NEW YORK, Nov. 21, 2024 /PRNewswire/ — Report on how AI is driving market transformation – The global artificial intelligence (AI) chips market size is estimated to grow by USD 389.25 billion from 2024-2028, according to Technavio. The market is estimated to grow at a CAGR of over 68.13% during the forecast period. Increasing adoption of ai chips in data centers 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., Alphabet Inc., Baidu Inc., Broadcom Inc., Cerebras, Fujitsu Ltd., 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. Deep learning and machine learning algorithms require powerful hardware components like AI chips. Advanced Micro and Nvidia lead the market with their Trainium2 chip and A100 chip, respectively. Quantum computing and highbandwidth memory are the next frontiers. Major cloud providers like Microsoft Azure, Amazon Web Services, and Google Cloud offer AI technologies. Edge computing reduces latency for real-time applications. AI chip lines, including CPU, GPU, FPGA, and ASICs, power data processing in centralized cloud servers and edge devices. Emerging trends include generative AI, cognitive computing, and image recognition. Ethical concerns are rising as AI is integrated into everyday life, from wearable devices to smart homes and connected cars. Energy efficiency is crucial as AI data centers grow. Patent filings for AI technologies are surging. ML and DL are key to computer vision, pose detection, and behavioral pattern analysis. AI applications in healthcare, elder care, and IoT devices are transforming industries. Industry 4.0 and smart manufacturing machines benefit from AI and parallel computing. Despite advancements, system failure and malfunctioning remain concerns. Mobile applications, health monitoring, and personalized health treatments are driving demand. ML and DL are essential for big data processing and AI applications. AI chips, GPUs, FPGAs, CPUs, DSPs, and microcontrollers power various applications, from graphic applications to frame buffer and display devices. Theoretical and algorithmic basis are crucial for visual understanding and human-like AI.

The Internet of Things (IoT) market is experiencing significant growth due to the numerous advantages it offers in various industries such as aerospace and defense, automotive, consumer electronics, healthcare, and others. IoT devices, which include cameras, drones, smart speakers, smartphones, smart TVs, and more, are making decisions based on data they receive without human intervention. To enable power-efficient data processing and machine learning computation in these devices, IoT manufacturers are integrating Artificial Intelligence (AI) chips. This integration allows IoT devices to perform complex tasks and learn from data, enhancing their functionality and value to users. The demand for AI chips in IoT devices is expected to continue growing as the market expands.

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Market Challenges

Artificial Intelligence (AI) is revolutionizing industries from healthcare to retail, finance, and automotive. However, the growth of AI technologies relies heavily on the development of efficient AI chips. These hardware components, including CPUs, GPUs, FPGAs, and ASICs, power deep learning and machine learning algorithms. Companies like Advanced Micro Devices and Nvidia are leading the AI chip market with their Trainium2 chip and A100 chip, respectively. However, challenges persist. Energy efficiency is a major concern as AI applications require high computing power, leading to increased energy consumption. Quantum computing and generative AI are pushing the boundaries of AI technologies, requiring even more powerful chips. Ethical concerns also arise as AI is integrated into various industries, from healthcare to manufacturing. Major cloud providers like Microsoft Azure, Amazon Web Services, and Google Cloud are investing in AI data centers, while edge computing gains popularity for real-time applications. Edge devices, such as IoT devices and autonomous vehicles, require specific integrated chips for data processing. As AI applications expand, so do the challenges. System failure and malfunctioning are concerns for mobile applications, while big data requires advanced parallel computing capabilities. Patent filing and theoretical/algorithmic basis are crucial for the development of AI technologies. In conclusion, the AI chip market is evolving rapidly, with companies investing in high-performance chips to meet the demands of various industries. However, challenges such as energy efficiency, ethical concerns, and system failure must be addressed to ensure the continued growth of AI technologies.The AI chips market is witnessing significant expansion due to the potential financial gains that businesses can reap from artificial intelligence. However, the absence of a sufficient workforce with technical expertise in AI is posing a significant challenge to market growth. Companies are keen on integrating AI into their operations but face high research and development costs and the need for information from experienced AI professionals. The scarcity of talent with the necessary knowledge of AI technology is currently impeding the growth of enterprise AI applications.

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Segment Overview

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

Product1.1 ASICs1.2 GPUs1.3 CPUs1.4 FPGAsEnd-user2.1 Media and advertising2.2 BFSI2.3 IT and telecommunication2.4 OthersGeography3.1 North America3.2 Europe3.3 APAC3.4 South America3.5 Middle East and Africa

1.1 ASICs- Artificial Intelligence (AI) chips, specifically Application-Specific Integrated Circuits (ASICs), are becoming increasingly popular in data center applications due to their superior performance and speed. ASICs are customized, non-configurable chips that offer an instruction set and libraries for local data processing, acting as an accelerator for parallel algorithms. Google’s Tensor Processing Unit (TPU) is a prime example, designed for deep neural networks and already in use for Google Search and Google Street View. ASICs provide faster performance than GPUs, FPGAs, and CPUs, making them a preferred choice for data centers. TPUs have an instruction set that allows TensorFlow programs to be modified and new algorithms to be developed, making them a valuable asset for managing data effectively. The use of ASIC-based AI chips is expected to witness significant growth in the forecast period.

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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. These chips are essential for robotics, quantum computing, and advanced AI applications. The market includes CPU, FPGA, GPU, System on Chip (SoC), and Multichip Module (MCM) solutions. AI chips are finding applications in sectors like healthcare, retail, finance, automotive, autonomous vehicles, IoT devices, and more. High-performance AI chips are crucial for training generative AI models and powering supercomputers. Ethical concerns surrounding AI are also fueling the development of specific integrated circuits. Key technologies include highbandwidth memory and Trainium2 chip. The market is evolving with the shift from cloud to edge computing.

Market Research Overview

Artificial Intelligence (AI) Chips Market: Overview The AI Chips Market is witnessing significant growth due to the increasing demand for advanced AI technologies such as deep learning and machine learning in various industries. AI chips are specialized hardware components designed to accelerate AI algorithms and technologies, including neural networks, quantum computing, and cognitive computing. These chips are essential for powering AI applications in robotics, computer vision, natural language processing, and other fields. The market for AI chips includes various types of hardware components, such as CPUs, GPUs, FPGAs, ASICs, DSPs, and microcontrollers. Companies are investing heavily in the development of AI chip lines, including Nvidia’s A100 chip, Ascend 910B chipset, and H200 chipset, to meet the growing demand for energy-efficient and high-performance AI solutions. AI applications are widespread across industries, including healthcare, retail, finance, automotive, and manufacturing. The use of AI in healthcare for health monitoring, health information access, personalized health, and treatment devices is gaining popularity, especially for the elderly population. In retail, AI is used for customer behavior analysis, inventory management, and personalized marketing. In finance, AI is used for fraud detection, risk assessment, and algorithmic trading. The automotive industry is also adopting AI technologies for autonomous vehicles, advanced driver assistance systems, and connected cars. The use of AI in manufacturing machines, smart homes, and IoT devices is increasing, leading to the growth of AI data centers and edge computing. However, ethical concerns surrounding AI and the potential for system failure or malfunctioning are major challenges for the market. The development of specific integrated chips and multichip modules is a potential solution to address these challenges. The AI Chips Market is expected to continue growing due to the increasing demand for real-time applications, low latency, and big data processing. The market is also being driven by the development of generative AI, large language models, and other advanced AI technologies. The use of AI in mobile applications, gaming consoles, and personal computers is also expected to drive market growth. In conclusion, the AI Chips Market is a rapidly growing market, driven by the increasing demand for advanced AI technologies and applications across various industries. The market is expected to continue growing due to the development of energy-efficient and high-performance AI solutions, the increasing use of AI in various industries, and the growing demand for real-time applications and low latency. However, ethical concerns and the potential for system failure or malfunctioning are major challenges that need to be addressed.

Table of Contents:

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

ProductASICsGPUsCPUsFPGAsEnd-userMedia And AdvertisingBFSIIT And TelecommunicationOthersGeographyNorth AmericaEuropeAPACSouth AmericaMiddle East And Africa

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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SOURCE Technavio