DUBLIN--(BUSINESS WIRE)--The "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" report has been added to ResearchAndMarkets.com's offering.
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%.
This research report segments the global AI infrastructure market on the basis of offering, technology, deployment, end-user, function geography. The report discusses major drivers, restraints, challenges, and opportunities pertaining to the AI infrastructure market and also includes value chain. The study also includes an in-depth competitive analysis of key players in the market, along with their company profiles, key observations related to product and business offerings, recent developments, and key market strategies.
A few AI infrastructure ecosystem players are as follows: SK HYNIX Inc. (South Korea), Wave Computing (US), Toshiba (Japan), Imagination Technologies (UK), Cambricon Technology (China), Cadence (US), Graphcore (UK), Gyrfalcon Technology Inc. (US), and Tensotorrent Inc. (Canada).
Drivers & Restraints
Major factors driving the AI infrastructure market growth 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. However, mature markets in North America and Europe is one of the key factors restraining the growth of the market.
Cloud deployment to hold largest share of AI infrastructure market by 2025
The cloud deployment mode provides several benefits, such as reduced operational costs, hassle-free deployment, and high scalability, easy data accessibility, faster access to critical data, and low capital requirement. The demand for the cloud deployment mode for NLP and ML tools in AI is expected to increase with the growing awareness of the benefits of cloud-based solutions.
AI solution providers are focusing on the development of robust cloud-based solutions for their clients as many organizations have migrated from on-premises to either private or public cloud. Moreover, the cloud provides additional flexibility for business operations and real-time data accessibility to companies.
The cloud platform provides improved predictive capability as this type of deployment mode enables faster alarm notification in critical situations. Further, it helps in maintaining a competitive edge by eliminating the administrative roadblocks of the supporting infrastructure and enables organizations to focus on improving their competencies. Major vendors offering cloud-based AI platforms include IBM (US), Microsoft (US), Amazon.com's AWS (US), Alibaba Cloud (China), and Google (US).
Processors to account for largest share of AI infrastructure hardware market
The processor segment includes CPUs, microprocessing units (MPUs), graphics processing units (GPUs), field-programmable gate arrays (FPGAs), and application-specific integrated circuits (ASICs). GPUs are being conventionally developed by companies such as NVIDIA (US) and ARM (UK).
High parallel processing capabilities and improved computing power are the major benefits leading to high adoption of processors in various AI applications. Tensor Processing Units (TPUs) were launched by Google (an Alphabet (US) company) in early 2016. Intel (US) has been a leading provider of CPUs, and Xilinx Inc. (US) is a major provider of FPGAs for AI applications.
APAC is likely to witness significant CAGR in AI infrastructure market during forecast period
APAC is expected to continue to lead the AI infrastructure market and is also likely to be the fastest-growing region. This is mainly attributed due to the increase in the number of manufacturing plants in various sectors, such as automotive, automation, power, and increasing adoption of cloud-based services and machine learning platform.
A few of the prolific automotive equipment manufacturers are present in APAC countries such as China, Japan, South Korea, and India. Therefore, the AI infrastructure market in APAC is likely to grow at the highest CAGR during the forecast period.
- 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
- Expanding AI Applications in Industries Such as Healthcare, Automotive, Finance, and Tourism
- Evolving Applications of Industrial IoT and Automation Technologies
- Dearth of AI Hardware Experts
- Surging Demand for FPGA-Based Accelerators
- Rising Need for Coprocessors Due to Slowdown of Moore's Law
- Increasing Focus on Developing Human-Aware AI Systems
- Unreliability of AI Algorithms
- Creation of Application-Specific Models and Mechanisms of AI in Cloud
- Concerns Regarding Data Privacy in AI Platforms
- No Assurance or Guarantee on Returns on Investment
- Availability of Limited Structured Data to Train and Develop Efficient AI Systems
- Advanced Micro Devices (AMD)
- Amazon Web Services
- Cadence Design Systems
- Cambricon Technologies
- Gyrfalcon Technology Inc.
- Habana Labs
- Imagination Technologies
- Intel Corporation
- Micron Technology
- NVIDIA Corporation
- S K Hynix Inc.
- Samsung Electronics
- Synopsys Inc
- Wave Computing
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