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North America Data Center GPU Market by Deployment (Cloud, On-premises), Function (Training, Inference), Application (Generative AI, Machine Learning, Natural Language Processing), End User (CSP, Enterprises), & Country - Forecast to 2030
USD 79.81 BN
MARKET SIZE, 2030
CAGR 13.1%
(2025-2030)
230
REPORT PAGES
150
MARKET TABLES
OVERVIEW
Source: Secondary Research, Interviews with Experts, MarketsandMarkets Analysis
The North America data center GPU market is projected to reach USD 79.81 billion by 2030 from USD 43.19 billion in 2025, at a CAGR of 13.1% from 2025 to 2030. The North America data-center GPU market is expanding rapidly as enterprises, hyperscalers, and research institutions accelerate the adoption of AI, machine learning, and high-performance computing across various sectors. Growth is driven by the increasing deployment of GPU-optimized workloads such as fraud analytics, autonomous systems, large-scale recommendation engines, and advanced natural language models, all of which demand parallel compute capabilities beyond traditional CPUs. Continued investments by major cloud providers, increasing enterprise AI integration, and the rise of GenAI applications are further propelling the region’s transition toward GPU-accelerated data-center architectures.
Market Size & Forecast
• 2025 Market Size: USD 43.19 billion
• 2030 Projected Market Size: USD 79.81 billion
• CAGR (2025-2030): 13.1%
• On-premises segment: Expected to register the highest CAGR
• US: Expected to dominate the market
KEY TAKEAWAYS
-
BY COUNTRY
The US is expected to dominate the North America data center GPU market, with a share of ~80–85% in terms of value in 2025.
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BY DEPLOYMENT
By deployment, the on-premises segment is expected to register the highest CAGR of ~9–13% during the forecast period.
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BY FUNCTION
By function, the inference segment is expected to register the highest CAGR of ~13–16% during the forecast period.
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COMPETITIVE LANDSCAPE (KEY PLAYERS)
Advanced Micro Devices, Inc., Intel Corporation, and NVIDIA Corporation were identified as star players in the North America data center GPU market due to their strong market share and extensive product footprint.
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COMPETITIVE LANDSCAPE (STARTUPS/SMES)
VULTR and Linode LLC, among others, have distinguished themselves among startups and SMEs by securing strong footholds in specialized niche areas, underscoring their potential as emerging market leaders in the North America data center GPU ecosystem.
The North America data center GPU market will expand as hyperscalers and enterprises accelerate investments in AI, generative AI, and high-performance computing, which require massive parallel processing capabilities. Growing adoption of cloud services, rising GPU-rich server deployments, and continuous product launches from players like NVIDIA, AMD, and Intel further reinforce sustained market growth.
TRENDS & DISRUPTIONS IMPACTING CUSTOMERS' CUSTOMERS
While today’s revenue mix is driven largely by M2M and deep learning applications, the shift toward new use cases—such as generative AI, edge intelligence, cloud virtualization, and HPC—is expected to significantly expand the market. As industries, including enterprise, financial services, manufacturing, healthcare, and government, accelerate their adoption of AI, clients are increasingly prioritizing real-time AI solutions, cloud-based services, digital twins, and metaverse-aligned capabilities, driving substantial long-term revenue transformation.
Source: Secondary Research, Interviews with Experts, MarketsandMarkets Analysis
MARKET DYNAMICS
Drivers
Impact
Level
-
Growing adoption of AI and machine learning
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Demand for high-performance computing (HPC)
RESTRAINTS
Impact
Level
-
Short product lifecycle
-
High costs of GPUs and infrastructure
OPPORTUNITIES
Impact
Level
-
Growth in autonomous systems
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Emergence of edge computing
CHALLENGES
Impact
Level
-
Existence of alternative technologies
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Stringent regulatory framework
Source: Secondary Research, Interviews with Experts, MarketsandMarkets Analysis
The rapid expansion of AI, generative AI, and machine learning workloads across hyperscalers, enterprises, and research institutions is sharply increasing the demand for high-performance GPUs. North America, led by companies such as AWS, Google, Microsoft, Meta, and NVIDIA, remains the global hub for AI model training and inference, directly accelerating GPU deployments.
The pace of GPU innovation in North America is extremely fast, with companies like NVIDIA, AMD, and Intel releasing new architectures every 12–18 months. This rapid cycle forces data centers to frequently upgrade infrastructure, increasing capital expenditure and limiting the effective utilization period of existing GPU clusters.
The rising development of autonomous vehicles, robotics, smart infrastructure, and defense-grade autonomous platforms in the US and Canada is expanding demand for data center GPU compute. These systems rely heavily on cloud- or edge-connected GPU clusters for real-time perception, simulation, and large-scale training, creating new growth avenues for GPU vendors.
The market faces competitive pressure from emerging accelerators such as custom AI chips, ASICs, TPUs, and dedicated inference hardware developed by hyperscalers and semiconductor firms. These alternatives can offer higher efficiency for specific workloads, potentially slowing the growth rate of traditional data center GPU adoption in North America.
NORTH AMERICA DATA CENTER GPU MARKET: COMMERCIAL USE CASES ACROSS INDUSTRIES
| COMPANY | USE CASE DESCRIPTION | BENEFITS |
|---|---|---|
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MSOE built an on-campus GPU-powered supercomputer (named “Rosie”), using NVIDIA DGX systems + GPU-based servers, to provide AI/deep-learning compute resources to undergraduates and researchers for coursework, experiments, and research in AI, machine learning, data analytics, and HPC. | Students and faculty can run AI, ML, and HPC workloads significantly faster than on CPU systems. The shared GPU cluster reduces hardware overhead and democratizes access to high-performance computing, enabling hands-on skill development across all academic levels. |
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Researchers analyzed real long-term job traces from SenseTime’s large-scale GPU data center to characterize deep-learning workloads and user behavior, then built a resource-management and scheduling framework tailored to deep-learning jobs. | Advanced scheduling reduced job completion time by up to 6.5 times and improved cluster-wide GPU utilization, thereby lowering energy consumption and operational costs. |
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Adobe integrated NVIDIA A100 and T4 GPUs into its data center infrastructure to accelerate Creative Cloud and Adobe Sensei AI services. | This improved inference speed for imaging and video workloads, enabling real-time features in Photoshop and Premiere Pro while reducing compute cost per workload. |
Logos and trademarks shown above are the property of their respective owners. Their use here is for informational and illustrative purposes only.
MARKET ECOSYSTEM
The North America data center GPU market in North America is supported by a strong combination of semiconductor leaders, server manufacturers, and hyperscale cloud providers that drive demand for accelerated computing. Leading chip manufacturers such as Intel, AMD, and NVIDIA supply high-performance GPUs and AI accelerators to support next-generation workloads. Downstream, major server OEMs and ODMs, including Inventec, Dell Technologies, and Hewlett Packard Enterprise, integrate these processors into scalable data center architectures. The supply chain is further strengthened by raw material and equipment specialists like SMC Global, Applied Materials, and SK Inc. Materials, who enable advanced manufacturing capabilities. On the demand side, hyperscalers such as AWS, Google Cloud, and Microsoft Azure remain the principal adopters, deploying GPU-rich infrastructure to power AI, HPC, analytics, and cloud-native applications across the region.
Logos and trademarks shown above are the property of their respective owners. Their use here is for informational and illustrative purposes only.
MARKET SEGMENTS
Source: Secondary Research, Interviews with Experts, MarketsandMarkets Analysis
In North America, the cloud deployment segment continues to dominate the data center GPU landscape as hyperscalers accelerate investments in AI and high-performance computing infrastructure. Cloud providers across the region—led by Amazon Web Services (AWS), Microsoft Azure, Google Cloud, and Oracle Cloud—are integrating advanced GPUs to support large language models, complex simulations, real-time inference, and scalable analytics. This shift toward cloud-based GPU consumption enables enterprises to minimize capital expenditures while gaining flexible access to high-performance computing resources. AWS has expanded its portfolio with NVIDIA H200 and GH200 instances, Azure has deepened its collaboration with NVIDIA for enterprise-grade AI clusters, Google Cloud offers NVIDIA A3 and L4-powered solutions for training and inference, and Oracle Cloud Infrastructure continues to scale its NVIDIA-based GPU pools to support on-demand AI workloads.
In North America, the inference function segment is projected to record strong growth as enterprises scale real-time AI applications across customer service, financial analytics, healthcare decision support, and content generation. As AI models become increasingly complex, data centers require high-performance GPUs that can sustain low-latency inference at large volumes. Leading regional deployments include NVIDIA’s L4, H100, and upcoming Blackwell GPUs, which are being adopted extensively by hyperscalers such as AWS, Azure, and Google Cloud for inference-centric workloads. The rapid rise of generative AI, recommendation engines, and personalization platforms further amplifies demand for inference-optimized GPU clusters, reinforcing their role in enabling efficient, scalable, and always-on processing across North America data centers.
In North America, generative AI remains the fastest-growing application area for data center GPUs as enterprises deploy large language models, multimodal AI systems, advanced analytics, and automated content creation tools. The region’s hyperscalers—AWS, Microsoft Azure, Google Cloud, and Meta—are significantly scaling GPU infrastructure to support demanding workloads based on models such as GPT-4, Gemini, and LLaMA. AWS continues expanding its NVIDIA-powered EC2 UltraClusters, Azure deepens GPU integration through its partnerships with OpenAI, and Google Cloud leverages both NVIDIA GPUs and in-house accelerators to power services like Bard and enterprise AI tools. Meta is also building large AI supercomputing clusters using tens of thousands of NVIDIA GPUs. As generative AI adoption accelerates across sectors like healthcare, finance, media, and manufacturing, the requirement for high-performance, scalable GPU infrastructure will further drive application-level growth in the North America market.
REGION
US to be fastest-growing country in North America data center GPU market during forecast period
The US is expected to be the fastest-growing market in North America, primarily due to the presence of major hyperscalers, including AWS, Microsoft, Google, and Meta. These companies are actively expanding their GPU-accelerated infrastructure to support AI, generative models, and high-performance computing (HPC) workloads. The country’s robust ecosystem of semiconductor innovators, including NVIDIA, AMD, and Intel, further drives the adoption of new technologies. Additionally, significant enterprise digitalization, substantial investments in AI, and the rapid deployment of large-scale data centers position the US as the primary growth engine in the region.
NORTH AMERICA DATA CENTER GPU MARKET: COMPANY EVALUATION MATRIX
In the North America data center GPU market matrix, NVIDIA (Star) maintains a commanding lead with its dominant market share, extensive product ecosystem, and advanced GPU architectures powering AI training, generative AI, HPC, and enterprise workloads across hyperscalers and large enterprises. AMD (Emerging Leader) is gaining strong traction with its Instinct series, offering high-performance accelerators that appeal to cloud providers and data center operators seeking competitive alternatives for scalable AI and compute-intensive applications. While NVIDIA continues to lead through innovation, ecosystem depth, and strategic partnerships with AWS, Azure, Google Cloud, and Oracle, AMD’s rapid advancements and expanding adoption signal its growing potential to move closer to the leaders' quadrant as demand for diverse and cost-efficient GPU solutions accelerates across the region.
Source: Secondary Research, Interviews with Experts, MarketsandMarkets Analysis
KEY MARKET PLAYERS
- NVIDIA Corporation (US)
- Advanced Micro Devices, Inc. (US)
- Intel Corporation (US)
- Google (US)
- Microsoft (US)
- Amazon Web Services, Inc. (US)
- IBM (US)
- Alibaba Cloud (China)
- Oracle (US)
- CoreWeave. (US)
- Tencent Cloud. (China)
- Vast.ai (US)
- Lambda (US)
- DigitalOcean (US)
- JarvisLabs.ai (India)
MARKET SCOPE
| REPORT METRIC | DETAILS |
|---|---|
| Market Size in 2024 (Value) | USD 31.61 Billion |
| Market Forecast in 2030 (Value) | USD 79.81 Billion |
| Growth Rate | CAGR of 13.1% from 2025–2030 |
| Years Considered | 2021–2030 |
| Base Year | 2024 |
| Forecast Period | 2025–2030 |
| Units Considered | Value (USD Million/Billion), Volume (Thousand Units) |
| Report Coverage | Revenue Forecast, Company Ranking, Competitive Landscape, Growth Factors, and Trends |
| Segments Covered |
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| Regions Covered | North America (US, Canada, & Mexico) |
WHAT IS IN IT FOR YOU: NORTH AMERICA DATA CENTER GPU MARKET REPORT CONTENT GUIDE
DELIVERED CUSTOMIZATIONS
We have successfully delivered the following deep-dive customizations:
| CLIENT REQUEST | CUSTOMIZATION DELIVERED | VALUE ADDS |
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| Hyperscale Cloud Provider |
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| AI Infrastructure Startup / Colocation Provider |
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| GPU Manufacturer |
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| Server OEM/System Integrator |
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| Private Equity/Investment Firm |
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| Enterprise End User |
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RECENT DEVELOPMENTS
- January 2024 : NVIDIA introduced the Blackwell family (GB200 / B200 family and Blackwell architecture) as its next-gen data-center GPUs and associated platforms (DGX, Grace + Blackwell superchips). Major hyperscalers announced plans or early integrations to adopt Blackwell-based systems for generative AI workloads.
- October 2024 : AMD expanded its Instinct MI300 family with the MI325X accelerators and confirmed system availability via OEMs (Dell, HPE, Supermicro and others). OEM server/platform launches and validated systems were announced to support large-scale AI training and inference deployments in North America.
- April 2024 : Intel announced Gaudi 3 as its next enterprise AI accelerator and outlined availability to OEMs and integrators (Dell, HPE, Supermicro) to accelerate lower-cost GenAI clusters for enterprise and cloud customers.
Table of Contents
Exclusive indicates content/data unique to MarketsandMarkets and not available with any competitors.
TITLE
PAGE NO
1
INTRODUCTION
25
2
EXECUTIVE SUMMARY
3
PREMIUM INSIGHTS
4
MARKET OVERVIEW
4.1
INTRODUCTION
4.2
MARKET DYNAMICS
4.2.1
DRIVERS
4.2.2
RESTRAINTS
4.2.3
OPPORTUNITIES
4.2.4
CHALLENGES
4.3
UNMET NEEDS AND WHITE SPACES
4.4
INTERCONNECTED MARKETS AND CROSS-SECTOR OPPORTUNITIES
4.5
STRATEGIC MOVES BY TIER-1/2/3 PLAYERS
5
INDUSTRY TRENDS
5.1
INTRODUCTION
5.2
PORTER’S FIVE FORCES ANALYSIS
5.2.1
THREAT OF NEW ENTRANTS
5.2.2
THREAT OF SUBSTITUTES
5.2.3
BARGAINING POWER OF SUPPLIERS
5.2.4
BARGAINING POWER OF BUYERS
5.2.5
INTENSITY OF COMPETITIVE RIVALRY
5.3
MACROECONOMIC INDICATORS
5.3.1
INTRODUCTION
5.3.2
GDP TRENDS AND FORECAST
5.3.3
TRENDS IN NORTH AMERICA DATA CENTER GPU MARKET
5.4
TRADE ANALYSIS
5.4.1
IMPORT SCENARIO
5.4.2
EXPORT SCENARIO
5.5
VALUE CHAIN ANALYSIS
5.6
ECOSYSTEM ANALYSIS
5.7
PRICING ANALYSIS
5.8
KEY CONFERENCES AND EVENTS, 2025–2026
5.9
TRENDS/DISRUPTIONS IMPACTING CUSTOMER BUSINESS
5.10
INVESTMENT AND FUNDING SCENARIO
5.11
CASE STUDY ANALYSIS
5.12
IMPACT OF 2025 US TARIFF – NORTH AMERICA DATA CENTER GPU MARKET
5.12.1
INTRODUCTION
5.12.2
KEY TARIFF RATES
5.12.3
PRICE IMPACT ANALYSIS
5.12.4
IMPACT ON COUNTRIES/REGIONS
5.12.5
IMPACT ON END USERS
6
STRATEGIC DISRUPTION THROUGH TECHNOLOGY, PATENTS, DIGITAL, AND AI ADOPTION
6.1
KEY EMERGING TECHNOLOGIES
6.2
COMPLEMENTARY TECHNOLOGIES
6.3
ADJACENT TECHNOLOGIES
6.4
TECHNOLOGY ROADMAP
6.5
PATENT ANALYSIS
6.6
FUTURE APPLICATIONS
6.7
NOTE: THE “IMPACT OF AI/GEN AI” IS NOT INCLUDED IN THE DATA CENTER GPU MARKET BECAUSE THE ENTIRE MARKET IS INHERENTLY AI-BASED, MAKING IT REDUNDANT TO SINGLE OUT AI/GEN AI AS A SEPARATE IMPACT FACTOR.
7
REGULATORY LANDSCAPE
7.1
REGIONAL REGULATIONS AND COMPLIANCE
7.1.1
REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
7.1.2
INDUSTRY STANDARDS
8
CUSTOMER LANDSCAPE & BUYER BEHAVIOR
8.1
DECISION-MAKING PROCESS
8.2
BUYER STAKEHOLDERS AND BUYING EVALUATION CRITERIA
8.2.1
KEY STAKEHOLDERS IN BUYING PROCESS
8.2.2
BUYING CRITERIA
8.3
ADOPTION BARRIERS & INTERNAL CHALLENGES
8.4
UNMET NEEDS FROM VARIOUS APPLICATIONS
8.5
MARKET PROFITABILITY
9
NORTH AMERICA DATA CENTER GPU MARKET, BY DEPLOYMENT
9.1
INTRODUCTION
9.2
CLOUD
9.3
ON-PREMISES
10
NORTH AMERICA DATA CENTER GPU MARKET, BY FUNCTION
10.1
INTRODUCTION
10.2
TRAINING
10.3
INFERENCE
11
NORTH AMERICA DATA CENTER GPU MARKET, BY TECHNOLOGY
11.1
INTRODUCTION
11.2
GENERATIVE AI
11.2.1
RULE-BASED MODELS
11.2.2
STATISTICAL MODELS
11.2.3
DEEP LEARNING
11.2.4
GENERATIVE ADVERSARIAL NETWORKS
11.2.5
AUTOENCODERS
11.2.6
CONVOLUTIONAL NEURAL NETWORKS
11.2.7
TRANSFORMER MODELS
11.3
MACHINE LEARNING
11.4
NATURAL LANGUAGE PROCESSING
11.5
COMPUTER VISION
12
NORTH AMERICA DATA CENTER GPU MARKET, BY END USER
12.1
INTRODUCTION
12.2
CLOUD SERVICE PROVIDERS
12.3
ENTERPRISES
12.3.1
HEALTHCARE
12.3.2
BFSI
12.3.3
AUTOMOTIVE
12.3.4
RETAIL & E-COMMERCE
12.3.5
MEDIA & ENTERTAINMENT
12.3.6
OTHERS
12.4
GOVERNMENT ORGANIZATIONS
13
NORTH AMERICA DATA CENTER GPU MARKET, BY COUNTRY
13.1
INTRODUCTION
13.2
US
13.3
CANADA
13.4
MEXICO
14
COMPETITIVE LANDSCAPE
14.1
INTRODUCTION
14.2
KEY PLAYER STRATEGIES/RIGHT TO WIN
14.3
REVENUE ANALYSIS
14.4
MARKET SHARE ANALYSIS,
14.5
COMPANY VALUATION AND FINANCIAL METRICS
14.6
PRODUCT/BRAND COMPARISON
14.7
COMPANY EVALUATION MATRIX: KEY PLAYERS,
14.7.1
STARS
14.7.2
EMERGING LEADERS
14.7.3
PERVASIVE PLAYERS
14.7.4
PARTICIPANTS
14.7.5
COMPANY FOOTPRINT: KEY PLAYERS,
14.7.5.1
COMPANY FOOTPRINT
14.7.5.2
REGION FOOTPRINT
14.7.5.3
END USER FOOTPRINT
14.7.5.4
FUNCTION FOOTPRINT
14.7.5.5
DEPLOYMENT FOOTPRINT
14.8
COMPANY EVALUATION MATRIX: STARTUPS/SMES,
14.8.1
PROGRESSIVE COMPANIES
14.8.2
RESPONSIVE COMPANIES
14.8.3
DYNAMIC COMPANIES
14.8.4
STARTING BLOCKS
14.8.5
COMPETITIVE BENCHMARKING: STARTUPS/SMES,
14.8.5.1
DETAILED LIST OF KEY STARTUPS/SMES
14.8.5.2
COMPETITIVE BENCHMARKING OF KEY STARTUPS/SMES
14.9
COMPETITIVE SCENARIO
14.9.1
PRODUCT LAUNCHES
14.9.2
DEALS
15
COMPANY PROFILES
15.1
KEY PLAYERS
15.1.1
NVIDIA CORPORATION
15.1.2
ADVANCED MICRO DEVICES, INC.
15.1.3
INTEL CORPORATION
15.1.4
MICROSOFT
15.1.5
AMAZON WEB SERVICES, INC.
15.1.6
15.1.7
IBM
15.1.8
COREWEAVE
15.1.9
ORACLE
15.1.10
HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP
15.2
OTHER PLAYERS
15.3
NOTE: THE ABOVE LIST OF COMPANIES IS TENTATIVE AND MAY CHANGE DURING THE COURSE OF RESEARCH.
16
RESEARCH METHODOLOGY
16.1
RESEARCH DATA
16.2
SECONDARY DATA
16.2.1
KEY DATA FROM SECONDARY SOURCES
16.2.2
PRIMARY DATA
16.2.2.1
KEY DATA FROM PRIMARY SOURCES
16.2.2.2
KEY PRIMARY PARTICIPANTS
16.2.2.3
BREAKDOWN OF PRIMARY INTERVIEWS
16.2.2.4
KEY INDUSTRY INSIGHTS
16.2.3
MARKET SIZE ESTIMATION
16.2.3.1
BOTTOM-UP APPROACH
16.2.3.2
TOP-DOWN APPROACH
16.2.3.3
BASE NUMBER CALCULATION
16.2.4
MARKET FORECAST APPROACH
16.2.4.1
SUPPLY SIDE
16.2.4.2
DEMAND SIDE
16.2.5
DATA TRIANGULATION
16.2.6
RESEARCH ASSUMPTIONS
16.2.7
RESEARCH LIMITATIONS AND RISK ASSESSMENT
17
APPENDIX
17.1
DISCUSSION GUIDE
17.2
KNOWLEDGESTORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL
17.3
AVAILABLE CUSTOMIZATIONS
17.4
RELATED REPORTS
17.5
AUTHOR DETAILS
Methodology
The study involved four major activities in estimating the current size of the North America 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 North America. This primary data was collected through questionnaires, emails, and telephonic interviews.
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 North America 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 North America 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 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 North America 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 North America Data Center GPU Market by deployment, function, application, end user, and region.
- To forecast the North America Data Center GPU Market by function in terms of volume
- 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 North America 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
Key Questions Addressed by the Report
The North America data center GPU market is projected to grow from USD 43.19 billion in 2025 to USD 79.81 billion by 2030, at a CAGR of 13.1%.
The United States dominates the market, accounting for approximately 80–85% of the total regional share.
The cloud deployment segment leads the market due to strong investments by hyperscalers in AI and high-performance computing infrastructure.
The generative AI segment is the fastest-growing application due to increasing adoption of large language models and advanced AI workloads.
Key players include NVIDIA Corporation, Advanced Micro Devices, Intel Corporation, Amazon Web Services, and Microsoft.
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VP - Marketing & Business Development
Leading Provider of Process Control Solutions
We engaged with MarketsandMarkets on a study to perform an analysis and recommend a Go-To-Market strategy for metrology and process control in the semiconductor market. The study was tailored to our targets and needs with well-defined milestones. Our overall experience with the MarketsandMarkets team was very good throughout the project in all aspects including the analysis methodologies used, the quality and depth of primary and secondary data sets, the professionalism and flexibility of the team and the ability to meet the target schedule and milestones. We want to thank MarketsandMarkets team for a job well done.
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