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Semiconductor and Electronics Ai Infrastructure Market

The AI infrastructure market is projected to grow at a CAGR 21.49% rate during the forecast period. 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.


Deep Learning Technology is estimated to grow at a 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.


Cloud Service Providers in APAC is estimated to grow at the 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).


North America is projected to hold the 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.


Key Market Players includes 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).


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?

1.     Introduction

1.1.  Market Definition

1.2.  Study Scope

1.3.  Study Period

1.4.  Market Stakeholders

2.     Research Methodology

2.1.  Secondary Research

2.2.  Primary Research

2.3.  Research Design

2.4.  Data Validation

2.5.  Limitations and Assumptions

3.     Market Dynamics

3.1.  Drivers

3.1.1.Increasing Adoption of Cloud Machine Learning Platform

3.1.2.Escalating Demand for AI Hardware in High-Performance Computing Data Centers

3.1.3.Rising Focus on Parallel Computing in AI Data Centers

3.1.4.Growing Volume of Data Generated in Industries Such as Automotive and Healthcare

3.1.5.Improving Computing Power and Declining Hardware Cost

3.1.6.Growing Number of Cross-Industry Partnerships and Collaborations

3.1.7.Expanding AI Applications in Industries Such as Healthcare, Automotive, Finance, and tourism

3.1.8.Evolving Applications of Industrial IoT and Automation Technologies

3.2.  Challenges

3.2.1.Dearth of AI Hardware Experts

3.2.2.Unreliability of AI Algorithms

3.2.3.Creation of Application-Specific Models and Mechanisms of AI in Cloud

3.2.4.Concerns Regarding Data Privacy in AI Platforms

3.2.5.No Assurance or Guarantee on Returns on Investment

3.2.6.Availability of Limited Structured Data to Train and Develop Efficient AI Systems

3.2.7.

3.3.  Opportunities

3.3.1.Surging Demand for FPGA-Based Accelerators

3.3.2.Rising Need for Coprocessors Due to Slowdown of Moore’s Law

3.3.3.Increasing Focus on Developing Human-Aware AI Systems

4.     Executive Summary

5.     AI Infrastructure Market, By Application

5.1.  Hardware

5.1.1.Processor

5.1.1.1.           CPU

5.1.1.2.           GPU

5.1.1.3.           FPGA

5.1.1.4.           ASIC

5.1.2.Memory

5.1.3.Storage

5.1.4.Networking

5.2.  Server software

6.     AI Infrastructure Market, By Technology

6.1.  Machine learning

6.2.  Deep learning

7.     AI Infrastructure Market, By Function

7.1.  Training

7.2.  Inference

8.     AI Infrastructure Market, By Deployment Type

8.1.  On-premises

8.2.  Cloud

8.3.  Hybrid

9.     AI Infrastructure Market, By End-User

9.1.  Enterprises

9.2.  Government organizations

9.3.  Cloud service providers (CSP)

10. AI Infrastructure Market, By Region

10.1.                 North America

10.1.1.  US

10.1.2.  Canada

10.1.3.  Mexico

10.2.                 Europe

10.2.1.  UK

10.2.2.  Germany

10.2.3.  France

10.2.4.  Rest of Europe

10.3.                 Asia Pacific

10.3.1.  China

10.3.2.  Japan

10.3.3.  India

10.3.4.  Rest of APAC

10.4.                 Rest of the World

10.4.1.  South America

10.4.2.  Middle East and Africa

11. Company Profiles

11.1.                 Leading Companies in AI Infrastructure Market

11.1.1.  Intel Corporation

11.1.2.  Nvidia Corporation

11.1.3.  Samsung Electronics

11.1.4.  Micron Technology

11.1.5.  Xilinx

11.1.6.  IBM

11.1.7.  Google

11.1.8.  Microsoft

11.1.9.  Amazon Web Services

11.1.10.                   Cisco

11.2.                 Other Companies in AI Infrastructure Market

*Company introduction, financial information, recent developments, SWOT analysis

12. Conclusion and Recommendations

13. Appendix

13.1.                 About

13.2.                 Related Reports

13.3.                 Glossary

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