The AI infrastructure market is expected to grow from USD 23.7 billion in 2021 to USD 79.3 billion by 2026, at a CAGR of 27.3%. Increased data traffic and need for high computing power, increasing adoption of cloud machine learning platform, increasingly large and complex dataset, rising focus on parallel computing in AI data centers, and growing number of cross-industry partnerships and collaborations ?are the key factors driving the AI infrastructure market. However, lack of AI hardware experts and skilled workforce may create hurdles for the market.
The COVID-19 pandemic is an accelerator for AI technology, helping people around the world get more and more comfortable with leveraging these tools for many applications, including healthcare. The adoption of remote patient monitoring, decoding genomic sequence for drug development, healthcare chatbots, and enhancement of CT scans using AI technology in diagnosis is expected to gain momentum during and after the COVID-19 pandemic.
Post-COVID-19, the manufacturing sector is expected to adopt smart manufacturing processes using AI, IoT, and blockchain technologies. Companies can reduce costs, increase process efficiency, and reduce human contact significantly by adopting these technologies. Currently, AI is being used for predictive maintenance and will further be implemented to forecast demand and returns in the supply chain.
Market for server software is expected to have a gradual growth during the forecast period
In the past three years, most developments have been witnessed in AI software and related software development kits. The software integrated into existing computer systems carries out complex operations. It synthesizes the data received from the hardware systems and processes it in an AI system to generate an intelligent response. The software itself can run the specific application without using any additional hardware.
Training function is expected to grow at a faster CAGR during the forecast period
The process of training an AI model involves providing the learning algorithm with training data to learn from. Training is computationally intensive and is best accelerated with GPUs. Using even a small dataset, the time taken to go through all the training samples can be reduced when using GPU compared to CPU. Hence, training function is expected to grow at a faster CAGR during the forecast period.
Enterprises to play a crucial role during the forecast period
High technological developments across various data centers of enterprises have generated and stored large volumes of data. The complexities within the IT infrastructure encourage these data centers to adopt virtualization technology, thereby fueling the growth of enterprise data centers. The utilization of advanced big data solutions for operational data explosion is impacting the future requirements for AI-based servers.
APAC is expected to register the highest CAGR in the market during the forecast period
The deployment of cloud-based services on a large scale is likely to usher in digital transformation in the APAC region. Increased demand for AI data centers and cloud resources led to the development of large-scale public cloud data centers. The demand for AI data centers in China has exceeded the available supply as organizations seek enhanced connectivity and scalable solutions for their growing businesses.
North America accounted for the largest share of the AI infrastructure market in 2020. The US and Canada are expected to adopt AI-based servers at a high rate. These countries are technologically developed economies in North America owing to their strong focus on investing in R&D activities for the development of new technologies.
Breakdown of primary participants:
- By Company Type: Tier 1 = 15%, Tier 2 = 50%, and Tier 3 = 35%
- By Designation: C-Level Executives = 45%, Directors = 35%, and Others = 20%
- By Region: North America = 45%, Europe = 35%, APAC = 12%, South America= 3%, Middle East & Africa = 5%,
Some of the major players in the AI infrastructure market include Intel Corporation (US), NVIDIA Corporation (US), IBM (US), Xilinx (US), Advanced Micro Devices (AMD) (US), Samsung Electronics (South Korea), Micron Technology (US), Google (US), Microsoft (US), Amazon Web Services (US), and so on.
In this report, the AI infrastructure market has been segmented on the basis of offering, technology, function, deployment type, end user, and geography. The report also discusses the drivers, restraints, opportunities, and challenges pertaining to the market. It gives a detailed view of the market across four main regions?North America, Europe, APAC, and RoW. Value chain analysis has been included in the report, along with the key players and their competitive analysis in the AI infrastructure ecosystem.
Key Benefits to Buy the Report:
- This report includes statistics for the AI infrastructure market based on offering, technology, function, deployment type, end user, and geography, along with their respective market sizes.
- Value chain analysis and key industry trends have been provided for the market.
- Major drivers, restraints, opportunities, and challenges for the AI infrastructure market have been provided in detail in this report.
- This report would help stakeholders to understand their competitors better and gain more insights to enhance their position in the market.
- The competitive landscape section includes the competitor ecosystem and the recent development strategies adopted by the key players in the market, such as product launches/developments, contracts/partnerships/agreements/acquisitions.