories
  • Hybrid AI Factories
• By Infrastructure Type
  • AI Server Clusters
  • Integrated AI Pod / Rack-Scale Systems
  • Large-Scale AI Superclusters
  • Custom AI Clusters
• By Application
  • Generative AI and Large Language Model Training
  • AI Inference and Deployment
  • Autonomous Systems Development
  • Scientific and Research Computing
  • Digital Twin and Industrial AI
  • Healthcare and Drug Discovery AI
• By End User
  • Hyperscalers and Cloud Providers
  • Enterprises
  • Research and Academic Institutions
  • Healthcare and Life Sciences Organizations
  • BFSI Companies
  • Industrial and Manufacturing Companies
  • Government and Defense Organizations
• By Geography
  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • Germany
    • United Kingdom
    • France
    • Italy
    • Rest of Europe
  • Asia-Pacific
    • China
    • Japan
    • South Korea
    • India
    • Southeast Asia
    • Rest of Asia-Pacific
  • South America
  • Middle East and Africa

Geography Analysis

North America held 62.35% of the AI factory infrastructure market share in 2025, which reflected the region's lead in hyperscaler spending and commissioned data center capacity. Amazon and Alphabet together reported more than USD 219 billion in capital expenditure in 2025, keeping North America at the center of the AI factory infrastructure market and reinforcing their advantage in scaling new projects. The region also benefits from a dense ecosystem of OEMs, cloud operators, specialized builders, and power and cooling vendors that can quickly move large projects from design to deployment. Canada is becoming a supporting node because its sovereign compute program committed up to CAD 1 billion (USD 730 million) for domestic high-performance AI infrastructure. Power access remains the main near-term limit, meaning operators with pre-secured grid capacity are likely to retain an advantage in pricing, delivery timing, and expansion options.

Asia-Pacific is projected to grow at a 16.91% CAGR through 2031, which makes it the fastest-growing geography in the AI factory infrastructure market. Growth is being supported by sovereign compute priorities in Japan, India, Southeast Asia, and by China's domestically funded expansion of AI infrastructure. The region is also seeing more interest in AI-grade data center design, where efficiency, thermal management, and localized control are becoming more important in procurement decisions. This keeps Asia-Pacific central to the next growth phase of the AI factory infrastructure market, even though North America still leads in current scale.

Europe, South America, and the Middle East and Africa remain smaller in current scale, but each has strategic importance in the AI factory infrastructure market. Europe is balancing stronger sovereign compute ambitions against grid and permitting delays, while the European Commission's June 2026 technology sovereignty package is expected to support domestic deployment and cloud sovereignty certification. The United Kingdom has also elevated AI infrastructure in national planning through its AI Hardware Plan and its support for AI Growth Zones. The Middle East and Africa, especially the UAE and Saudi Arabia, are attracting interest because of available power and favorable data center economics, while South America remains earlier in development and more concentrated in Brazil's primary urban markets.

1. NVIDIA Corporation
2. Amazon Web Services, Inc.
3. Microsoft Corporation
4. Google LLC
5. Oracle Corporation
6. Advanced Micro Devices, Inc.
7. Intel Corporation
8. Dell Technologies Inc.
9. Hewlett Packard Enterprise Development LP
10. Super Micro Computer, Inc.
11. Lenovo Group Limited
12. Cisco Systems, Inc.
13. CoreWeave, Inc.
14. Lambda Labs, Inc.
15. Alibaba Cloud
16. Tencent Holdings Limited
17. Vertiv Group Corp.
18. Schneider Electric SE

Additional Benefits:

• The market estimate (ME) sheet in Excel format
• 3 months of analyst support

TABLE OF CONTENTS

1 INTRODUCTION

• 1.1 Study Assumptions and Market Definition
• 1.2 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET LANDSCAPE

• 4.1 Market Overview
• 4.2 Market Drivers
  • 4.2.1 Rapid Expansion of Hyperscale AI Buildouts
  • 4.2.2 Rising Generative AI and Large Language Model Training Demand
  • 4.2.3 Shift Toward AI-Native Data Center Architectures
  • 4.2.4 Enterprise Preference for Hybrid AI Deployment Models
  • 4.2.5 Power-Density Engineering as a Competitive Differentiator
  • 4.2.6 Localized AI Sovereignty and Onshore Compute Mandates
• 4.3 Market Restraints
  • 4.3.1 High Capex for Compute, Power, and Cooling Systems
  • 4.3.2 Grid Interconnection Delays and Power Availability Constraints
  • 4.3.3 Semiconductor and High-Density Hardware Supply Bottlenecks
  • 4.3.4 Scarcity of AI Infrastructure Engineering Talent
• 4.4 Industry Value Chain Analysis
• 4.5 Technological Outlook
• 4.6 Impact of Macroeconomic Factors on the Market
• 4.7 Porter's Five Forces Analysis
  • 4.7.1 Bargaining Power of Suppliers
  • 4.7.2 Bargaining Power of Buyers
  • 4.7.3 Threat of New Entrants
  • 4.7.4 Threat of Substitutes
  • 4.7.5 Industry Rivalry

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

• 5.1 By Component
  • 5.1.1 Compute Infrastructure
  • 5.1.2 Networking Infrastructure
  • 5.1.3 Storage Infrastructure
  • 5.1.4 Infrastructure Management Software
  • 5.1.5 Other Components
• 5.2 By Deployment Model
  • 5.2.1 Cloud-Based AI Factories
  • 5.2.2 On-Premise AI Factories
  • 5.2.3 Hybrid AI Factories
• 5.3 By Infrastructure Type
  • 5.3.1 AI Server Clusters
  • 5.3.2 Integrated AI Pod / Rack-Scale Systems
  • 5.3.3 Large-Scale AI Superclusters
  • 5.3.4 Custom AI Clusters
• 5.4 By Application
  • 5.4.1 Generative AI and Large Language Model Training
  • 5.4.2 AI Inference and Deployment
  • 5.4.3 Autonomous Systems Development
  • 5.4.4 Scientific and Research Computing
  • 5.4.5 Digital Twin and Industrial AI
  • 5.4.6 Healthcare and Drug Discovery AI
• 5.5 By End User
  • 5.5.1 Hyperscalers and Cloud Providers
  • 5.5.2 Enterprises
  • 5.5.3 Research and Academic Institutions
  • 5.5.4 Healthcare and Life Sciences Organizations
  • 5.5.5 BFSI Companies
  • 5.5.6 Industrial and Manufacturing Companies
  • 5.5.7 Government and Defense Organizations
• 5.6 By Geography
  • 5.6.1 North America
    • 5.6.1.1 United States
    • 5.6.1.2 Canada
    • 5.6.1.3 Mexico
  • 5.6.2 Europe
    • 5.6.2.1 Germany
    • 5.6.2.2 United Kingdom
    • 5.6.2.3 France
    • 5.6.2.4 Italy
    • 5.6.2.5 Rest of Europe
  • 5.6.3 Asia-Pacific
    • 5.6.3.1 China
    • 5.6.3.2 Japan
    • 5.6.3.3 South Korea
    • 5.6.3.4 India
    • 5.6.3.5 Southeast Asia
    • 5.6.3.6 Rest of Asia-Pacific
  • 5.6.4 South America
  • 5.6.5 Middle East and Africa

6 COMPETITIVE LANDSCAPE

• 6.1 Market Concentration
• 6.2 Strategic Moves
• 6.3 Vendor Positioning Analysis
• 6.4 Company Profiles (includes Global Level Overview, Market Level Overview, Core Segments, Financials as available, Strategic Information, Market Rank/Share, Products and Services, Recent Developments)
  • 6.4.1 NVIDIA Corporation
  • 6.4.2 Amazon Web Services, Inc.
  • 6.4.3 Microsoft Corporation
  • 6.4.4 Google LLC
  • 6.4.5 Oracle Corporation
  • 6.4.6 Advanced Micro Devices, Inc.
  • 6.4.7 Intel Corporation
  • 6.4.8 Dell Technologies Inc.
  • 6.4.9 Hewlett Packard Enterprise Development LP
  • 6.4.10 Super Micro Computer, Inc.
  • 6.4.11 Lenovo Group Limited
  • 6.4.12 Cisco Systems, Inc.
  • 6.4.13 CoreWeave, Inc.
  • 6.4.14 Lambda Labs, Inc.
  • 6.4.15 Alibaba Cloud
  • 6.4.16 Tencent Holdings Limited
  • 6.4.17 Vertiv Group Corp.
  • 6.4.18 Schneider Electric SE

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

• 7.1 White-Space and Unmet-Need Assessment