AI Infrastructure Market by Offering (Hardware, Software), Technology (Machine Learning, Deep Learning), Function (Training, Inference), Deployment Type (On-Premises, Cloud, Hybrid), End User, and Region - Global Forecast to 2025
[189 pages Report] The global AI infrastructure market is projected to grow from USD 14.6 billion in 2019 to USD 50.6 billion by 2025, at a CAGR of 23.1%. Major factors driving the market for AI infrastructure include increasing adoption of cloud machine learning platform, escalating demand for AI hardware in high-performance computing data centers, rising focus on parallel computing in AI data centers, growing volume of data generated in industries such as automotive and healthcare, improving computing power and declining hardware cost, growing number of cross-industry partnerships and collaborations, and expanding AI applications in industries such as healthcare, automotive, finance, and tourism. The markets in North America and Europe is one of the key factors restraining the growth of the AI infrastructure market.
AI Infrastructure Market for Deep Learning Technology is Estimated to Grow at Higher CAGR During Forecast Period
AI infrastructure for deep learning technology enables a machine to build a hierarchical representation. For instance, the first layer of the captured image could scan for simple edges, followed by a layer that collects edge-forming shapes (such as rectangle or circle). The final layer could identify machine parts. After scanning several layers to identify the required data, the neural network can collate the features into an algorithm that can recognize the overall image. The growing adoption of robots, cybersecurity applications, IoT, industrial automation, and machine vision technology has created a large volume of data. This data serves as a training module in deep learning technology, which helps in testing and diagnosis processes. Deep learning technology helps to manage data consistently. The deep learning platform learns from different sources and creates a consolidated data environment. Moreover, this platform reduces the workload of end-user industries such as semiconductor and electronics, energy & power, pharmaceuticals, automotive, heavy metals and machine manufacturing, and food & beverages. The extensive use of big data, industrial IoT (IIoT), and robotics is fueling the growth of the AI infrastructure market for deep learning technology.
AI Infrastructure Market for Cloud Service Providers in APAC is Estimated to Grow at Highest CAGR During Forecast Period
Cloud service providers (CSPs) offer network services, infrastructure, or business applications in the cloud to various companies from industries such as automotive, healthcare, retail, and manufacturing. The cloud mainly addresses 3 areas of operations: software-as-a-service (SaaS), infrastructure-as-a-service (IaaS), and platform-as-a-service (PaaS). The number of data center providers and cloud companies is likely to increase owing to the high efficiency and economies of scale offered by cloud computing. Cloud service providers offer services to several customers from a common shared infrastructure (i.e., equipment for operations, networking, data storage, and hardware) and help companies to save their IT infrastructure cost.
China Is Expected to Grow at Highest CAGR in AI Infrastructure Market During Forecast Period
The AI infrastructure market in China is growing rapidly. As multinational and domestic enterprises increasingly transit to cloud services providers (CSPs) and colocation solutions, the AI data center growth in China continues to evolve. The demand for AI data centers in the country has exceeded the available supply as organizations seek enhanced connectivity and scalable solutions for their growing businesses. Investments by the Chinese government for stimulating technological developments have led to an increase in the adoption of cloud-based services such as Big Data Analytics and Internet of Things (IoT). Various government reforms, such as the establishment of free trade in Shanghai, are attracting international investors. The growing demand for high-density, redundant facilities is triggering a shift in the design and development of the country’s data centers.
North America is Projected to Hold Largest Market Share During Forecast Period
At present, North America accounts for the largest share of the global AI infrastructure market, and a similar trend is likely to continue in the near future. The US and Canada are expected to adopt AI-based servers at a high rate. These countries are technologically developed economies in North America because of their strong focus on investing in R&D activities for the development of new technologies. The North American AI infrastructure is further segmented into the US, Canada, and Mexico. The US is one of the major contributors to the North American AI market. The US is one of the leading countries in the world to adopt AI technology. In addition, the presence of prominent AI technology providers in the country, such as IBM, Google, Microsoft, NVIDIA, Intel, Facebook, MetaMind, Tute Genomics, and Amazon.com, is boosting the growth of the AI infrastructure market in this region.
Key Market Players
Key players operating in the AI infrastructure market are Intel Corporation (US), NVIDIA Corporation (US), IBM (US), Samsung Electronics (South Korea), Google (US), Microsoft (US), Micron Technology (US), Amazon Web Services (US), CISCO (US), Oracle (US), ARM (UK), Xilinx (US), Advanced Micro Devices (AMD) (US), Dell (US), HPE (US), Habana Labs (Israel), and Synopsys Inc. (US).
Intel Corporation (US)
Intel is a leading designer and manufacturer of advanced integrated digital technology platforms. The company has robust market presence, especially in the PC and data center market, and invests significantly in R&D, which has resulted in its strong position in the AI infrastructure market. In the past 2 years, Intel significantly adopted inorganic strategies such as acquisitions and partnerships. The acquisition of companies such as Saffron Technology, Altera, Nervana Systems, Movidius, and Mobileye has further improved its AI product portfolio. Moreover, Intel’s partnership with AI software solution and service providers, such as C3 IoT, Inc., JianPei Tech Ltd., and Mphasis, is expected to remain a vital growth strategy of the company in the coming years as these solutions will lead to mainstream AI adoption that will drive improvements in efficiency, responsiveness, and personalization.
NVIDIA Corporation (US)
NVIDIA has 2 reportable segments: GPU and Tegra Processor. The GPU segment provides products under several brands, including GeForce, Quadro, GeForce NOW, Quadro, Tesla, and GRID. GeForce is used for visual computing in PC gaming. Quadro is used for computer-aided design, video editing, and other applications. GeForce NOW is for cloud-based game-streaming service. Quadro is for designers in the field of computer-aided design, special effects, video editing, and other applications. Tesla is designed to meet deep learning and accelerated computing applications, and GRID is used to power NVIDIA graphics through the cloud and data centers. The company has significantly invested in R&D; for instance, it increased its investment from USD 1.33 billion in 2017 to USD 1.80 billion in 2018. Since its inception, it has made a total R&D investment of over USD 15 billion. Moreover, it has more than 7,300 patents with inventions pertaining to modern computing. This has resulted in its strong organic growth and robust market position in terms of hardware product development in the AI market.
Samsung Electronics (South Korea)
Samsung has been a leader in the memory segment with product offerings such as dynamic random-access memory (DRAM), NAND flash solutions, and advanced solid-state drive (SSD) products. DRAM solutions include components and modules for PC, server, and mobile applications. Samsung offers a broad portfolio of high-performance, high-density NAND flash solutions combined with its preceding controller technologies that encompass embedded and non-embedded memory storage solutions, as well as SSDs that can be used as data storage devices for PCs and enterprise systems. Samsung’s advanced SSD products offer the next-generation storage solutions that overcome the shortcomings of conventional hard disk drives, suggest attractive alternatives for server applications, and improve the stability and performance of systems in which they are used.
Scope of the Report
|
Report Metric |
Details |
|
Market size available for years |
2016–2025 |
|
Base year |
2018 |
|
Forecast period |
2019–2025 |
|
Forecast units |
Value (USD billion/million) and volume (million/billion units) |
|
Segments Covered |
By offering, by technology, by function, by deployment type, by end user, by geography |
|
Geographies Covered |
North America, Asia Pacific, Europe, and Rest of the World |
|
Companies Covered |
Intel Corporation (US), NVIDIA Corporation (US), IBM (US), Samsung Electronics (South Korea), Google (US), Microsoft (US), Micron Technology (US), Amazon Web Services (US), CISCO (US), Oracle (US), ARM (UK), Xilinx (US), Advanced Micro Devices (AMD) (US), Dell (US), HPE (US), Habana Labs (Israel), and Synopsys Inc. (US). |
This research report categorizes the market based on offering, technology, function, deployment type, end-user and region.
Based on Offering, the AI infrastructure market has been segmented as follows:
- Hardware
- Processor
- CPU
- GPU
- FPGA
- ASIC
- Memory
- Storage
- Networking
- Server Software
Based on Technology, the AI infrastructure market has been segmented as follows:
- Machine learning
- Deep learning
Based on Function, the AI infrastructure market has been segmented as follows:
- Training
- Inference
Based on Deployment type, the AI infrastructure market has been segmented as follows:
- On-premises
- Cloud
- Hybrid
Based on End-user, the AI infrastructure market has been segmented as follows:
- Enterprises
- Government organizations
- Cloud service providers
Based on Region, the AI infrastructure market has been segmented as follows:
- North America
- Europe
- Asia Pacific
- Rest of the World
Recent Developments
- In February 2019, IBM launched a new portfolio of IoT solutions that team AI and advanced analytics to help asset-intensive organizations, such as the Metropolitan Atlanta Rapid Transit Authority (MARTA), to improve maintenance strategies. The solution is designed to help organizations to lower costs and reduce the risk of failure from physical assets such as vehicles, manufacturing robots, turbines, mining equipment, elevators, and electrical transformers.
- In April 2019, AMD and HOSTKEY (Moscow), an equipment leasing and reliable cloud solution provider, announced the release of AMD EPYC processor-based servers across HOSTKEY’s infrastructure. The deployment of AMD EPYC CPUs offers differentiated features, such as core-count, connectivity, and memory bandwidth, to HOSTKEY customers running virtualized environments and high-performance computing workloads.
- In October 2018, Xilinx launched Alveo, a portfolio of powerful accelerator cards designed to dramatically increase performance in industry-standard servers across cloud and on-premise data centers. With Alveo, customers can expect breakthrough performance improvement at low latency when running critical data center applications such as real-time machine learning inference, video processing, genomics, and data analytics.
- In March 2019, Micron Technology launched the portfolio of Micron 2200 PCIe NVMe SSDs. These drives are available in capacities ranging from 256 GB through 1 TB in an M.2 22x80 mm form factor. This portfolio of SSDs brings increased bandwidth and reduced latency to client computing markets by addressing growing needs across original equipment manufacturers (OEMs) and other clients.
Key Questions Addressed by the Report
- Where will all these developments take the industry in the mid to long term?
- What will be the upcoming industries for AI infrastructure?
- What are the drivers, challenges, and restraints impacting the AI infrastructure market growth?
- Which hardware components and software solutions are expected to be used widely to build AI infrastructure in the mid to long term?
- Which region is expected to witness significant demand for AI infrastructure?
To speak to our analyst for a discussion on the above findings, click Speak to Analyst
Table of Contents
1 Introduction (Page No. - 18)
1.1 Study Objectives
1.2 Definition
1.3 Study Scope
1.3.1 Markets Covered
1.3.2 Geographic Scope
1.3.3 Years Considered
1.4 Currency
1.5 Limitations
1.6 Stakeholders
2 Research Methodology (Page No. - 22)
2.1 Research Data
2.1.1 Secondary Data
2.1.1.1 Secondary Sources
2.1.2 Primary Data
2.1.2.1 Primaries Sources
2.1.2.2 Key Industry Insights
2.1.2.3 Breakdown of Primaries
2.2 Market Size Estimation
2.2.1 Bottom-Up Approach
2.2.2 Top-Down Approach
2.3 Market Breakdown and Data Triangulation
2.4 Research Assumptions
3 Executive Summary (Page No. - 31)
4 Premium Insights (Page No. - 35)
4.1 Attractive Opportunities in AI Infrastructure Market
4.2 AI Infrastructure Hardware Market, By Type
4.3 Market, By Deployment
4.4 Market, By Function
4.5 Market for Enterprise, By Region
4.6 Market, By Technology
4.7 Market, By Geography
5 Market Overview (Page No. - 39)
5.1 Introduction
5.2 Market Dynamics
5.2.1 Drivers
5.2.1.1 Increasing Adoption of Cloud Machine Learning Platform
5.2.1.2 Escalating Demand for AI Hardware in High-Performance Computing Data Centers
5.2.1.3 Rising Focus on Parallel Computing in AI Data Centers
5.2.1.4 Growing Volume of Data Generated in Industries Such as Automotive and Healthcare
5.2.1.5 Improving Computing Power and Declining Hardware Cost
5.2.1.6 Growing Number of Cross-Industry Partnerships and Collaborations
5.2.1.7 Expanding AI Applications in Industries Such as Healthcare, Automotive, Finance, and Tourism
5.2.1.8 Evolving Applications of Industrial IoT and Automation Technologies
5.2.2 Restraints
5.2.2.1 Dearth of AI Hardware Experts
5.2.3 Opportunities
5.2.3.1 Surging Demand for FPGA-Based Accelerators
5.2.3.2 Rising Need for Coprocessors Due to Slowdown of Moore’s Law
5.2.3.3 Increasing Focus on Developing Human-Aware AI Systems
5.2.4 Challenges
5.2.4.1 Unreliability of AI Algorithms
5.2.4.2 Creation of Application-Specific Models and Mechanisms of AI in Cloud
5.2.4.3 Concerns Regarding Data Privacy in AI Platforms
5.2.4.4 No Assurance or Guarantee on Returns on Investment
5.2.4.5 Availability of Limited Structured Data to Train and Develop Efficient AI Systems
6 AI Infrastructure Market, By Offering (Page No. - 51)
6.1 Introduction
6.2 Hardware
6.2.1 Processor
6.2.1.1 CPU
6.2.1.2 GPU
6.2.1.3 FPGA
6.2.1.4 ASIC
6.2.2 Memory
6.2.2.1 High-Bandwidth Memory, Independent of Its Computing Architecture, is Being Developed and Deployed for AI Applications
6.2.3 Storage
6.2.3.1 Artificial Intelligence, Along With Analytics Tools, is Used in Sorting Necessary and Unessential Data
6.2.4 Networking
6.2.4.1 NVIDIA (US), Intel (US)And CISCO (US)Are Key Providers of Network Interconnect Adapters for AI Applications
6.3 Server Software
6.3.1 Creating Intelligent Software Involves Simulating Several Capabilities Such as Problem-Solving, Perception, and Knowledge Representation
7 AI Infrastructure Market, By Technology (Page No. - 61)
7.1 Introduction
7.2 Machine Learning
7.2.1 Machine Learning Enables Systems to Automatically Improve Their Performance With Experience
7.3 Deep Learning
7.3.1 Deep Learning Uses Artificial Neural Networks to Learn Multiple Levels of Data
8 AI Infrastructure Market, By Function (Page No. - 65)
8.1 Introduction
8.2 Training
8.2.1 Building Good Model is Directly Related to Quality and Quantity of Data Used in Process of Learning Model
8.3 Inference
8.3.1 On-Premises Inference Platform is Adopted to Gain Faster Results Than That of Cloud
9 AI Infrastructure Market, By Deployment Type (Page No. - 70)
9.1 Introduction
9.2 On-Premises
9.2.1 Data-Sensitive Enterprises Prefer On-Premises AI Solutions Based on Advanced Nlp Techniques and Ml Models
9.3 Cloud
9.3.1 Cloud-Based AI Solutions Provide Additional Flexibility and More Accurate Real-Time Data Essential for Effective Business Operations
9.4 Hybrid
9.4.1 Hybrid Infrastructure Would Help in Fast Work Processes, Saving Time, and Money
10 AI Infrastructure Market, By End-User (Page No. - 75)
10.1 Introduction
10.2 Enterprises
10.2.1 Utilization of Advanced Big Data Solutions for Operational Data Explosion is Impacting Future Requirements for AI-Based Servers
10.3 Government Organizations
10.3.1 Governments Worldwide are Working Toward Implementing AI in Security Solutions to Protect Critical Government and Defense-Related Infrastructure
10.4 Cloud Service Providers (CSP)
10.4.1 Cloud Service Providers Need to Deliver Industry-Specific Functionality or Help Users Meet Certain Regulatory Requirements
11 Geographic Analysis (Page No. - 88)
11.1 Introduction
11.2 North America
11.2.1 US
11.2.1.1 Improved Economy and High Disposable Income in US Lead to Increased Demand for Modern Technologies, Which, in Turn, Boosts AI
Infrastructure Market Growth
11.2.2 Canada
11.2.2.1 High Adoption of AI Technologies, Especially Ml and Nlp, is Fueling Canadian Market Growth
11.2.3 Mexico
11.2.3.1 Mexican Market Growth is Driven By Growing Penetration of AI in Security and BFSI Industries in Country
11.3 Europe
11.3.1 UK
11.3.1.1 Adoption of Supercomputers Would Drive Market in UK
11.3.2 Germany
11.3.2.1 Adoption of Cloud Computing and Industry 4.0 has Created Increased Demand for Data Centers in Germany
11.3.3 France
11.3.3.1 Investment of Venture Capitalists in French Start-Ups for Development of AI Ecosystem Surge AI Infrastructure Market Growth in Country
11.3.4 Rest of Europe
11.3.4.1 Spain, Italy, Sweden, Norway, Netherlands, Belgium, Russia, and Poland Drive AI Infrastructure Market Growth in Rest of Europe
11.4 APAC
11.4.1 China
11.4.1.1 Growing Demand for High-Density, Redundant Facilities is Triggering Deployment of Data Centers, Thereby Escalating Demand for AI Infrastructure
11.4.2 Japan
11.4.2.1 Small and Medium-Sized Companies in Japan are Utilizing Infrastructure-As-A-Service (Through Cloud)
11.4.3 India
11.4.3.1 Growing Adoption of Cloud Based Services to Have Positive Impact on AI Infrastructure Market
11.4.4 Rest of APAC
11.4.4.1 Australia, Thailand, South Korea, and Indonesia are Major Countries Responsible for Growth in Rest of APAC
11.5 RoW
11.5.1 South America
11.5.1.1 Brazil has Largest Computing Services Market in South America, Followed By Chile and Argentina
11.5.2 Middle East and Africa
11.5.2.1 Smart Mobile Data Traffic Would Lead to Increased Workload on Data Centers, Resulting in AI Server Growth
12 Competitive Landscape (Page No. - 114)
12.1 Overview
12.2 Ranking Analysis of Key Players in AI Infrastructure Market
12.3 Competitive Situations and Trends
12.3.1 Product Launches
12.3.2 Agreements, Partnerships, Collaborations, & Contracts
12.3.3 Acquisitions
12.3.4 AI Infrastructure Market (Global) Competitive Leadership Mapping, 2018
12.3.4.1 Visionary Leaders
12.3.4.2 Dynamic Differentiators
12.3.4.3 Innovators
12.3.4.4 Emerging Companies
13 Company Profiles (Page No. - 120)
13.1.1 Intel Corporation
13.1.1.1 Business Overview
13.1.1.2 Products and Solutions Offered
13.1.1.3 Recent Developments
13.1.1.4 SWOT Analysis
13.1.1.5 MnM View
13.1.2 NVIDIA Corporation
13.1.2.1 Business Overview
13.1.2.2 Products and Solutions Offered
13.1.2.3 Recent Developments
13.1.2.4 SWOT Analysis
13.1.2.5 MnM View
13.1.3 Samsung Electronics
13.1.3.1 Business Overview
13.1.3.2 Products Offered
13.1.3.3 Recent Developments
13.1.3.4 SWOT Analysis
13.1.3.5 MnM View
13.1.4 Micron Technology
13.1.4.1 Business Overview
13.1.4.2 Products and Solutions Offered
13.1.4.3 Recent Developments
13.1.4.4 SWOT Analysis
13.1.4.5 MnM View
13.1.5 Xilinx
13.1.5.1 Business Overview
13.1.5.2 Products and Solutions Offered
13.1.5.3 Recent Developments
13.1.5.4 SWOT Analysis
13.1.5.5 MnM View
13.1.6 Advanced Micro Devices (AMD)
13.1.6.1 Business Overview
13.1.6.2 Products Offered
13.1.6.3 Recent Developments
13.1.6.4 SWOT Analysis
13.1.6.5 MnM View
13.1.7 IBM
13.1.7.1 Business Overview
13.1.7.2 Products, Solutions, and Services Offered
13.1.7.3 Recent Developments
13.1.7.4 SWOT Analysis
13.1.7.5 MnM View
13.1.8 Google
13.1.8.1 Business Overview
13.1.8.2 Products and Solutions
13.1.8.3 Recent Developments
13.1.8.4 SWOT Analysis
13.1.8.5 MnM View
13.1.9 Microsoft
13.1.9.1 Business Overview
13.1.9.2 Products and Solutions Offered
13.1.9.3 Recent Developments
13.1.9.4 SWOT Analysis
13.1.9.5 MnM View
13.1.10 Amazon Web Services
13.1.10.1 Business Overview
13.1.10.2 Products and Services Offered
13.1.10.3 Recent Developments
13.1.10.4 SWOT Analysis
13.1.10.5 MnM View
13.1.11 CISCO
13.1.11.1 Business Overview
13.1.11.2 Products and Solutions
13.1.11.3 Recent Developments
13.1.11.4 MnM View
13.1.12 ARM
13.1.12.1 Business Overview
13.1.12.2 Products Offered
13.1.12.3 Recent Developments
13.1.12.4 SWOT Analysis
13.1.12.5 MnM View
13.1.13 Dell
13.1.13.1 Business Overview
13.1.13.2 Solutions Offered
13.1.13.3 Recent Developments
13.1.13.4 MnM View
13.1.14 HPE
13.1.14.1 Business Overview
13.1.14.2 Products and Solutions Offered
13.1.14.3 Recent Developments
13.1.14.4 MnM View
13.1.15 Habana Labs
13.1.15.1 Business Overview
13.1.15.2 Products Offered
13.1.15.3 Recent Developments
13.1.15.4 MnM View
13.1.16 Synopsys Inc
13.1.16.1 Business Overview
13.1.16.2 Products Offered
13.1.16.3 Recent Developments
13.1.16.4 MnM View
13.2 Other Key Players
13.2.1 S K Hynix Inc.
13.2.2 Wave Computing
13.2.3 Toshiba
13.2.4 Imagination Technologies
13.2.5 Cambricon Technologies
13.2.6 Graphcore
13.2.7 Gyrfalcon Technology Inc.
13.2.8 Cadence Design Systems
13.2.9 Tenstorrent
14 Appendix (Page No. - 182)
14.1 Insights of Industry Experts
14.2 Discussion Guide
14.3 Knowledge Store: Marketsandmarkets’ Subscription Portal
14.4 Available Customizations
14.5 Related Reports
14.6 Author Details
List of Tables (71 Tables)
Table 1 Increase in Neuroimaging and Genetics Data and Complexity Related to Computational Power From 1985 to 2019
Table 2 Average Number of Connected Devices Per Capita for Each Region, 2016 Vs. 2021
Table 3 Price Comparison of AI Hardware (Leading Companies), 2016 Vs. 2017
Table 4 Prominent Investments in AI Market in Past 3 Years
Table 5 AI Infrastructure Market, By Offering, 2016–2025 (USD Billion)
Table 6 AI Infrastructure Market for Hardware Offerings, By Type, 2016–2025 (USD Billion)
Table 7 AI Infrastructure Market for Hardware Offerings, By Deployment, 2016–2025 (USD Billion)
Table 8 AI Infrastructure Market for Hardware Offerings, By Function, 2016–2025 (USD Billion)
Table 9 AI Infrastructure Market for Hardware Offerings, By Technology, 2016–2025 (USD Billion)
Table 10 AI Infrastructure Market for Hardware Offerings, By End User, 2016–2025 (USD Billion)
Table 11 Market for Processors, By Type, 2016–2025 (USD Billion)
Table 12 Market for Processors, By Type, 2016–2025 (Million Units)
Table 13 Market for Server Software, By Deployment Type, 2016–2025 (USD Billion)
Table 14 Market for Server Software, By Function, 2016–2025 (USD Billion)
Table 15 Market for Server Software, By Technology, 2016–2025 (USD Billion)
Table 16 Market for Server Software, By End User, 2016–2025 (USD Billion)
Table 17 Market, By Technology, 2016–2025 (USD Billion)
Table 18 Market for Machine Learning, By Offering, 2016–2025 (USD Billion)
Table 19 Market for Deep Learning, By Offering, 2016–2025 (USD Billion)
Table 20 Market, By Function, 2016–2025 (USD Billion)
Table 21 Market for Training, By Offering, 2016–2025 (USD Billion)
Table 22 Market for Inference, By Offering, 2016–2025 (USD Billion)
Table 23 Market, By Deployment Type, 2016–2025 (USD Billion)
Table 24 Market for On-Premise, By Offering, 2016–2025 (USD Billion)
Table 25 Market for Cloud, By Offering, 2016–2025 (USD Billion)
Table 26 Market for Hybrid, By Offering, 2016–2025 (USD Billion)
Table 27 Market, By End User, 2016–2025 (USD Billion)
Table 28 Market for Enterprises, By Region, 2016–2025 (USD Billion)
Table 29 Market for Enterprises, By Offering, 2016–2025 (USD Billion)
Table 30 Market for Enterprises in North America, By Country, 2016–2025 (USD Million)
Table 31 Market for Enterprises in Europe, By Country, 2016–2025 (USD Million)
Table 32 Market for Enterprises in APAC, By Country, 2016–2025 (USD Million)
Table 33 Market for Enterprises in RoW, By Region, 2016–2025 (USD Million)
Table 34 Market for Government Organizations, By Offering, 2016–2025 (USD Billion)
Table 35 Market for Government Organizations, By Region, 2016–2025 (USD Million)
Table 36 Market for Government Organizations in North America, By Country, 2016–2025 (USD Million)
Table 37 Market for Government Organizations in Europe, By Country, 2016–2025 (USD Million)
Table 38 Market for Government Organizations in APAC, By Country, 2016–2025 (USD Million)
Table 39 Market for Government Organizations in RoW, By Region, 2016–2025 (USD Million)
Table 40 Market for Cloud Service Providers, By Region, 2016–2025 (USD Billion)
Table 41 Market for Cloud Service Providers, By Offering, 2016–2025 (USD Billion)
Table 42 Market for Cloud Service Providers in North America, By Country, 2016–2025 (USD Billion)
Table 43 Market for Cloud Service Providers in Europe, By Country, 2016–2025 (USD Billion)
Table 44 Market for Cloud Service Providers in APAC, By Country, 2016–2025 (USD Billion)
Table 45 Market for Cloud Service Providers in RoW, By Region, 2016–2025 (USD Billion)
Table 46 Market, By Region, 2016–2025 (USD Billion)
Table 47 Market in North America, By Country, 2016–2025 (USD Billion)
Table 48 Market in North America, By End User, 2016–2025 (USD Billion)
Table 49 Market in US, By End User, 2016–2025 (USD Billion)
Table 50 Market in Canada, By End User, 2016–2025 (USD Billion)
Table 51 Market in Mexico, By End User, 2016–2025 (USD Billion)
Table 52 Market in Europe, By Country, 2016–2025 (USD Billion)
Table 53 Market in Europe, By End User, 2016–2025 (USD Billion)
Table 54 Market in UK, By End User, 2016–2025 (USD Billion)
Table 55 Market in Germany, By End User, 2016–2025 (USD Billion)
Table 56 Market in France, By End User, 2016–2025 (USD Billion)
Table 57 Market in Rest of Europe, By End User, 2016–2025 (USD Billion)
Table 58 Market in APAC, By Country, 2016–2025 (USD Billion)
Table 59 Market in APAC, By End User, 2016–2025 (USD Billion)
Table 60 Market in China, By End User, 2016–2025 (USD Billion)
Table 61 Market in Japan, By End User, 2016–2025 (USD Billion)
Table 62 Market in India, By End User, 2016–2025 (USD Billion)
Table 63 Market in Rest of APAC, By End User, 2016–2025 (USD Billion)
Table 64 Market in RoW, By Region, 2016–2025 (USD Billion)
Table 65 Market in RoW, By End User, 2016–2025 (USD Billion)
Table 66 Market in South America, By End User, 2016–2025 (USD Billion)
Table 67 Market in Middle East and Africa, By End User, 2016–2025 (USD Billion)
Table 68 Ranking Analysis of Key Companies in AI Infrastructure Market
Table 69 Product Launches, 2017–2018
Table 70 Agreements, Partnerships, Collaborations, Contracts, and Joint Ventures, 2017–2018
Table 71 Acquisitions, 2016–2018
List of Figures (54 Figures)
Figure 1 AI Infrastructure Market: Segmentation
Figure 2 AI Infrastructure Market: Research Design
Figure 3 Bottom-Up Approach to Arrive at Market Size
Figure 4 Top-Down Approach to Arrive at Market Size
Figure 5 Data Triangulation
Figure 6 AI Infrastructure Market, By Offering, 2019 Vs. 2025 (USD Billion)
Figure 7 Training Function to Exhibit Higher CAGR in AI Infrastructure Market During Forecast Period
Figure 8 AI Infrastructure Market, By End User, 2019 Vs. 2025 (USD Billion)
Figure 9 AI Infrastructure Market, By Region, 2019–2025
Figure 10 APAC to Grow at Highest CAGR in AI Infrastructure Market During 2019–2025
Figure 11 Processors to Account for the Largest Share of AI Infrastructure Hardware Market
Figure 12 Cloud Deployment to Hold Largest Share of AI Infrastructure Market By 2025
Figure 13 Inference Function to Hold Largest Share of AI Infrastructure Market During Forecast Period
Figure 14 AI Infrastructure Market for Enterprise in APAC is Estimated to Grow at the Highest CAGR During the Forecast Period
Figure 15 AI Infrastructure Market, By End User and Region
Figure 16 The AI Infrastructure Market for Deep Learning is Estimated to Grow at the Highest CAGR During the Forecast Period
Figure 17 China is Expected to Grow at Highest CAGR in AI Infrastructure Market During Forecast Period
Figure 18 AI Infrastructure Market: Drivers, Restraints, Opportunities, and Challenges
Figure 19 Cost Per Transistor
Figure 20 Percentage and Types of Healthcare Breaches Reported By U.S. Department of Health and Human Services
Figure 21 Hardware Offerings to Command AI Infrastructure Market During 2019–2025
Figure 22 Memory to Exhibit Highest CAGR in AI Infrastructure Market, By Offering, During Forecast Period
Figure 23 Deep Learning Technology to Witness Highest CAGR in AI Infrastructure Market During Forecast Period
Figure 24 AI Infrastructure Market for Training Function is Estimated to Grow at Higher CAGR During Forecast Period
Figure 25 AI Infrastructure Market for Cloud Deployment is Estimated to Grow at Highest CAGR During Forecast Period
Figure 26 AI Infrastructure Market, By End User, 2016–2025 (USD Billion)
Figure 27 AI Infrastructure Market for Enterprises in APAC is Estimated to Grow at Highest CAGR During Forecast Period.
Figure 28 Hardware Offerings in Government Organizations is Estimated to Grow at Higher CAGR During Forecast Period
Figure 29 AI Infrastructure Market for Cloud Service Providers in APAC is Estimated to Grow at Highest CAGR During Forecast Period
Figure 30 China, US, Germany, India, and Japan Emerging as New Hot Spots in AI Infrastructure Market
Figure 31 North America to Account for Largest Share of AI Infrastructure Market, in Terms of Value, During Forecast Period
Figure 32 North America: Market Snapshot
Figure 33 Europe: Market Snapshot
Figure 34 Cloud Service Providers to Record Highest CAGR in AI Infrastructure Market in UK During Forecast Period
Figure 35 APAC: Market Snapshot
Figure 36 Cloud Service Providers Areexpected to Commandai Infrastructure Market in China During Forecast Period
Figure 37 RoW: Market Snapshot
Figure 38 Companies Adopted Product Launches as Key Growth Strategy From 2017 to 2019
Figure 39 Competitive Leadership Mapping, 2018
Figure 40 Intel Corporation: Company Snapshot
Figure 41 NVIDIA Corporation: Company Snapshot
Figure 42 Samsung Electronics: Company Snapshot
Figure 43 Micron Technology: Company Snapshot
Figure 44 Xilinx: Company Snapshot
Figure 45 Advanced Micro Devices (AMD): Company Snapshot
Figure 46 IBM: Company Snapshot
Figure 47 Google: Company Snapshot
Figure 48 Microsoft: Company Snapshot
Figure 49 Amazon Web Services: Company Snapshot
Figure 50 CISCO: Company Snapshot
Figure 51 ARM: Company Snapshot
Figure 52 Dell: Company Snapshot
Figure 53 HPE: Company Snapshot
Figure 54 Synopsys: Company Snapshot
The study involves 4 major activities to estimate the size of the AI infrastructure market at present. Exhaustive secondary research has been conducted to collect information about the market, the peer market, and the parent market. To validate these findings, assumptions, and sizing with industry experts across the value chain through primary research has been the next step. Both top-down and bottom-up approaches have been employed to estimate the complete market size. After that, market breakdown and data triangulation methods have been used to estimate the market size of segments and subsegments.
Secondary Research
In the secondary research process, secondary sources such as Hoovers, Bloomberg BusinessWeek, and Dun & Bradstreet have been referred to for the identification and collection of information for this study. The other secondary sources used during the study included annual reports, press releases and investor presentations of companies, white papers, certified publications, articles by recognized authors, gold-standard and silver-standard websites, regulatory bodies, trade directories, and databases.
Primary Research
The AI infrastructure market comprises stakeholders such as raw material suppliers, processors, end product manufacturers, and regulatory organizations in the supply chain. The demand side of this market is characterized by the development of industrial IoT, automation systems, and industrial monitoring and control. The supply side is characterized by advancements in technology and diverse use cases. Various primary sources from both the supply and demand sides of the market have been interviewed to obtain qualitative and quantitative information. Following is the breakdown of primary respondents
To know about the assumptions considered for the study, download the pdf brochure
Market Size Estimation
Both top-down and bottom-up approaches have been used to estimate and validate the total size of the AI infrastructure market. These methods have also been used extensively to estimate the size of various subsegments in the market. The research methodology used to estimate the market size includes the following:
- Key players in the industry and markets have been identified through extensive secondary research.
- The industry’s supply chain and market size, in terms of value, have been determined through primary and secondary research processes.
- All percentage shares, splits, and breakdowns have been determined using secondary sources and verified through primary sources.
Data Triangulation
After arriving at the overall market size through the estimation process explained above, the total market has been split into several segments. To complete the overall market engineering process and arrive at the exact statistics for all segments, the market breakdown and data triangulation procedures have been employed, wherever applicable. The data has been triangulated by studying various factors and trends from both the demand and supply sides. Along with this, the market has been validated using both top-down and bottom-up approaches.
Report Objectives
- To describe, segment, and forecast the AI infrastructure market, by offering, deployment, function, technology, end user, in terms of value
- To describe, segment, and forecast the AI infrastructure market, by offering, in terms of volume
- To describe and forecast the market for various segments, by region—North America, Europe, Asia Pacific (APAC), and Rest of the World (RoW)
- To provide detailed information regarding drivers, restraints, opportunities, and challenges that influence the growth of the AI infrastructure market
- To provide a detailed overview of the AI infrastructure value chain
- To analyze micromarkets1 with respect to individual growth trends, prospects, and contribution to the overall AI infrastructure market
- To analyze opportunities in the market for stakeholders by identifying the high-growth segments of the AI infrastructure market
- To profile key players in the AI infrastructure market and comprehensively analyze their market ranking in terms of revenues, shares, and core competencies2
- To analyze the competitive strategies such as product launches and developments, partnerships and collaborations, and mergers and acquisitions in the global 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
- Detailed analysis and profiling of additional market players based on various blocks of the value chain
Regional Analysis
- Further breakdown of regions into countries, by offering
- Further breakdown of regions into countries, by end-user
- Triangulate with your Own Data
- Get Data as per your Format and Definition
- Gain a Deeper Dive on a Specific Application, Geography, Customer or Competitor
- Any level of Personalization
- What are the Known and Unknown Adjacencies Impacting the AI Infrastructure Market
- What will your New Revenue Sources be?
- Who will be your Top Customer; what will make them switch?
- Defend your Market Share or Win Competitors
- Get a Scorecard for Target Partners
Growth opportunities and latent adjacency in AI Infrastructure Market