ARTIFICIAL INTELLIGENCE IN HARDWARE MARKET SIZE AND SHARE ANALYSIS - GROWTH TRENDS AND FORECASTS (2024-2031)

Artificial Intelligence in Hardware Market Size and Trends

Global artificial intelligence in hardware market is estimated to be valued at USD 56.21 Billion in 2024 and is expected to reach USD 158.46 Billion by 2031, exhibiting a compound annual growth rate (CAGR) of 16% from 2024 to 2031.

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Rapid adoption of AI technologies across industries for applications such as robotics, smart homes, and autonomous vehicles can boost demand for specialized AI hardware such as GPUs, chips, and other acceleration hardware during the forecast period. The market growth is driven by increasing investments by major technology players such as Intel, IBM, and Google in AI hardware startups. Widespread availability of low-cost cloud-based AI platforms and growing need for edge analytics and computing can open new avenues for AI hardware vendors in the near future.

Market Driver - Increasing Demand for AI in Various Industries

With companies adopting advanced technologies to streamline operations and improve productivity, AI find wide applications across many sectors. In manufacturing, AI is being used for predictive maintenance of machinery, quality control, and supply chain optimization.

In healthcare, AI aid diagnosis by analyzing medical images like CT scans and MRIs. Deep learning algorithms can detect patterns and abnormalities that may be missed by humans. Researchers are also working on AI assistants for nurses and caregivers. The transportation industry witness significant transformation with self-driving cars requiring sophisticated hardware for computer vision, path planning and decision making. Other emerging applications of AI include drone inspection of energy assets like pipelines and wind turbines, predictive policing based on analyzing crime patterns, and virtual assistants or chatbots for customer support.

As AI capabilities grow rapidly due to greater computing power and availability of vast amounts of data, more industries will adopt AI in the near future. Sectors like agriculture, education, customer service, and urban infrastructure can benefit from AI applications in resources optimization, adaptive learning, personalized recommendations, and traffic management respectively. Although challenges remain around integration, safety and fairness of algorithms, rising dependence of most industries on digital technologies boosts demand for specialized AI hardware that can power next generation applications. This growing importance of AI across multiple domains can drive AI in hardware market growth.

Market Concentration and Competitive Landscape

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Advancements in Machine Learning Algorithms

Advanced algorithms are able to solve complex problems by recognizing patterns in large, unstructured datasets. New deep learning architectures like convolutions neural networks and recurrent neural networks have achieved human-level performance in tasks like image recognition, natural language processing and prediction/forecasting.

Transfer learning and self-supervised learning approaches are helping ML models achieve more with less data by leveraging existing knowledge. Models are also becoming increasingly multimodal to handle diverse inputs like text, images, audio, and others. Developments in reinforcement learning have enabled systems to learn complex behaviors and take actions autonomously by trial-and-error interaction with the environment. GANs or generative adversarial networks have found applications in generating synthetic images, video and other media content. Meanwhile, self-supervised models utilize vast amounts of unlabeled data which previously went underutilized.

Advancements in computational hardware, particularly Graphics Processing Units (GPUs) and Tensor Processing Units (TPUs), have accelerated the creation of larger and more complex neural networks. To leverage these computational enhancements, algorithm researchers are developing increasingly data-intensive models that necessitate specialized hardware accelerators. The relentless pace of innovation in machine learning presents significant opportunities for AI chip manufacturers to create customizable solutions for training and deploying sophisticated algorithms across various industries. This ongoing drive for enhanced performance, efficiency, and specialized capabilities is expected to significantly boost long-term demand in the AI hardware market.

Key Takeaways from Analyst:

Global artificial intelligence in hardware market growth is driven by increasing adoption of AI technologies across industries such as IT and Telecommunications, Manufacturing, Retail, Automotive, Healthcare, Others. Growing processing power needs of deep learning workloads and demand for accelerated computing can drive the market growth. Edge devices and IoT will be a key growth area for specialized AI hardware due to rising need to analyze large volumes of data in real-time. However, high initial development costs of AI chips and lack of standards can hamper the market growth.

North America currently dominates the market due to presence of large tech companies and major research hubs. Meanwhile, Asia Pacific is aggressively investing in AI and is expected to witness the fastest growth. The countries like China aims to become the global leader in AI by 2030, and this will boost hardware demand. Furthermore, countries like Japan, South Korea and those in Western Europe are also rapidly deploying AI and supporting hardware development to gain competitive advantage.

Specialized AI processors present a significant opportunity for hardware manufacturers to take advantage of the AI boom by creating application-specific chips. Additionally, the increasing automation in factories is expected to enhance the integration of vision solutions, thereby propelling the growth of the computer vision hardware segment. Nevertheless, GPUs will remain crucial in training AI models for the foreseeable future, thanks to their flexibility and extensive parallel architecture.

Market Challenge - Complexity in Design and Development of AI Hardware

Complexity in design and development of specialized AI hardware can hamper the global artificial intelligence in hardware market growth. Developing application-specific integrated circuits (ASICs) or systems-on-chips (SoCs) that can handle the computational requirements of deep learning and neural networks is an immense technical challenge. It requires expertise in hardware design, semiconductor fabrication, parallel computing architectures and software-hardware co-design. Finding the optimal level of processing power, memory bandwidth, power consumption and cost is difficult to achieve. Transitioning AI models trained on general-purpose GPUs to run efficiently on custom hardware also pose challenges. Hardware vendors must invest heavily in R&D to continuously innovate and deliver performance improvements with each new generation of AI chips. This sustained engineering effort coupled with design cycles that can extend years adds to the complexity faced by hardware companies in commercializing specialized AI solutions.

In December 2023, Intel Corporation, a leading technology company that is known for its innovations in semiconductor design and manufacturing, announced the launch of its new AI products and hardware. The introduction of the 5th Gen Intel Xeon and Intel Core Ultra processors expands the company's extensive AI hardware portfolio, positioning it to compete effectively with top AI hardware providers.

Opportunity- Growing Demand for AI in Gaming and Entertainment

Gaming and entertainment industry has emerged as a major opportunity area for artificial intelligence in hardware. Applications of AI such as computer vision, motion tracking, predictive analytics, facial recognition, and natural language processing enhances the user experience in games, interactive content and simulations. The industry is investing heavily in AI techniques to power immersive VR/AR experiences, personalized recommendations, contextual storytelling, lifelike virtual characters and integrated social experiences. This growing demand from gaming and entertainment customers for specialized AI capabilities can boost development of efficient AI hardware accelerators optimized for computer vision, voice and language processing workloads.

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Insights By Type of Hardware - Growing processing demands of AI can drive AI processors segment growth

In terms of type of hardware, AI processors segment is estimated to contribute the 45.1% market share in 2024, owing to growing processing demands of advanced AI techniques. As AI applications become more sophisticated, utilizing techniques such as deep learning, computer vision, and natural language processing, the processing requirements to power these applications have exponentially increased. This has boosted demand for specialized AI processors that can handle these intensive workloads much more efficiently than traditional CPUs or GPUs.

AI processors such as GPUs, Application-Specific Integrated Circuits (ASICs), and neuromorphic chips are designed from the ground up specifically for AI tasks, with dedicated architectures and instruction sets tailored for common AI operations. These offer significantly higher AI performance per watt than alternatives, allowing for the training and deployment of larger and more complex AI models. Leading companies developing AI processors include Nvidia, Intel, Qualcomm, and Graphcore. As AI continues to advance and be applied across more industries, the need for high-performance and power-efficient AI hardware will boost demand for AI processors.

In December 2023, AMD, a renowned semiconductor manufacturer, launched its new AI hardware- the MI300. The MI300A is notable for being the world’s first 2 Exaflop supercomputer and demonstrates 10% to 20% better performance on GROMACS and HPCG benchmarks compared to the H100.

Insights By Application - Robotic automation boosts demand for AI hardware in robotics applications

In terms of application, robotics segment is estimated to contribute the 29.6% market share in 2024, owing to rising adoption of AI and automation across robotic systems. The integration of AI hardware such as AI chips, accelerators, and edge servers enables sophisticated computer vision, decision making, and motion planning capabilities in robotics. This allows robots to perform complex automated tasks that were previously not possible. Applications include industrial robot arms, autonomous vehicles, medical surgical robots, service robots, and more.

As the economic benefits of robotic automation become clear, many companies are incorporating AI and robotics into their operations to improve productivity, quality control, and workflow efficiency. Demand is especially high from logistics warehouses needing to scale operations as well as factories aiming to enhance production through robotic assembly lines. AI hardware acts as the "brain" powering these robotic systems, interpreting sensor inputs and executing responsive motions in real-time. The increased cognitive abilities brought by AI position robotics for even broader adoption across more industries going forward.

Insights By End-use Industry - AI Infrastructure Demands Drive IT Adoption in Data Centers

In terms of end-use industry, IT and telecommunications segment is estimated to contribute the 31.4% market share in 2024, owing to immense AI infrastructure requirements of modern data centers. As enterprises increasingly rely on cloud platforms and AI-powered web services, data centers witness exponential growth in data volumes and computational workloads. Training and deploying advanced ML models requires vast amounts of processing power, memory, networking, and data storage.

Major cloud providers such as AWS, Microsoft Azure, Google Cloud, IBM Cloud, and Alibaba have embarked on initiatives to integrate AI throughout their infrastructure stacks to support these necessities. This includes deploying AI ASICs, AI accelerators as well as AI frameworks and software to handle recommendation systems, forecasting, security monitoring, maintenance scheduling, and a multitude of other functions. AI chips and AI edge servers are also being distributed to the network edge to enable low-latency AI-based services and applications.

Telecommunication companies are applying AI in 5G networks for dynamic resource allocation, predictive maintenance, fraud detection and more. This widespread adoption of AI hardware and solutions across IT and cloud infrastructure cements the lead of this industry in driving AI hardware demand overall.

Regional Insights

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North America has established itself as the dominant region in the global artificial intelligence in hardware market with 35.1% in 2024. With major technology companies like Intel, Nvidia and AMD headquartered in the U.S., the region accounts for the largest number of AI chip startups and suppliers. These companies have made huge investments in innovating AI hardware like processors, systems and edge devices optimized for neural network computations. Sustained focus on R&D has enabled North American firms to commercialize cutting-edge AI chip technologies well ahead of competitors. These also wield significant influence over global technology standards and supply chains catering to AI deployment. The presence of large tech customer base has further accelerated adoption of AI hardware goods and solutions across industries in the region.

Asia Pacific is rapidly emerging as the fastest-growing market, largely fueled by China's significant investments. The Chinese government is heavily funding and promoting indigenous innovation, leading to substantial advancements in domestic AI chip manufacturing. The robust electronics manufacturing sector in China enables startups to quickly scale their hardware production. Initiatives like 'Made in China 2025' are enhancing this growth by designating AI processors and edge devices as key strategic areas. Moreover, several Chinese companies are successfully expanding internationally by utilizing their competitive manufacturing capabilities and expertise. Government policies mandating AI deployment across essential industries are further boosting domestic demand. Other countries in Asia are also increasing their investments to take advantage of the economic opportunities presented by China's ascent as a global AI leader. As local capabilities strengthen, the Asia Pacific region is progressively transforming into a net exporter of AI hardware technologies rather than an importer.

Market Report Scope

Artificial Intelligence in Hardware Industry News

• In February 2024, Luxonis, a company specializing in AI-powered vision systems, launched its first thermal camera, the OAK Thermal. This innovative camera features waterproof M12 and M8 connectors and is equipped with a Myriad AI accelerator. It is designed to detect leaks and fires, as well as to identify humans and animals with greater accuracy than traditional vision-only cameras.
• In January 2024, Sidus Space, a multi-faceted Space and Data-as-a-Service company focused on mission-critical hardware manufacturing, multi-disciplinary engineering services, satellite design, production, launch planning, mission operations, and in-orbit support, reached significant hardware contract and artificial intelligence (AI) milestones as the Company prepared for the launch of LizzieSat-1, the first satellite in its constellation, in March 2024 as part of SpaceX's Transporter-10 mission from Vandenberg Space Force Base in California.
• In April 2020, Intellifusion, a China-based AI chip maker specializing in visual intelligence, completed a pre-IPO financing round of nearly USD 141 million (approximately CNY 1 billion). This round was led by Utrust VC, Forebright Capital, and existing investor Walden International. Intellifusion focuses on developing AI technologies, including its recently launched second-generation chip, DeepEye1000, which is a heterogeneous multi-core visual analysis SoC featuring a custom instruction set neural network processor.
• In March 2020, Socionext Inc., a company specializing in the design and development of System-on-Chip (SoC) products, developed a prototype chip that integrates newly developed quantized Deep Neural Network (DNN) technology. This advancement enables highly sophisticated AI processing tailored for small, low-power edge computing devices. The prototype is part of a research initiative titled “Updatable and Low Power AI-Edge LSI Technology Development,” commissioned by Japan's New Energy and Industrial Technology Development Organization (NEDO).

*Definition: Global Artificial Intelligence in Hardware Market refers to the worldwide sale and development of hardware and chips designed specifically for artificial intelligence applications like machine learning and deep learning. This includes hardware accelerators, chips, processors and other devices that can speed up AI training and inference. The hardware is boosting the performance of AI systems used across industries like automotive, healthcare, manufacturing, agriculture.

Market Segmentation

• By Type of Hardware Insights (Revenue, USD Bn, 2019 - 2031)
  • AI Processors
  • AI Accelerators
  • AI Chips
  • AI-enabled Servers
• By Application Insights (Revenue, USD Bn, 2019 - 2031)
  • Robotics
  • Automotive
  • Healthcare
  • Consumer Electronics
  • Data Centers
  • Others
• By End-use Industry Insights (Revenue, USD Bn, 2019 - 2031)
  • IT and Telecommunications
  • Manufacturing
  • Retail
  • Automotive
  • Healthcare
  • Others
• Regional Insights (Revenue, USD Bn, 2019 - 2031)
  • North America
    • U.S.
    • Canada
  • Latin America
    • Brazil
    • Argentina
    • Mexico
    • Rest of Latin America
  • Europe
    • Germany
    • U.K.
    • Spain
    • France
    • Italy
    • Russia
    • Rest of Europe
  • Asia Pacific
    • China
    • India
    • Japan
    • Australia
    • South Korea
    • ASEAN
    • Rest of Asia Pacific
  • Middle East
    • GCC Countries
    • Israel
    • Rest of Middle East
  • Africa
    • South Africa
    • North Africa
    • Central Africa
• Key Players Insights
  • Advanced Micro Devices Inc.
  • com, Inc.
  • Apple Inc.
  • Baidu, Inc.
  • Facebook, Inc.
  • Google LLC
  • ai
  • Huawei Technologies Co., Ltd.
  • IBM Corporation
  • Intel Corporation
  • Lifegraph
  • MediaTek Inc.
  • Microsoft Corporation
  • NVIDIA Corporation
  • Qualcomm Technologies, Inc.