Artificial Intelligence Chipset Market – Global Industry Size, Share, Trends, Opportunity, and Forecast, Segmented by Type of AI Chipset (GPU (Graphics Processing Unit) AI Chipsets, CPU (Central Processing Unit) AI Chipsets), FPGA (Field-Programmable Gate Array) AI Chipsets, ASIC (Application-Specific Integrated Circuit) AI Chipsets) By Technology (Deep Learning, Machine Learning, Quantum AI Chipsets), By End-User Industry (Consumer, IT and Telecom, Healthcare, Banking, Financial Services, and Insurance (BFSI), Manufacturing and Industrial, Others), By Region, By Competition, 2021-2031F

Forecast Period	2027-2031
Market Size (2025)	USD 25.39 Billion
CAGR (2026-2031)	29.02%
Fastest Growing Segment	Machine Learning
Largest Market	North America
Market Size (2031)	USD 117.11 Billion

Market Overview

The Global Artificial Intelligence Chipset Market will grow from USD 25.39 Billion in 2025 to USD 117.11 Billion by 2031 at a 29.02% CAGR. Artificial intelligence chipsets are specialized electronic components engineered to accelerate the computational processing required for machine learning and deep learning workloads. These processors utilize parallel computing architectures to handle complex mathematical operations with greater efficiency than general-purpose central processing units. The market is primarily supported by the escalating requirement for high-performance computing in hyperscale data centers and the widespread deployment of autonomous technologies in the automotive sector. Furthermore, the increasing incorporation of intelligence into consumer electronics drives substantial demand for improved on-device processing capabilities.

However, the industry encounters a significant impediment regarding the high energy consumption of these powerful processors, which necessitates complex thermal management solutions and raises operational costs. This power efficiency challenge remains a critical hurdle for broader hardware deployment. According to the World Semiconductor Trade Statistics, in 2025, global semiconductor sales are forecast to rise by 22.5 percent to USD 772.2 billion, a growth trajectory heavily reliant on the demand for logic and memory components utilized in artificial intelligence acceleration.

Key Market Drivers

The explosive growth in Generative AI and Large Language Model deployment serves as the primary catalyst for the market, fundamentally altering hardware requirements. Training complex models with billions of parameters necessitates processors capable of massive parallel processing and high-bandwidth memory utilization, shifting demand heavily toward high-performance graphics processing units and tensor processing units. This workload intensity has compelled chip designers to prioritize architectures that optimize matrix multiplication and floating-point operations. According to NVIDIA Corporation, August 2024, in the 'NVIDIA Announces Financial Results for Second Quarter Fiscal 2025', the company reported record Data Center revenue of USD 26.3 billion, a 154 percent increase from the previous year driven largely by demand for the Hopper GPU computing platform.

Simultaneously, the rapid expansion of hyperscale data centers and cloud AI infrastructure provides the physical foundation for scaling these computational capabilities. Cloud service providers are aggressively increasing capital expenditures to construct facilities equipped with custom application-specific integrated circuits and accelerators designed to handle both training and inference tasks efficiently. This infrastructure build-out is essential to support the heavy computational loads requested by enterprise clients and consumer applications. According to Microsoft Corporation, April 2024, in the 'Third Quarter Fiscal Year 2024 Results', capital expenditures reached USD 14.0 billion, a figure explicitly attributed to scaling cloud and artificial intelligence infrastructure. This systemic growth is reflected in broader component trends; according to the Semiconductor Industry Association, in 2024, global semiconductor sales reached USD 53.1 billion for the month of August, an increase of 20.6 percent year-over-year.

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Key Market Challenges

The high energy consumption associated with artificial intelligence chipsets constitutes a substantial impediment to market growth. These processors utilize massive amounts of electricity to execute parallel processing tasks, which directly results in elevated operational costs for data center operators and enterprises. The requirement for advanced power delivery systems and specialized thermal management infrastructure complicates the deployment process and increases the total cost of ownership. This economic burden discourages smaller organizations from adopting high-performance hardware and limits the density at which these processors can be installed within existing facilities.

Furthermore, the strain on energy infrastructure poses a physical limit to how quickly the market can scale. As computational requirements expand, the aggregate power demand threatens to exceed the capacity of regional power grids, delaying the construction of necessary data center capacity. According to the Electric Power Research Institute, in 2024, it was projected that data centers could consume up to 9 percent of United States electricity generation by 2030, a surge driven primarily by artificial intelligence workloads. This trajectory suggests that energy availability will increasingly act as a bottleneck, forcing the market to decelerate regardless of the demand for processing capabilities.

Key Market Trends

The proliferation of Edge AI and on-device inference capabilities is reshaping the market by moving computational workloads from centralized data centers to local devices like personal computers. This shift addresses latency concerns and reduces cloud connectivity costs, prompting manufacturers to integrate neural processing units directly into consumer hardware to support local task execution. This momentum is evident in the production targets of major industry players seeking to decentralize processing power. According to The Elec, October 2024, in the 'Intel aims to ship 100 million units of AI PC processors by 2025', the company announced a strategic objective to supply 100 million processors equipped with dedicated artificial intelligence acceleration capabilities by the end of 2025.

Simultaneously, the integration of Advanced High-Bandwidth Memory (HBM) solutions has become essential for maximizing logic chipset performance in high-performance computing environments. As models expand in parameter size, the lag in data transfer speeds hinders training efficiency, compelling architects to utilize packaging technologies that stack memory dies adjacent to compute units for increased throughput. This technical requirement is directly influencing supplier revenue streams as demand for faster memory protocols surges. According to Samsung Electronics, July 2024, in the 'Earnings Presentation: 2Q 2024 Financial Results', the company reported that sales revenue for its High Bandwidth Memory products increased by approximately 50 percent compared to the previous quarter.

Segmental Insights

Machine Learning represents the fastest-growing segment within the Global Artificial Intelligence Chipset Market due to the escalating demand for hardware capable of processing large datasets efficiently. This expansion is fueled by the widespread adoption of predictive analytics and automated processes across various industries, necessitating processors that can manage intensive computational workloads. Additionally, the proliferation of cloud computing and the integration of intelligent features into consumer devices further stimulate the requirement for specialized machine learning architectures. Consequently, manufacturers are prioritizing the development of chipsets optimized for these algorithmic tasks to support enterprise modernization strategies.

Regional Insights

North America dominates the Global Artificial Intelligence Chipset Market, underpinned by a mature technological infrastructure and the concentration of major industry innovators like NVIDIA, Intel, and AMD. The region exhibits extensive adoption of high-performance computing across diverse sectors, including healthcare and automotive, which fuels consistent demand for advanced hardware. This market leadership is strengthened by strategic federal support, specifically through the U.S. Department of Commerce and the CHIPS and Science Act. These initiatives incentivize domestic semiconductor manufacturing and research, creating a resilient environment that sustains North America’s premier status in the global AI landscape.

Recent Developments

• In December 2024, Amazon Web Services (AWS) announced the general availability of its Trainium2 chips and Trn2 instances during the re:Invent conference in Las Vegas. The new custom silicon was engineered to provide up to 40% better price-performance for training deep learning models compared to the previous generation. The company revealed that each Trn2 instance utilized 16 Trainium2 chips to deliver 20.8 petaflops of compute power, specifically optimized for large language models with hundreds of billions of parameters. This release highlighted the growing trend of cloud providers developing proprietary solutions within the Global Artificial Intelligence Chipset Market to support massive-scale machine learning clusters.
• In October 2024, Advanced Micro Devices (AMD) announced the Instinct MI325X accelerators at its "Advancing AI" event, targeting leadership performance in the generative AI market. The new processors, built on the CDNA 3 architecture, featured 256GB of HBM3E memory and 6.0TB/s of memory bandwidth, which the company claimed surpassed the capacity of competing NVIDIA H200 chips by 1.8 times. Designed to handle demanding tasks like foundation model training and fine-tuning, the MI325X was scheduled for production shipments in the fourth quarter of 2024. This launch underscored the intensifying competition in the Global Artificial Intelligence Chipset Market to deliver superior memory capabilities for complex data processing.
• In April 2024, Intel Corporation launched the Gaudi 3 AI accelerator during its Vision event, positioning the device as a cost-effective and open alternative for enterprise artificial intelligence workloads. The company reported that the Gaudi 3 chip provided 50% better inference performance and 40% superior power efficiency on average compared to the NVIDIA H100. Manufactured using a 5nm process, the accelerator was engineered to support standard Ethernet networking, facilitating scalable AI systems for original equipment manufacturers such as Dell Technologies and Lenovo. This development marked a strategic expansion in the AI chipset sector, aiming to provide flexible solutions for large language model deployment.
• In March 2024, NVIDIA unveiled the Blackwell platform and the B200 GPU at its GTC conference, introducing a new architecture with 208 billion transistors designed to power the next generation of accelerated computing. The company stated that the B200 GPU delivered up to 20 petaflops of FP4 performance, offering a significant upgrade over the previous Hopper architecture to handle massive generative AI models. This launch addressed the Global Artificial Intelligence Chipset Market's need for higher efficiency, with the new chips enabling real-time inference for multi-trillion-parameter models. The platform also featured a second-generation Transformer Engine to optimize training and inference costs for data center operators.

Key Market Players

• NVIDIA Corporation
• Advanced Micro Devices, Inc.
• Intel Corporation
• Qualcomm Incorporated
• Apple Inc.
• Google LLC
• Samsung Electronics Co., Ltd.
• MediaTek Inc.
• Huawei Technologies Co., Ltd.
• Graphcore Limited

By Technology	By End-User Industry	By Region
Deep Learning Machine Learning Quantum AI Chipsets	Consumer IT and Telecom Healthcare Banking Financial Services Insurance (BFSI) Manufacturing and Industrial Others	North America Europe Asia Pacific South America Middle East & Africa

Report Scope:

In this report, the Global Artificial Intelligence Chipset Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:

• Artificial Intelligence Chipset Market, By Technology:

• Deep Learning
• Machine Learning
• Quantum AI Chipsets

• Artificial Intelligence Chipset Market, By End-User Industry:

• Consumer
• IT and Telecom
• Healthcare
• Banking
• Financial Services
• Insurance (BFSI)
• Manufacturing and Industrial
• Others

• Artificial Intelligence Chipset Market, By Region:

• North America
• United States
• Canada
• Mexico
• Europe
• France
• United Kingdom
• Italy
• Germany
• Spain
• Asia Pacific
• China
• India
• Japan
• Australia
• South Korea
• South America
• Brazil
• Argentina
• Colombia
• Middle East & Africa
• South Africa
• Saudi Arabia
• UAE