Data Center GPU Market by Product, Memory Capacity, Deployment Model, End-User

List of Tables

The Data Center GPU Market was valued at USD 20.80 billion in 2023, expected to reach USD 25.13 billion in 2024, and is projected to grow at a CAGR of 21.44%, to USD 81.07 billion by 2030.

The Data Center GPU market is integral to the burgeoning fields of artificial intelligence, machine learning, and large-scale data processing. Defined as graphical processing units specialized for high-performance computing beyond traditional graphics applications, these GPUs are vital for data centers to meet the escalating computational demands. The necessity for data center GPUs stems from their unparalleled ability to accelerate complex computations, optimize data throughput, and improve energy efficiency. They find applications across various sectors like cloud computing, scientific research, financial modeling, and real-time data analytics. End-users primarily include tech giants, governmental agencies, and research institutions seeking robust solutions for massive data operations. Growth is influenced by the explosion of data generation, increasing adoption of AI and machine learning, and the ongoing shift from traditional CPU-based processing to GPU-based systems. Opportunities lie in the expanding AI sector, with potential in sectors like autonomous driving, personalized medicine, and smart cities. However, the market faces limitations such as high initial investment, heat management issues, and power consumption challenges. Emerging trends and innovations like quantum computing integration, edge computing enhancements, and AI-optimized GPU architectures present avenues for research and business growth. Companies should focus on developing GPUs that efficiently balance power consumption with performance and integrate better cooling solutions. Collaborations with AI-focused enterprises and research institutes can also unlock new capabilities and expand market reach. However, regulatory challenges and the fast-paced evolution of technology require businesses to remain agile. The Data Center GPU market is competitive and rapidly evolving, pushing firms to continuously innovate and adapt to new technological developments to maintain relevance and drive growth. Understanding these dynamics is crucial for players within the market to harness its full potential while navigating its inherent challenges.

KEY MARKET STATISTICS
Base Year [2023]	USD 20.80 billion
Estimated Year [2024]	USD 25.13 billion
Forecast Year [2030]	USD 81.07 billion
CAGR (%)	21.44%

Market Dynamics: Unveiling Key Market Insights in the Rapidly Evolving Data Center GPU Market

The Data Center GPU Market is undergoing transformative changes driven by a dynamic interplay of supply and demand factors. Understanding these evolving market dynamics prepares business organizations to make informed investment decisions, refine strategic decisions, and seize new opportunities. By gaining a comprehensive view of these trends, business organizations can mitigate various risks across political, geographic, technical, social, and economic domains while also gaining a clearer understanding of consumer behavior and its impact on manufacturing costs and purchasing trends.

Market Drivers
- The rapid adoption of Industry 4.0 across manufacturing industries with supportive government policies
- Global businesses and individuals shift toward cloud-based solutions
- Growing implementation of smart city projects and IoT applications
Market Restraints
- High cost associated with the deployment of high-performance GPUs
Market Opportunities
- Continuous innovation and advancements in GPU architectures
- Ongoing expansion of edge computing demands robust data center GPUs
Market Challenges
- Issues related to heat output and energy consumption of GPUs

Porter's Five Forces: A Strategic Tool for Navigating the Data Center GPU Market

Porter's five forces framework is a critical tool for understanding the competitive landscape of the Data Center GPU Market. It offers business organizations with a clear methodology for evaluating their competitive positioning and exploring strategic opportunities. This framework helps businesses assess the power dynamics within the market and determine the profitability of new ventures. With these insights, business organizations can leverage their strengths, address weaknesses, and avoid potential challenges, ensuring a more resilient market positioning.

PESTLE Analysis: Navigating External Influences in the Data Center GPU Market

External macro-environmental factors play a pivotal role in shaping the performance dynamics of the Data Center GPU Market. Political, Economic, Social, Technological, Legal, and Environmental factors analysis provides the necessary information to navigate these influences. By examining PESTLE factors, businesses can better understand potential risks and opportunities. This analysis enables business organizations to anticipate changes in regulations, consumer preferences, and economic trends, ensuring they are prepared to make proactive, forward-thinking decisions.

Market Share Analysis: Understanding the Competitive Landscape in the Data Center GPU Market

A detailed market share analysis in the Data Center GPU Market provides a comprehensive assessment of vendors' performance. Companies can identify their competitive positioning by comparing key metrics, including revenue, customer base, and growth rates. This analysis highlights market concentration, fragmentation, and trends in consolidation, offering vendors the insights required to make strategic decisions that enhance their position in an increasingly competitive landscape.

FPNV Positioning Matrix: Evaluating Vendors' Performance in the Data Center GPU Market

The Forefront, Pathfinder, Niche, Vital (FPNV) Positioning Matrix is a critical tool for evaluating vendors within the Data Center GPU Market. This matrix enables business organizations to make well-informed decisions that align with their goals by assessing vendors based on their business strategy and product satisfaction. The four quadrants provide a clear and precise segmentation of vendors, helping users identify the right partners and solutions that best fit their strategic objectives.

Strategy Analysis & Recommendation: Charting a Path to Success in the Data Center GPU Market

A strategic analysis of the Data Center GPU Market is essential for businesses looking to strengthen their global market presence. By reviewing key resources, capabilities, and performance indicators, business organizations can identify growth opportunities and work toward improvement. This approach helps businesses navigate challenges in the competitive landscape and ensures they are well-positioned to capitalize on newer opportunities and drive long-term success.

Key Company Profiles

The report delves into recent significant developments in the Data Center GPU Market, highlighting leading vendors and their innovative profiles. These include Advanced Micro Devices, Inc., Analog Devices, Inc., Arm Holdings PLC, ASUSTeK Computer Inc., Broadcom Inc., Fujitsu Limited, Google LLC by Alphabet Inc., Hewlett Packard Enterprise Company, Huawei Investment & Holding Co., Ltd., Imagination Technologies Limited, Intel Corporation, International Business Machines Corporation, Microsoft Corporation, NVIDIA Corporation, Oracle Corporation, and VeriSilicon Microelectronics (Shanghai) Co., Ltd..

Market Segmentation & Coverage

This research report categorizes the Data Center GPU Market to forecast the revenues and analyze trends in each of the following sub-markets:

Based on Product, market is studied across Discrete and Integrated.
Based on Memory Capacity, market is studied across 4GB to 8GB, 8GB to 16GB, Above 16GB, and Below 4 GB.
Based on Deployment Model, market is studied across Cloud and On-premise.
Based on End-User, market is studied across BFSI, Education, Energy & Utilities, Government, Healthcare, IT & Telecommunications, Manufacturing, Media & Entertainment, and Retail. The BFSI is further studied across BFSI - Generation - Content Creation, BFSI - Generation - Synthetic Data Generation, BFSI - Generation - Text Generation, BFSI - Inference - Real-time Image & Video Analytics, BFSI - Inference - Recommender Systems, BFSI - Inference - Speech Recognition & Translation, BFSI - Learning - Data Analytics & Big Data Processing, BFSI - Learning - Deep Learning Model Training, and BFSI - Learning - Reinforcement Learning. The Education is further studied across Education - Generation - Content Creation, Education - Generation - Synthetic Data Generation, Education - Generation - Text Generation, Education - Inference - Real-time Image & Video Analytics, Education - Inference - Recommender Systems, Education - Inference - Speech Recognition & Translation, Education - Learning - Data Analytics & Big Data Processing, Education - Learning - Deep Learning Model Training, and Education - Learning - Reinforcement Learning. The Energy & Utilities is further studied across Energy & Utilities - Generation - Content Creation, Energy & Utilities - Generation - Synthetic Data Generation, Energy & Utilities - Generation - Text Generation, Energy & Utilities - Inference - Real-time Image & Video Analytics, Energy & Utilities - Inference - Recommender Systems, Energy & Utilities - Inference - Speech Recognition & Translation, Energy & Utilities - Learning - Data Analytics & Big Data Processing, Energy & Utilities - Learning - Deep Learning Model Training, and Energy & Utilities - Learning - Reinforcement Learning. The Government is further studied across Government - Generation - Content Creation, Government - Generation - Synthetic Data Generation, Government - Generation - Text Generation, Government - Inference - Real-time Image & Video Analytics, Government - Inference - Recommender Systems, Government - Inference - Speech Recognition & Translation, Government - Learning - Data Analytics & Big Data Processing, Government - Learning - Deep Learning Model Training, and Government - Learning - Reinforcement Learning. The Healthcare is further studied across Healthcare - Generation - Content Creation, Healthcare - Generation - Synthetic Data Generation, Healthcare - Generation - Text Generation, Healthcare - Inference - Real-time Image & Video Analytics, Healthcare - Inference - Recommender Systems, Healthcare - Inference - Speech Recognition & Translation, Healthcare - Learning - Data Analytics & Big Data Processing, Healthcare - Learning - Deep Learning Model Training, and Healthcare - Learning - Reinforcement Learning. The IT & Telecommunications is further studied across IT & Telecommunications - Generation - Content Creation, IT & Telecommunications - Generation - Synthetic Data Generation, IT & Telecommunications - Generation - Text Generation, IT & Telecommunications - Inference - Real-time Image & Video Analytics, IT & Telecommunications - Inference - Recommender Systems, IT & Telecommunications - Inference - Speech Recognition & Translation, IT & Telecommunications - Learning - Data Analytics & Big Data Processing, IT & Telecommunications - Learning - Deep Learning Model Training, and IT & Telecommunications - Learning - Reinforcement Learning. The Manufacturing is further studied across Manufacturing - Generation - Content Creation, Manufacturing - Generation - Synthetic Data Generation, Manufacturing - Generation - Text Generation, Manufacturing - Inference - Real-time Image & Video Analytics, Manufacturing - Inference - Recommender Systems, Manufacturing - Inference - Speech Recognition & Translation, Manufacturing - Learning - Data Analytics & Big Data Processing, Manufacturing - Learning - Deep Learning Model Training, and Manufacturing - Learning - Reinforcement Learning. The Media & Entertainment is further studied across Media & Entertainment - Generation - Content Creation, Media & Entertainment - Generation - Synthetic Data Generation, Media & Entertainment - Generation - Text Generation, Media & Entertainment - Inference - Real-time Image & Video Analytics, Media & Entertainment - Inference - Recommender Systems, Media & Entertainment - Inference - Speech Recognition & Translation, Media & Entertainment - Learning - Data Analytics & Big Data Processing, Media & Entertainment - Learning - Deep Learning Model Training, and Media & Entertainment - Learning - Reinforcement Learning. The Retail is further studied across Retail - Generation - Content Creation, Retail - Generation - Synthetic Data Generation, Retail - Generation - Text Generation, Retail - Inference - Real-time Image & Video Analytics, Retail - Inference - Recommender Systems, Retail - Inference - Speech Recognition & Translation, Retail - Learning - Data Analytics & Big Data Processing, Retail - Learning - Deep Learning Model Training, and Retail - Learning - Reinforcement Learning.
Based on Region, market is studied across Americas, Asia-Pacific, and Europe, Middle East & Africa. The Americas is further studied across Argentina, Brazil, Canada, Mexico, and United States. The United States is further studied across California, Florida, Illinois, New York, Ohio, Pennsylvania, and Texas. The Asia-Pacific is further studied across Australia, China, India, Indonesia, Japan, Malaysia, Philippines, Singapore, South Korea, Taiwan, Thailand, and Vietnam. The Europe, Middle East & Africa is further studied across Denmark, Egypt, Finland, France, Germany, Israel, Italy, Netherlands, Nigeria, Norway, Poland, Qatar, Russia, Saudi Arabia, South Africa, Spain, Sweden, Switzerland, Turkey, United Arab Emirates, and United Kingdom.

The report offers a comprehensive analysis of the market, covering key focus areas:

1. Market Penetration: A detailed review of the current market environment, including extensive data from top industry players, evaluating their market reach and overall influence.

2. Market Development: Identifies growth opportunities in emerging markets and assesses expansion potential in established sectors, providing a strategic roadmap for future growth.

3. Market Diversification: Analyzes recent product launches, untapped geographic regions, major industry advancements, and strategic investments reshaping the market.

4. Competitive Assessment & Intelligence: Provides a thorough analysis of the competitive landscape, examining market share, business strategies, product portfolios, certifications, regulatory approvals, patent trends, and technological advancements of key players.

5. Product Development & Innovation: Highlights cutting-edge technologies, R&D activities, and product innovations expected to drive future market growth.

The report also answers critical questions to aid stakeholders in making informed decisions:

1. What is the current market size, and what is the forecasted growth?

2. Which products, segments, and regions offer the best investment opportunities?

3. What are the key technology trends and regulatory influences shaping the market?

4. How do leading vendors rank in terms of market share and competitive positioning?

5. What revenue sources and strategic opportunities drive vendors' market entry or exit strategies?

Product Code: MRR-92249FEC2CC3

1. Preface

1.1. Objectives of the Study
1.2. Market Segmentation & Coverage
1.3. Years Considered for the Study
1.4. Currency & Pricing
1.5. Language
1.6. Stakeholders

2. Research Methodology

2.1. Define: Research Objective
2.2. Determine: Research Design
2.3. Prepare: Research Instrument
2.4. Collect: Data Source
2.5. Analyze: Data Interpretation
2.6. Formulate: Data Verification
2.7. Publish: Research Report
2.8. Repeat: Report Update

3. Executive Summary

4. Market Overview

5. Market Insights

5.1. Market Dynamics
- 5.1.1. Drivers
  - 5.1.1.1. The rapid adoption of Industry 4.0 across manufacturing industries with supportive government policies
  - 5.1.1.2. Global businesses and individuals shift toward cloud-based solutions
  - 5.1.1.3. Growing implementation of smart city projects and IoT applications
- 5.1.2. Restraints
  - 5.1.2.1. High cost associated with the deployment of high-performance GPUs
- 5.1.3. Opportunities
  - 5.1.3.1. Continuous innovation and advancements in GPU architectures
  - 5.1.3.2. Ongoing expansion of edge computing demands robust data center GPUs
- 5.1.4. Challenges
  - 5.1.4.1. Issues related to heat output and energy consumption of GPUs
5.2. Market Segmentation Analysis
- 5.2.1. Product: Growing utilization of discrete data centers GPU for high graphics performance and complex data computations
- 5.2.2. Memory Capacity: Growing adoption of 8GB to 16GB memory capacity to provide a balance of power and affordability
- 5.2.3. Deployment Model: Increasing preference for cloud deployment model due to its flexibility and scalability
- 5.2.4. Application: Rising application of data center GPUs in learning for rapid analysis and handling complex mathematical computations
- 5.2.5. End-User: Ongoing expansion of IT & telecommunications sector accelerates demand for robust data center GPU
5.3. Porter's Five Forces Analysis
- 5.3.1. Threat of New Entrants
- 5.3.2. Threat of Substitutes
- 5.3.3. Bargaining Power of Customers
- 5.3.4. Bargaining Power of Suppliers
- 5.3.5. Industry Rivalry
5.4. PESTLE Analysis
- 5.4.1. Political
- 5.4.2. Economic
- 5.4.3. Social
- 5.4.4. Technological
- 5.4.5. Legal
- 5.4.6. Environmental
5.5. Client Customization
- 5.5.1. Use cases for application by BFSI sector
- 5.5.2. Use cases for application by energy & utilities sector
- 5.5.3. Use cases for application by education sector
- 5.5.4. Use cases for application by government sector
- 5.5.5. Use cases for application by healthcare sector
- 5.5.6. Use cases for application by IT & telecommunications sector

5.5.7. Use cases for application by manufacturing sector
5.5.8. Use cases for application by media & entertainment sector
5.5.9. Use cases for application by retail sector

6. Data Center GPU Market, by Product

6.1. Introduction
6.2. Discrete
6.3. Integrated

7. Data Center GPU Market, by Memory Capacity

7.1. Introduction
7.2. 4GB to 8GB
7.3. 8GB to 16GB
7.4. Above 16GB
7.5. Below 4 GB

8. Data Center GPU Market, by Deployment Model

8.1. Introduction
8.2. Cloud
8.3. On-premise

9. Data Center GPU Market, by End-User

9.1. Introduction
9.2. BFSI
- 9.2.1. BFSI - Generation - Content Creation
- 9.2.2. BFSI - Generation - Synthetic Data Generation
- 9.2.3. BFSI - Generation - Text Generation
- 9.2.4. BFSI - Inference - Real-time Image & Video Analytics
- 9.2.5. BFSI - Inference - Recommender Systems
- 9.2.6. BFSI - Inference - Speech Recognition & Translation
- 9.2.7. BFSI - Learning - Data Analytics & Big Data Processing
- 9.2.8. BFSI - Learning - Deep Learning Model Training
- 9.2.9. BFSI - Learning - Reinforcement Learning
9.3. Education
- 9.3.1. Education - Generation - Content Creation
- 9.3.2. Education - Generation - Synthetic Data Generation
- 9.3.3. Education - Generation - Text Generation
- 9.3.4. Education - Inference - Real-time Image & Video Analytics
- 9.3.5. Education - Inference - Recommender Systems
- 9.3.6. Education - Inference - Speech Recognition & Translation
- 9.3.7. Education - Learning - Data Analytics & Big Data Processing
- 9.3.8. Education - Learning - Deep Learning Model Training
- 9.3.9. Education - Learning - Reinforcement Learning
9.4. Energy & Utilities
- 9.4.1. Energy & Utilities - Generation - Content Creation
- 9.4.2. Energy & Utilities - Generation - Synthetic Data Generation
- 9.4.3. Energy & Utilities - Generation - Text Generation
- 9.4.4. Energy & Utilities - Inference - Real-time Image & Video Analytics
- 9.4.5. Energy & Utilities - Inference - Recommender Systems
- 9.4.6. Energy & Utilities - Inference - Speech Recognition & Translation
- 9.4.7. Energy & Utilities - Learning - Data Analytics & Big Data Processing
- 9.4.8. Energy & Utilities - Learning - Deep Learning Model Training
- 9.4.9. Energy & Utilities - Learning - Reinforcement Learning
9.5. Government
- 9.5.1. Government - Generation - Content Creation
- 9.5.2. Government - Generation - Synthetic Data Generation
- 9.5.3. Government - Generation - Text Generation
- 9.5.4. Government - Inference - Real-time Image & Video Analytics
- 9.5.5. Government - Inference - Recommender Systems
- 9.5.6. Government - Inference - Speech Recognition & Translation
- 9.5.7. Government - Learning - Data Analytics & Big Data Processing
- 9.5.8. Government - Learning - Deep Learning Model Training
- 9.5.9. Government - Learning - Reinforcement Learning
9.6. Healthcare
- 9.6.1. Healthcare - Generation - Content Creation
- 9.6.2. Healthcare - Generation - Synthetic Data Generation
- 9.6.3. Healthcare - Generation - Text Generation
- 9.6.4. Healthcare - Inference - Real-time Image & Video Analytics
- 9.6.5. Healthcare - Inference - Recommender Systems
- 9.6.6. Healthcare - Inference - Speech Recognition & Translation
- 9.6.7. Healthcare - Learning - Data Analytics & Big Data Processing
- 9.6.8. Healthcare - Learning - Deep Learning Model Training
- 9.6.9. Healthcare - Learning - Reinforcement Learning
9.7. IT & Telecommunications
- 9.7.1. IT & Telecommunications - Generation - Content Creation
- 9.7.2. IT & Telecommunications - Generation - Synthetic Data Generation
- 9.7.3. IT & Telecommunications - Generation - Text Generation
- 9.7.4. IT & Telecommunications - Inference - Real-time Image & Video Analytics
- 9.7.5. IT & Telecommunications - Inference - Recommender Systems
- 9.7.6. IT & Telecommunications - Inference - Speech Recognition & Translation
- 9.7.7. IT & Telecommunications - Learning - Data Analytics & Big Data Processing
- 9.7.8. IT & Telecommunications - Learning - Deep Learning Model Training
- 9.7.9. IT & Telecommunications - Learning - Reinforcement Learning
9.8. Manufacturing
- 9.8.1. Manufacturing - Generation - Content Creation
- 9.8.2. Manufacturing - Generation - Synthetic Data Generation
- 9.8.3. Manufacturing - Generation - Text Generation
- 9.8.4. Manufacturing - Inference - Real-time Image & Video Analytics
- 9.8.5. Manufacturing - Inference - Recommender Systems
- 9.8.6. Manufacturing - Inference - Speech Recognition & Translation
- 9.8.7. Manufacturing - Learning - Data Analytics & Big Data Processing
- 9.8.8. Manufacturing - Learning - Deep Learning Model Training
- 9.8.9. Manufacturing - Learning - Reinforcement Learning
9.9. Media & Entertainment
- 9.9.1. Media & Entertainment - Generation - Content Creation
- 9.9.2. Media & Entertainment - Generation - Synthetic Data Generation
- 9.9.3. Media & Entertainment - Generation - Text Generation
- 9.9.4. Media & Entertainment - Inference - Real-time Image & Video Analytics
- 9.9.5. Media & Entertainment - Inference - Recommender Systems
- 9.9.6. Media & Entertainment - Inference - Speech Recognition & Translation
- 9.9.7. Media & Entertainment - Learning - Data Analytics & Big Data Processing
- 9.9.8. Media & Entertainment - Learning - Deep Learning Model Training
- 9.9.9. Media & Entertainment - Learning - Reinforcement Learning
9.10. Retail
- 9.10.1. Retail - Generation - Content Creation
- 9.10.2. Retail - Generation - Synthetic Data Generation
- 9.10.3. Retail - Generation - Text Generation
- 9.10.4. Retail - Inference - Real-time Image & Video Analytics
- 9.10.5. Retail - Inference - Recommender Systems
- 9.10.6. Retail - Inference - Speech Recognition & Translation
- 9.10.7. Retail - Learning - Data Analytics & Big Data Processing
- 9.10.8. Retail - Learning - Deep Learning Model Training
- 9.10.9. Retail - Learning - Reinforcement Learning

10. Americas Data Center GPU Market

10.1. Introduction
10.2. Argentina
10.3. Brazil
10.4. Canada
10.5. Mexico
10.6. United States

11. Asia-Pacific Data Center GPU Market

11.1. Introduction
11.2. Australia
11.3. China
11.4. India
11.5. Indonesia
11.6. Japan
11.7. Malaysia
11.8. Philippines
11.9. Singapore
11.10. South Korea
11.11. Taiwan
11.12. Thailand
11.13. Vietnam

12. Europe, Middle East & Africa Data Center GPU Market

12.1. Introduction
12.2. Denmark
12.3. Egypt
12.4. Finland
12.5. France
12.6. Germany
12.7. Israel
12.8. Italy
12.9. Netherlands
12.10. Nigeria
12.11. Norway
12.12. Poland
12.13. Qatar
12.14. Russia
12.15. Saudi Arabia
12.16. South Africa
12.17. Spain
12.18. Sweden
12.19. Switzerland
12.20. Turkey
12.21. United Arab Emirates
12.22. United Kingdom

13. Competitive Landscape

13.1. Market Share Analysis, 2023
13.2. FPNV Positioning Matrix, 2023
13.3. Competitive Scenario Analysis
- 13.3.1. Advanced Micro Devices, Inc. enhances GPU server capabilities with the acquisition of ZT systems
- 13.3.2. Imagination Technologies Limited secures strategic investment from Fortress Investment Group
- 13.3.3. HPE launches 'Turnkey' AI data center solution featuring NVIDIA GPUs
- 13.3.4. Intel Corporation unveils next-gen Xeon chips to elevate AI performance in cloud and data centers
- 13.3.5. NVIDIA Corporation to Acquire Run:AI to enhance GPU orchestration capabilities
- 13.3.6. NVIDIA Corporation unveiled a platform Blackwell engineered with GPU technology
- 13.3.7. Yotta expands GPU infrastructure with USD 500 million purchase of NVIDIA Corporation GPU for data centers
- 13.3.8. Imagination Technologies Limited unveils high-performance GPU IP with DirectX support
- 13.3.9. Google enhances AI infrastructure with new TPU and GPU innovations for large-scale data center AI workloads
13.4. Strategy Analysis & Recommendation
- 13.4.1. NVIDIA Corporation
- 13.4.2. Imagination Technologies Limited
- 13.4.3. Intel Corporation
- 13.4.4. Advanced Micro Devices, Inc.

Companies Mentioned

1. Advanced Micro Devices, Inc.
2. Analog Devices, Inc.
3. Arm Holdings PLC
4. ASUSTeK Computer Inc.
5. Broadcom Inc.
6. Fujitsu Limited
7. Google LLC by Alphabet Inc.
8. Hewlett Packard Enterprise Company
9. Huawei Investment & Holding Co., Ltd.
10. Imagination Technologies Limited
11. Intel Corporation
12. International Business Machines Corporation
13. Microsoft Corporation
14. NVIDIA Corporation
15. Oracle Corporation
16. VeriSilicon Microelectronics (Shanghai) Co., Ltd.