Artificial Intelligence (AI) in Hardware Market Size, Share, Report 2024 To 2033

Artificial Intelligence (AI) in Hardware Market Size and Growth 2024 To 2033

The global artificial intelligence (AI) in hardware market size was valued at USD 66.99 billion in 2024 and is expected to reach around USD 307.33 billion by 2033, growing at a compound annual growth rate (CAGR) of 19.05% over the forecast period 2024 to 2033.

The Artificial Intelligence (AI) in hardware market is undergoing rapid transformation, fueled by the increasing adoption of AI across a wide range of industries. Companies are shifting toward specialized hardware solutions to meet the computational demands of AI applications. Traditional processors are being supplemented with purpose-built AI chips, such as GPUs, TPUs, and FPGAs, which are optimized for tasks like deep learning, data analytics, and real-time decision-making. This shift is particularly noticeable in industries like automotive, healthcare, and manufacturing, where AI is integral to innovations such as self-driving vehicles, predictive maintenance, and medical diagnostics. Companies like NVIDIA, Intel, and AMD are leading the charge by releasing next-generation hardware that supports faster data processing and energy-efficient AI model deployment. The growing demand for AI hardware is also being driven by the increasing need for edge computing.

Artificial Intelligence (AI) in hardware refers to the integration of AI technologies with physical computing systems, such as processors, chips, and specialized circuits. These AI-powered hardware devices are designed to accelerate machine learning tasks, enabling faster data processing, and improving the efficiency of AI algorithms. Key components of AI hardware include Graphics Processing Units (GPUs), Tensor Processing Units (TPUs), and Field-Programmable Gate Arrays (FPGAs). GPUs, for instance, are widely used in training and inference tasks, particularly for deep learning models. TPUs are custom-built by Google to enhance neural network processing, while FPGAs allow for hardware customization, providing a balance between performance and flexibility. These devices help offload tasks from traditional processors, improving the speed and scalability of AI applications in fields such as autonomous driving, robotics, healthcare, and more.

• In December 2023, AMD, a leading semiconductor manufacturer, introduced the MI300A, a groundbreaking AI hardware. The MI300A is notable for powering the world’s first 2 Exaflop supercomputers and demonstrating 10% to 20% superior performance in GROMACS and HPCG benchmarks compared to the H100.
• In 2024, a CNN Business article reports that the South Korean government plans to invest USD 6.94 billion in artificial intelligence by 2027. This investment is part of a strategic initiative to maintain a global leadership position in advanced semiconductor technologies. The initiative includes establishing a prominent research center to serve as a pivotal hub for the country’s AI R&D efforts.

Report Highlights

• By Regions, The North America region has dominated the market by holding revenue share of 37.82% in 2023.
• By Regions, Asia Pacific is experiencing significant growth by generating revenue share of 23.08% in 2023.
• By Type, The processor segment has reported largest revenue share of 55.95% in 2023.
• By End User, IT & Telecommunication Segment has acquired revenue share of 21.45% in 2023.
• By End User, In 2023 the others segemt has registered market revenue share of 29.21% in 2023.
• By Technology, Machine learning segment has captured dominating revenue revenue share of 46.8% in 2023.
• By Deployment, The cloud-based segment has achieved revenue share of 55.21% in 2023.

Artificial Intelligence (AI) in Hardware Market Growth Factors

• Advancements in AI Technologies: Continuous improvements in AI algorithms and models necessitate more powerful and specialized hardware to execute complex computations efficiently. Innovations in AI chips, such as GPUs, TPUs, and neuromorphic processors, are tailored to meet these demands, driving market growth.
• Proliferation of Edge Computing: The shift towards edge computing, where data processing occurs near the data source rather than in centralized data centers, is a major growth factor. Edge AI hardware enables real-time data analysis and decision-making in applications like autonomous vehicles, industrial automation, and smart devices, reducing latency and bandwidth usage.
• Increased Investment in AI by Governments and Corporations: Significant investments from governments and private enterprises are propelling the AI hardware market. For instance, South Korea's commitment of USD 6.94 billion by 2027 to advance AI and semiconductor technologies highlights the global push to dominate AI innovations. Such investments foster R&D activities and the development of cutting-edge AI hardware solutions.
• Rising Demand in Various Sectors: Diverse industries such as healthcare, automotive, finance, and retail are increasingly adopting AI technologies. The need for efficient AI hardware to support applications like predictive analytics, personalized medicine, autonomous driving, and customer insights is expanding the market. For example, AMD's MI300A, launched in 2023, exemplifies advancements catering to high-performance computing needs.
• Emergence of AI-Driven Applications: The growing adoption of AI-driven applications, from natural language processing to computer vision and robotics, requires robust hardware infrastructure. The need for real-time processing and enhanced performance in these applications fuels demand for specialized AI hardware, encouraging continuous market expansion.

Artificial Intelligence (AI) in Hardware Market Trends

• Integration of AI and Edge Computing: There is a growing trend towards integrating AI capabilities directly into edge devices. This allows for real-time data processing and analytics at the source, reducing latency and bandwidth requirements. Edge AI hardware is becoming increasingly vital in applications such as autonomous vehicles, smart cities, and IoT devices.
• Proliferation of AI Accelerators: Specialized AI accelerators, such as GPUs, TPUs, and custom ASICs, are being developed to enhance the performance and efficiency of AI workloads. These accelerators are designed to handle the intensive computational demands of AI tasks, providing significant performance boosts over traditional CPUs.
• Advancements in Neuromorphic Computing: Neuromorphic computing, which mimics the neural structures of the human brain, is gaining traction. This technology promises to revolutionize AI hardware by providing more efficient and powerful computing capabilities, particularly for complex tasks like pattern recognition and sensory processing.
• Rise of Quantum Computing: Quantum computing is emerging as a potential game-changer for AI. Quantum processors can handle vast amounts of data and perform complex calculations at unprecedented speeds, offering new possibilities for AI applications and pushing the boundaries of what current hardware can achieve.
• Growing Investment in AI R&D: Governments and corporations are investing heavily in AI research and development. For example, the South Korean government's USD 6.94 billion investment by 2027 aims to bolster AI and semiconductor advancements, driving innovation and growth in the AI hardware market.
• Energy-Efficient AI Hardware: As AI applications become more widespread, there is an increasing focus on developing energy-efficient AI hardware. Reducing power consumption while maintaining high performance is crucial for sustainable growth, particularly in large-scale data centers and edge deployments.
• Adoption in Diverse Industries: AI hardware is being adopted across various sectors, including healthcare, automotive, finance, and retail. Each industry leverages AI for specific applications, such as personalized medicine, autonomous driving, fraud detection, and customer insights, driving demand for specialized AI hardware solutions.

Report Scope

Artificial Intelligence (AI) in Hardware Market Dynamics

Drivers

Collaboration and Partnerships

• Strategic collaborations and partnerships between technology companies and research institutions are significantly driving the AI in Hardware market. These collaborations facilitate the sharing of expertise, resources, and technology, accelerating the development of advanced AI hardware solutions. For instance, partnerships between semiconductor manufacturers and AI software developers help create integrated solutions that enhance performance and efficiency.

Regulatory Support and Incentives

• Government policies and incentives aimed at promoting technological innovation are crucial drivers. Regulatory frameworks that support AI research and development, alongside financial incentives such as grants, tax breaks, and subsidies, encourage companies to invest in AI hardware advancements. This regulatory support fosters a conducive environment for market growth and innovation.
  Restraints

High Development Costs

• The development of advanced AI hardware, such as specialized chips and accelerators, involves substantial research and development expenditures. These high costs can be a significant barrier for smaller companies and startups, limiting their ability to compete and innovate. This financial burden can slow down the overall pace of advancements and market expansion.

Complexity and Technical Challenges

• Designing and manufacturing AI hardware that meets the rigorous demands of AI applications is technically challenging. Issues such as heat dissipation, energy consumption, and integration with existing systems pose significant hurdles. Additionally, ensuring compatibility with diverse AI frameworks and software environments adds complexity, potentially slowing down the deployment and adoption of new AI hardware solutions.

Opportunities

Expansion into Emerging Markets

• There is significant potential for AI hardware manufacturers to expand into emerging markets where AI adoption is accelerating. Countries in Asia, Africa, and Latin America are increasingly investing in AI technologies for various applications, such as healthcare, agriculture, and education. These regions offer a growing customer base and less saturated markets, presenting substantial growth opportunities for AI hardware providers.

Development of Custom AI Solutions

• As businesses across various industries seek to implement AI, there is an increasing demand for customized AI hardware solutions tailored to specific applications. Companies that can develop and offer bespoke AI hardware designed for particular industry needs, such as healthcare diagnostics or financial modeling, can tap into niche markets and create differentiated value propositions, driving new revenue streams.

Challenges

Rapid Technological Obsolescence

• The pace of innovation in AI and hardware technologies is extremely fast, leading to rapid obsolescence of existing products. Companies in the AI hardware market must continuously innovate and upgrade their offerings to stay competitive. This constant need for innovation requires significant investment in R&D and can strain resources, making it challenging to keep up with the latest advancements.

Supply Chain Disruptions

• The AI hardware market is heavily dependent on a complex global supply chain for components such as semiconductors, memory chips, and other critical materials. Disruptions in the supply chain, caused by geopolitical tensions, natural disasters, or pandemics, can lead to delays in production, increased costs, and challenges in meeting market demand, impacting overall market growth.

Artificial Intelligence (AI) in Hardware Market Segmental Analysis

The AI in hardware market is segmented into type, application, deployment, technology, end user, and region. Based on type, the market is classified into processor, memory, network, and storage. Based on application, the market is classified into training & simulation, driver monitoring systems, surveillance & security, imaging & diagnosis, robotic surgery, disaster management, visual inspection, and others. Based on deployment, the market is classified into cloud-based and on-premise. Based on technology, the market is classified into machine learning, computer vision and others. Based on end user, the market is classified into telecommunication and IT industry, banking and finance sectors, education, e-commerce, navigation, robotics, agriculture, healthcare, automotive, retail and others.

Type Analysis

Processor: This segment has covered largest market share of 55.95% in the year of 2023. The processor segment in the AI hardware market is driven by the increasing demand for powerful computing capabilities to handle complex AI algorithms and data processing tasks. Trends include the development of specialized AI processors like GPUs, TPUs, and custom ASICs, which are optimized for high-speed parallel processing, essential for machine learning and deep learning applications.

Memory: This segment has captured market share of 12.06% in 2023. In the memory segment, the market is propelled by the need for high-speed, high-capacity memory solutions that can support the vast amounts of data processed by AI systems. Trends involve the adoption of advanced memory technologies like HBM (High Bandwidth Memory) and persistent memory to enhance data throughput and storage efficiency, critical for AI workloads.

Network: In 2023 network segment has confired market share of 20.42%. The network segment focuses on the infrastructure required for seamless data transfer and communication between AI hardware components. Market drivers include the growing necessity for low-latency, high-bandwidth networking solutions to support real-time AI applications. Trends highlight the implementation of advanced networking technologies like 5G and InfiniBand to improve connectivity and data exchange rates.

Storage: This segment has reported 11.57% market share in 2023. The storage segment is driven by the exponential increase in data generation and the need for efficient data management solutions. Trends in this segment include the development of high-capacity, fast-access storage systems like NVMe (Non-Volatile Memory Express) SSDs and AI-optimized storage architectures, which facilitate quick retrieval and analysis of large datasets essential for AI operations.

Application Analysis

Training & Simulation: This segment is driven by the need for sophisticated AI models that require extensive computational power for training and simulation. Trends include the adoption of powerful AI hardware such as GPUs and TPUs to accelerate the training process and the use of simulation environments to develop and test AI algorithms efficiently. 

Driver Monitoring Systems: AI hardware in driver monitoring systems is driven by the automotive industry's focus on enhancing vehicle safety and autonomous driving features. Trends involve integrating AI chips and sensors to monitor driver behavior in real-time, improving response times, and reducing accidents through advanced driver assistance systems (ADAS).

Surveillance & Security: The surveillance and security segment benefits from AI hardware advancements that enable real-time data processing and analytics for threat detection and monitoring. Trends include the use of AI-optimized processors and edge computing devices to enhance video analytics, facial recognition, and anomaly detection in various security applications.

Imaging & Diagnosis: In the imaging and diagnosis segment, AI hardware is driven by the healthcare industry's need for accurate and fast diagnostic tools. Trends focus on the integration of AI accelerators and high-performance computing systems to support medical imaging technologies, such as MRI and CT scans, enhancing diagnostic accuracy and efficiency.

Robotic Surgery: AI hardware in robotic surgery is propelled by the demand for precision and minimally invasive procedures. Trends include the use of advanced AI processors and real-time data processing units to enhance the capabilities of surgical robots, providing better control, precision, and outcomes in complex surgeries.

Disaster Management: The disaster management segment relies on AI hardware to improve response times and decision-making during emergencies. Trends highlight the deployment of AI-enabled drones and sensors for real-time data collection and analysis, improving disaster prediction, monitoring, and response strategies through enhanced computational power.

Visual Inspection: AI hardware in visual inspection is driven by the need for automated quality control and defect detection in manufacturing. Trends involve using AI accelerators and high-resolution cameras to enhance visual inspection systems' accuracy and speed, reducing human error and increasing production efficiency.

Others: This segment includes diverse applications such as natural language processing, financial modeling, and customer service automation. Trends indicate the widespread adoption of AI hardware across these varied fields, driven by the need for efficient data processing, real-time analytics, and improved decision-making capabilities, supported by specialized AI chips and advanced computing architectures.

Technology Analysis

Based on technology the market is segmented into machine learning, computer vision and others: The machine learning segment market has reported highest market share of 46.8% in the year of 2023. The computer vision and others segment has accounted combined market share of 53.20% in 2023.

End User Analysis

Telecommunication and IT Industry: This segment has accounted market share of 21.45% in th year of 2023. The AI hardware market in telecommunications and IT is driven by the need for enhanced network performance and data management. Trends include the deployment of AI-optimized processors for network optimization, predictive maintenance, and customer service automation, improving operational efficiency and service quality.

Banking and Finance Sectors: The banking and finance sectors has calculated share of 17.54% in 2023. In banking and finance, AI hardware is driven by the demand for high-speed data processing for fraud detection, algorithmic trading, and customer analytics. Trends involve the integration of AI accelerators to enhance real-time data analysis, risk management, and personalized financial services, boosting security and customer satisfaction.

Education: The education sector leverages AI hardware to enhance personalized learning, administrative efficiency, and research capabilities. Trends include using AI processors in educational tools and platforms for adaptive learning, real-time feedback, and data-driven decision-making, improving learning outcomes and operational efficiency.

Ecommerce: AI hardware in ecommerce is driven by the need for personalized shopping experiences and efficient logistics. Trends involve deploying AI-optimized systems for recommendation engines, customer service chatbots, and inventory management, enhancing customer engagement and operational efficiency.

Navigation: The navigation segment relies on AI hardware for accurate and real-time data processing. Trends include using AI accelerators in GPS systems, autonomous vehicles, and mapping applications to improve route planning, traffic management, and situational awareness, ensuring safety and efficiency.

Robotics: AI hardware in robotics is driven by the demand for advanced automation and precision in various applications. Trends involve integrating high-performance AI chips to enhance robots' capabilities in manufacturing, healthcare, and service industries, improving productivity and accuracy. 

Agriculture: In agriculture, AI hardware is driven by the need for precision farming and resource optimization. Trends include using AI-enabled sensors and processors for real-time monitoring, predictive analytics, and automation of farming activities, improving crop yields and sustainability.

Healthcare: The healthcare sector's demand for AI hardware is driven by the need for improved diagnostics, treatment planning, and patient care. Trends involve integrating AI accelerators in medical devices and imaging systems, enabling real-time data processing and analysis, enhancing healthcare outcomes and efficiency. 12.57% market share has reported for healthcare segment in 2023.

Others: The others segment has generated highest maket share of 29.21% in 2023. Others includes various sectors like entertainment, logistics, and energy. Trends indicate the widespread adoption of AI hardware to enhance data processing, predictive analytics, and automation across these fields, driven by the need for efficiency, accuracy, and improved decision-making capabilities.

Artificial Intelligence (AI) in Hardware Market Regional Analysis

Why North America is leading in the artificial intelligence (AI) in hardware market?

The North America market size is expected to reach around USD 116.23 billion by 2033 increasing from USD 25.34 billion in 2024 with a CAGR of 19.02%. North America leads the AI in Hardware market due to its robust technological infrastructure, significant R&D investments, and strong presence of key market players such as NVIDIA and Intel. The region's emphasis on innovation, supported by government initiatives and substantial funding, drives advancements in AI hardware. The widespread adoption of AI across industries like healthcare, automotive, and finance further boosts market growth. Additionally, North America's advanced network infrastructure and early adoption of cutting-edge technologies position it as a pivotal player in the global AI hardware landscape. The U.S market size is estimated to reach around USD 87.18 billion by 2033 increasing from USD 19.00 billion in 2024 with a CAGR of 19%.

Why Asia Pacific is experiencing significant growth in the artificial intelligence (AI) in hardware market?

The Asia Pacific market size is calculated at USD 15.46 billion in 2024 and is projected to grow around USD 70.93 billion by 2033 with a CAGR of 19.36%. Asia-Pacific is experiencing rapid growth in the AI in Hardware market, fueled by significant investments from countries like China, Japan, and South Korea. The region's strong manufacturing base and advancements in semiconductor technologies contribute to its leadership in AI hardware development. Government initiatives and strategic partnerships drive innovation, while the increasing adoption of AI across sectors such as telecommunications, automotive, and consumer electronics propels market expansion. Asia-Pacific's focus on smart cities, 5G deployment, and digital transformation further accelerates the demand for advanced AI hardware solutions.

Europe Artificial Intelligence (AI) in Hardware Market Trends

The Europe market size is measured at USD 19.99 billion in 2024 and is expected to grow around USD 91.71 billion by 2033 with a CAGR of 19.15%. Europe exhibits substantial growth in the AI in Hardware market, driven by strong government support, strategic investments, and collaborations between tech companies and research institutions. The region prioritizes ethical AI development and regulatory frameworks that foster innovation. Key industries, including automotive, manufacturing, and healthcare, are increasingly integrating AI hardware to enhance efficiency and competitiveness. Germany, the UK, and France are notable contributors, with initiatives like the European Commission's AI strategy promoting research and development, positioning Europe as a significant player in AI hardware advancements.

LAMEA Artificial Intelligence (AI) in Hardware Market Trends

The LAMEA market size is forecasted to reach around USD 28.46 billion by 2033 from USD 6.20 billion in 2024 with a CAGR of 18.70%. The LAMEA region is emerging as a promising market for AI in Hardware, driven by increasing digitalization efforts and investments in AI infrastructure. Countries like Brazil and the UAE are spearheading AI initiatives, focusing on sectors such as healthcare, agriculture, and finance. Challenges such as infrastructure limitations and skill gaps are being addressed through strategic partnerships and government support. The region's growing interest in leveraging AI for economic growth and addressing societal challenges highlights its potential for significant contributions to the AI hardware market.

Artificial Intelligence (AI) in Hardware Market Top Companies

• NVIDIA Corporation
• Intel Corporation
• Advanced Micro Devices (AMD)
• Google LLC
• IBM Corporation
• Qualcomm Incorporated
• Apple Inc.
• Samsung Electronics Co., Ltd.
• Huawei Technologies Co., Ltd.
• Microsoft Corporation
• Xilinx, Inc.
• Graphcore Limited
• Amazon Web Services (AWS)
• Baidu, Inc.
• ARM Holdings

Among the new players, Graphcore Limited and Baidu, Inc. are notable. Graphcore leverages its Intelligence Processing Unit (IPU) for efficient AI workloads, while Baidu's Kunlun AI chips drive advancements in autonomous driving and cloud services. Dominating players like NVIDIA Corporation and Intel Corporation lead through robust GPUs and AI accelerators. NVIDIA's collaborations, such as with Mercedes-Benz for autonomous vehicles, and Intel's innovations in AI chips and partnerships with Mobileye for advanced driver-assistance systems (ADAS), underscore their dominance. These companies continually innovate and collaborate, driving market growth and technological advancements in AI hardware.

CEO Statements

Jensen Huang, CEO of NVIDIA Corporation

• "AI is the most powerful technology force of our time, and NVIDIA is at the forefront of this revolution. Our GPUs and AI platforms are enabling breakthroughs in scientific research, autonomous vehicles, and healthcare."

Pat Gelsinger, CEO of Intel Corporation

• "Intel is committed to leading the AI hardware market with our innovative processors and accelerators. Our focus on AI extends across our product portfolio, empowering our customers to harness the full potential of artificial intelligence."

Lisa Su, CEO of Advanced Micro Devices (AMD)

• "The launch of our MI300A AI hardware marks a significant milestone in our AI journey. We are dedicated to delivering high-performance computing solutions that drive AI advancements and transform industries."

Sundar Pichai, CEO of Google LLC

• "Our Tensor Processing Units (TPUs) are designed to accelerate AI research and applications. We continue to invest in AI hardware to push the boundaries of what's possible and make AI more accessible to everyone."

Arvind Krishna, CEO of IBM Corporation

• "IBM's AI-optimized hardware, combined with our expertise in quantum computing, positions us uniquely in the market. We are committed to driving AI innovations that address real-world challenges and create new opportunities."

Andrew Feldman, CEO of Cerebras Systems (an emerging player)

• "At Cerebras, our mission is to revolutionize AI computing. Our Wafer-Scale Engine is designed to deliver unprecedented performance for AI workloads, enabling researchers and enterprises to achieve breakthroughs faster than ever before."

Recent Developments

• In January 2024, Sidus Space, a versatile space and data-as-a-service provider, marked significant milestones in hardware contracts and AI initiatives. The company is preparing for the March 2024 launch of LizzieSat-1, its inaugural satellite, aboard SpaceX's Transporter-10 mission from Vandenberg Space Force Base, California.
• In January 2024, IBM announced its engagement with Korea Quantum Computing (KQC) to provide its most advanced AI software, infrastructure, and quantum computing services. KQC's ecosystem will benefit from IBM's comprehensive AI solutions, including the Watsonx platform for training, tuning, and deploying advanced AI models.
• In April 2024, Apple Inc. plans to revolutionize its Mac lineup with the introduction of M4 processors featuring advanced AI processing capabilities. According to Bloomberg, these processors will be integrated across all Mac models, underscoring Apple's commitment to cutting-edge technology.
• In July 2023, Huawei Technologies Co., Ltd., alongside various partners, launched the "Global Alliance on Artificial Intelligence for Industry and Manufacturing" (AIM Global). Led by UNIDO (Austria), this initiative aims to integrate public and private entities to advance AI innovation and application in industry and manufacturing sectors.
• In 2021, the partnership formed between Green Tea, an eco-friendly shopping mall, and Samsung Electronics has significantly enhanced the promotion and sales of consumer electronic products and appliances within the mall, leveraging their combined environmental and technological strengths.

Market Segmentation

By Type

• Processor
• Memory
• Network
• Storage

By Application

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

By Deployment

• Cloud-Based
• On-Premise

By Technology

• Machine Learning
• Computer Vision
• Others

By End User

• Telecommunication and IT industry
• banking and finance sectors
• Education
• Ecommerce
• Navigation
• Robotics
• Agriculture
• Health care
• Automotive
• Retail
• Others

By Region

• North America
• APAC
• Europe
• LAMEA