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

According to Mordor Intelligence, the aI data center GPU market size is expected to grow from USD 36.56 billion in 2025 to USD 45.04 billion in 2026 and is forecast to reach USD 90.46 billion by 2031 at a 14.97% CAGR over 2026-2031.

This report is Segmented by Deployment Mode (Cloud Data Centers, Enterprise and Private Data Centers, and More), GPU Type (Training GPUs, and Inference GPUs), Interconnect (PCIe-Based GPUs, and High-Bandwidth Interconnect GPUs), End-User (Hyperscalers and Cloud Service Providers, Enterprises, and Government and Research Institutions), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Global AI Data Center GPU Market Trends and Insights

Explosive Growth in Generative AI Model Size

Large language and multimodal models are ballooning past the trillion-parameter mark, and post-training scaling steps such as reinforcement learning from human feedback, synthetic data expansion, and long-context reasoning now consume up to 30 times the compute of the original pre-training run. Operators therefore prioritize GPUs with enormous on-package memory; AMD's MI325X offers 288 GB of HBM3e, enabling a single server to host a 1-trillion-parameter model and eliminating cross-node sharding delays. NVIDIA's Blackwell architecture improves cost per million tokens by 15-fold, down to roughly USD 0.02 per million tokens, making pay-as-you-go API economics viable at enterprise scale. Hyperscalers are responding with record capex, and prepayment contracts are locking in both wafer starts and advanced packaging slots, effectively pulling demand forward and solidifying the AI data center GPU market's growth trajectory.

Rapid Adoption of GPU-Accelerated Cloud Services

Embedding generative AI directly into productivity software is proving sticky and high-margin, prompting cloud providers to reserve unprecedented quantities of GPUs. Microsoft sold more than 8 million paid Gemini Enterprise seats within four months, while Google Cloud revenue surged 48% year-over-year in Q4 2025 on the back of Gemini roll-outs across 2,800 corporate customers. These workloads amortize GPU fleets in under two years, reinforcing aggressive procurement. Parallel multiyear supply contracts, such as Microsoft's 30,000-GPU order from Nscale for a 230-megawatt site in Norway, highlight the cash-flow confidence underpinning the AI data center GPU market.

Persistent Supply-Demand Imbalance for Advanced Packaging

High-bandwidth memory (HBM) stacks and CoWoS interposers remain in chronic shortage. HBM die areas are roughly 2.5 times those of conventional DRAM, and TSV complexity raises defect rates, forcing suppliers to reserve wafer area for yield loss. Micron's 2026 HBM output is already presold, Samsung is tripling HBM revenue yet still hiking prices by high-teens percentages, and TSMC's 9.5-reticle-limit expansion will not meaningfully lift CoWoS capacity until 2027. Scarcity slows Rubin and MI400 volume ramps and may compel vendors to allocate early lots to the highest-margin buyers, delaying access for smaller cloud and enterprise users.

Other drivers and restraints analyzed in the detailed report include:

1. Data-Center-Scale GPU Clusters Crossing the 100 K-GPU Threshold
2. Rise of Sovereign AI Initiatives in Smaller Economies
3. Growing Preference for Custom AI Accelerators Over GPUs

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

Segment Analysis

Cloud facilities accounted for 66.38% revenue in 2025, anchored by multi-gigawatt campuses that integrate liquid-cooled rack pods housing more than 100,000 GPUs each. Enterprises rely on this centralized capacity to amortize compute across thousands of tenants, but rising outbound data fees and privacy mandates are nudging some workloads back on-prem or toward sovereign centers. Edge data centers, though still niche, are forecast to expand at a 15.57% CAGR through 2031 as autonomous vehicles, robotic cells, and real-time industrial inspection demand sub-10-millisecond round-trip latency.

Vendors are increasingly re-architecting software to facilitate seamless model migration across different environments. For instance, NVIDIA's BlueField-4 Data Processing Unit (DPU) layer plays a pivotal role by tunneling key-value caches from the core to the edge. This approach significantly reduces redundant GPU memory allocations, thereby optimizing resource utilization. Collectively, these advancements are driving the AI data center GPU market along a dual-track scaling trajectory. On one hand, hyperscale hubs are witnessing substantial growth, while on the other, federated micro-sites are also expanding, albeit starting from vastly different foundational levels. These developments highlight the diverse strategies being adopted to meet the evolving demands of AI workloads.

Inference accelerators accounted for 54.23% of 2025 revenue and will grow faster than training GPUs, with a 15.37% CAGR, thanks to steady, token-based monetization models. Fine-tuning, retrieval-augmented generation, and real-time personalization drive continuous inference cycles that now represent roughly two-thirds of 2026 compute spend. Training GPUs remain indispensable for frontier model creation, but their share erodes as marginal parameter increases yield diminishing performance gains.

Hardware vendors are responding with mixed-precision pipelines, NVIDIA Rubin packs a third-generation Transformer Engine, and AMD MI325X doubles HBM capacity to squeeze trillion-parameter interpreters onto a single board, both innovations that tilt economics further toward inference. As a result, hyperscalers increasingly bifurcate their fleets, reserving the newest interconnect-rich GPUs for large-batch training while backfilling inference clusters with memory-dense cards optimized for cost per token.

Geography Analysis

North America retained 37.50% of 2025 revenue, buoyed by the proximity of top cloud providers' headquarters and abundant power capacity in Texas, the Midwest, and the Pacific Northwest. U.S. policy continues to favor domestic allocation: January 2026 export-control revisions imposed a 25% tariff on certain high-end GPUs shipped abroad, effectively preserving local supply. Mega-leases such as Applied Digital's 300-megawatt deal at Delta Forge 1 underscore the long-term runway for U.S.-based construction. Europe follows with concentrated but strategic growth; Microsoft's 30,000-Rubin-GPU contract in Narvik, Norway, reveals appetite for cold-climate, renewable-powered campuses that mitigate rising carbon taxes. The United Kingdom is channeling GBP 500 million (USD 630 million) into its Sovereign AI Unit, pledging one-million-GPU-hour grants per startup and direct equity stakes in infrastructure orchestration firms.

Asia-Pacific is projected to log the fastest regional expansion at a 15.97% CAGR through 2031. Japan's USD 12 billion GMI Cloud sovereign site in Kagoshima aims for 1 gigawatt of capacity, positioning the country as a domestic manufacturing hub for robotics, autonomous vehicles, and heavy-industry AI workloads. China, facing tightened U.S. export rules and customs hurdles on imports of NVIDIA H200 chips, is pivoting toward homegrown accelerators from Huawei, Cambricon, and Biren, even though yield and software maturity gaps suggest short-term performance lags. Elsewhere, India accelerates approvals for multi-megawatt campuses, while Samsung and SK Hynix in South Korea ramp HBM4 lines to capture value upstream in the GPU supply chain.

South America, the Middle East, and Africa hold smaller shares but serve as fast-follower destinations for low-cost renewable energy. Policy shifts in May 2025 opened Saudi Arabia and the UAE to advanced GPU imports under a Validated End User framework, leveraging their vast natural gas and solar assets to deliver competitive power purchase agreements. Although these regions will not challenge the scale of North America or Asia-Pacific in absolute dollars, they offer incremental upside and geographic risk diversification for vendors marketing into the AI data center GPU market.

1. NVIDIA Corporation
2. Advanced Micro Devices, Inc.
3. Intel Corporation
4. Google LLC
5. Amazon Web Services, Inc.
6. Microsoft Corporation
7. Alibaba Group Holding Limited
8. Baidu, Inc.
9. Huawei Technologies Co., Ltd.
10. Graphcore Ltd.
11. SambaNova Systems, Inc.
12. Cerebras Systems Inc.
13. Tenstorrent Inc.
14. Qualcomm Technologies, Inc.
15. IBM Corporation
16. Giga Computing Technology Co., Ltd.
17. Super Micro Computer, Inc.
18. ASUStek Computer Inc.
19. Dell Technologies Inc.

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 Explosive Growth in Generative AI Model Size
  • 4.2.2 Rapid Adoption of GPU-Accelerated Cloud Services
  • 4.2.3 Data-Center-Scale GPU Clusters Crossing the 100K-GPU Threshold
  • 4.2.4 Standardization of MLPerf Benchmarks in Procurement
  • 4.2.5 Rise of Sovereign AI Initiatives in Smaller Economies
  • 4.2.6 Liquid-Cooling Retrofits Driving Refresh Sales
• 4.3 Market Restraints
  • 4.3.1 Persistent Supply-Demand Imbalance for Advanced Packaging
  • 4.3.2 Escalating Total Cost of Ownership for Air-Cooled Racks
  • 4.3.3 Export Control Restrictions on High-End GPUs
  • 4.3.4 Growing Preference for Custom AI Accelerators Over GPUs
• 4.4 Impact of Macroeconomic Factors on the Market
• 4.5 Industry Value Chain Analysis
• 4.6 Regulatory Landscape
• 4.7 Technological Outlook
• 4.8 Porter's Five Forces Analysis
  • 4.8.1 Bargaining Power of Suppliers
  • 4.8.2 Bargaining Power of Buyers
  • 4.8.3 Threat of New Entrants
  • 4.8.4 Threat of Substitutes
  • 4.8.5 Intensity of Competitive Rivalry

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

• 5.1 By Deployment Mode
  • 5.1.1 Cloud Data Centers
  • 5.1.2 Enterprise and Private Data Centers
  • 5.1.3 Edge Data Centers
• 5.2 By GPU Type
  • 5.2.1 Training GPUs
  • 5.2.2 Inference GPUs
• 5.3 By Interconnect
  • 5.3.1 PCIe-Based GPUs
  • 5.3.2 High-Bandwidth Interconnect GPUs
• 5.4 By End-User
  • 5.4.1 Hyperscalers and Cloud Service Providers
  • 5.4.2 Enterprises
  • 5.4.3 Government and Research Institutions
• 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 Europe
    • 5.5.2.1 United Kingdom
    • 5.5.2.2 Germany
    • 5.5.2.3 France
    • 5.5.2.4 Italy
    • 5.5.2.5 Rest of Europe
  • 5.5.3 Asia-Pacific
    • 5.5.3.1 China
    • 5.5.3.2 Japan
    • 5.5.3.3 India
    • 5.5.3.4 South Korea
    • 5.5.3.5 Rest of Asia-Pacific
  • 5.5.4 South America
  • 5.5.5 Middle East and 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, Products and Services, Recent Developments)
  • 6.4.1 NVIDIA Corporation
  • 6.4.2 Advanced Micro Devices, Inc.
  • 6.4.3 Intel Corporation
  • 6.4.4 Google LLC
  • 6.4.5 Amazon Web Services, Inc.
  • 6.4.6 Microsoft Corporation
  • 6.4.7 Alibaba Group Holding Limited
  • 6.4.8 Baidu, Inc.
  • 6.4.9 Huawei Technologies Co., Ltd.
  • 6.4.10 Graphcore Ltd.
  • 6.4.11 SambaNova Systems, Inc.
  • 6.4.12 Cerebras Systems Inc.
  • 6.4.13 Tenstorrent Inc.
  • 6.4.14 Qualcomm Technologies, Inc.
  • 6.4.15 IBM Corporation
  • 6.4.16 Giga Computing Technology Co., Ltd.
  • 6.4.17 Super Micro Computer, Inc.
  • 6.4.18 ASUStek Computer Inc.
  • 6.4.19 Dell Technologies Inc.

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

• 7.1 White-Space and Unmet-Need Assessment