Artificial Intelligence Chipsets

Global Artificial Intelligence Chipsets Market to Reach US$291.8 Billion by 2030

The global market for Artificial Intelligence Chipsets estimated at US$97.4 Billion in the year 2024, is expected to reach US$291.8 Billion by 2030, growing at a CAGR of 20.1% over the analysis period 2024-2030. Processor, one of the segments analyzed in the report, is expected to record a 19.2% CAGR and reach US$124.3 Billion by the end of the analysis period. Growth in the Memory segment is estimated at 21.2% CAGR over the analysis period.

The U.S. Market is Estimated at US$28.5 Billion While China is Forecast to Grow at 18.6% CAGR

The Artificial Intelligence Chipsets market in the U.S. is estimated at US$28.5 Billion in the year 2024. China, the world`s second largest economy, is forecast to reach a projected market size of US$42.5 Billion by the year 2030 trailing a CAGR of 18.6% over the analysis period 2024-2030. Among the other noteworthy geographic markets are Japan and Canada, each forecast to grow at a CAGR of 17.4% and 16.4% respectively over the analysis period. Within Europe, Germany is forecast to grow at approximately 12.1% CAGR.

Global Artificial Intelligence Chipsets Market - Key Trends and Drivers Summarized

How Are AI Chipsets Revolutionizing Computing Power and Efficiency?

AI chipsets are spearheading a technological transformation by delivering unprecedented levels of computing power and efficiency, specifically tailored to meet the demands of artificial intelligence workloads. Unlike traditional CPUs, which are optimized for general-purpose computing tasks, AI chipsets—such as Graphics Processing Units (GPUs), Tensor Processing Units (TPUs), and application-specific integrated circuits (ASICs)—are designed to handle the massive data requirements and parallel processing demands of AI models. These specialized chips are capable of processing complex computations at accelerated speeds, thereby significantly reducing the time required to train deep learning models or perform real-time inference. For instance, GPUs are now the industry standard for training large neural networks, offering thousands of cores to perform parallel calculations that traditional CPUs cannot match. TPUs, developed by Google, further enhance performance for neural network processing, enabling the training of large models in a fraction of the time it would take with conventional hardware. The efficiency and speed provided by AI chipsets are powering a range of applications—from language processing and image recognition to real-time video analytics and autonomous driving—across diverse industries. Their ability to deliver high-speed processing with reduced power consumption is not only transforming AI research but also making advanced AI applications feasible for real-world use cases, establishing AI chipsets as a cornerstone in the evolution of intelligent computing systems.

What Role Do AI Chipsets Play in Edge Computing and IoT?

AI chipsets are also central to the advancement of edge computing and the Internet of Things (IoT), where real-time, localized data processing is crucial for responsive and efficient operations. In edge computing, data processing occurs on the device itself or near the data source, rather than relying on a distant cloud server, thereby reducing latency and improving response times—critical factors for applications that demand instantaneous decision-making, such as autonomous vehicles, industrial robotics, and smart city infrastructure. By embedding AI chipsets in edge devices, these systems can run sophisticated algorithms and perform local data analysis without the need for continuous internet connectivity. This capability is transformative in IoT ecosystems, where devices need to interpret data, learn from patterns, and make decisions autonomously. In smart homes, for example, devices powered by AI chipsets can adjust settings in real time based on user behavior, while in healthcare, wearable devices can analyze vital signs on the spot and alert medical staff to anomalies. Beyond performance benefits, edge AI chipsets address privacy and security concerns by keeping sensitive data local to the device, reducing exposure to potential breaches. By enabling efficient and secure on-device processing, AI chipsets are redefining the scope of IoT applications, from remote agricultural sensors to industrial automation and urban surveillance, fundamentally changing how data is processed and utilized across diverse environments.

How Do AI Chipsets Enhance Energy Efficiency in Data Centers and Consumer Electronics?

AI chipsets are setting new standards in energy efficiency, crucial for data centers and consumer electronics, where power consumption directly impacts operational costs and environmental sustainability. Data centers, which are essential to cloud computing and support massive AI workloads, are among the largest consumers of energy in the tech industry, creating demand for energy-efficient solutions to manage escalating operational costs. AI chipsets, particularly ASICs and TPUs, are designed to execute specific AI tasks efficiently, using a fraction of the power required by general-purpose CPUs and GPUs. By optimizing power usage without compromising on computational performance, these chipsets are helping data centers reduce their energy footprint, a significant advantage as companies strive to meet environmental standards. In consumer electronics, AI chipsets have a similarly transformative effect on power management. Smartphones, for example, are now equipped with AI processors that manage power distribution intelligently, enabling tasks like image recognition, voice processing, and augmented reality without draining the battery rapidly. This efficiency extends battery life and enhances device performance, making these chipsets an attractive option in energy-sensitive applications like wearables, smart home devices, and laptops. As industries increasingly prioritize sustainability, AI chipsets offer a practical solution, enabling high-performance computing while reducing power consumption and supporting a global shift towards greener, eco-friendly technology practices.

What Are the Factors Fueling the Growth of the AI Chipset Market?

Several key drivers are propelling growth in the AI chipset market. One primary driver is the rapid adoption of AI across various industries, as companies in fields such as healthcare, finance, automotive, and retail recognize the value of AI for applications like predictive analytics, automation, and decision-making support. AI chipsets provide the high-performance processing power needed to execute these complex tasks effectively, making them essential for real-time analytics, image and speech recognition, and intelligent automation. The growth of edge computing and IoT further accelerates demand for AI chipsets, as businesses need localized processing power to run AI algorithms on devices at the network’s edge. This demand is especially high in sectors like autonomous vehicles, industrial automation, and smart cities, where AI chipsets enable real-time decision-making without relying on cloud connectivity, thereby reducing latency and increasing operational efficiency. Additionally, the need for energy-efficient solutions in data centers has become a significant market driver. As companies look to reduce operational costs and minimize their environmental footprint, AI chipsets offer a solution that optimizes power consumption, making them essential for data-intensive environments. Furthermore, the evolution of AI itself—marked by advancements in deep learning architectures, natural language processing, and computer vision—continues to push the development of more specialized AI chip architectures, capable of handling increasingly complex algorithms. Government investments and strategic initiatives, particularly in smart infrastructure and AI research, are also bolstering market growth, as public and private sectors alike invest in AI-enabled technologies to remain competitive. These diverse growth drivers illustrate the critical role AI chipsets play in enabling the next generation of intelligent systems, supporting both high-performance computing needs and the transition to energy-efficient, decentralized data processing solutions. As industries continue to innovate and expand their AI applications, the AI chipset market is positioned for sustained growth, reshaping the future of computing with specialized, powerful, and scalable processing capabilities.

SCOPE OF STUDY:

The report analyzes the Artificial Intelligence Chipsets market in terms of units by the following Segments, and Geographic Regions/Countries:

Segments:
Hardware (Processor, Memory, Network); End-Use (Cybersecurity, Manufacturing, Healthcare, Automotive, Retail, Agriculture, Human Resources, Other End-Uses)

Geographic Regions/Countries:
World; United States; Canada; Japan; China; Europe (France; Germany; Italy; United Kingdom; and Rest of Europe); Asia-Pacific; Rest of World.

Select Competitors (Total 37 Featured) -

• Advanced Micro Devices, Inc.
• Amazon Web Services (AWS)
• Fujitsu
• General Vision
• Google LLC
• Graphcore
• Greenwaves Technologies
• Huawei Technologies
• IBM Corporation
• Intel Corporation
• Kalray
• Koniku
• Mediatek
• Microsoft Corporation
• Mythic
• Nvidia Corporation
• Qualcomm Technologies
• Sambanova
• Samsung Group
• Tenstorrent
• Wave Computing
• Xmos
• Zero Asic

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