Artificial Intelligence (AI) in Hardware Market Size to Reach USD 473.53 Bn By 2033

The global artificial intelligence (AI) in hardware market size was estimated at USD 53.71 billion in 2023 and is anticipated to reach around USD 473.53 billion by 2033, expanding at a double-digit CAGR of 24.5% from 2024 to 2033.

Ottawa, March 06, 2024 (GLOBE NEWSWIRE) — The global artificial intelligence in hardware market size accounted for USD 66.96 billion in 2024 and is projected to hit around USD 381.98 billion by 2032, according to Precedence Research. North America dominated the market with the largest market share of 37.90% in 2023.

Artificial Intelligence (AI) in Hardware Market Revenue, by Regions ($Billion)

Region 2019 2020 2021 2022 2023
North America 7.98 10.53 13.13 16.35 20.36
Europe 6.14 8.15 10.22 12.81 16.05
Asia Pacific 4.66 6.21 7.82 9.83 12.37
LAMEA 2.11 2.73 3.33 4.06 4.95

Artificial Intelligence (AI) in Hardware Market Revenue, by Types ($ Billion)

Type 2019 2020 2021 2022 2023
Processor 11.97 15.74 19.54 24.24 30.06
Memory 2.55 3.36 4.18 5.21 6.47
Network 4.18 5.56 6.98 8.75 10.98
Storage 2.18 2.96 3.80 4.86 6.21

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Artificial intelligence in hardware market is a significant trend aimed at improving the efficiency of AI-related tasks. AI accelerators, such as GPUs and TPUs, are critical components. GPUs, initially built for graphics visualization, excel at parallel computing, making them essential for training and operating deep learning models. Google TPUs specialize in tensor operations, which are critical to several deep-learning algorithms.

FGAs provide customization for accelerating specific AI workloads, allowing specific neural network architectures to be implemented. ASICs are custom-designed circuits that deliver high performance and energy efficiency for focused AI applications. Neuromorphic computing, inspired by the human brain’s architecture, seeks parallelism and efficiency in cognitive computing. 

Edge AI and IoT devices require low-power consumption and real-time processing technology. These devices require specialized circuits for image recognition, voice processing, and sensor data analysis. Quantum computing, in its early stages, holds the potential for transforming AI by handling complex computations at unprecedented speeds.

Memory and storage technology optimizations, such as HBM and SCM, are significant for effectively processing vast amounts of data. Interconnects and networking advancements allow AI accelerators and other components to communicate effortlessly, reducing latency and improving overall system performance.

The continuous integration of hardware and software advancements is vital for delivering superior performance, energy efficiency, and scalability in AI applications, which is expected to shape the future of computing.

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Key Insights:

  • By type, the processor segment has contributed the maximum market share of 55.96% in 2023.
  • By deployment, the cloud-based segment has accounted the largest market share of 54.51% in 2023.
  • By technology, the machine learning segment led the market with a major market share of 46.85% in 2023.
  • By end user, the IT & telecommunication segment has recorded the highest market share of 21.45% in 2023.
  • By deployment, the cloud-based segment dominated the market with the biggest market share of 54.51% in 2023.

Regional Stance:

North America dominated the artificial intelligence in hardware market in 2023, and it is expected that the market will be dominated by North America in the forecast period. One of the significant reasons behind the domination is the continuous demand for the integration of AI in different sectors, including the hardware sector. Governments and private organizations are highly interested in using AI and checking its full potential for improving products, procedures, and services. The major key players in the market, like Apple, Google, Intel, NVIDIA, Qualcomm Technologies, IBM, Micron Technology, Xilinx, Microsoft, and AMD, are all from the North American regions that are boosting artificial intelligence in hardware market.

In 2023, Europe was the second largest region for artificial intelligence in hardware market, owing to technological advancements and strategic alliances. For instance, European Startup Graphcore introduced the “Intelligence Processing Unit” (IPU), an innovative in-memory computing system based on a developing three-dimensional chip packing process. This approach improves processing density while minimizing communication times. The IPU comprises a large grid of thousands of “IPU tiles,” each with memory and processing capabilities. This innovative design is expected to transform computing power in the European market, offering a remarkable 350 teraflops performance. Graphcore’s IPU is a noteworthy advancement for companies across Europe, improving the technological landscape by tackling fundamental obstacles in processing efficiency and communication speed.

Moreover, in 2022, the European Commission financed a consortium of 9 million euros to develop Europe’s AI-on-demand platform. Under the Horizon Europe Programme (Artificial Intelligence and Robotics), the AI4Europe project, spearheaded by University College Cork (UCC) in Ireland, secures €9 million for platform development. The collaboration, which includes 23 partners from 14 European countries (Finland, Spain, France, Norway, Germany, Sweden, Italy, the Netherlands, Belgium, Portugal, Romania, Slovakia, Slovenia, and Greece), will contribute to the project’s success jointly. AI4Europe is expected to use specific hardware for an interoperable platform, raising research collaboration and productivity. It will build on AI4EU and other EU-funded efforts. The Commission plans to invest €1 billion annually for the next decade to promote industry-academia collaboration. The Commission’s AI-on-demand platform is critical to strengthening Europe’s global leadership in trustworthy AI.

Artificial Intelligence in Hardware Market Scope

Report Coverage Details
Growth Rate from 2024 to 2033 CAGR of 24.3%
Global Market Size in 2023 USD 53.71 Billion
Global Market Size by 2033 USD 473.53 Billion
U.S. Market Size in 2023 USD 15.04 Billion
U.S. Market Size by 2033 USD 128.69 Billion
Base Year 2023
Forecast Period 2024 to 2033
Segments Covered Type, End User, Product, Application, Technology, Material, Devices, Deployment, Geography
Regions Covered North America, Asia-Pacific, Latin America and Middle East & Africa (MEA)

Type Insights

The processor segment dominated the market with the largest share of 55.96% in 2023. AI processors are optimized for AI workloads, featuring specialized architectures and instruction sets tailored to the demands of machine learning, deep learning, and other AI tasks. These architectures often include dedicated cores, memory structures, and hardware accelerators optimized for matrix multiplications, convolution operations, and other mathematical computations commonly used in AI algorithms.

Deployment Insights

The cloud-based segment led the market with the highest share of 54.51% in 2023. Cloud-based AI hardware solutions offer scalability and flexibility, allowing users to scale computing resources up or down based on their specific needs and workload requirements. This flexibility enables organizations to efficiently manage fluctuating demand and optimize resource utilization, leading to cost savings and improved performance. 

End-use Insights

The IT and telecommunication segment held the largest share of 21.45% in 2023. The dominance of the segment is attributed to the wider application of AI in the sector owing to the availability of large database. The intense competition in the IT and telecommunication industry drives companies to invest in AI hardware innovation to gain a competitive edge, enhance product offerings, and differentiate themselves in the market. Leading players in the sector invest heavily in AI research and development, partnerships with AI hardware vendors, and deployment of AI-driven solutions to meet evolving customer demands and market trends.
 
Devices Insights

The autonomous robots segment is expected to grow faster in the artificial intelligence in hardware market during the forecast period due to several factors, such as rising deployment in various industries, advancements in AI and Machine Learning, and demand for logistics and warehousing automation. Artificial intelligence (AI) and machine learning advancements have given robots the ability to learn, adapt, and make decisions in real time. Industries are shifting to autonomous robots to automate challenging and hazardous activities, lowering operational costs and increasing productivity. Moreover, Logistics and warehousing are experiencing an increase in demand for these robots as they optimize operations in response to the growing e-commerce sector.

  • Recently, the integration of autonomous mobile robots was seen in the e-fulfillment center operated by GEODIS in Columbus, Ohio. The robots are designed for omnichannel operations and are capable of store replenishments, fulfilling direct-to-consumer orders, and processing ship-to-store/buy online requirements in a single inventory with very little human interaction.

Application Insights

The training & simulation segment of artificial intelligence in hardware market is experiencing rapid growth across a wide range of industries. AI-powered simulators are transforming training in automotive applications by providing realistic virtual environments for drivers, engineers, and maintenance professionals. These simulations improve skills and safety while avoiding real-world risks. Similarly, the aerospace industry primarily relies on AI-powered simulations to teach pilots and maintenance workers, enhancing skill and lowering the chance of errors. In healthcare, the section transforms medical training by allowing practitioners to practice operations and surgeries in a realistic virtual setting, increasing patient safety.

  • A virtual reality brain surgery was demonstrated by a medical student at the Neurological Simulation Center of McGill University. The virtual reality experience was the world’s first experience in which VR was able to simulate the most realistic experience of neurosurgery apart from an OR.

Moreover, the game industry uses AI in Training and simulation to produce immersive gaming experiences, demonstrating the breadth of AI applications. Incorporating augmented reality (AR) and virtual reality (VR) technologies adds another degree of reality, allowing trainees to participate in interactive learning experiences. This growth represents an overall shift in workforce training, accelerating learning curves, lowering costs, and raising safety standards across industries.

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Market Dynamics

Driver: Real-time deep learning and AI hardware accelerators

In the artificial intelligence in hardware market, the demand for real-time deep learning workloads has fueled an enormous shift toward Edge AI, demanding specialized AI hardware accelerators. This shift addresses issues such as bandwidth, data protection, and low-latency needs, bringing AI functions closer to the data source. Machine learning inference at the edge involves implementing neural network models, often developed using deep learning, onto computer devices for real-time data processing, particularly image or video processing. Specialized AI hardware, such as Vision Processing Units (VPUs), Graphics Processing Units (GPUs), and Tensor Processing Units (TPUs), are vital for improving edge computing efficiency. Intel Movidius Myriad X VPUs excel in high-efficiency computer vision and edge-computing AI applications.

GPUs, such as the NVIDIA Jetson, use their fast-processing capabilities for graphics and image processing to boost AI at the edge. TPUs, such as the Google Coral Edge TPU, are ASICs that are purpose-built for the efficient execution of machine learning algorithms. The adoption of AI hardware accelerators at the edge provides benefits such as lower latency, increased security, cost-effectiveness, scalability, offline capabilities, and improved handling of data. Artificial Intelligence in hardware market is experiencing considerable growth as companies progressively deploy analytics and business intelligence closer to data-generating points, driven by the developing landscape of edge computing and the need for fast on-device machine learning inference.

  • Luxonis launched its first thermal camera in February 2024. The camera consists of OAK thermal, waterproof M12 and M8 connectors and a Myriad AI accelerator. The cameras are capable of detecting leaks and fires. It is also able to detect humans and animals more accurately than traditional cameras, which are based on vision only.

Restraint: Cyber threat

The security of AI tools is a significant concern as the technology sector seeks to protect itself against malicious threats. Protecting the confidentiality, integrity, and availability of hardware resources and data critical to AI applications is essential. Encryption is necessary because it prevents unauthorized access and reduces the risk of data breaches. Authentication and verification mechanisms are essential to avoid tampering and to ensure that only authorized organizations can communicate with AI tools.
The isolation of sensitive processes within the hardware architecture improves security by compartmentalizing operations, reducing the risk of lateral movement by potential attackers. Continuous monitoring detects unusual behavior and allows for real-time reactions to potential security incidents. Recovery mechanisms are vital for swiftly restoring functionality after a breach, minimizing downtime, and ensuring system availability.

Hardware security is an ongoing issue due to the constantly changing characteristics of AI environments, interactions with various unknown parties, and the intelligence of developing rivals. Computer engineers are expected to continually innovate hardware security measures that include encryption, authentication, isolation, monitoring, and recovery to counter the evolving threat landscape in artificial intelligence in hardware market.

Opportunity: Integration in semiconductor companies

The rise of artificial intelligence in hardware market offers a transformative opportunity for semiconductor companies, indicating a fundamental shift in value generation within the technology sector. Previously, these companies obtained a small portion of the value generated by technological stacks. However, the growth of AI forecasts that these companies are expected now to command 40 to 50 percent of the entire value. Moreover, across its nine-layer technology stack, AI depends mostly on hardware for functions such as logic and memory. Semiconductor companies are critical in addressing AI concerns.  To capitalize on this advantage, companies are expected to focus on personalized solutions for specific sectors, emphasizing customization and providing specialized memory and accelerators for increased efficiency. Strategic targeting, personalization, and end-to-end solutions are crucial to success in the AI era, allowing semiconductor companies to dominate the hardware environment.

  • Nvidia launched ‘Chat with RTX’ in February 2024, which will blur the line between hardware and software. People who bought the latest series of graphic cards will be able to use an AI-powered chatbot that will be able to operate offline on the Windows PC. The chatbot will be linked with the notes, documents, and files. The linkage will help the users to resolve their queries.

Related Reports:

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  • Artificial Intelligence for IT Operations Platform Market: The global artificial intelligence for IT operations platform market size surpassed USD 10.62 billion in 2022 and is projected to hit around USD 55.32 billion by 2032, expanding at a CAGR of 17.95% during the forecast period from 2023 to 2032.
  • Mobile Artificial Intelligence (AI) Market: The global mobile artificial intelligence (AI) market size was estimated at USD 15.41 billion in 2022, and it is expected to hit around USD 170.07 billion by 2032, growing at a CAGR of 27.14% during the forecast period from 2023 to 2032.
  • Artificial Intelligence (AI) in Manufacturing Market: The global artificial intelligence (AI) in manufacturing market size was accounted at USD 3.8 billion in 2022 and is expected to be worth around USD 68.36 billion by 2032 with a registering growth at a CAGR of 33.5% during the forecast period 2023 to 2032.
  • Enterprise Artificial Intelligence (AI) Market: The global enterprise artificial intelligence (AI) market size reached USD 7.02 billion in 2022, and it is expected to hit around USD 270.06 billion by 2032, growing at a CAGR of 44.1% over the forecast period 2023 to 2032.

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Recent Developments:

  • In December 2023, Rabbit Inc., an AI startup, announced that it had raised $10 million in a Series A round led by existing investor Khosla Ventures, bringing its total funding to $30 million. This is expected to help Rabbit grow its staff and accelerate the development of its first standalone hardware, the r1, which will be powered by the proprietary foundation model Large Action Model (LAM) and run on the Rabbit OS. The Rabbit OS and the r1 device will be introduced in an online launch event in conjunction with the Consumer Electronics Show (CES 2024) in Las Vegas. Through its revolutionary hardware and operating system, the company aims to influence the future of the human-machine interface.
  • In December 2023, Intel introduced Gaudi3, an AI CPU focused on generative AI software that will compete with Nvidia and AMD. The processor is designed to compete with Nvidia’s dominance in AI models. Core Ultra CPUs for Windows laptops with NPUs for quicker AI processing and greater gaming capabilities were also announced. In addition, fifth-generation Xeon CPUs for AI model deployment in servers were announced. The move is consistent with a broader industry trend of traditional processor manufacturers reacting to rising demand for AI-focused devices.
  • In November 2023, Microsoft introduced the Maia 100 AI chip, which will compete with Nvidia GPUs, as well as the Cobalt 100 Arm chip, which will compete with Intel processors. The Maia chip is now being tested for applications such as Bing and GitHub Copilot. In 2024, virtual machine instances based on Cobalt chips will be available on Azure. Unlike its competitors, Microsoft will not sell servers equipped with its CPUs. The Maia chip was created in response to user input. In Teams and Azure SQL Database services, Cobalt processors outscored current Arm-based CPUs by 40%. Graviton chips, which AWS’s top 100 clients use, provide a 40% price-performance gain. Microsoft’s decision is consistent with the trend of tech behemoths providing a variety of cloud infrastructure solutions.

Key Market Players:

  • Apple (US)
  • Intel (US)
  • NVIDIA (US)
  • Qualcomm Technologies (US)
  • Huawei Technologies (China)
  • Samsung Electronics (South Korea)
  • IBM (US)
  • Micron Technology (US)
  • Xilinx (US)
  • Google (US)
  • Microsoft (US) 
  • AMD (US)

Market Segmentation

By Type

  • Processor
  • Memory
  • Network
  • Storage

By End User

  • Telecommunication and IT industry
  • banking and finance sectors
  • Education
  • E-commerce
  • Navigation
  • Robotics
  • Agriculture
  • Health care
  • Others

By Product Type

  • CPU
  • GPU
  • ASIC
  • FPGA
  • Memory
  • Storage
  • Modules

By Application

  • Training & Simulation
  • Driver Monitoring Systems
  • Surveillance & Security
  • Imaging & Diagnosis
  • Robotic Surgery
  • Disaster Management
  • Visual Inspection
  • Others

By Technology

  • Machine Learning
    • Supervised Learning
    • Un-supervised Learning
    • Deep Learning
    • Others
  • Computer Vision
  • Others

By Material

  • Silicon
  • GaN
  • Glass
  • Metal
  • Others

By Devices

  • Smartphones & Tablets
  • Personal Computing Devices
  • Autonomous Robots
  • UAVs/UGVs
  • HUD
  • Others

By Deployment

  • Cloud
    • Cloud Platforms
      • Private Cloud
      • Public Cloud
      • Hybrid Cloud
      • Community Cloud
    • Cloud Services
      • SaaS
      • IaaS
      • PaaS
      • Others
  • On-premise

By Geography

  • North America
  • Europe
  • Asia-Pacific
  • Latin America
  • Middle East & Africa (MEA)

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