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Data Center GPU Market Size, Share & Trends

Report Code SE 8838
Published in May, 2025, By MarketsandMarkets™
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Data Center GPU Market by Deployment (Cloud, On-premises), Function (Training, Inference), Application (Generative AI, Machine Learning, Natural Language Processing, Computer Vision), End User (CSP, Enterprises) & Region - Global Forecast to 2030

Data Center GPU Market Size, Share & Trends

The global data center GPU market is anticipated to grow from USD 119.97 billion in 2025 to USD 228.04 billion by 2030 at a CAGR of 13.7% during the forecast period.

The demand for GPUs in data centers is experiencing significant growth, mainly due to the rapid advancements and widespread adoption of artificial intelligence (AI) and machine learning (ML) across various industries. The increasing complexity of AI models and the large datasets needed for training necessitate the high-performance parallel processing capabilities that GPUs provide. Performance benchmarks for AI applications drive significant investments in robust GPU infrastructure within data centers. Real-time inference and the growing need for accelerated data analytics enabled by GPUs are becoming crucial for businesses seeking a competitive advantage. At the same time, there is an increasing emphasis on energy-efficient GPU solutions to reduce the substantial power consumption associated with large-scale data center operations and to support sustainability initiatives.

Data Center GPU Market

Attractive Opportunities in the Data Center GPU Market

NORTH AMERICA

North America accounted for the largest share of 36.2% of the global data center GPU market in 2024

The growing demand for high-end GPUs in AI-powered applications, scientific research, and large-scale simulations will drive demand for data center GPUs.

Product launches are expected to offer growth opportunities for market players in the next five years.

North America’s robust technological ecosystem, including advanced AI cloud infrastructure and the presence of several industry leaders in the region, will fuel market growth.

NVIDIA Corporation (US), AMD (US), and Intel Corporation (US) are the major players in the data center GPU market.

Global Data Center GPU Market Dynamics

DRIVER: Growing adoption of AI and machine learning

The rising use of machine learning (ML) and artificial intelligence (AI) drives demand for high-performance datacenter GPUs. Applications like fraud detection, autonomous vehicles, recommendation systems, and natural language processing require strong parallel processing capabilities, making accelerated computing essential for data centers. NVIDIA's Tensor Core GPUs, such as the A100 and H100, are widely used for AI training and inference. In 2024, Microsoft and NVIDIA expanded their collaboration to build AI supercomputing infrastructure for Azure. AMD is also gaining ground with partnerships, as its MI300 GPUs see increased adoption in hyperscale environments. While Google, Meta, and Amazon are investing in custom AI chips, they still depend on third-party GPUs for specific workloads. As the AI landscape evolves, the datacenter GPU market is set to grow with the demand for compute-intensive AI models.

RESTRAINTS: Short product lifecycle

One of the main challenges in the data center GPU market is the short lifespan of GPU hardware. Rapid advancements and rising performance demands from AI, ML, and high-performance computing require GPU vendors to release new versions frequently. This situation places pressure on data center operators, who face significant initial investments and the constant need for infrastructure upgrades. For instance, NVIDIA launched its A100 GPU in mid-2020, followed by the H100 GPU based on the Hopper architecture just two years later, while AMD released its Instinct MI250 series in 2021 and the MI300 series in 2023. These constant upgrades lead to quicker depreciation of older models, hampering long-term ROI, especially for smaller enterprises that cannot easily absorb regular upgrades. Additionally, each new GPU generation requires adjustments to software stacks and workloads, adding complexity and operational overhead. As a result, these short product lifecycles can discourage adoption in markets that value stability and long-term value.

 

OPPORTUNITY: Growth in autonomous systems

The emergence of autonomous technologies, including self-driving cars, drones, and robots, is creating significant opportunities for the data center GPU industry. These technologies rely on advanced AI algorithms like computer vision and real-time decision-making, all requiring substantial computational power. For example, Tesla's Dojo supercomputer trains autonomous driving software, showcasing the need for GPU-accelerated infrastructure. NVIDIA partners with automotive companies such as Mercedes-Benz and Volvo, providing platforms like NVIDIA DRIVE for autonomous development. In 2023, NVIDIA and Foxconn collaborated to create AI factories focused on autonomous platforms.

Amazon also uses GPU-supported AWS instances for autonomous drone navigation. As businesses increasingly adopt autonomy, the demand for scalable, GPU-driven data centers will rise, positioning the GPU market to capitalize on this revolution in mobility and logistics.

CHALLENGE: Existence of alternative technologies

The growth of data center GPUs faces challenges from alternative technologies like application-specific integrated circuits (ASICs) and field-programmable gate arrays (FPGAs). These alternatives often provide better performance per watt for specialized workloads. For instance, Google’s Tensor Processing Units (TPUs) are ASICs optimized for machine learning and AI, offering faster training and inference for TensorFlow models than general-purpose GPUs. Similarly, Amazon Web Services (AWS) provides FPGAs in its EC2 F1 instances, which allow custom hardware acceleration for tasks like genomics and video encoding. Microsoft Azure has integrated FPGAs through Project Brainwave to improve real-time AI performance. As these more power-efficient technologies gain traction, especially in hyperscale environments, GPU vendors must adapt to remain competitive and justify their costs and power requirements.

Additionally, these exoskeletons typically operate for only a few hours on a single charge, leading to frequent downtimes for recharging or battery replacement. In environments where continuous operation is critical, such as in military missions or time-sensitive industrial settings, this can pose serious risks. For example, in military applications, a sudden power failure could hinder a soldier's mobility or put them in a vulnerable position. In industrial settings, power limitations can disrupt workflow and reduce productivity, as users must either pause work or switch devices. The challenge is compounded in remote or high-demand locations where charging infrastructure is limited or impractical. These constraints highlight a major trade-off between technological enhancement and operational reliability.

Global Data Center GPU Market Ecosystem Analysis

The data center GPU ecosystem is a dynamic and rapidly evolving landscape based on high-end graphics processing units designed to accelerate computationally intensive workloads in data center environments. This ecosystem includes stakeholders such as raw material suppliers, foundries, data center GPU manufacturers, server OEMs and ODMs, and end users. End users predominantly consist of cloud service providers such as Microsoft (US), Google (US), AWS (US), etc. Key companies that are involved in the data center GPU market include Nvidia Corporation (US), Advanced Micro Devices, Inc., and Intel Corporation. (US).

Top Companies in Data Center GPU Market

Source: Secondary Research, Interviews with Experts, and MarketsandMarkets Analysis

By deployment, the cloud segment is expected to maintain a leading market position during the forecast period

Cloud deployment is expected to see significant growth in the data center GPU market due to the rising demand for flexible and affordable AI and high-performance computing. Cloud providers are integrating powerful GPUs into their infrastructure to support AI, machine learning, analytics, and complex simulations. This cloud-based GPU access helps businesses reduce costs associated with physical infrastructure, making high-powered computing resources more accessible. Major players like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud are competing to enhance their GPU offerings. AWS offers NVIDIA-powered EC2 instances for AI training, Azure has partnered with NVIDIA for advanced GPUs for enterprise workloads, Google Cloud supports NVIDIA L4 GPUs for scalable tasks, and Oracle Cloud Infrastructure also collaborates with NVIDIA for on-demand AI solutions.

By function, the inference segment is expected to witness a high CAGR from 2025 to 2030.

The inference segment for data center GPUs is set to grow significantly due to the rising use of AI in real-time applications like chatbots and recommendation engines. After training, AI models require substantial GPU capacity for low-latency inference under heavy loads, driving demand for inference-optimized GPUs. NVIDIA’s latest L4 and H100 GPUs, Meta’s extensive GPU use, and Microsoft Azure’s AI services integration highlight this trend. Generative AI applications like ChatGPT and DALL-E further increase inference workloads, boosting the need for efficient and scalable computing in data centers.

Generative AI will showcase a substantial market share from 2025 to 2030

Generative AI is poised for significant growth in the data center GPU market, driven by applications like LLMs, video & image creation, code writing, and voice assistants. High-performance GPUs are in demand for these computationally intensive models, including OpenAI’s GPT-4, Google Gemini, and Meta’s LLaMA. Companies are investing heavily in meeting this demand. Microsoft has partnered with OpenAI to integrate ChatGPT into Azure, relying on NVIDIA GPUs. Google Cloud uses its Tensor Processing Units (TPUs) and NVIDIA GPUs for services like Bard and Duet AI. Meta is building an AI supercomputer, the Research SuperCluster, with thousands of NVIDIA GPUs. Amazon Web Services (AWS) is also expanding its EC2 UltraClusters with NVIDIA GPUs for generative model development. As generative AI expands into healthcare, finance, marketing, and design, the need for high-quality GPU-based infrastructure will drive growth in this sector.

Asia Pacific is Projected to Record the Highest CAGR During the Forecast Period.

The Asia Pacific region is expected to see significant growth in the data center GPU market due to rapid digitalization, increasing AI adoption, and expanding cloud infrastructure in countries like China, India, Japan, South Korea, and Southeast Asia. China leads with its "New Infrastructure" policy, promoting AI and cloud computing. Major companies, including Alibaba Cloud, Tencent Cloud, and Baidu, are utilizing high-performance GPUs for AI model training, with Baidu's PaddlePaddle relying on NVIDIA GPUs. In India, Google Cloud and Microsoft Azure are building GPU-backed data centers for startups, while South Korea's Naver and Kakao, along with Japan’s Fujitsu and NEC, are advancing AI initiatives. Collaborations with NVIDIA and AMD are further enhancing GPU-based cloud services. Overall, the region's data center GPU market is positioned for substantial growth, driven by government initiatives and rising demand for AI applications.

LARGEST MARKET SHARE IN 2025-2030
CHINA FASTER-GROWING MARKET IN REGION
Data Center GPU Market
 Size and Share

Recent Developments of Data Center GPU Market

  • On January 29, 2025, Alibaba Cloud released an updated version of its AI model, Qwen 2.5, named Qwen 2.5-Max. According to Alibaba Cloud, Qwen 2.5-Max outperforms models such as DeepSeek-V3 and Meta's Llama 3.1 across 11 benchmarks. This release reflects the company's ongoing efforts to advance AI capabilities and maintain competitiveness in the rapidly evolving AI landscape.
  • In December 2024, AWS launched Amazon Nova, a new family of foundation models integrated into Amazon Bedrock. These models are designed to enhance generative AI capabilities, enabling businesses to create text, images, and videos more efficiently.
  • Launched in October 2024, the AMD Instinct MI325X is designed for AI and machine learning workloads in data centers. It offers high computational performance and is optimized for complex rendering and simulation tasks. This GPU is suitable for AI-driven visualization, media rendering, and data-intensive applications.
  • In August 2024, AMD agreed to acquire ZT Systems, a data center hardware company, in a cash and stock transaction valued at USD 4.9 billion. ZT Systems' expertise in designing and optimizing cloud computing solutions was expected to enhance AMD's data center AI infrastructure offerings.
  • In April 2024, NVIDIA completed the acquisition of Run:ai, an Israel-based AI infrastructure platform. This acquisition aimed to strengthen NVIDIA's AI infrastructure offerings, providing advanced tools for managing and optimizing AI workloads in data centers.
  • In July 2024, AMD acquired Silo AI, an AI software company. This acquisition aimed to strengthen AMD's AI capabilities, particularly in developing advanced AI solutions for data centers.

Key Market Players

List of Top Data Center GPU Market

The following players dominate the Data Center GPU Market:

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Scope of the Report

Report Attribute Details
Market size available for years 2021–2030
Base year considered 2024
Forecast period 2025–2030
Forecast units Value (USD Million/Billion) (Thousand Units)
Segments Covered By Deployment, Function, Application, End User, and Region
Regions covered North America, Europe, Asia Pacific, and RoW

Key Questions Addressed by the Report

Which are the major companies in the data center GPU market, and what are their significant strategies to strengthen their market presence?
Major companies in the aerospace testing market are Nvidia Corporation (US), Advanced Micro Devices, Inc. (US), Intel Corporation (US), Google Cloud (US), Microsoft (US), Amazon Web Services, Inc. (US), IBM (US), Alibaba Cloud (Singapore), Oracle (US), Tencent Cloud (China), CoreWeave (US), Vast.ai (US), Lambda (US), DigitalOcean (US), and JarvisLabs.ai (India).
Which end-user in the data center GPU market will likely have the most significant impact over the coming years?
The cloud service providers (CSPs) segment is expected to grow at the highest rate due to the expanding applications and growing computing demand.
Which function in the data center GPU market will show strong growth over the coming years?
The inference as a function segment is expected to grow due to the surge in real-time AI applications such as chatbots, recommendation engines, and generative AI tools. Enterprises require high-performance GPUs to deliver low-latency responses at scale, making inference infrastructure a critical investment.
What are the drivers and opportunities for the data center GPU market?
The growing adoption of AI & ML, the demand for high-performance computing, and the expansion of cloud computing are the major drivers of the data center GPU market. Opportunity areas in this market include the growth of autonomous systems and the emergence of edge computing.
What are the restraints and challenges for the data center GPU market?
Higher costs of GPUs and infrastructure and short product lifecycles are the major restraints for the data center GPU market. Market growth faces challenges due to alternative technologies, stringent frameworks, and supply chain disruptions.

 

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Table of Contents

Exclusive indicates content/data unique to MarketsandMarkets and not available with any competitors.

TITLE
PAGE NO
INTRODUCTION
15
RESEARCH METHODOLOGY
20
EXECUTIVE SUMMARY
25
PREMIUM INSIGHTS
30
MARKET OVERVIEW
35
  • 5.1 INTRODUCTION
  • 5.2 MARKET DYNAMICS
  • 5.3 TRENDS/DISRUPTIONS IMPACTING CUSTOMER’S BUSINESS
  • 5.4 PRICING ANALYSIS
    AVERAGE SELLING PRICE TREND OF KEY PLAYERS, BY FUNCTION
    AVERAGE SELLING PRICE TREND, BY REGION
  • 5.5 VALUE CHAIN ANALYSIS
  • 5.6 ECOSYSTEM ANALYSIS
  • 5.7 TECHNOLOGY ANALYSIS
    KEY TECHNOLOGIES
    - HBM3E/HBM4
    - Parallel Processing Architectures
    COMPLEMENTARY TECHNOLOGIES
    - Non-Volatile Memory Express (NVMe)
    - InfiniBand
    ADJACENT TECHNOLOGIES
    - Application Specific Integrated Circuit (ASIC)
    - Field Programmable Gate Arrays (FPGA)
  • 5.8 PATENT ANALYSIS
  • 5.9 TRADE ANALYSIS
    IMPORT SCENARIO (HS CODE 847330)
    EXPORT SCENARIO (HS CODE 847330)
    KEY CONFERENCES AND EVENTS (2025-2026)
    CASE STUDY ANALYSIS
    INVESTMENT AND FUNDING SCENARIO
    TARIFF AND REGULATORY LANDSCAPE
    - Tariff Data (HS Code 847330 - Parts and accessories of automatic data-processing machines or for other machines of heading 8471, n.e.s..)
    - Regulatory Bodies, Government Agencies, and Other Organizations
    - Regulatory Standards
    PORTERS FIVE FORCE ANALYSIS
    - Threat from New Entrants
    - Threat of Substitutes
    - Bargaining Power of Suppliers
    - Bargaining Power of Buyers
    - Intensity of Competitive Rivalry
    KEY STAKEHOLDERS AND BUYING CRITERIA
    - Key Stakeholders in Buying Process
    - Buying Criteria
GPU-AS-A-SERVICE (GPUAAS) INDUSTRY LANDSCAPE
70
  • 6.1 INTRODUCTION
  • 6.2 BY SERVICE MODEL
    PLATFORM-AS-A-SERVICE (PAAS)
    INFRASTRUCTURE-AS-A-SERVICE (IAAS)
    SOFTWARE-AS-A-SERVICE (SAAS)
  • 6.3 BY DEPLOYMENT
    PUBLIC CLOUD
    PRIVATE CLOUD
    HYBRID CLOUD
DATA CENTER GPU MARKET, BY DEPLOYMENT
100
  • 7.1 INTRODUCTION
  • 7.2 CLOUD
  • 7.3 ON-PREMISES
DATA CENTER GPU MARKET, BY FUNCTION
120
  • 8.1 INTRODUCTION
  • 8.2 TRAINING
  • 8.3 INFERENCE
DATA CENTER GPU MARKET, BY TECHNOLOGY
140
  • 9.1 INTRODUCTION
  • 9.2 GENERATIVE AI
    RULE BASED MODELS
    STATISTICAL MODELS
    DEEP LEARNING
    GENERATIVE ADVERSARIAL NETWORKS (GANS)
    AUTOENCODERS
    CONVOLUTIONAL NEURAL NETWORKS (CNNS)
    TRANSFORMER MODELS
  • 9.3 MACHINE LEARNING
  • 9.4 NATURAL LANGUAGE PROCESSING
  • 9.5 COMPUTER VISION
    DATA CENTER GPU MARKET, BY END USER
DATA CENTER GPU MARKET, BY END USER
190
  • 10.1 INTRODUCTION
  • 10.2 CLOUD SERVICE PROVIDERS
  • 10.3 ENTERPRISES
    HEALTHCARE
    BFSI
    AUTOMOTIVE
    RETAIL & E-COMMERCE
    MEDIA & ENTERTAINMENT
    OTHERS
  • 10.4 GOVERNMENT ORGANIZATIONS
DATA CENTER GPU MARKET, BY REGION
210
  • 11.1 INTRODUCTION
  • 11.2 NORTH AMERICA
    MACRO-ECONOMIC OUTLOOK
    US
    CANADA
    MEXICO
  • 11.3 EUROPE
    MACRO-ECONOMIC OUTLOOK
    UK
    GERMANY
    FRANCE
    ITALY
    SPAIN
    POLAND
    NORDICS
    REST OF EUROPE
  • 11.4 ASIA PACIFIC
    MACRO-ECONOMIC OUTLOOK
    CHINA
    JAPAN
    SOUTH KOREA
    INDIA
    AUSTRALIA
    INDONESIA
    MALAYSIA
    THAILAND
    - Vietnam
    - Rest of Asia Pacific
  • 11.5 ROW
    MACRO-ECONOMIC OUTLOOK
    MIDDLE EAST
    - GCC Countries
    - Rest of Middle East
    AFRICA
    SOUTH AMERICA
DATA CENTER GPU MARKET, COMPETITIVE LANDSCAPE
270
  • 12.1 INTRODUCTION
  • 12.2 KEY PLAYER STRATEGIES/RIGHT TO WIN
  • 12.3 REVENUE ANALYSIS
  • 12.4 MARKET SHARE ANALYSIS
  • 12.5 COMPANY VALUATION AND FINANCIAL METRICS
  • 12.6 BRAND/PRODUCT COMPARISON
  • 12.7 COMPANY EVALUATION MATRIX: KEY PLAYERS, 2024
    STARS
    EMERGING LEADERS
    PERVASIVE PLAYERS
    PARTICIPANTS
    COMPANY FOOTPRINT: KEY PLAYERS, 2024
    - Company Footprint
    - Region Footprint
    - Deployment Footprint
    - Function Footprint
    - Technology Footprint
    - End user Footprint
  • 12.8 COMPANY EVALUATION MATRIX: STARTUPS/SMES, 2024
    PROGRESSIVE COMPANIES
    RESPONSIVE COMPANIES
    DYNAMIC COMPANIES
    STARTING BLOCKS
    COMPETITIVE BENCHMARKING: STARTUPS/SMES, 2024
    - Detailed List of Key Startups/SMEs
    - Competitive Benchmarking of Key Startups/SMEs
  • 12.9 COMPETITIVE SITUATION AND TRENDS
DATA CENTER GPU MARKET, COMPANY PROFILES
320
  • 13.1 KEY PLAYERS
    NVIDIA CORPORATION
    ADVANCED MICRO DEVICES, INC.
    INTEL CORPORATION
    MICROSOFT
    AMAZON WEB SERVICES, INC.
    GOOGLE
    ALIBABA CLOUD
    IBM
    COREWEAVE.
    - Oracle
  • 13.2 OTHER PLAYERS
    HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP
    TENCENT CLOUD.
    OVH SAS.
    MIPS
    DIGITAL MEDIA PROFESSIONALS INC.
    ANDES TECHNOLOGY CORPORATION.
    IMAGINATION TECHNOLOGIES
    VAST.AI
    FLUIDSTACK
APPENDIX
360
  • 14.1 DISCUSSION GUIDE
  • 14.2 KNOWLEDGE STORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL
  • 14.3 AVAILABLE CUSTOMIZATIONS
  • 14.4 RELATED REPORTS
  • 14.5 AUTHOR DETAILS

The study involved four major activities in estimating the current size of the data center GPU market. Exhaustive secondary research was done to collect information on the market, peer, and parent markets. The next step was to validate these findings, assumptions, and sizing with industry experts across the value chain through primary research. Both top-down and bottom-up approaches were employed to estimate the complete market size. After that, market breakdown and data triangulation were used to estimate the market size of segments and subsegments.

Secondary Research

Various secondary sources have been referred to in the secondary research process to identify and collect important information for this study. The secondary sources include annual reports, press releases, and investor presentations of companies; white papers; journals and certified publications; and articles from recognized authors, websites, directories, and databases. Secondary research has been conducted to obtain critical information about the industry’s supply chain, the market’s value chain, the total pool of key players, market segmentation according to the industry trends (to the bottom-most level), regional markets, and key developments from market- and technology-oriented perspectives. The secondary data has been collected and analyzed to determine the overall market size, further validated by primary research.

Primary Research

Extensive primary research was conducted after gaining knowledge about the current scenario of the image sensor market through secondary research. Several primary interviews were conducted with experts from the demand and supply sides across four major regions: North America, Europe, the Asia Pacific, and the Rest of the World. This primary data was collected through questionnaires, emails, and telephonic interviews.

Data Center GPU Market
 Size, and Share

Note: Other designations include technology heads, media analysts, sales managers, marketing managers, and product managers.

Companies are classified into tiers based on their total revenues as of 2024?Tier 1: >USD 1 billion, Tier 2: USD 500 million–1 billion, and Tier 3: USD 500 million.

To know about the assumptions considered for the study, download the pdf brochure

Market Size Estimation

In the complete market engineering process, top-down and bottom-up approaches and several data triangulation methods have been used to estimate and forecast the overall market segments and subsegments listed in this report. Key players in the market have been identified through secondary research, and their market shares in the respective regions have been determined through primary and secondary research. This entire procedure includes the study of annual and financial reports of the top market players and extensive interviews for key insights (quantitative and qualitative) with industry experts (CEOs, VPs, directors, and marketing executives).

All percentage shares, splits, and breakdowns have been determined using secondary sources and verified through primary sources. All the parameters affecting the markets covered in this research study have been accounted for, viewed in detail, verified through primary research, and analyzed to obtain the final quantitative and qualitative data. This data has been consolidated and supplemented with detailed input and analysis from MarketsandMarkets and presented in this report. The following figure represents this study’s overall market size estimation process.

Bottom-Up Approach

  • Identifying key participants that influence the entire market, along with the related component players
  • Analyzing major manufacturers of data center GPUs, studying their portfolios, and understanding different types
  • Analyzing trends pertaining to the use of data center GPUs in different kinds of industries
  • Tracking the ongoing and upcoming developments in the market, such as investments, R&D activities, product/service launches, collaborations, and partnerships, and forecasting the market based on these developments and other critical parameters
  • Carrying out multiple discussions with key opinion leaders to understand different types of data center GPU trends in the market, thereby analyzing the breakup of the scope of work carried out by major companies
  • Arriving at the market estimates by analyzing the revenues of companies generated and then combining them to get the market estimate
  • Segmenting the overall market into various other market segments
  • Verifying and cross-checking the estimate at every level, from discussions with key opinion leaders such as CXOs, directors, and operations managers, and finally with the domain experts at MarketsandMarkets
  • Studying various paid and unpaid sources of information, such as annual reports, press releases, white papers, and databases

Top-Down Approach

  • Focusing initially on the top-line investments and expenditures being made in the ecosystem of the data center GPU market, splitting the key market areas based on type, function, end user, and region, and listing the key developments
  • Identifying all leading players in the data center GPU market based on type, function, and end user through secondary research and fully verifying them through a brief discussion with Industry experts
  • Analyzing revenues, product mix, geographic presence, and key applications served by all identified players to estimate and arrive at percentage splits for all key segments
  • Discussing splits with industry experts to validate the information and identify key growth pockets across all key segments
  • Breaking down the total market based on verified splits and key growth pockets across all segments

Data Center GPU Market : Top-Down and Bottom-Up Approach

Data Center GPU Market Top Down and Bottom Up Approach

Data Triangulation

After arriving at the overall market size using the market size estimation processes explained above, the market has been split into several segments and subsegments. Data triangulation and market breakdown procedures have been employed to complete the entire market engineering process and arrive at the exact statistics of each market segment and subsegment. The data has been triangulated by studying various factors and trends from the demand and supply sides in the data center GPU market.

Market Definition

A data center GPU is a specialized processor optimized for parallel processing in large-scale computing environments. It is used primarily to accelerate workloads such as artificial intelligence (AI) inference and training, high-performance computing (HPC), data analytics, and video processing in cloud and enterprise data centers.

Key Stakeholders

  • Raw Material Suppliers
  • Original Equipment Manufacturers (OEMs)
  • Original Design Manufacturers (ODM) and Technology Solution Providers
  • Research Institutes
  • Data Center GPU Manufacturers
  • Data Center GPU Forums, Alliances, and Associations
  • Governments and Financial Institutions
  • Analysts and Strategic Business Planners

Report Objectives

  • To describe and forecast the data center GPU market by deployment, function, application, end user, and region.
  • To forecast the data center GPU market by function in terms of volume
  • To provide the market size estimation for North America, Europe, Asia Pacific, and the Rest of the World (RoW), along with their respective country-level market sizes, in terms of value
  • To provide detailed information regarding drivers, restraints, opportunities, and challenges influencing market growth
  • To provide a value chain analysis, ecosystem analysis, case study analysis, patent analysis, trade analysis, technology analysis, pricing analysis, key conferences and events, key stakeholders and buying criteria, Porter’s five forces analysis, investment and funding scenario, and regulations pertaining to the market.
  • To strategically analyze micromarkets1 with regard to individual growth trends, prospects, and contributions to the total market
  • To analyze opportunities for stakeholders by identifying high-growth segments of the market
  • To strategically profile the key players, comprehensively analyze their market positions in terms of ranking and core competencies2, and provide a competitive market landscape
  • To analyze strategic approaches, such as product launches, expansions, and partnerships, in the data center GPU market
  • To analyze the impact of the macroeconomic outlook for each region

Customization Options:

With the given market data, MarketsandMarkets offers customizations according to the specific requirements of companies. The following customization options are available for the report:

  • Detailed analysis and profiling of additional market players based on various blocks of the supply chain

Previous Versions of this Report

Data Center GPU Market by Deployment (Cloud, On-premises), Function (Training, Inference), Application (Generative AI, Machine Learning, Natural Language Processing, Computer Vision), End User (CSP, Enterprises) & Region - Global Forecast to 2030

Report Code SE 8838
Published in Nov, 2023, By MarketsandMarkets™
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Growth opportunities and latent adjacency in Data Center GPU Market

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