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AI Infrastructure Market - Growth, Trends, COVID-19 Impact, and Forecasts (2023-2028)

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  • 120 Pages
  • April 2023
  • Region: Global
  • Mordor Intelligence
  • ID: 5026107
UP TO OFF until Jun 30th 2023
The AI Infrastructure Market is expected to register a CAGR of 20.59% over the forecasted period. Artificial intelligence has seen significant growth and development in recent years and will be even more prevalent in a few short years. AI Infrastructure optimizes and streamlines the world of corporate data. AI Infrastructure trains machine learning algorithms that operate across databases and message queuing systems to deliver data delivery flow.

Key Highlights

  • According to IBM Global AI Adoption Index, AI usage remained steady last year, with more than a third of businesses (35%) reporting the use of AI in their operations, a four-point rise over the previous year. Accessibility, which made AI easy to apply across the enterprise, was a big driver of adoption. Still, businesses are also looking to AI to assist them in enhancing the automation of jobs and cutting costs. The AI adoption gap between larger and smaller businesses has also grown dramatically. Larger organizations are now 100% more likely to have applied AI in their organization than smaller companies, compared to 69% in 2021.
  • Further, to take advantage of the increasing AI opportunities, one of the first considerations for any organization is to have a suitable infrastructure to support AI developments. Moreover, AI solutions frequently demand new hardware and software integration to function. For instance, for collation and annotation of data sources, scalable processing, or creating and fine-tuning models as new data become available requires AI solutions, such as repurposing existing hardware and buying a one-off AI solution, building a broader platform to support multiple AI solutions, and outsourcing AI solution delivery. Thus, infrastructure plays a vital role in the growth of the AI landscape.
  • According to Nvidia, Capital market firms, hedge funds, asset managers, and exchanges are the most frequent consumers of deep learning, accounting for 58 % of all users. In contrast, machine learning is used by 80 % of fintechs, which have business AI capabilities available from the cloud but may need more data to allow many deep learning use cases.
  • The rapid digitization brought on by the pandemic is driving industry and academia to come together in India to produce more skilled labor. As per Salesforce, demand for artificial intelligence, and talent with AI expertise, has surged in recent years and more since the pandemic. On their Trailhead platform, through the pandemic, AI-related certifications/badges saw an increase of 148%, followed by blockchain-related certifications/ badges at 54%,
  • On the flip side, O’Reilly’s AI Adoption in the Enterprise report, which surveyed more than 3,500 business leaders, found that a lack of skilled people and difficulty hiring topped the challenges in AI, with 19% of respondents citing it as a “significant” barrier. The O’Reilly report suggests that the second-most significant barrier to AI adoption is a lack of quality data, with 18% of respondents saying their organization is only beginning to realize the importance of high-quality data.

AI Infrastructure Market Trends

Increasing Demand for AI Hardware in High-Performance Computing Data Centers

  • The rapid growth of smart connected devices and a massive rise in data consumption is placing enormous pressure on the underlying data center infrastructure. Data centers have become so complicated that it is now possible for only human beings to manage this surging complexity. Artificial intelligence hardware in data centers can help improve the efficiency of data operation in a significant manner.
  • Training an ML model on thousands of datasets is a compute-intensive activity best conducted in data centers. GPUs have performed this function well, and much other hardware is added to the options. For instance, the Wafer-Scale Engine (WSE) delivers much more computing power and memory. However, inference can occur in the data center by moving the information back and forth to the cloud. In general, low latency is needed for applications at the edge, along with chips that soak up less energy. Edge and data center AI call for different chip infrastructures.
  • One of Nvidia’s newer concepts in AI hardware for data centers is the BlueField DPU (data processing unit) for data centers. The company unveiled BlueField-3, a DPU explicitly designed for “AI and accelerated computing.” BlueField-3 is the world’s first 400GbE DPU, Nvidia said. It is ten times faster than its predecessor, BlueField-2. The same month, the company announced an Arm-based data center CPU for AI and high-performance computing. The new data center CPU, Grace, created new competition for x86 CPU rivals Intel and AMD when it arrived earlier this year.
  • According to cloudscene, As of January last year, there were 2,701 data centers in the United States, with 487 more in Germany. With 456, the United Kingdom placed third among countries in terms of the number of data centers, while China had 443. Such a huge number of data centers would create an opportunity for the study market to grow.
  • In March last year, NVIDIA introduced powerful new technology that would serve as the foundation for its aim of transforming data centers into AI factories, opening up new vistas in technical computing. NVIDIA unveiled its new Hopper GPU architecture and H100 GPU to power this transformation and new systems that will optimize the latest hardware for massive computing tasks, such as creating digital twins of million-square-foot Amazon warehouses, which will make it easier to train robotic systems to manage these facilities.

Asia-Pacific is Expected to Register the Fastest Growth During the Forecast Period

  • The Chinese government announced the establishment of the Next Generation Artificial Intelligence Development Plan, which promises policy support, central coordination, and investments totaling more than USD 150 billion by 2030. By the end of this decade, China's AI business is expected to produce USD 160 billion in yearly revenues, with allied industries generating USD 1.6 trillion in annual sales.
  • China's digital behemoths have been encouraged by the government to develop artificial intelligence. More relationships with industry incumbents will be catalyzed by libraries, platforms, and frameworks enabling small and medium businesses to use artificial intelligence at a lower price. It also has the added benefit of ensuring that each ecosystem develops a more equitable collection of complementors, allowing the digital behemoths to take a more significant portion of the value that artificial intelligence generates and creates.
  • Further, the government and organizations are investing in the Research & Development of AI technologies for governance. For instance, in March last year, the Government of India launched the Artificial Intelligence and Robotics Technology Park (ARTPARK) at the Indian Institute of Science (IISc) in Bengaluru. This ARTPARK is a public-private collaboration with seed money of INR 230 Crores. This is a premier research translation park with the global collaborative ecosystem, which is a joint initiative of the IISc & AI Foundry,
  • According to the Nomura Research Institute, Artificial Intelligence in Japan is expected to expand exponentially, with AI robots performing half of all occupations in Japan by 2035. While the Japanese AI market has been focused on robotics, overseas companies have focused on software development, which is an area of opportunity for foreign companies trying to enter the Japanese AI sector.
  • Further, companies such as NEC and Toshiba are developing software and equipment integrating software based on AI, ML, and other new technologies. For instance, NEC stated that it had developed a control technology for autonomous mobile robots (AMR) in warehouse applications which can increase efficiency by 100% while maintaining safety features.

AI Infrastructure Market Competitor Analysis

The AI Infrastructure Market is highly competitive, owing to multiple prominent players operating in domestic and international markets. The market is moderately concentrated, with the significant players primarily adopting effective strategies such as product innovations and mergers and acquisitions. The market is a technology-driven market that witnesses players putting substantial efforts into R&D to widen the functionality of their solutions. Some major players in the market are Nvidia Corporation, Microsoft Corporation, Google, and IBM.
  • December 2022: EnCharge AI has announced a successful Series A financing round of USD 21.7 million from investment firms Anzu Partners, AlleyCorp, Scout Ventures, Silicon Catalyst Angels, Schams Ventures, E14 Fund, and Alumni Ventures to further their AI hardware accelerators. Encharge AI promises excellent efficiency, with test chips hitting 150 TOPS/W for 8-b computing, seamless hardware-software interface with major AI frameworks such as PyTorch and TF, and 20x higher performance-per-Watt and 14x better performance-per-dollar than comparable AI accelerators.
  • May 2022: IBM has revealed the Elastic Storage System 3500, a 2U storage system intended exclusively for AI training workloads. The new machine includes 24 drive bays and a raw capacity of 368TB of NVMe storage. The ESS 3500 may achieve up to 91GB/s of throughput by utilizing Spectrum Scale, IBM's high-performance clustered file system software.

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Table of Contents

1.1 Study Assumptions and Market Definition
1.2 Scope of the Study
4.1 Market Overview
4.2 Industry Attractiveness - Porter's Five Forces Analysis
4.2.1 Bargaining Power of Consumers
4.2.2 Bargaining Power of Suppliers
4.2.3 Threat of New Entrants
4.2.4 Intensity of Competitive Rivalry
4.2.5 Threat of Substitute Products
4.3 Assessment of the Impact of COVID-19 on the Market
4.4 Market Drivers
4.4.1 Increasing Demand for AI Hardware in High-performance Computing Data Centers
4.4.2 Increasing Applications of IIoT and Automation Technologies
4.4.3 Rising Application of Machine Leaning and Deep Learning Technologies
4.4.4 Huge Volume of Data Being Generated in Industries such as Automotive and Healthcare
4.5 Market Restraints
4.5.1 Lack of Skilled Professional in the Industry
5.1 By Offering
5.1.1 Hardware Processor Storage Memory
5.1.2 Software
5.2 By Deployment
5.2.1 On-premise
5.2.2 Cloud
5.3 By End User
5.3.1 Enterprises
5.3.2 Government
5.3.3 Cloud Service Providers
5.4 By Geography
5.4.1 North America United States Canada
5.4.2 Europe United Kingdom Germany France Italy Spain Rest of Europe
5.4.3 Asia-Pacific China India South Korea Japan Rest of Asia-Pacific
5.4.4 Latin America Brazil Mexico Rest of Latin America
5.4.5 Middle-East and Africa Saudi Arabia United Arab Emirates Qatar Israel South Africa Rest of Middle-East and Africa
6.1 Company Profiles
6.1.1 Intel Corporation
6.1.2 Nvidia Corporation
6.1.3 Samsung Electronics Co. Ltd
6.1.4 Micron Technology Inc.
6.1.5 Xilinx Inc.
6.1.6 IBM Corporation
6.1.7 Google LLC
6.1.8 Microsoft Corporation
6.1.9 Amazon Web Services Inc.
6.1.10 Cisco Systems Inc.
6.1.11 Arm Holdings
6.1.12 Dell Inc.
6.1.13 Hewlett Packard Enterprise Company
6.1.14 Advanced Micro Devices
6.1.15 Synopsys Inc.

Companies Mentioned

A selection of companies mentioned in this report includes:

  • Intel Corporation
  • Nvidia Corporation
  • Samsung Electronics Co. Ltd
  • Micron Technology Inc.
  • Xilinx Inc.
  • IBM Corporation
  • Google LLC
  • Microsoft Corporation
  • Amazon Web Services Inc.
  • Cisco Systems Inc.
  • Arm Holdings
  • Dell Inc.
  • Hewlett Packard Enterprise Company
  • Advanced Micro Devices
  • Synopsys Inc.