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

According to Mordor Intelligence, the AI infrastructure market size reached USD 101.17 billion in 2026 and is projected to reach USD 202.48 billion by 2031, reflecting a 14.89% CAGR over the forecast period.

This report is Segmented by Offering (Hardware [Processor, Storage, and Memory], and Software [System Optimization, and AI Middleware and MLOps]), Deployment (On-Premises, and Cloud), End User (Enterprises, Government and Defense, and Cloud Service Providers), Processor Architecture (CPU, GPU, FPGA/ASIC (TPU, Gaudi, and More), and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Global AI Infrastructure Market Trends and Insights

Soaring H100 and H200 GPU Backlogs Among Hyperscalers

NVIDIA reported that 2025 pre-orders for H100 and H200 devices tripled available supply, prompting Microsoft to earmark USD 80 billion for multi-year allocations and AWS to expand its infrastructure budget by USD 100 billion through 2028. High-bandwidth memory bottlenecks intensified the imbalance, as SK Hynix and Samsung controlled 95% of HBM3E output. Hyperscalers now co-design memory packaging directly with fabs, weakening the negotiating leverage of traditional GPU vendors. TSMC's 3-nanometer capacity remained oversubscribed, extending device lead times past 12 months and accelerating a pivot toward custom ASICs such as Google TPU v6e. Enterprises, facing unpredictable delivery schedules, increasingly rent guaranteed instances from cloud providers even when on-demand prices exceed USD 30 per hour for eight-GPU bundles.

Rapid AI-Specific Network Fabrics (InfiniBand NDR, Ethernet 800G)

InfiniBand NDR operated at 400 Gbps and connected about 70% of 2025 AI training clusters, delivering latency that was 40% lower than traditional Ethernet. Hyperscalers, however, began evaluating 800 Gbps Ethernet as Broadcom's Tomahawk 5 and Spectrum-X switched traffic at competitive latencies with a 25% reduction in capital cost. Meta validated Ethernet performance by scaling its 10,000-GPU AI Research SuperCluster on 800 Gbps links, widening vendor choice and eroding InfiniBand lock-in. IEEE 802.3df work on 1.6 Tbps Ethernet continues, signaling more convergence between AI and standard data-center workloads.

AI-Class GPUs in Chronic Short Supply Through 2026

Lead times for H200 cards lengthened past 52 weeks in 2025, while AMD's MI300X backlog mirrored the constraint. CoWoS packaging capacity at TSMC hit 35,000 wafer starts per month, far below demand estimates above 100,000 equivalents. High-bandwidth memory remains scarce because each H100 device needs 80 GB of HBM3 stacked across five layers. Enterprises consequently delayed large-scale deployments and reprioritized model architectures that require fewer parameters. Cloud platforms countered by over-provisioning inventory, dropping utilization rates, and charging elevated spot prices, a tactic that distorts supply signals and suppresses near-term market adoption.

Other drivers and restraints analyzed in the detailed report include:

1. Energy-Efficient Liquid Cooling Adoption
2. Government CHIPS-Type Subsidies for AI Fabs
3. Sovereign-AI Export Controls

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

Segment Analysis

Hardware commanded 68.42% of 2025 spending, reflecting capital-intensive GPU clusters, high-bandwidth memory, and NVMe fabrics that push rack densities beyond 100 kilowatts. Software is projected to rise at a 16.02% CAGR to 2031 as enterprises emphasize inferencing efficiency, model observability, and MLOps automation. Tools like Triton Inference Server compress latency by as much as 50% through quantization and kernel fusion. System vendors now bundle orchestration frameworks with observability dashboards, converting one-off licenses into subscriptions. The AI infrastructure market size attributed to software is therefore expanding faster than GPU capital investment, even though absolute spending on accelerators remains larger. Training workloads will stay GPU-centric, but inference is already moving toward purpose-built ASICs that lower total cost of ownership for production pipelines. Enterprises gaining cost relief redeploy freed budgets into data-quality initiatives and retrieval-augmented generation pipelines, pushing middleware adoption higher.

A second catalyst is the rise of large-language-model-as-a-service offerings that embed guardrails for content safety and bias mitigation. Vendors who package middleware with pre-trained models secure recurring revenue and deepen customer lock-in. Independent software providers respond by hardening open-source deployment stacks, ensuring that proprietary licensing does not impede model portability. The emergent dynamic elevates software gross margins toward 75%, well above hardware reselling levels, underscoring why investors favor code over silicon in later-stage funding rounds. The AI infrastructure market therefore shifts from a capital-expenditure cycle to a blended model where subscription revenue stabilizes earnings and mitigates hardware refresh volatility.

On-premise infrastructure held 57.46% of spending in 2025, driven by data-residency mandates and sectoral frameworks like HIPAA. Cloud deployments are forecast to grow at 15.76% CAGR as AWS Trainium2 and Google TPU v6e instances deliver multi-petaflop performance at favorable economics. The AI infrastructure market size associated with cloud offerings is thus expanding faster than enterprise capex, especially as hyperscalers standardize pay-per-inference pricing. Financial institutions that once insisted on sovereign hosting now pilot confidential-compute enclaves that keep encryption keys under customer control, reducing regulatory friction.

Hybrid patterns proliferate as enterprises train sensitive models on-premise then shift inference to geographic edge nodes that lower latency for end users. Sovereign AI initiatives in Saudi Arabia and the United Arab Emirates inject more than USD 140 billion to build domestic hyperscale campuses, sustaining a countervailing demand for local deployments. Cloud providers accommodate sovereignty by offering dedicated regions with jurisdictionally ring-fenced networking, certifications, and auditing. Over the long term, however, hardware obsolescence cycles of 18-24 months tilt the cost curve toward shared infrastructure, compelling on-premise defenders to adopt modular designs that swap node boards without re-cabling entire halls.

Complete Report Scope:

• By Offering
  • Hardware
    • Processor
    • Storage
    • Memory
  • Software
    • System Optimisation
    • AI Middleware and MLOps
• By Deployment
  • On-Premise
  • Cloud
• By End User
  • Enterprises
  • Government and Defence
  • Cloud Service Providers
• By Processor Architecture
  • CPU
  • GPU
  • FPGA/ASIC (TPU, Inferentia, Gaudi, Cerebras)
  • Other Processor Architectures
• By Geography
  • North America
    • United States
    • Canada
    • Mexico
  • South America
    • Brazil
    • Argentina
    • Rest of South America
  • Europe
    • United Kingdom
    • Germany
    • France
    • Sweden
    • Rest of Europe
  • Asia Pacific
    • China
    • Japan
    • India
    • Australia
    • South Korea
    • Rest of Asia Pacific
  • Middle East
    • Saudi Arabia
    • United Arab Emirates
    • Turkey
    • Rest of Middle East
  • Africa
    • South Africa
    • Nigeria
    • Rest of Africa

Geography Analysis

North America commanded 39.56% of 2025 spending, supported by USD 52.7 billion in CHIPS Act grants and by hyperscalers that operate roughly 60% of global AI capacity. The Semiconductor Industry Association warns of a 67,000-worker talent shortage by 2030, which could slow fab ramp-ups even as capital is plentiful. Canada positions Toronto and Montreal as research hubs backed by supportive immigration policy, whereas Mexico's grid reliability questions dampen large-scale build-outs. The United States Department of Defense awarded Amazon a USD 50 billion cloud contract, underscoring that sovereign security concerns coexist with a broader shift toward centrally managed compute.

Asia Pacific is expected to grow at a 16.44% CAGR through 2031, propelled by China's USD 50 billion semiconductor fund and India's USD 15 billion hyperscaler commitments. Alibaba deployed 100,000 Huawei Ascend 910C accelerators in 2025, illustrating rapid indigenous progress despite export curbs. Japan allocated JPY 2 trillion (USD 13.5 billion) for TSMC's Kumamoto site and 2-nanometer R&D to hedge geopolitical exposure. South Korea enjoys 95% share of HBM3E supply, an essential choke point in the AI supply chain. Australia's high power tariffs limit hyperscale, but Sydney and Melbourne still attract colocation players looking for resilient connectivity to submarine cables.

Europe's growth moderates as AI Act compliance layers EUR 5-15 million (USD 5.5-16.5 million) in incremental cost per multi-nation deployment. Germany and France lead semiconductor subsidies, while Sweden leverages cold climate and hydroelectric power to tempt hyperscalers; Microsoft confirmed a USD 3.2 billion Stockholm campus for 2026. The United Kingdom confronts post-Brexit data transfer frictions that add latency and legal overhead to continent-wide services. Middle East sovereign wealth funds pledge USD 140 billion to converge energy advantage with AI ambitions, supporting Riyadh and Abu Dhabi data center corridors that operate largely outside Western export control regimes.

1. NVIDIA Corporation
2. Intel Corporation
3. Advanced Micro Devices (AMD)
4. Amazon Web Services, Inc.
5. Microsoft Corporation
6. Google LLC
7. IBM Corporation
8. Cisco Systems, Inc.
9. Hewlett Packard Enterprise
10. Dell Technologies, Inc.
11. Samsung Electronics Co., Ltd.
12. Micron Technology, Inc.
13. Arm Holdings plc
14. Synopsys, Inc.
15. Baidu, Inc.
16. Alibaba Cloud
17. Tencent Cloud
18. Cerebras Systems
19. Graphcore
20. Huawei Technologies Co., Ltd.

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 Soaring H100/G100 GPU Backlogs Among Hyperscalers
  • 4.2.2 Rapid AI-Specific Network Fabrics (InfiniBand NDR, Ethernet 800 G)
  • 4.2.3 Energy-Efficient Liquid Cooling Adoption
  • 4.2.4 Government CHIPS-Type Subsidies for AI Fabs
  • 4.2.5 Cloud-Native AI Accelerator Instances Democratise Access
  • 4.2.6 Open-Source AI Framework Optimisation (e.g., Triton, TVM)
• 4.3 Market Restraints
  • 4.3.1 AI-Class GPUs in Chronic Short Supply Through 2026
  • 4.3.2 400 V / 48 V Power-Conversion Limitations in Legacy Data Centres
  • 4.3.3 Sovereign-AI Export Controls (US-China, EU)
  • 4.3.4 Rising Scope-2 Emissions Compliance Costs
• 4.4 Industry Value Chain Analysis
• 4.5 Regulatory Landscape
• 4.6 Technological Outlook
• 4.7 Porter's Five Forces Analysis
  • 4.7.1 Bargaining Power of Buyers
  • 4.7.2 Bargaining Power of Suppliers
  • 4.7.3 Threat of New Entrants
  • 4.7.4 Threat of Substitutes
  • 4.7.5 Competitive Rivalry
• 4.8 Impact of Macroeconomic Factors on the Market

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

• 5.1 By Offering
  • 5.1.1 Hardware
    • 5.1.1.1 Processor
    • 5.1.1.2 Storage
    • 5.1.1.3 Memory
  • 5.1.2 Software
    • 5.1.2.1 System Optimisation
    • 5.1.2.2 AI Middleware and MLOps
• 5.2 By Deployment
  • 5.2.1 On-Premise
  • 5.2.2 Cloud
• 5.3 By End User
  • 5.3.1 Enterprises
  • 5.3.2 Government and Defence
  • 5.3.3 Cloud Service Providers
• 5.4 By Processor Architecture
  • 5.4.1 CPU
  • 5.4.2 GPU
  • 5.4.3 FPGA/ASIC (TPU, Inferentia, Gaudi, Cerebras)
  • 5.4.4 Other Processor Architectures
• 5.5 By Geography
  • 5.5.1 North America
    • 5.5.1.1 United States
    • 5.5.1.2 Canada
    • 5.5.1.3 Mexico
  • 5.5.2 South America
    • 5.5.2.1 Brazil
    • 5.5.2.2 Argentina
    • 5.5.2.3 Rest of South America
  • 5.5.3 Europe
    • 5.5.3.1 United Kingdom
    • 5.5.3.2 Germany
    • 5.5.3.3 France
    • 5.5.3.4 Sweden
    • 5.5.3.5 Rest of Europe
  • 5.5.4 Asia Pacific
    • 5.5.4.1 China
    • 5.5.4.2 Japan
    • 5.5.4.3 India
    • 5.5.4.4 Australia
    • 5.5.4.5 South Korea
    • 5.5.4.6 Rest of Asia Pacific
  • 5.5.5 Middle East
    • 5.5.5.1 Saudi Arabia
    • 5.5.5.2 United Arab Emirates
    • 5.5.5.3 Turkey
    • 5.5.5.4 Rest of Middle East
  • 5.5.6 Africa
    • 5.5.6.1 South Africa
    • 5.5.6.2 Nigeria
    • 5.5.6.3 Rest of Africa

6 COMPETITIVE LANDSCAPE

• 6.1 Market Concentration
• 6.2 Strategic Moves
• 6.3 Market Share Analysis
• 6.4 Company Profiles (includes Global Level Overview, Market Level Overview, Core Segments, Financials as Available, Strategic Information, Market Rank/Share for Key Companies, Products and Services, Recent Developments)
  • 6.4.1 NVIDIA Corporation
  • 6.4.2 Intel Corporation
  • 6.4.3 Advanced Micro Devices (AMD)
  • 6.4.4 Amazon Web Services, Inc.
  • 6.4.5 Microsoft Corporation
  • 6.4.6 Google LLC
  • 6.4.7 IBM Corporation
  • 6.4.8 Cisco Systems, Inc.
  • 6.4.9 Hewlett Packard Enterprise
  • 6.4.10 Dell Technologies, Inc.
  • 6.4.11 Samsung Electronics Co., Ltd.
  • 6.4.12 Micron Technology, Inc.
  • 6.4.13 Arm Holdings plc
  • 6.4.14 Synopsys, Inc.
  • 6.4.15 Baidu, Inc.
  • 6.4.16 Alibaba Cloud
  • 6.4.17 Tencent Cloud
  • 6.4.18 Cerebras Systems
  • 6.4.19 Graphcore
  • 6.4.20 Huawei Technologies Co., Ltd.

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

• 7.1 White-Space and Unmet-Need Assessment