Artificial Intelligence (AI) Chips Market to Grow by USD 902.65 Billion by 2029, Boosted by Focus on AI Chips for Smartphones, Report on AI Impact on Market Trends- Technavio

NEW YORK, Feb. 3, 2025 -- Report on how AI is redefining market landscape - The global artificial intelligence (AI) chips market size is estimated to grow by USD 902.65 billion from 2025-2029, according to Technavio. The market is estimated to grow at a CAGR of over 81.2%  during the forecast period. Increased focus on developing ai chips for smartphones is driving market growth, with a trend towards convergence of AI and IoT. However, dearth of technically skilled workers for ai chips development  poses a challenge. Key market players include Advanced Micro Devices Inc., Baidu Inc., Broadcom Inc., Cerebras, Fujitsu Ltd., Google LLC, Graphcore Ltd., Huawei Technologies Co. Ltd., Intel Corp., International Business Machines Corp., MediaTek Inc., Microchip Technology Inc., NVIDIA Corp., NXP Semiconductors NV, Qualcomm Inc., SambaNova Systems Inc., Samsung Electronics Co. Ltd., SenseTime Group Inc., Taiwan Semiconductor Manufacturing Co. Ltd., and Tesla Inc..

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Market Driver

Artificial Intelligence (AI) is revolutionizing industries from healthcare to retail, finance, and automotive with deep learning and machine learning algorithms. The demand for AI technologies is driving the growth of AI chips market. Companies like Advanced Micro Devices, Nvidia, and Huawei are leading the way with AI chip lines, such as Trainium2 and A100 chip, designed for high computing and parallel processing. Quantum computing and generative AI are the next frontiers. Hardware components like CPUs, GPUs, FPGAs, and ASICs are essential for AI technologies. Energy efficiency is a key consideration, with AI chipmakers focusing on highbandwidth memory and system on chip designs. Cloud providers like Microsoft Azure, Amazon Web Services, and Google Cloud offer AI services, while edge computing enables real-time applications on Edge devices. Ethical concerns around AI use are rising, with specific integrated circuits and multichip modules being developed to address these issues. AI data centers and cognitive computing are powering AI applications in various sectors, including healthcare, retail, finance, automotive, and manufacturing. Patent filings for AI technologies continue, with potential applications in mobile health monitoring, elderly population care, and image recognition. The future of AI lies in the intersection of AI, robotics, and quantum computing, with potential use cases in autonomous vehicles, IoT devices, and Industry 4.0. The market for AI chips is expected to grow significantly, with investments in research and development and collaboration between tech giants and startups. However, system failure and malfunctioning remain challenges, with ongoing efforts to improve reliability. 

The Internet of Things (IoT) market is experiencing significant growth due to the advantages it offers in various industries, including aerospace and defense, automotive, consumer electronics, healthcare, and more. IoT devices make decisions using data they receive, eliminating the need for human intervention. To enhance their capabilities, IoT device manufacturers integrate Human-Machine Interface (HMI) technologies into devices such as cameras, drones, smart speakers, smartphones, smart TVs, and others. This integration leads to the deployment of Artificial Intelligence (AI) chips in IoT devices, enabling power-efficient data processing and machine learning computation. The high demand for IoT devices and the integration of AI chips make the AI Chips Market a lucrative business opportunity. 

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 Market Challenges

• Artificial Intelligence (AI) is revolutionizing industries from healthcare to retail, finance, and automotive. However, the growing demand for advanced AI technologies, including deep learning and robotics, poses challenges for hardware components like AI chips. Traditional CPUs, GPUs, FPGAs, and ASICs struggle to meet the high computing requirements of AI algorithms and machine learning models. Leading tech companies like Advanced Micro Devices, Nvidia, and Huawei are investing in AI chip lines to address these challenges. For instance, Nvidia's A100 chip and Huawei's Ascend 910B chipset are designed for AI data centers, while the Trainium2 chip focuses on edge computing. Energy efficiency is another concern, as AI applications, such as generative AI and large language models, require massive data processing. Highbandwidth memory and quantum computing are potential solutions, but they present their own challenges. Moreover, ethical concerns surrounding AI technologies, such as system failure and malfunctioning, require careful consideration. Edge computing and centralized cloud servers offer alternatives for real-time applications and reducing latency. AI applications span various industries, including healthcare, retail, finance, and automotive. Ethical concerns and energy efficiency challenges must be addressed to ensure the widespread adoption of AI technologies. AI technologies like cognitive computing, machine intelligence, image recognition, and pose detection are transforming industries, from healthcare to retail, finance, automotive, and more. Patent filing and system failure are concerns, but the potential benefits far outweigh the challenges. AI technologies are also being integrated into everyday devices, such as mobile phones, personal computers, gaming consoles, and even wearable devices and smart homes. Theoretical and algorithmic basis, visual understanding, and automatic analysis are essential for these applications. In conclusion, the AI chip market is experiencing significant growth, driven by the increasing demand for AI technologies in various industries. However, challenges related to energy efficiency, ethical concerns, and system failure must be addressed to ensure the widespread adoption of AI technologies. Companies are investing in AI chip lines, such as Nvidia's A100 chip and Huawei's Ascend 910B chipset, to meet the demands of AI applications in industries like healthcare, retail, finance, and automotive. Edge computing and centralized cloud servers offer alternatives for real-time applications and reducing latency. The future of AI technologies is bright, with potential applications in manufacturing machines, processors, GPUs, FPGAs, CPUs, DSPs, microcontrollers, frame buffers, display devices, and programmable logic chips.
• The AI chips market is experiencing significant growth due to the potential revenue increases that companies can achieve through artificial intelligence implementation. However, the market's expansion is hindered by a shortage of skilled professionals with expertise in AI technology. The high research and development costs associated with AI integration and the lack of available talent pose challenges for enterprises. To address this issue, companies must carefully evaluate the benefits and requirements before implementing AI solutions. The scarcity of experienced AI professionals is currently the greatest barrier to the widespread adoption of AI within business operations.

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Segment Overview 

This artificial intelligence (ai) chips market report extensively covers market segmentation by  

• Product
  • ASICs
  • GPUs
  • CPUs
  • FPGAs
• End-user
  • Media And Advertising
  • BFSI
  • IT And Telecommunication
  • Others
• Geography
  • North America
  • Europe
  • APAC
  • South America
  • Middle East And Africa
• Processing Type
  • Edge
  • Cloud
• Application
  • Nature Language Processing
  • Robotics
  • Computer Vision
  • Network Security
  • Others
• Technology
  • System On Chip
  • System In Package
  • Multi Chip Module
  • Others

1.1 ASICs-  Artificial Intelligence (AI) chips are experiencing significant growth due to the increasing adoption of application-specific integrated circuits (ASICs) in data centers. ASICs are customized, non-configurable chips that offer faster performance than GPUs and FPGAs. Google's Tensor Processing Unit (TPU) is an example of an ASIC designed for deep neural networks. TPUs provide higher performance and speed compared to GPUs, FPGAs, and CPUs. The TPU is a network of software and hardware that learns specific tasks by analyzing large data sets. TensorFlow, an open-source machine learning library, is used with TPUs to develop new algorithms. ASIC-based AI chips are gaining market share due to their ability to offer superior performance and speed in data center applications. The use of ASICs in cloud-based data centers is driving the growth of the AI chips market.

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Research Analysis

Artificial Intelligence (AI) Chips Market: The global AI Chips Market is experiencing significant growth due to the increasing adoption of AI technologies in various industries. Deep learning and machine learning algorithms are driving the demand for AI chips, which are specialized hardware components designed to accelerate AI computations. AI chips include specific integrated circuits like CPUs, GPUs, FPGAs, and system on chips. Quantum computing is also expected to revolutionize AI technologies, leading to the development of advanced AI chips. The market is witnessing innovation with the introduction of new AI chips like Trainium2, designed for high-performance AI applications. Industries such as healthcare, retail, finance, automotive, and IoT devices are major consumers of AI chips. Ethical concerns surrounding AI are also pushing the need for more advanced AI chips to ensure transparency and accountability. The market is segmented into cloud and edge computing, with the edge computing segment expected to grow due to the increasing demand for real-time AI processing. Highbandwidth memory is another crucial component in the development of advanced AI chips. The market is expected to grow significantly in the coming years, with new applications and use cases emerging in robotics, autonomous vehicles, and generative AI.

Market Research Overview

Artificial Intelligence (AI) Chips Market: Overview The AI Chips Market is a rapidly growing sector driven by the increasing demand for advanced AI technologies, including deep learning and machine learning, in various industries. AI Chips are specialized hardware components designed to accelerate AI algorithms and technologies, such as neural networks, quantum computing, and cognitive computing. These chips are integral to powering AI applications in robotics, computer vision, natural language processing, and other fields. The market for AI Chips encompasses a range of hardware components, including CPUs, GPUs, FPGAs, ASICs, DSPs, and microcontrollers. Companies are investing heavily in research and development to create more energy-efficient and high-performance AI chips to meet the demands of real-time applications and big data processing. Key trends in the AI Chips Market include the development of AI chip lines, such as Nvidia's A100 chip and Ascend 910B chipset, and H200 chipset, as well as the integration of highbandwidth memory and other advanced technologies. The market is also seeing the emergence of edge computing and the use of AI in centralized cloud servers and edge devices, such as mobile phones and IoT devices. Applications of AI Chips span various industries, including healthcare, retail, finance, automotive, and manufacturing. Ethical concerns surrounding AI and the potential for system failure or malfunctioning are also driving the need for more advanced and reliable AI chips. The market is expected to continue growing as AI technologies become increasingly integrated into everyday life, from health monitoring and personalized health to smart homes, connected cars, and manufacturing machines. The theoretical and algorithmic basis of AI technologies, including deep learning and machine learning, continue to evolve, with advancements in image recognition, pose detection, behavioral patterns, and structured data analysis. Patent filing and innovation are key drivers in the AI Chips Market, with companies investing in research and development to stay ahead of the competition. The market for AI Chips is diverse and dynamic, with applications ranging from mobile phones and embedded systems to personal computers, gaming consoles, and data centers. The future of AI Chips lies in their ability to provide high computing power, parallel processing, and energy efficiency, making them essential for powering the next generation of AI technologies and applications.

Table of Contents:

1 Executive Summary
2 Market Landscape
3 Market Sizing
4 Historic Market Size
5 Five Forces Analysis
6 Market Segmentation

• Product
  • ASICs
  • GPUs
  • CPUs
  • FPGAs
• End-user
  • Media And Advertising
  • BFSI
  • IT And Telecommunication
  • Others
• Geography
  • North America
  • Europe
  • APAC
  • South America
  • Middle East And Africa
• Processing Type
  • Edge
  • Cloud
• Application
  • Nature Language Processing
  • Robotics
  • Computer Vision
  • Network Security
  • Others
• Technology
  • System On Chip
  • System In Package
  • Multi Chip Module
  • Others

7 Customer Landscape
8 Geographic Landscape
9 Drivers, Challenges, and Trends
10 Company Landscape
11 Company Analysis
12 Appendix

About Technavio

Technavio is a leading global technology research and advisory company. Their research and analysis focuses on emerging market trends and provides actionable insights to help businesses identify market opportunities and develop effective strategies to optimize their market positions.

With over 500 specialized analysts, Technavio's report library consists of more than 17,000 reports and counting, covering 800 technologies, spanning across 50 countries. Their client base consists of enterprises of all sizes, including more than 100 Fortune 500 companies. This growing client base relies on Technavio's comprehensive coverage, extensive research, and actionable market insights to identify opportunities in existing and potential markets and assess their competitive positions within changing market scenarios.

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Email: media@technavio.com
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