AI Infrastructure Market Size, Share and Trends

Report Code SE 7201
Published in Nov, 2024, By MarketsandMarkets™
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AI Infrastructure Market by Offerings (Compute (GPU, CPU, FPGA), Memory (DDR, HBM), Network (NIC/Network Adapters, Interconnect), Storage, Software), Function (Training, Inference), Deployment (On-premises, Cloud, Hybrid) – Global Forecast to 2030

 

AI Infrastructure Market Size, Share & Trends

The global AI Infrastructure market is expected to grow from USD 135.81 billion in 2024 to USD 394.46 billion by 2030 at a CAGR of 19.4% during the estimated period 2024-2030.

The AI infrastructure market is experiencing robust growth, driven by the rising demand for high-performance computing (HPC) to manage complex AI workloads, enabling faster and more efficient data processing. The surge in generative AI (GenAI) applications and large language models (LLMs) is further amplifying the need for advanced AI infrastructure, as these models require immense computational power for training and inference of AI workloads. Cloud service providers (CSPs) are increasingly adopting AI infrastructure to deliver scalable and cost-effective solutions, fueling market expansion. Technology advancements, such as NVIDIA’s cutting-edge Blackwell GPU architecture, are accelerating AI infrastructure adoption by offering unparalleled performance, and scalability, making them ideal for supporting the growing demands of GenAI and LLM applications..

AI Infrastructure Market

Attractive Opportunities in the AI Infrastructure Market

NORTH AMERICA

North America accounted for the largest share of 37.0% of the AI Infrastructure market in 2023.

Cloud service providers and enterprises, including those in BFSI, healthcare, retail, and e-commerce, are likely to create lucrative opportunities for the players in the AI infrastructure market.

Favorable initiatives and subsidies by the US government to develop AI infrastructure in North America to fuel market growth.

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

NVIDIA Corporation (US), Advanced Micro Devices, Inc. (US), Intel Corporation (US), SK HYNIX INC. (South Korea), and SAMSUNG (South Korea) are the major players in the AI infrastructure market.

Global AI Infrastructure Market Dynamics

DRIVER: - Rising demand for high-performance computing in AI workloads

AI workloads, especially machine and deep learning, require unprecedented levels of data processing that traditional computing systems cannot support. High-performance computing (HPC) systems has emerged as the solution to large dataset management and executing complex algorithms at incredulous speeds, which adequately address the computational requirements of increasingly sophisticated AI models. Such models require substantial resources for training and inference, and HPC reduces the development and deployment times, that allows quicker decision-making and improved operational efficiency. Industries such as healthcare, finance and automotive are now rapidly integrating artificial intelligence into core operations, which is driving a stronger dependence on HPC infrastructure to increase scalability and accuracy in outcomes. Along with this, the exponential rise of data has increased the demand for powerful systems to analyze big datasets and deliver actionable insights. HPC ensures that AI can meet such demands. In response to this increasing demand, organization’s investment in advanced infrastructure, including HPC clusters, cutting-edge GPUs, and other specialized hardware is rising. This rise in investment underpins the high growth of the AI infrastructure market, which is driven by the need to support highly intensive computational tasks associated with AI, enabling next wave of AI-driven innovation across industries.

RESTRAINTS: Compatibility issues with legacy systems

One of the significant restraint in the growth of AI infrastructure markets is the compatibility issues with legacy systems. Enterprises in traditional sectors such as manufacturing, finance, and government, depends on legacy IT systems that were not developed to support such high computational demands associated with AI and machine learning workloads. These legacy systems lack processing power, storage capabilities, and flexibility required for AI infrastructure. Therefore, such organizations cannot integrate highly advanced AI solutions into the organization. As a result, companies are experiencing technical and operational issues when trying to upgrade or replace their legacy systems to absorb AI technologies. The primary challenge is the high cost and complexity required to transition legacy infrastructure on to AI-optimized platforms. Moreover, many legacy systems are deeply integrated into the core operations of the organization, thus becoming risky to disrupt the existing workflow with new AI infrastructure. The need to maintain business continuity in this transition can slow down the speed of adopting AI technologies, thus becoming a barrier for those companies seeking to access the full potential of AI.

 

OPPORTUNITY: - Rise of AI-as-a-Service platforms

The rise of AI-as-a-Service (AIaaS) platforms presents a key opportunity for growth of AI infrastructure market especially for smaller enterprises looking to harness the power of artificial intelligence without massive upfront investments in hardware and expertise. AIaaS enable subscription or pay-as-you-go-based access to AI tools and infrastructure for firms looking to take a proactive step to deploy more advanced AI. Major cloud providers such as Amazon Web Services, Inc. (US), Microsoft Azure (US), and Google Cloud (US) offer AIaaS platforms which save SMEs from investing in expensive AI hardware like GPUs, TPUs, or specially designed processors. Instead, business can access powerful AI infrastructure through scalable, cloud-based solutions, that would reduce the entry barrier appreciably. This shift is especially beneficial to retail, healthcare, and financial services industries, where AI is used more for decision-making quality enhancement, improved customer experiences, and operational optimization. The growing use of AIaaS in SMEs due to rise in competitiveness will fuel further growth in AI infrastructure, because cloud service providers are continuously expanding their capabilities to be able to cope with this huge rise in demand.

CHALLENGE: - Maintaining data security and integrity in distributed AI systems

One of the most critical challenges of AI infrastructure market is maintaining data security and integrity. Distributed AI systems, within multiple data centers, edge devices, or cloud environments, involve inherent transmission, storage, and processing of large amounts of data across different locations. This widespread distribution enhances the risk of cybersecurity breaches, hack attacks, and unauthorized access, because sensitive information needs to travel over a number of networks each with different levels of security. In distributed AI systems, data needs to be shared in real-time to be processed. Thus, it has to be safeguarded in terms of data integrity along its transmission. Ensuring that data is not tampered or corrupted by the time it is transmitted to other infrastructures in a network is one of the more significant challenges. Problems in data integrity may affect prediction in AI models, faulty decision-making, or compromised business outcome. The distributed systems often involve edge computing, where processing of data is done locally on edge devices such as lot sensors, smartphones, or autonomous vehicles. Such devices are the most vulnerable to physical access or cyber threats, which may undermine the security and reliability of the entire AI infrastructure, which as a big challenge for the AI Infrastructure market.

Global AI Infrastructure Market Ecosystem Analysis

The ecosystem of AI Infrastructure comprises designers, WFE/SEMICAP companies, manufacturers and end users. Each one of these collaborates towards the aim of advancing AI infrastructure by sharing knowledge, resources, and expertise to attain end innovation in this field. Manufacturers such as such as NVIDIA Corporation (US), Advanced Micro Devices, Inc. (US), Intel Corporation (US), are at the core of the AI infrastructure market that are responsible for developing AI infrastructure offerings for various applications.

Top Companies in AI Infrastructure Market

GPU segment to hold the high market share during the forecast period

GPU segment holds the highest market share in the AI infrastructure market due to its unmatched ability in handling highly parallel tasks, which is essential for AI applications like machine learning, deep learning and data analytics. The hyperscale cloud providers, including Amazon Web Services (US), Microsoft Azure (US), Google (US), IBM (US), and Oracle (US), rely on GPUs to expand scalable AI capabilities for enterprises and researchers. This versatility allows GPUs to support a wide range of AI tasks, from training large models to real-time inference, optimizing data center infrastructure for diverse workloads. Leading companies like NVIDIA Corporation (US), Advanced Micro Devices, Inc (US) and Intel Corporation (US) drive GPU innovation to meet escalating AI demands. NVIDIA’s recent launch of its Blackwell platform in March 2024 marked a significant leap, offering up to 25x reductions in cost and energy consumption, addressing sustainability concerns in AI infrastructure. Blackwell’s transformative technologies extend beyond AI to fields like engineering simulation and computer-aided drug design. With increasing reliance on AI for complex challenges across sectors such as healthcare, finance, and automotive, GPUs’ exceptional performance, energy efficiency, and scalability solidify their dominant role in the AI infrastructure market.

Inference segment to hold largest market share during forecast period

The inference function will dominate the AI infrastructure market due to the exponentially increasing demand for real-time AI applications in specialized hardware. As AI models are being increasingly deployed in production across industries like healthcare, finance, autonomous vehicles, and customer service, there is an increasing requirement for effective yet efficient computing infrastructure that supports inference at scale. This trend is further fueled by generative AI models such as natural language processing and image generation, which give rise to a high demand for inference resources. Widespread adoption of AI-based chatbots, predictive analytics and recommendation systems underscores the importance of scalable inference solutions. For example, in June 2024, Cisco (US) and NVIDIA Corporation (US) partnered to launch the Cisco Nexus HyperFabric AI cluster solution for data centers to manage, build, and optimize the software and infrastructure and scale generative AI workloads. With its cloud management capabilities, customers can easily deploy and manage large-scale fabrics across data centers, colocation facilities and edge sites. As business increasingly adopt generative AI technologies, the need for efficient inference infrastructure will increase proportionally and solidify its market leadership in the AI infrastructure market.

Cloud Service Providers (CSP) to hold the largest market share during the forecast period

Cloud services providers (CSP) segment will dominate the AI infrastructure market, as demand for scalable and cost-effective AI computing solutions escalates. Businesses are increasingly relying on CSPs to avoid the high costs of building in-house infrastructure and scaling it. It has resulted in high investments in advanced hardware, networking equipment, and storage. For example, in May 2024, Microsoft announced the plan to develop cloud and AI infrastructure in Thailand, expanding data center capabilities to support sophisticated AI workloads. CSPs are further democratizing AI adoption by offering pre-built models, development tools, and infrastructure-as-a-service solutions, enabling industries such as finance, healthcare and retail to accelerate deployment. Regulatory requirements and data sovereignty concerns have spurred the establishment of regional data centers, enhancing performance by reducing latency and improving local data processing. Collaborations such as Airtel and Google Cloud’s partnership in May 2024 to deploy generative AI solutions highlight CSPs’ focus on delivering advanced AI-powered tools for applications like geospatial and voice analytics. These tools demand robust computational resources, driving CSPs to invest in high-speed interconnects and intelligent networking. By meeting these diverse needs, CSPs are cementing their leadership in providing seamless, high-performance AI infrastructure solutions across the globe.

Asia Pacific Region to Hold High CAGR in the AI Infrastructure Market in the Forecast Period

The AI infrastructure market in Asia Pacific will grow at the highest CAGR, due to significant advancements in AI research, development, and deployment. High investments in AI technologies by China, Japan, South Korea, and Singapore have fostered collaborations among academia, industry, and government. The governments of the region are putting in large amounts of funds into AI infrastructure development, including optimized data centers for AI workloads. China's "Next Generation Artificial Intelligence Development Plan" aims to position the country as a world leader in AI by 2030. This is to be achieved through creating a robust ecosystem of strong AI infrastructure deployment. Similarly, South Korea’s AI National Strategy and Japan’s Society 5.0 initiative are bolstering AI infrastructure capabilities to support innovation. Businesses in Asia Pacific are rapidly adopting AI to enhance competitiveness, leading to increased demand for high-performance computing systems, advanced GPUs, and scalable cloud-based infrastructure. The region’s strong focus on digital transformation and regulatory support further accelerates AI adoption. As AI technologies become integral to sectors like healthcare, finance, and manufacturing, Asia Pacific is poised to lead the global AI infrastructure market, leveraging its investments and advancements to drive substantial growth.

LARGEST MARKET SHARE IN 2024-2030
CHINA FASTER-GROWING MARKET IN REGION
AI Infrastructure Market Size and Share

Recent Developments of AI Infrastructure Market

  • In June 2024, SK HYNIX INC. (South Korea) launched PCB01, SSD for PCs optimized for on-device AI. It offers the capabilities of 14GB and 12GB per second of sequential read and write speeds that allow the operation of a large language model4, or LLM, for AI training and inference.
  • In June 2024, Advanced Micro Devices, Inc. (US) partnered with Microsoft to deliver CoPilot+ PCs powered by Ryzen AI. This partnership supports AI's rapid acceleration, driving the increased demand for high-performance computing platforms.
  • In April 2024, Micron Technology, Inc. (US) and Silvaco Group, Inc. (US) extended their partnership to develop an AI-based solution named Fab Technology Co-Optimization (FTCO). It enables customers to use manufacturing data to perform machine learning software simulations and create computer models to simulate the wafer fabrication process.
  • In March 2024, NVIDIA Corporation (US) launched NVIDIA Quantum-X800 InfiniBand and NVIDIA Spectrum-X800 Ethernet networking platforms for computing and AI workloads. They are capable of 800 Gbps throughput and are designed for massive-scale AI. These platforms, designed for various data centers, feature software that accelerates AI and data processing applications and are integrated with NVIDIA’s new Blackwell architecture.
  • In February 2024, SAMSUNG (South Korea) developed a 36 GB HBM3E 12H DRAM to cater to the high-capacity requirements of AI service providers. It can increase the AI training speed by 34% and reduce total ownership costs for data centers.

Key Market Players

List of Top AI Infrastructure Market Companies

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Scope of the Report

Report Attribute Details
Market size available for years 2020–2030
Base year considered 2023
Forecast period 2024–2030
Forecast units Value (USD Million/Billion)
Segments Covered Offerings, Function, Deployment, Application, End User, and Region.
Regions covered North America, Europe, Asia Pacific, and Rest of the world (RoW)

Key Questions Addressed by the Report

What is the AI Infrastructure market's major driving factors and opportunities?
The major driving factors for AI Infrastructure market include rising demand for high-performance computing in AI workloads and Increasing government initiatives and investments in AI research and development (R&D). Key opportunities lie in advancements in neuromorphic and quantum computing for AI and Increasing investments in data centers by cloud service providers.
Which region is expected to hold the highest market share?
North America holds larger market share of the AI Infrastructure market. Rising government investments and the presence of major market players in the region is driving the demand for AI Infrastructure in North America.
Who are the leading players in the global AI Infrastructure market?
Leading players operating in the AI Infrastructure market are NVIDIA Corporation (US), Advanced Micro Devices, Inc. (US), SK HYNIX INC. (South Korea), SAMSUNG (South Korea), Micron Technology, Inc. (US).
What are some of the technological advancements in the market?
Generative AI, conversational AI, and AI-optimized cloud platforms are major technological advancements. Edge computing is another advancement which is expected to drive market growth.
What is the size of the global AI Infrastructure market?
The global AI Infrastructure market is expected to be valued at USD 135.81 billion in 2024 and is projected to reach USD 394.46 billion by 2030, growing at a CAGR of 19.4% from 2024-2030.

 

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Table of Contents

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

TITLE
PAGE NO
INTRODUCTION
18
RESEARCH METHODOLOGY
20
EXECUTIVE SUMMARY
22
PREMIUM INSIGHTS
24
MARKET OVERVIEW
40
  • 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 COMPUTE
    AVERAGE SELLING PRICE TREND, BY REGION
  • 5.5 VALUE CHAIN ANALYSIS
  • 5.6 ECOSYSTEM ANALYSIS
  • 5.7 INVESTMENT AND FUNDING SCENARIO
  • 5.8 TECHNOLOGY ANALYSIS
    KEY TECHNOLOGIES
    - Generative AI
    - Conversational AI
    - AI-Optimized Cloud Platforms
    COMPLEMENTARY TECHNOLOGIES
    - Blockchain
    - Edge Computing
    - Cybersecurity
    ADJACENT TECHNOLOGIES
    - Big Data
    - Predictive Analysis
  • 5.9 UPCOMING DEPLOYMENTS OF DATA CENTER BY CLOUD SERVICE PROVIDERS (CSPS)
    CLOUD SERVICE PROVIDERS’ CAPEX
    PROCESSOR BENCHMARKING
    - GPU Benchmarking
    - CPU Benchmarking
    PATENT ANALYSIS
    TRADE ANALYSIS
    - Import Scenario
    - Export Scenario
    KEY CONFERENCES AND EVENTS (2024-2025)
    CASE STUDY ANALYSIS
    REGULATORY LANDSCAPE
    - 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
AI SERVER INDUSTRY LANDSCAPE
80
  • 6.1 INTRODUCTION
  • 6.2 AI SERVER’S CURRENT PENETRATION AND GROWTH FORECAST
  • 6.3 AI SERVER MARKET, BY PROCESSOR TYPE
    GPU-BASED SERVER
    FPGA-BASED SERVER
    ASIC-BASED SERVER
  • 6.4 AI SERVER MARKET, BY FUNCTION
    TRAINING
    INFERENCE
  • 6.5 AI SERVER MARKET SHARE ANALYSIS, 2023
AI INFRASTRUCTURE MARKET, BY OFFERING
100
  • 7.1 INTRODUCTION
  • 7.2 COMPUTE
    GPU
    CPU
    FPGA
    TPU
    DOJO & FSD
    TRAINIUM & INFERENTIA
    ATHENA
    T-HEAD
    MTIA
    - LPU
    - Other ASIC
  • 7.3 MEMORY
    DDR
    HBM
  • 7.4 NETWORK
    NIC/NETWORK ADAPTERS
    - Infiniband
    - Ethernet
    INTERCONNECTS
  • 7.5 STORAGE
  • 7.6 SERVER SOFTWARE
AI INFRASTRUCTURE MARKET, BY FUNCTION
120
  • 8.1 INTRODUCTION
  • 8.2 TRAINING
  • 8.3 INFERENCE
AI INFRASTRUCTURE MARKET, BY DEPLOYMENT
150
  • 9.1 INTRODUCTION
  • 9.2 ON-PREMISES
  • 9.3 CLOUD
  • 9.4 HYBRID
    INTRODUCTION
    GENERATIVE AI
    - Rule Based Models
    - Statistical Models
    - Deep Learning
    - Generative Adversarial Networks (GANs)
    - Autoencoders
    - Convolutional Neural Networks (CNNs)
    - Transformer Models
    MACHINE LEARNING
    NATURAL LANGUAGE PROCESSING
    COMPUTER VISION
AI INFRASTRUCTURE MARKET, BY END USER
190
INTRODUCTION
CLOUD SERVICE PROVIDERS (CSP)
ENTERPRISES
- Healthcare
- BFSI
- Automotive
- Retail & E-commerce
- Media & Entertainment
- Others
GOVERNMENT ORGANIZATIONS
AI INFRASTRUCTURE MARKET, BY REGION
240
INTRODUCTION
NORTH AMERICA
- Macro-Economic Outlook
- US
- Canada
- Mexico
EUROPE
- Macro-Economic Outlook
- UK
- Germany
- France
- Italy
- Spain
- Rest of Europe
ASIA PACIFIC
- Macro-Economic Outlook
- China
- Japan
- South Korea
- India
- Rest of Asia Pacific
ROW
- Macro-Economic Outlook
- Middle East
- Africa
- South America
AI INFRASTRUCTURE MARKET, COMPETITIVE LANDSCAPE
260
INTRODUCTION
KEY PLAYER STRATEGIES/RIGHT TO WIN
REVENUE ANALYSIS
MARKET SHARE ANALYSIS
COMPANY VALUATION AND FINANCIAL METRICS
PRODUCT/BRAND COMPARISON
COMPANY EVALUATION MATRIX: KEY PLAYERS, 2023
- Stars
- Emerging Leaders
- Pervasive Players
- Participants
- Company Footprint: Key Players, 2023
COMPANY EVALUATION MATRIX: STARTUPS/SMES, 2023
- Progressive Companies
- Responsive Companies
- Dynamic Companies
- Starting Blocks
- Competitive Benchmarking: Startups/SMEs, 2023
COMPETITIVE SCENARIO AND TRENDS
- Product Launches
- Deals
AI INFRASTRUCTURE MARKET, COMPANY PROFILES
290
KEY PLAYERS
- NVIDIA Corporation
- Advanced Micro Devices, Inc.
- SK HYNIX INC.
- Samsung
- Micron Technology, Inc.
- Intel Corporation
- Google
- Amazon Web Services, Inc.
- Tesla
- Microsoft
- Meta
- Graphcore
- Cerebras
OTHER PLAYERS
- Kioxia
- Western Digital Corporation
- Mythic
- Blaize
- Groq, Inc.
- HAILO TECHNOLOGIES LTD
- SiMa Technologies, Inc.
- Kneron, Inc.
- Rain Neuromorphics Inc.
- Tenstorrent
- SambaNova Systems, Inc.
- Taalas
- SAPEON Inc.
- Rebellions Inc.
- Rivos Inc.
- Shanghai BiRen Technology Co., Ltd.
APPENDIX
310
DISCUSSION GUIDE
KNOWLEDGE STORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL
AVAILABLE CUSTOMIZATIONS
RELATED REPORTS
AUTHOR DETAILS

The research process for this technical, market-oriented, and commercial study of the AI infrastructure market included the systematic gathering, recording, and analysis of data about companies operating in the market. It involved the extensive use of secondary sources, directories, and databases (Factiva, Oanda, and OneSource) to identify and collect relevant information. In-depth interviews were conducted with various primary respondents, including experts from core and related industries and preferred manufacturers, to obtain and verify critical qualitative and quantitative information as well as to assess the growth prospects of the market. Key players in the AI infrastructure market were identified through secondary research, and their market rankings were determined through primary and secondary research. This included studying annual reports of top players and interviewing key industry experts, such as CEOs, directors, and marketing executives.

Secondary Research

In the secondary research process, various secondary sources were used to identify and collect information for this study. These include annual reports, press releases, and investor presentations of companies, whitepapers, certified publications, and articles from recognized associations and government publishing sources. Research reports from a few consortiums and councils were also consulted to structure qualitative content. Secondary sources included corporate filings (such as annual reports, investor presentations, and financial statements); trade, business, and professional associations; white papers; Journals and certified publications; articles by recognized authors; gold-standard and silver-standard websites; directories; and databases. Data was also collected from secondary sources, such as the International Trade Centre (ITC) (Switzerland), and the International Monetary Fund (IMF).

List of key secondary sources

Source

Web Link

Generative AI Association (GENAIA)

https://www.generativeaiassociation.org/

Association for Machine Learning and Application (AMLA)

https://www.icmla-conference.org/

Association for the Advancement of Artificial Intelligence

https://aaai.org/

European Association for Artificial Intelligence

https://eurai.org/

International Monetary Fund

https://www.umaconferences.com/

Institute of Electrical and Electronics Engineers (IEEE)

https://ieeexplore.ieee.org/

Primary Research

Extensive primary research was accomplished after understanding and analyzing the AI infrastructure market scenario through secondary research. Several primary interviews were conducted with key opinion leaders from both demand- and supply-side vendors across four major regions—North America, Europe, Asia Pacific, and RoW. Approximately 30% of the primary interviews were conducted with the demand side, and 70% with the supply side. Primary data was collected through questionnaires, emails, and telephonic interviews. Various departments within organizations, such as sales, operations, and administration, were contacted to provide a holistic viewpoint in the report.

AI Infrastructure Market Size, and Share

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

The three tiers of the companies are based on their total revenues as of 2023 ? 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 perform the market size estimation and forecasting for the overall market segments and subsegments listed in this report. Extensive qualitative and quantitative analyses have been performed on the complete market engineering process to list the key information/insights throughout the report. The following table explains the process flow of the market size estimation.

The key players in the market were identified through secondary research, and their rankings in the respective regions determined through primary and secondary research. This entire procedure involved the study of the annual and financial reports of top players, and interviews with industry experts such as chief executive officers, vice presidents, directors, and marketing executives for quantitative and qualitative key insights. All percentage shares, splits, and breakdowns were determined using secondary sources and verified through primary sources. All parameters that affect the markets covered in this research study were accounted for, viewed in extensive detail, verified through primary research, and analyzed to obtain the final quantitative and qualitative data. This data was consolidated, supplemented with detailed inputs and analysis from MarketsandMarkets, and presented in this report.

AI Infrastructure Market : Top-Down and Bottom-Up Approach

Bottom-Up Approach

  • Initially, the companies offering AI infrastructure were identified. Their products were mapped based on offering, function, deployment, application, and end user.
  • After understanding the different types of AI infrastructure offering by various manufacturers, the market was categorized into segments based on the data gathered through primary and secondary sources.
  • To derive the global AI infrastructure market, global server shipments of top players for AI servers considered in the report's scope were tracked
  • A suitable penetration rate was assigned for computing, memory, network, storage, and server software offerings to derive the shipments of AI infrastructure.
  • We derived the AI infrastructure market based on different offerings using the average selling price (ASP) at which a particular company offers its devices. The ASP of each offering was identified based on secondary sources and validated from primaries.
  • For the CAGR, the market trend analysis was carried out by understanding the industry penetration rate and the demand and supply of AI infrastructure offerings for different end users.
  • The AI infrastructure market is also tracked through the data sanity method. The revenues of key providers were analyzed through annual reports and press releases and summed to derive the overall market.
  • For each company, a percentage is assigned to its overall revenue or, in a few cases, segmental revenue to derive its revenue for the AI Infrastructure. This percentage for each company is assigned based on its product portfolio and range of AI infrastructure offerings.
  • The estimates at every level, by discussing them with key opinion leaders, including CXOs, directors, and operation managers, have been verified and cross-checked, and finally, with the domain experts at MarketsandMarkets.
  • Various paid and unpaid sources of information, such as annual reports, press releases, white papers, and databases, have been studied.

Top-Down Approach

  • The global market size of AI infrastructure was estimated through the data sanity of major companies.
  • The growth of the AI infrastructure market witnessed an upward trend during the studied period, as it is currently in the initial stage of the product cycle, with major players beginning to expand their business into various application areas of the market.
  • Types of AI infrastructure offerings, their features and properties, geographical presence, and key applications served by all players in the AI infrastructure market were studied to estimate and arrive at the percentage split of the segments.
  • Different types of AI infrastructure offerings, such as compute, memory, and network, storage and server software and their penetration for end users were also studied.
  • The market split for AI infrastructure by offering, function, deployment, application, and end user was estimated based on secondary research.
  • The demand generated by companies operating in different end-use application segments was analyzed.
  • Multiple discussions with key opinion leaders across major companies involved in developing the AI Infrastructure offerings and related components were conducted to validate the offering, function, deployment, application, and end user market split.
  • The regional splits were estimated using secondary sources based on factors such as the number of players in a specific country and region and the adoption and use cases of each implementation type with respect to applications in the region.
AI Infrastructure Market Top Down and Bottom Up Approach

Data Triangulation

After arriving at the overall size of the AI infrastructure market through the process explained above, the overall market has been split into several segments. Data triangulation procedures have been employed to complete the overall market engineering process and arrive at the exact statistics for all the segments, wherever applicable. The data has been triangulated by studying various factors and trends from both the demand and supply sides. The market has also been validated using both top-down and bottom-up approaches.

Market Definition

AI infrastructure refers to the foundational technological ecosystem required to develop, deploy, and scale artificial intelligence applications. It encompasses a combination of high-performance computing resources (e.g., GPUs, CPUs, FPGAs, etc.), memory solutions (e.g., DDR, HBM), networking components (e.g., network adapters, interconnects), software, and storage systems optimized for handling AI workloads. AI infrastructure supports both training and inference functions across diverse deployment models, including on-premises, cloud, and hybrid environments. It is utilized in generative AI, machine learning, natural language processing (NLP), and computer vision applications.

Key Stakeholders

  • Government and financial institutions and investment communities
  • Analysts and strategic business planners
  • Semiconductor product designers and fabricators
  • Application providers
  • AI solution providers
  • AI platform providers
  • AI system providers
  • Manufacturers and AI technology users
  • Business providers
  • Component and device suppliers and distributors
  • Professional service/solution providers
  • Research organizations
  • Technology standard organizations, forums, alliances, and associations
  • Technology investors
  • Investors (private equity firms, venture capitalists, and others)

Report Objectives

  • To define, describe, segment, and forecast the size of the AI infrastructure market, in terms of value, based on offering, function, deployment, application, end user, and region
  • To forecast the size of the market segments for four major regions—North America, Europe, Asia Pacific, and RoW
  • To define, describe, segment, and forecast the size of the AI infrastructure market, in terms of volume, based on offering.
  • To give detailed information regarding drivers, restraints, opportunities, and challenges influencing the growth of the market
  • To provide an 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 provide a detailed overview of the value chain analysis of the AI infrastructure ecosystem
  • 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, acquisitions, agreements, and partnerships in the AI infrastructure market

Available Customizations

With the given market data, MarketsandMarkets offers customizations according to the company’s specific needs. The following customization options are available for the report:

Company Information:

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Previous Versions of this Report

AI Infrastructure Market by Offering (Hardware, Server Software), Technology (Machine Learning, Deep Learning), Function (Training, Inference), Deployment Type (On-premises, Hybrid, Cloud), End user and Region - Global Forecast to 2027

Report Code SE 7201
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AI Infrastructure Market with COVID-19 Impact Analysis by Offering (Hardware, Software), Technology (Machine Learning, Deep Learning), Function (Training, Inference), Deployment Type (On-Premises, Cloud), End User, and Region - Global Forecast to 2026

Report Code SE 7201
Published in Jul, 2021, By MarketsandMarkets™

AI Infrastructure Market with COVID-19 Impact Analysis by Offering (Hardware, Software), Technology (Machine Learning, Deep Learning), Function (Training, Inference), Deployment Type (On-Premises, Cloud), End User, and Region - Global Forecast to 2026

Report Code SE 7201
Published in Jun, 2019, By MarketsandMarkets™
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Growth opportunities and latent adjacency in AI Infrastructure Market

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