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

According to Mordor Intelligence, the GPU semiconductor market size is expected to increase from USD 189.43 billion in 2025 to USD 228.61 billion in 2026 and reach USD 647.34 billion by 2031, growing at a CAGR of 23.14% over 2026-2031.

This report is Segmented by Integration Type (Integrated GPUs and Discrete GPUs), Device Application (Mobile Devices and Tablets, Pcs and Workstations, Data Center and Server Accelerators, Gaming Consoles and Handheld Devices, and More), End User (Consumer Electronics and Gaming, and More), Memory Type (GDDR-Based GPUs, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Global GPU Semiconductor Market Trends and Insights

Hyperscale AI Training and Inference Cluster Expansion

The GPU semiconductor market is being pushed higher by the decision of the largest cloud operators to treat GPU capacity as foundational infrastructure. The combined capital expenditure of Amazon, Alphabet, Meta, Microsoft, and Oracle approached USD 600 billion in 2026, with a large share directed toward AI infrastructure build-outs. NVIDIA reported Q1 FY2027 data center revenue of USD 75.2 billion in May 2026, up 92% year over year, which shows how quickly this procurement cycle translated into supplier revenue. The Semiconductor Industry Association also showed that AI data center semiconductor revenue reached USD 670 billion in 2026, confirming that AI infrastructure has become the largest demand engine in the broader chip stack. The same source indicated that inference workloads are expected to grow faster than training workloads, suggesting the GPU semiconductor market should remain robust even after the first wave of large-model training clusters matures. Each leading-edge AI rack carries a very high system cost, so even a modest expansion in hyperscale capacity has an outsized effect on GPU volumes, mix, and pricing across the GPU semiconductor market.

Enterprise AI Factory and Sovereign Compute Procurement

The GPU semiconductor market is also gaining support from enterprise AI factories and sovereign compute programs that follow policy goals rather than short-cycle return thresholds. The source text shows that this demand is tied to data sovereignty, national industrial planning, and the need to keep sensitive model development under domestic control. NVIDIA stated, in the context of the Sovereign AI Index cited in the draft, that its platforms account for a large share of tracked sovereign infrastructure projects, suggesting long procurement cycles once a national platform is selected. That pattern matters because governments and regulated institutions often buy premium systems even as commercial buyers slow down decision-making, helping keep a pricing floor in the GPU semiconductor market. The result is a broader demand base that is less dependent on the timing of hyperscaler ordering waves. It also gives leading vendors a chance to build durable relationships around hardware, software stacks, and ongoing support, which can lock in future replacement cycles across the GPU semiconductor market.

Export Controls and Tariff Volatility

The GPU semiconductor market faces a clear restraint from export control changes and tariff actions that affect pricing, compliance, and deal timing. The US Bureau of Industry and Security revised its license review policy for advanced semiconductors exported to China in January 2026, moving certain reviews to a case-by-case process subject to end-user certification and verification. BIS then clarified in May 2026 that license requirements for advanced computing items still apply to entities headquartered in Country Group D:5, regardless of their physical location. That keeps a structural limit on how freely high-end suppliers can address some overseas demand pools. The draft also notes that tariffs added a direct cost to cross-border AI chip transactions, complicating procurement planning for distributors and cloud operators. Together, these measures slow decisions, raise documentation burdens, and make revenue conversion less predictable across the GPU semiconductor market.

Other drivers and restraints analyzed in the detailed report include:

1. Edge AI Upgrade Cycle in PCs and Mobile Devices
2. Rising ADAS and In-Cabin Compute Content per Vehicle
3. Elevated GPU And Memory ASPs Slowing Mainstream Adoption

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

Segment Analysis

Discrete GPUs accounted for 84.31% of revenue in the GPU semiconductor market in 2025, underscoring how AI computing needs now shape the overall mix. That share reflects the simple fact that integrated graphics still cannot provide the memory bandwidth, dedicated VRAM capacity, and floating-point throughput needed for large-scale model training and inference. The Semiconductor Industry Association reported that AI accelerators account for most of the semiconductor value in AI server racks, which helps explain why discrete designs hold such a large share of the GPU semiconductor market. Product launches across NVIDIA and AMD have reinforced that position because buyers can adopt new hardware while keeping much of the same software environment and deployment logic. That continuity matters in the GPU semiconductor industry because replacement decisions now depend as much on ecosystem stability as on raw performance gains.

Intel's Crescent Island launch at Computex 2026 showed that the discrete data center segment remains open enough to attract major entrants with server compute relationships and packaging capabilities. At the same time, integrated GPUs remain strategically relevant in mobile devices, notebooks, display-centric automotive systems, and lighter edge inference tasks where power efficiency matters more than peak throughput. The draft also highlights the role of chiplet-based architecture, especially in AMD's Instinct roadmap, because it improves yield economics for large compute designs and allows suppliers to scale products without relying on a single monolithic die. That architecture supports broader product planning because one design base can be paired with different memory approaches for AI, gaming, and professional use cases. The result is a GPU semiconductor market where discrete GPUs keep control of high-value AI workloads, while integrated designs retain an important role in high-volume client and embedded deployments.

Data center and server accelerators held a 73.52% share of the GPU semiconductor market in 2025 and are projected to expand at a 24.35% CAGR through 2031. The Semiconductor Industry Association reported that AI data center semiconductor revenue reached USD 670 billion in 2026, underscoring the centrality of this application to the broader GPU semiconductor market. The same report also shows that AI accelerators capture the majority of semiconductor content value in AI server racks, so application mix now follows infrastructure investment more than consumer unit shipments. Another important shift is that inference demand is expected to grow faster than training demand, which should keep installed platforms productive for longer while still supporting new deployments. That balance is useful because it supports continuing volume demand even if per-rack pricing becomes less aggressive over time.

Other device applications still matter because they spread GPU demand across several usage models and replacement cycles. PCs and workstations are benefiting from local AI inference needs, and Microsoft's 2026 software support expansion for discrete RTX systems widened the refresh path for installed users. Automotive and ADAS remains the fastest-moving non-data-center application in the draft because higher compute content is moving into larger vehicle segments. Embedded and edge devices also provide a meaningful layer of demand because GPU IP licensing and modular platform design let chipmakers integrate graphics and AI capability without building full custom stacks from scratch. This leaves the GPU semiconductor market with a clear center of gravity in data centers, but not a single-point dependence on one application alone.

Complete Report Scope:

• By Integration Type
  • Integrated GPUs
  • Discrete GPUs
• By Device Application
  • Mobile Devices and Tablets
  • PCs and Workstations
  • Data Center and Server Accelerators
  • Gaming Consoles and Handheld Devices
  • Automotive and ADAS
  • Embedded and Edge Devices
• By End User
  • Consumer Electronics and Gaming
  • Cloud, Data Center, and Enterprise IT
  • Telecom
  • Automotive
  • Industrial and Robotics
  • Other End Users
• By Memory Type
  • GDDR-Based GPUs
  • HBM-Based GPUs
  • Shared DDR/LPDDR Memory GPUs
  • Other Memory Types
• 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 a 49.51% share of the GPU semiconductor market in 2025, keeping the region in the lead entering 2026. The main reason is that the largest hyperscaler buyers are headquartered in the United States, and their AI infrastructure budgets continue to drive demand for GPUs in the semiconductor market. The draft states that Amazon, Microsoft, Alphabet, Meta, and Oracle were on track to spend more than USD 600 billion on AI infrastructure in 2026, which helps explain why regional demand remained concentrated even when data center build-outs were geographically distributed. NVIDIA's FY2026 revenue and AMD's Q1 2026 data center performance both reflect that spending pattern, since North American buyers remain central to high-end procurement and product qualification. Canada is also drawing more AI compute investment through policy support, while Mexico is gaining relevance as a secondary data center location tied to nearshoring and regional diversification. Together, these factors keep North America at the core of demand, platform testing, and early deployment activity in the GPU semiconductor market.

Asia-Pacific is projected to expand at a 24.57% CAGR through 2031, making it the fastest-growing regional layer in the GPU semiconductor market. The region is growing through a different mix of drivers, since it combines end demand, memory supply, packaging capacity, equipment investment, and sovereign compute programs in one broad ecosystem. South Korea remains especially important because Samsung Electronics and SK Hynix anchor HBM production, which makes the region central to the premium AI supply chain even when final system demand is located elsewhere. Japan also supports the GPU semiconductor market upstream, and the Semiconductor Equipment Association of Japan forecast that the country's semiconductor and FPD manufacturing equipment market would reach JPY 5,500.4 billion (USD 36.7 billion) in FY2026. That rise reflects AI-linked investment in advanced logic and memory capacity, which means GPU demand is spreading back through tools, materials, and production infrastructure.

Europe's role in the GPU semiconductor market is being supported by compliance, data residency, and the preference for locally operated AI compute in regulated use cases. Germany, the United Kingdom, and France remain the largest demand pools in the region, and domestic cloud operators are scaling local GPU capacity in response to enterprise requirements. The region's demand profile is less tied to consumer replacement cycles and more tied to trusted deployment conditions for finance, healthcare, and public sector workloads. Outside Europe, South America and the Middle East and Africa remain smaller in share but important in strategic terms because energy availability, digital infrastructure expansion, and sovereign technology priorities are creating new pockets of premium AI compute demand. This leaves the GPU semiconductor market with a regional structure where North America leads in current demand, Asia-Pacific grows fastest through supply and deployment depth, and other regions expand where regulation, infrastructure, or state-backed programs create clear buying triggers.

1. NVIDIA Corporation
2. Advanced Micro Devices, Inc.
3. Intel Corporation
4. Qualcomm Incorporated
5. Samsung Electronics Co., Ltd.
6. MediaTek Inc.
7. Imagination Technologies Group Ltd.
8. Huawei Technologies Co., Ltd.
9. Moore Threads Intelligent Technology (Beijing) Co., Ltd.
10. Biren Technology Co., Ltd.
11. VeriSilicon Microelectronics (Shanghai) Co., Ltd.
12. Zhaoxin Semiconductor Co., Ltd.
13. UNISOC Technologies Co., Ltd.
14. Rockchip Electronics Co., Ltd.
15. Bolt Graphics, 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 Hyperscale AI Training and Inference Cluster Expansion
  • 4.2.2 Enterprise AI Factory and Sovereign Compute Procurement
  • 4.2.3 Edge AI Upgrade Cycle in PCs and Mobile Devices
  • 4.2.4 Rising ADAS and In-Cabin Compute Content per Vehicle
  • 4.2.5 Chiplet-Based GPU Roadmaps Improving Yield and Product Scaling
  • 4.2.6 GPU-as-a-Service Broadening Access Beyond Hyperscalers
• 4.3 Market Restraints
  • 4.3.1 Export Controls and Tariff Volatility
  • 4.3.2 Elevated GPU and Memory ASPs Slowing Mainstream Adoption
  • 4.3.3 HBM and CoWoS Allocation Bias Toward AI Racks
  • 4.3.4 Grid Interconnection Delays for High-Density GPU Campuses
• 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 Suppliers
  • 4.7.2 Bargaining Power of Buyers
  • 4.7.3 Threat of New Entrants
  • 4.7.4 Threat of Substitutes
  • 4.7.5 Competitive Rivalry

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

• 5.1 By Integration Type
  • 5.1.1 Integrated GPUs
  • 5.1.2 Discrete GPUs
• 5.2 By Device Application
  • 5.2.1 Mobile Devices and Tablets
  • 5.2.2 PCs and Workstations
  • 5.2.3 Data Center and Server Accelerators
  • 5.2.4 Gaming Consoles and Handheld Devices
  • 5.2.5 Automotive and ADAS
  • 5.2.6 Embedded and Edge Devices
• 5.3 By End User
  • 5.3.1 Consumer Electronics and Gaming
  • 5.3.2 Cloud, Data Center, and Enterprise IT
  • 5.3.3 Telecom
  • 5.3.4 Automotive
  • 5.3.5 Industrial and Robotics
  • 5.3.6 Other End Users
• 5.4 By Memory Type
  • 5.4.1 GDDR-Based GPUs
  • 5.4.2 HBM-Based GPUs
  • 5.4.3 Shared DDR/LPDDR Memory GPUs
  • 5.4.4 Other Memory Types
• 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 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 Advanced Micro Devices, Inc.
  • 6.4.3 Intel Corporation
  • 6.4.4 Qualcomm Incorporated
  • 6.4.5 Samsung Electronics Co., Ltd.
  • 6.4.6 MediaTek Inc.
  • 6.4.7 Imagination Technologies Group Ltd.
  • 6.4.8 Huawei Technologies Co., Ltd.
  • 6.4.9 Moore Threads Intelligent Technology (Beijing) Co., Ltd.
  • 6.4.10 Biren Technology Co., Ltd.
  • 6.4.11 VeriSilicon Microelectronics (Shanghai) Co., Ltd.
  • 6.4.12 Zhaoxin Semiconductor Co., Ltd.
  • 6.4.13 UNISOC Technologies Co., Ltd.
  • 6.4.14 Rockchip Electronics Co., Ltd.
  • 6.4.15 Bolt Graphics, Inc.

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

• 7.1 White-Space and Unmet-Need Assessment