The AI Infrastructure Market size is projected to reach USD 63.7 Billion by 2027, from USD 16.5 Billion in 2020 growing at a CAGR of 25.2% during 2021-2027.
The AI Infrastructure Market research report thoroughly explains each and every aspect related to the Global AI Infrastructure Market, which facilitates the report’s reader to study and evaluate the upcoming market trend and execute the analytical data to promote the business.
A large-scale AI Infrastructure Market document offers an all-inclusive study about production capacity, consumption, import, and export for all the major regions across the world. Furthermore, the statistical and numerical data such as facts and figures are represented very neatly in the report by using charts, tables, or graphs. This market report also involves strategic profiling of the major players in the market, comprehensive analysis of their basic competencies, and thereby keeping the competitive landscape of the market in front of the client. The winning AI Infrastructure report covers all the market shares and approaches of the major competitors or the key players in the market.
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Scroll down 100s of data Tables, charts and graphs spread through Pages and in-depth Table of Content on “AI Infrastructure Market, By Offering (Hardware, Software), By Deployment Mode (On-premise, Cloud Based, Hybrid), Technology (Machine learning, Deep learning), End-user and Geography – Global Forecast to 2026”. Early buyers will get 10% customization on study.
To Avail deep insights of AI Infrastructure Market Size, competition landscape is provided i.e. Revenue Analysis (M $US) by Company (2018-2020), Segment Revenue Market Share (%) by Players (2018-2020) and further a qualitative analysis is made towards market concentration rate, product/service differences, new entrants and the technological trends in future.
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The AI infrastructure market size is projected to reach USD 63.7 Billion by 2027, from USD 16.5 Billion in 2020 growing at a CAGR of 25.2% during 2021-2027. 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.
Based on Offering, the AI infrastructure market has been segmented as follows:
- 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:
Based on Deployment type, the AI infrastructure market has been segmented as follows:
Based on End-user, the AI infrastructure market has been segmented as follows:
- Government organizations
- Cloud service providers
Based on Region, the AI infrastructure market has been segmented as follows:
- North America
- Asia Pacific
- Rest of the World
- 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 22×80 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.
What Porter’s Five Forces of Competitive Analysis Provides?
Competitive rivalry:- The main driver is the number and capability of competitors in the market. Many competitors, offering undifferentiated products and services, will reduce market attractiveness.
Threat of substitution:- Where close substitute products exist in a market, it increases the likelihood of customers switching to alternatives in response to price increases. This reduces both the power of suppliers and the attractiveness of the market.
Threat of new entry:- Profitable markets attract new entrants, which erodes profitability. Unless incumbents have strong and durable barriers to entry, for example, patents, economies of scale, capital requirements or government policies, then profitability will decline to a competitive rate.
Supplier power:- An assessment of how easy it is for suppliers to drive up prices. This is driven by the: number of suppliers of each essential input; uniqueness of their product or service; relative size and strength of the supplier; and cost of switching from one supplier to another.
Buyer power:- An assessment of how easy it is for buyers to drive prices down. This is driven by the: number of buyers in the market; importance of each individual buyer to the organisation; and cost to the buyer of switching from one supplier to another. If a business has just a few powerful buyers, they are often able to dictate terms.
Five forces analysis helps organizations to understand the factors affecting profitability in a specific industry, and can help to inform decisions relating to: whether to enter a specific industry; whether to increase capacity in a specific industry; and developing competitive strategies.
The country section of the report also provides individual market impacting factors & changes in regulation in the market domestically that impacts the current & future trends of the market. Data points such as consumption volumes, production sites & volumes, import export analysis, price trend analysis, cost of raw materials, down-stream & upstream value chain analysis are some of the major pointers used to forecast the market scenario for individual countries. Also, presence & availability of global brands & their challenges faced due to large or scarce competition from local & domestic brands, impact of domestic tariffs & trade routes are considered while providing forecast analysis of the country data.
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