Artificial Intelligence Chip Market
The global artificial intelligence chip market witnessed remarkable growth in 2024, driven by rapid advancements in AI applications across industries such as automotive, healthcare, finance, and manufacturing. With rising demand for high-performance, energy-efficient chips to power edge and cloud AI workloads, major players are investing in next-gen technologies like SoC, chiplets, and 3nm fabrication. As AI adoption accelerates globally, the market is poised for sustained expansion, with Asia-Pacific emerging as a key hub for innovation and manufacturing
According to a new report published by Allied Market Research, titled, “Artificial Intelligence Chip Market,” The Artificial Intelligence Chip Market Size was valued at $44.9 billion in 2024, and is estimated to reach $460.9 billion by 2034, growing at a CAGR of 27.6% from 2025 to 2034.
Get a Sample PDF Report to understand our report before you purchase: https://www.alliedmarketresearch.com/request-sample/4515
Artificial intelligence is a technology that is used to develop methods and application systems for simulating and expanding human intelligence. In the current market scenario, factors such as emergence of big data, rise in computing power, and rapid digitalization influence the Artificial Intelligence Chip Market Growth. The big tech players such as Google Inc., Facebook, Tesla, and others are either investing or planning to do so for the development of AI Chips.
Rise in demand for smart homes and smart cities, increase in investment in AI startups, and emergence in quantum computing are the factors expected to drive the growth of the artificial intelligence chip market during the Artificial Intelligence Chip Market Forecastperiod. In addition, increase in adoption of AI chips in developing economies is anticipated to be opportunistic for the market growth during the forecast period.
Quantum computing holds the potential to revolutionize the AI chip industry by accelerating the processing of intricate AI algorithms. Quantum systems augment tasks such as optimization, machine learning training, and data analysis by leveraging their capacity to execute specific computations at an exponentially faster rate than classical computers. This acceleration facilitates the creation of more sophisticated AI models and algorithms, fostering a demand for dedicated quantum AI chips designed to align with quantum computing architectures. As quantum technology advances, it has the potential to stimulate innovation in AI chip design, leading to hybrid or quantum-accelerated AI chips that harness quantum effects for more efficient resolution of AI-related challenges, consequently propelling the expansion of the AI chip market.
Make a Direct Purchase: https://www.alliedmarketresearch.com/checkout-final/dc24389d92d79e96de1b6c7bc29beb33
However, lack of skilled workforce hampers the market growth. Increase in development of smarter robots based on 5G and widespread use of AI chips in smart homes are anticipated to present prospective growth opportunities for the global Artificial Intelligence Chip Market Trends during the forecast period.
One of the significant restraints in the AI chip market is the high power consumption and excessive heat generation associated with advanced processors. AI chips such as GPUs, TPUs, and custom accelerators are designed for intense parallel processing tasks like deep learning and generative AI, which require immense computational power. For instance, chips like the NVIDIA H100 can consume over 700 watts, significantly straining power and cooling systems. According to an IEEE report, cooling infrastructure accounts for nearly 40% of total energy use in AI data centers, making operations costly and less sustainable. This thermal output not only increases electricity expenses but also requires advanced thermal management systems, further escalating capital and operational expenditures. Moreover, excessive heat limits chip performance in compact or edge devices, where space for active cooling is minimal. These energy and thermal challenges pose a barrier to scaling AI applications while aligning with environmental and efficiency goals.
The development of smarter robots is a promising opportunity driving innovation across the AI chip and robotics ecosystem. Companies are increasingly focused on building advanced robotic brains capable of autonomous decision-making and contextual awareness. For example, Rethink Robotics developed Baxter, a research-oriented collaborative robot that can be trained through demonstration, enabling adaptive learning in dynamic environments. Similarly, Hanson Robotics has introduced human-like robots such as Sophia, which are designed to engage in natural conversations and recall personal interactions, enhancing human-machine relationships. Additionally, the rise of intelligent virtual assistants presents further opportunities. A case in point is Jarvis Corp, a start-up in its conceptual phase, aiming to develop an AI-powered assistant capable of retrieving online information, acting as an internet server, and managing connected smart devices. These advancements highlight the growing convergence of AI, robotics, and IoT, expanding the role of AI chips in enabling real-time, intelligent processing.
The artificial intelligence chip market is segmented based on chip type, processing type, technology, application, industry vertical, and region. By chip type, the Artificial Intelligence Chip industryis categorized into GPU, ASIC, FPGA, CPU, and others. By processing type, it is categorized into edge and cloud. By technology, the Artificial Intelligence Chip industry is categorized into system-on-chip, system-in- package, multi-chip module, and others. By application, the Artificial Intelligence Chip Market Share is classified into natural language processing (NLP), robotics, computer vision, network security, and others. By industry vertical, the market is fragmented into media & advertising, BFSI, IT & telecom, retail, healthcare, automotive & transportation, and others. By region, the artificial intelligence chip market is analyzed across North America (the U.S., Canada, and Mexico), Europe (UK, Germany, France, Russia and rest of Europe), Asia-Pacific (China, Japan, India, South Korea, Australia, and rest of Asia-Pacific), and LAMEA (Latin America, Middle East, and Africa).
To Ask About Report Availability or Customization, Click Here: https://www.alliedmarketresearch.com/purchase-enquiry/4515
Key players profiled in the report include SoftBank Corp., Qualcomm Technologies Inc., Alphabet Inc., Mythic, Baidu, NXP Semiconductors, Intel Corporation, MediaTek, Advanced Micro Devices, Inc., NVIDIA Corporation (Mellanox Technologies), and Samsung. Market players have adopted various strategies such as product launch, and business expansion to expand their foothold in the artificial intelligence chip market.
Contact:
United States
1209 Orange Street,
Corporation Trust Center,
Wilmington, New Castle,
Delaware 19801 USA.
Int’l: +1-503-894-6022
Toll Free: +1-800-792-5285
Fax: +1-800-792-5285
help@alliedmarketresearch.com
About Us
Allied Market Research (AMR) is a full-service market research and business-consulting wing of Allied Analytics LLP based in Portland, Oregon. Allied Market Research provides global enterprises as well as medium and small businesses with unmatched quality of “Market Research Reports” and “Business Intelligence Solutions.” AMR has a targeted view to provide business insights and consulting to assist its clients to make strategic business decisions and achieve sustainable growth in their respective market domain.
Pawan Kumar, the CEO of Allied Market Research, is leading the organization toward providing high-quality data and insights. We are in professional corporate relations with various companies and this helps us in digging out market data that helps us generate accurate research data tables and confirms utmost accuracy in our market forecasting. Each and every data presented in the reports published by us is extracted through primary interviews with top officials from leading companies of domain concerned. Our secondary data procurement methodology includes deep online and offline research and discussion with knowledgeable professionals and analysts in the industry.
This release was published on openPR.
