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

According to Mordor Intelligence, the AI GPU chip market size is expected to grow from USD 167.5 billion in 2025 to USD 288.4 billion in 2026 and is forecast to reach USD 621.7 billion by 2031 at 16.60% CAGR over 2026-2031.

This report is Segmented by Product Type (Data Center AI GPUs, Edge AI GPUs, and Client AI GPUs), Compute Function (Mixed Training and Inference GPUs, and More), Deployment Environment (Hyperscale and Cloud, Enterprise Data Centers, and More), Workload (Generative AI and Large Language Models, Computer Vision and Robotics, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Global AI GPU Chip Market Trends and Insights

Expanding Enterprise Fine-Tuning of Proprietary Models

Enterprise fine-tuning has moved beyond one-off pilot work and is becoming a recurring operating practice for companies that want models trained on proprietary data. That shift matters for the AI GPU chip market because repeated retraining, evaluation, and deployment create ongoing hardware demand rather than a single purchase cycle. Many enterprises also want mixed clusters that support both fine-tuning and inference, which increases the value of versatile GPU configurations instead of narrow, fixed-purpose systems. On-premise economics are becoming easier to justify for high-utilization AI work, and Lenovo reported that on-premise generative AI deployments can reach breakeven against cloud in under 4 months for sustained workloads. Data control requirements in regulated sectors are also keeping some model customization closer to internal infrastructure, which broadens the buyer base of the AI GPU chip market beyond hyperscalers.

Rapid Scale-Up of Hyperscale AI Training Clusters

The AI GPU chip market is still being shaped by larger hyperscale training clusters that need tightly integrated racks, dense networking, and more advanced cooling. These purchases are no longer limited to one training wave because serving fleets also need to grow after model deployment, which keeps procurement cycles active across both training and inference estates. The newest rack-scale systems are being ordered for large frontier workloads, and that raises demand for high-end accelerators, switching, power delivery, and memory in the same build cycle. AMD reinforced this pattern in February 2026 when it announced a multi-year 6-gigawatt partnership with Meta to deploy AMD Instinct GPUs across Meta's AI data centers. As long as hyperscalers continue to separate frontier training fleets from large inference fleets, the AI GPU chip market is likely to see more continuous buying than in earlier compute upgrade cycles.

Advanced Packaging Capacity Bottlenecks

Advanced packaging remains a practical limit on how fast the AI GPU chip market can convert design demand into shipped systems. Modern AI accelerators depend on complex integration of logic dies and stacked high-bandwidth memory, and that makes packaging yield and throughput as important as wafer supply. Siemens highlighted the growing complexity of HBM4 integration, and the move to higher bandwidth and denser stack configurations increases the burden on packaging lines. Even when vendors have strong product demand, delivery schedules can still stretch because memory, packaging, and backend assembly must all scale together. This restraint slows volume growth, favors vendors with stronger supply relationships, and keeps the AI GPU chip market dependent on a narrow manufacturing base in the near term.

Other drivers and restraints analyzed in the detailed report include:

1. HBM4 Readiness and Advanced Packaging Upgrade Cycle
2. Sovereign AI Procurement and Domestic Compute Security
3. Rising Total Cost of Ownership for Cluster-Scale Deployments

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

Segment Analysis

Data center AI GPUs held 93.11% of the AI GPU chip market share in 2025, and that concentration reflected where the newest hardware could be deployed at scale. The leading products are designed around dense racks, high-speed interconnects, and specialized cooling, which makes large data center environments the natural fit for current flagship platforms. This also keeps vendor competition centered on full system design rather than on the chip alone, because deployment success depends on memory, networking, and thermal management working together. The AI GPU chip market therefore still leans heavily toward centralized compute environments even as new demand pockets begin to appear.

Edge AI GPUs are projected to expand at a 17.44% CAGR through 2031, and that growth is tied to robotics, industrial automation, and localized inference needs. NVIDIA's robotics platform design, which links DGX systems for training with RTX PRO servers for simulation and Jetson hardware for on-device inference, shows how the edge stack is becoming part of a broader AI deployment model. Client AI GPUs remain a smaller part of the AI GPU chip industry, but they are gaining relevance as device makers add AI-native features to workstations and laptops. NVIDIA's RTX Spark announcement in 2026 showed that client devices are becoming another entry point for AI GPU adoption, especially where local model execution, design workflows, and compact inferencing are important.

Training GPUs accounted for 52.33% of the AI GPU chip market size in 2025, and that lead came from frontier model development and large public compute programs. Training platforms still need the highest interconnect density and the most aggressive scaling behavior, which supports continued demand for premium rack architectures. Inference GPUs, however, are projected to expand at a 17.62% CAGR through 2031, and that difference shows how model serving is becoming the larger recurring compute task after training is complete. The AI GPU chip market is therefore shifting from a training-first narrative to a more balanced model where deployment intensity matters as much as model creation.

Mixed training and inference platforms are gaining a practical role in enterprise environments that cannot justify separate fleets for each workload. These buyers often need a shared cluster that can fine-tune models, run evaluation cycles, and serve applications from the same installed base. That operating pattern broadens the middle of the market and keeps demand from concentrating only in the most expensive training hardware. It also explains why the AI GPU chip industry is seeing more interest in memory-rich and flexible configurations that trade some peak specialization for higher overall utilization.

Complete Report Scope:

• By Product Type
  • Data Center AI GPUs
  • Edge AI GPUs
  • Client AI GPUs
• By Compute Function
  • Training GPUs
  • Inference GPUs
  • Mixed Training and Inference GPUs
• By Deployment Environment
  • Hyperscale and Cloud
  • Enterprise Data Centers
  • Government and Research Institutions
  • Edge and Endpoint Deployments
• By Workload
  • Generative AI and Large Language Models
  • Computer Vision and Robotics
  • Speech and Natural Language Processing
  • Recommendation, Search, and Graph Analytics
  • Scientific Computing and Other AI Workloads
• 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 38.44% of the global AI GPU chip market in 2025, and the region remained the largest buyer because it combines hyperscale spending with the deepest developer ecosystem. The United States still anchors most of that demand through cloud platform investment, software compatibility, and system-level integration around CUDA and NVLink. Canada is becoming more active in sovereign compute, and Bell and Cohere signed a USD 220 million agreement in June 2026 to deploy 2,304 NVIDIA Grace Blackwell GB200 NVL72 systems in British Columbia. Mexico benefits more through manufacturing and assembly ties to the United States than through large domestic AI GPU deployments at this stage. This keeps North America at the center of near-term volume for the AI GPU chip market even as more regions build local compute agendas.

Europe is building a larger sovereign compute role in the AI GPU chip market, with policy, public funding, and compliance all pushing demand toward domestic infrastructure. OECD work on public cloud compute availability supports the view that public-sector AI capacity is increasingly being evaluated through resilience and sovereignty criteria. Asia-Pacific presents a broader mix, from South Korea's USD 1.4 billion national GPU program to growing demand in India and Southeast Asia as domestic model development expands. France also signaled willingness to diversify vendors for sovereign systems, which suggests the region may support more than one software and hardware stack as procurement matures.

The Middle East and Africa is projected to expand at a 17.42% CAGR through 2031, which gives it the fastest regional growth rate in the AI GPU chip market. The UAE continues to build institutional AI capacity, and the Technology Innovation Institute's partnership with NVIDIA gives the region a formal research base in robotics and advanced AI systems. Africa is also adding academic compute infrastructure, and the University of Cape Town launched the African Compute Initiative in 2026 to expand research access to high-end AI systems. South America remains smaller in current scale, but Brazil's plan for a USD 360 million AI supercomputer due in 2027 shows that the region is entering the procurement cycle with more visible public ambition.

1. NVIDIA Corporation
2. Advanced Micro Devices, Inc.
3. Intel Corporation
4. Moore Threads Technology Co., Ltd.
5. Biren Technology Co., Ltd.
6. Huawei Technologies Co., Ltd.
7. Google LLC
8. Amazon.com, Inc.
9. Microsoft Corporation
10. Alibaba Group Holding Limited
11. Meta Platforms, Inc.
12. Cerebras Systems, Inc.
13. Graphcore Limited
14. SambaNova Systems, Inc.
15. Qualcomm Incorporated
16. Tenstorrent Inc.
17. Shanghai Iluvatar CoreX Semiconductor Co., Ltd.
18. Shanghai Denglin Technology Co., Ltd.
19. Hygon Information Technology Co., Ltd.
20. Glenfly Tech 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 Expanding Enterprise Fine-Tuning of Proprietary Models
  • 4.2.2 Rapid Scale-Up of Hyperscale AI Training Clusters
  • 4.2.3 HBM4 Readiness and Advanced Packaging Upgrade Cycle
  • 4.2.4 Sovereign AI Procurement and Domestic Compute Security
  • 4.2.5 NVLink-CXL and UALink Pooling of Accelerator Capacity
  • 4.2.6 Liquid Cooling Standardization for High-TDP GPU Racks
• 4.3 Market Restraints
  • 4.3.1 Advanced Packaging Capacity Bottlenecks
  • 4.3.2 Rising Total Cost of Ownership for Cluster-Scale Deployments
  • 4.3.3 Export Controls and Geopolitical Supply Friction
  • 4.3.4 Competition From Custom ASICs and Proprietary Accelerators
• 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 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 Industry Rivalry

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

• 5.1 By Product Type
  • 5.1.1 Data Center AI GPUs
  • 5.1.2 Edge AI GPUs
  • 5.1.3 Client AI GPUs
• 5.2 By Compute Function
  • 5.2.1 Training GPUs
  • 5.2.2 Inference GPUs
  • 5.2.3 Mixed Training and Inference GPUs
• 5.3 By Deployment Environment
  • 5.3.1 Hyperscale and Cloud
  • 5.3.2 Enterprise Data Centers
  • 5.3.3 Government and Research Institutions
  • 5.3.4 Edge and Endpoint Deployments
• 5.4 By Workload
  • 5.4.1 Generative AI and Large Language Models
  • 5.4.2 Computer Vision and Robotics
  • 5.4.3 Speech and Natural Language Processing
  • 5.4.4 Recommendation, Search, and Graph Analytics
  • 5.4.5 Scientific Computing and Other AI Workloads
• 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 Germany
    • 5.5.2.2 United Kingdom
    • 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 South Korea
    • 5.5.3.4 India
    • 5.5.3.5 Southeast Asia
    • 5.5.3.6 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 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 Moore Threads Technology Co., Ltd.
  • 6.4.5 Biren Technology Co., Ltd.
  • 6.4.6 Huawei Technologies Co., Ltd.
  • 6.4.7 Google LLC
  • 6.4.8 Amazon.com, Inc.
  • 6.4.9 Microsoft Corporation
  • 6.4.10 Alibaba Group Holding Limited
  • 6.4.11 Meta Platforms, Inc.
  • 6.4.12 Cerebras Systems, Inc.
  • 6.4.13 Graphcore Limited
  • 6.4.14 SambaNova Systems, Inc.
  • 6.4.15 Qualcomm Incorporated
  • 6.4.16 Tenstorrent Inc.
  • 6.4.17 Shanghai Iluvatar CoreX Semiconductor Co., Ltd.
  • 6.4.18 Shanghai Denglin Technology Co., Ltd.
  • 6.4.19 Hygon Information Technology Co., Ltd.
  • 6.4.20 Glenfly Tech Co., Ltd.

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

• 7.1 White Space and Unmet-Need Assessment