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

According to Mordor Intelligence, the generative AI GPU market size is projected to expand from USD 87.63 billion in 2025 and USD 101.97 billion in 2026 to USD 214.22 billion by 2031, registering a CAGR of 16.01% between 2026 to 2031.

This report is Segmented by Deployment Type (Cloud, and On-Premise), Function (Training, and Inference), GPU Type (Data Center Training, Inference, and More), Model Type (LLMs, Multimodal, Image/Video, and Speech and Audio Models), End User (Cloud Service Providers, Enterprises, Government and Research Institutions, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Global Generative AI GPU Market Trends and Insights

Rising Enterprise Demand For Private GenAI Training Clusters

Private generative AI infrastructure is moving into standard enterprise capital planning, and that is giving the generative AI GPU market a demand stream that does not depend only on hyperscaler spending. Buyers are focusing on data control, compliance, cost visibility, and the ability to fine tune proprietary models inside controlled environments rather than through third-party processing layers. This shift matters because the generative AI GPU market is now pulling demand from organizations that intend to run continuous internal workloads instead of short experimental projects. The economics also improve as utilization rises, which makes dedicated capacity easier to justify for stable inference and fine-tuning programs. As more vendors package managed private AI systems behind the customer firewall, the generative AI GPU market is likely to see broader enterprise participation without requiring every buyer to build deep in-house infrastructure teams.

Hyperscaler Capex Expansion For Model Training And Inference Infrastructure

The generative AI GPU market remains closely tied to hyperscaler capital spending because the largest training and serving environments still sit inside cloud platforms. These companies are committing capital across multi-generation roadmaps rather than short replacement cycles, which gives the generative AI GPU market stronger demand visibility than a normal semiconductor upgrade pattern. That pattern now extends beyond the GPU itself because large deployments also require networking fabrics, power systems, and data center expansion, which makes each compute order part of a larger infrastructure build. NVIDIA's fiscal 2026 results show how tightly AI compute and adjacent infrastructure are now linked, with data center revenue reaching USD 193.7 billion and data center networking revenue rising 263% year over year in Q4. The commitment by AWS, Google Cloud, Microsoft Azure, and Oracle Cloud Infrastructure to deploy the Vera Rubin platform shows that the generative AI GPU market is being supported by forward capacity plans rather than one product cycle at a time.

Advanced Packaging And HBM Supply Constraints

The main supply ceiling for the generative AI GPU market is no longer limited to chip design demand; it is now limited to packaging throughput and memory availability. Even when budgets are approved, orders can still face delays because the generative AI GPU market depends on a narrow set of suppliers for advanced memory and packaging steps that cannot be expanded overnight. NVIDIA's multiyear memory partnership with SK Hynix reflects how central HBM access has become to future platform rollouts. Planned capacity additions from major memory suppliers target later production windows, which means short-term tightness is still likely to shape availability through the current forecast period. As a result, the generative AI GPU market can show strong order demand while still converting that demand into revenue more slowly than buyers intend.

Other drivers and restraints analyzed in the detailed report include:

1. Rapid Shift To HBM-Heavy GPU Platforms For Large Model Training
2. Sovereign AI Programs Accelerating National GPU Procurement
3. High Power Density, Cooling, And Facility Upgrade Costs

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

Segment Analysis

Cloud deployments accounted for 74.19% of the generative AI GPU market in 2025, which kept this model well ahead of on-premise installations by revenue. That lead reflects a long infrastructure advantage built by hyperscalers through earlier GPU data center investment and closer ties to the largest model developers. The cloud model also remains attractive because it lets buyers provision capacity quickly without carrying the full upfront cost of hardware, facility work, and operations. For many organizations, especially those still testing workload patterns, the generative AI GPU market is easiest to access through elastic cloud infrastructure. That access advantage continues to support cloud leadership even as cost discipline becomes a bigger factor in 2026.

On-premise deployments are the fastest-growing segment at 16.38% CAGR through 2026-2031, which shows where the next wave of buyer behavior is shifting. Enterprises that have moved past pilot programs now have better visibility into usage intensity, latency needs, and data handling requirements, so the case for owned capacity is becoming more concrete. Lenovo's 2026 analysis shows that on-premise systems can reach cost parity with cloud rental at 75% GPU utilization across fleets of more than 50 GPUs, which supports the move toward dedicated infrastructure for stable workloads. The generative AI GPU market is also benefiting from managed private AI platforms, colocation-backed clusters, and subscription-style offers that reduce the operational burden on enterprise buyers. This gives the generative AI GPU industry a broader path into regulated and data-sensitive environments where public cloud dependency is harder to justify over time.

Training commanded 64.88% of the generative AI GPU market size in 2025, which shows how much spending is still centered on building frontier models. That share came from the exceptional compute intensity of pre-training large language, vision, and multimodal systems, where each run can consume very large GPU-hour volumes. The early commercial phase of the generative AI GPU market was therefore built on a training-heavy spending mix because the first priority was model creation and capability expansion. Large clusters, premium hardware, and concentrated cloud buying all reinforced that pattern. Training still anchors revenue because the most advanced models continue to require the highest-performance systems available.

Inference is the fastest-growing function at 16.97% CAGR through 2026-2031, and that growth is changing the operating profile of the generative AI GPU market. Once models enter production, they serve users continuously, which means inference demand can last far longer than the original training cycle. Enterprises are also shifting from per-token API spending toward owned or dedicated inference nodes when usage becomes frequent enough to make hardware amortization more attractive. This matters for the generative AI GPU market because inference demand is more geographically distributed than the concentrated training spend inside a small number of hyperscaler campuses. The result is a growth curve that broadens the buyer base while keeping total compute demand elevated after the training phase has already passed.

Complete Report Scope:

• By Deployment Type
  • Cloud
  • On-Premise
• By Function
  • Training
  • Inference
• By GPU Type
  • Data Center Training GPUs
  • Data Center Inference GPUs
  • Edge and Enterprise AI GPUs
• By Model Type
  • Large Language Models (LLMs)
  • Multimodal Models
  • Image and Video Generation Models
  • Speech and Audio Models
• By End User
  • Cloud Service Providers
  • Enterprises
  • Government and Research Institutions
  • AI Model Developers and AI Labs
• 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 46.74% of the generative AI GPU market in 2025, which kept it as the clear revenue leader by region. The region benefits from the concentration of hyperscaler headquarters, frontier AI labs, and GPU-optimized data center capacity within the United States. That combination gives the generative AI GPU market its deepest commercial base in North America because procurement, software development, and infrastructure deployment are closely linked there. Large cloud platforms also continue to secure a major share of next-generation allocation, which supports the region's lead in both training and production inference environments. Canada adds a public compute layer through its sovereign AI compute strategy, which complements the commercial strength of the broader regional market.

Europe remains important to the generative AI GPU market because demand is shaped by both public investment and regulatory pressure around data handling and model oversight. Compliance requirements under the EU AI Act support interest in domestic and on-premise deployments, especially among regulated sectors that prefer tighter control over where processing occurs. France has made one of the region's largest national AI infrastructure commitments, which is expected to support future data center buildout and GPU procurement. The UK also formalized its hardware plan with funding for specialized chip procurement inside its broader AI research resource, showing that national compute capability is now an explicit policy target.

Asia-Pacific is the fastest-growing regional segment at 17.36% CAGR through 2026-2031, and this gives it the most rapid expansion path within the generative AI GPU market size over the forecast period. Growth is being supported by sovereign AI programs, local hyperscaler investment, and rising interest in domestic alternatives where export restrictions affect access to leading U.S. hardware. The generative AI GPU market in China is developing under a different policy setting because U.S. export controls continue to shape procurement routes and encourage local accelerator development. That divergence matters because it creates separate competitive tracks within Asia-Pacific, one centered on imported premium systems and another centered on domestic substitutes. South America and the Middle East and Africa remain earlier-stage regions in the generative AI GPU market, though sovereign investment and local data center expansion could support stronger procurement volumes later in the forecast period.

1. NVIDIA Corporation
2. Advanced Micro Devices, Inc.
3. Intel Corporation
4. Google LLC
5. Amazon Web Services, Inc.
6. Microsoft Corporation
7. Huawei Technologies Co., Ltd.
8. Baidu, Inc.
9. Groq, Inc.
10. xAI Corp.
11. Marvell Technology, Inc.
12. Cerebras Systems, Inc.
13. Tenstorrent Inc.
14. Mistral AI
15. Qualcomm Incorporated
16. IBM Corporation
17. CoreWeave, Inc.
18. Oracle Corporation
19. Alibaba Group Holding Limited
20. Tencent Holdings Limited
21. Lambda, 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 Rising Enterprise Demand for Private GenAI Training Clusters
  • 4.2.2 Hyperscaler Capex Expansion for Model Training and Inference Infrastructure
  • 4.2.3 Rapid Shift to HBM-Heavy GPU Platforms for Large Model Training
  • 4.2.4 Sovereign AI Programs Accelerating National GPU Procurement
  • 4.2.5 Liquid Cooling Adoption for High-TDP Generative AI Racks
  • 4.2.6 GenAI Inferencing Migration From API Consumption to Owned GPU Capacity
• 4.3 Market Restraints
  • 4.3.1 Advanced Packaging and HBM Supply Constraints
  • 4.3.2 High Power Density, Cooling, and Facility Upgrade Costs
  • 4.3.3 Export Controls and Geopolitical Procurement Restrictions
  • 4.3.4 Custom ASIC Substitution Risk in Inference Workloads
• 4.4 Industry Value Chain Analysis
• 4.5 Regulatory Landscape
• 4.6 Technological Outlook
• 4.7 Impact of Macroeconomic Factors on the Market
• 4.8 Porter's Five Forces Analysis
  • 4.8.1 Threat of New Entrants
  • 4.8.2 Bargaining Power of Buyers
  • 4.8.3 Bargaining Power of Suppliers
  • 4.8.4 Threat of Substitutes
  • 4.8.5 Industry Rivalry

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

• 5.1 By Deployment Type
  • 5.1.1 Cloud
  • 5.1.2 On-Premise
• 5.2 By Function
  • 5.2.1 Training
  • 5.2.2 Inference
• 5.3 By GPU Type
  • 5.3.1 Data Center Training GPUs
  • 5.3.2 Data Center Inference GPUs
  • 5.3.3 Edge and Enterprise AI GPUs
• 5.4 By Model Type
  • 5.4.1 Large Language Models (LLMs)
  • 5.4.2 Multimodal Models
  • 5.4.3 Image and Video Generation Models
  • 5.4.4 Speech and Audio Models
• 5.5 By End User
  • 5.5.1 Cloud Service Providers
  • 5.5.2 Enterprises
  • 5.5.3 Government and Research Institutions
  • 5.5.4 AI Model Developers and AI Labs
• 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 Market 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 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 Huawei Technologies Co., Ltd.
  • 6.4.8 Baidu, Inc.
  • 6.4.9 Groq, Inc.
  • 6.4.10 xAI Corp.
  • 6.4.11 Marvell Technology, Inc.
  • 6.4.12 Cerebras Systems, Inc.
  • 6.4.13 Tenstorrent Inc.
  • 6.4.14 Mistral AI
  • 6.4.15 Qualcomm Incorporated
  • 6.4.16 IBM Corporation
  • 6.4.17 CoreWeave, Inc.
  • 6.4.18 Oracle Corporation
  • 6.4.19 Alibaba Group Holding Limited
  • 6.4.20 Tencent Holdings Limited
  • 6.4.21 Lambda, Inc.

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

• 7.1 White-Space and Unmet-Need Assessment