Global AI GPU Industry Chain Analysis Report 2025

Global Info Research‘s report is a detailed and comprehensive analysis for global AI GPU market. Both quantitative and qualitative analyses are presented by manufacturers, by region & country, by Type and by Application. As the AI GPU market is constantly changing, this report explores the competition, supply and demand trends, as well as key factors that contribute to its changing demands across many markets. Company profiles and product examples of selected competitors, along with market share estimates of some of the selected leaders for the year 2025, are provided.

**The global AI GPU market size is expected to reach $ 750641 million by 2031, rising at a market growth of 35.0% CAGR during the forecast period (2025-2031).
In this report, we will assess the current U.S. tariff framework alongside international policy adaptations, analyzing their effects on competitive market structures, regional economic dynamics, and supply chain resilience.
In 2024, the global AI GPU production will be around 10.442 million units, with an average price of US,200 per unit.
Broadly speaking, AI chips refer to chips that run artificial intelligence algorithms. AI algorithms mainly include deep learning algorithms and machine learning algorithms. In a narrow sense, AI chips refer to chips specially designed to accelerate artificial intelligence algorithms.
AI chips mainly include GPU, TPU, FPGA, ASIC, etc.
GPU is a hardware component similar to CPU, but more professional. It can handle complex mathematical operations running in parallel more efficiently than a regular CPU.
The GPU was initially used to simulate human imagination, enabling the virtual worlds of video games and films. Today, it also simulates human intelligence, enabling a deeper understanding of the physical world. Its parallel processing capabilities, supported by thousands of computing cores, are essential to running deep learning algorithms.
This form of AI, in which software writes itself by learning from large amounts of data, can serve as the brain of computers, robots and self-driving cars that can perceive and understand the world.
Since artificial intelligence tasks often require a large number of computationally intensive operations such as matrix multiplication and convolution, these operations can be parallelized to speed up calculations. In contrast, CPUs have weak parallelism and their relatively small number of cores cannot handle this type of task efficiently. Therefore, in artificial intelligence tasks, using GPUs for calculations can significantly speed up calculations and improve calculation efficiency.
The AI ​​GPU application scenarios in this article include AI training and reasoning in data centers, edge AI, and cloud computing AI.
With the rapid development of large models and generative AI, AI GPUs are the core engine supporting computing infrastructure. The market is moving from single-purpose training or inference acceleration to a new stage of integrated development of training, inference, and training-inference. From supercomputing centers to cloud computing platforms, to edge devices and smart terminals, AI GPUs are building an integrated "cloud-edge-end" computing network, making AI as readily available as water and electricity. With compatibility with mainstream ecosystems, a unified software stack, and continuously iterating hardware architecture, AI GPUs not only significantly lower the development and migration threshold, but also significantly improve efficiency through mixed-precision computing and distributed parallelism, helping customers quickly implement large models while maintaining manageable costs. Globally, the leading AI GPU companies are NVIDIA, AMD, and Moore Threads, with NVIDIA holding over 80% market share.
This report studies the global AI GPU production, demand, key manufacturers, and key regions.
This report is a detailed and comprehensive analysis of the world market for AI GPU and provides market size (US$ million) and Year-over-Year (YoY) Growth, considering 2024 as the base year. This report explores demand trends and competition, as well as details the characteristics of AI GPU that contribute to its increasing demand across many markets.

Market segment by Type: AI Training GPU、 AI Inference GPU、 Edge & Endpoint AI GPU Market segment by Application:Data Center、 Enterprises、 HPC & Academia Major players covered:** NVIDIA、 AMD、 Intel、 Shanghai Denglin、 Vastai Technologies、 Shanghai Iluvatar、 Metax Tech、 Moore Threads、 BIRENTECH、 Innosilicon、 Shenzhen Siroywe、 Lisuan Technology、 Glenfly Tech Co., Ltd、 Sietium、 Hygon Information Technology

The overall report focuses on primary sections such as – market segments, market outlook, competitive landscape, and company profiles. The segments provide details in terms of various perspectives such as end-use industry, product or service type, and any other relevant segmentation as per the market’s current scenario which includes various aspects to perform further marketing activity. The market outlook section gives a detailed analysis of market evolution, growth drivers, restraints, opportunities, and challenges, Porter’s 5 Force’s Framework, macroeconomic analysis, value chain analysis and pricing analysis that directly shape the market at present and over the forecasted period. The drivers and restraints cover the internal factors of the market whereas opportunities and challenges are the external factors that are affecting the market. The market outlook section also gives an indication of the trends influencing new business development and investment opportunities.

The Primary Objectives in This Report determine the size of the total market opportunity of global and key countries,assess the growth potential for AI GPU and competitive factors affecting the marketplace,forecast future growth in each product and end-use market. Also,this report profiles key players in the global AI GPU market based on the following parameters - company overview, sales quantity, revenue, price, gross margin, product portfolio, geographical presence, and key developments.

AI GPU market is split by Type and by Application. For the period 2020-2031, the growth among segments provides accurate calculations and forecasts for consumption value by Type, and by Application in terms of volume and value. This analysis can help you expand your business by targeting qualified niche markets.

Market segment by region, regional analysis covers North America (United States, Canada, and Mexico),Europe (Germany, France, United Kingdom, Russia, Italy, and Rest of Europe),Asia-Pacific (China, Japan, Korea, India, Southeast Asia, and Australia),South America (Brazil, Argentina, Colombia, and Rest of South America),Middle East & Africa (Saudi Arabia, UAE, Egypt, South Africa, and Rest of Middle East & Africa).

The report provides insights regarding the lucrative opportunities in the AI GPU Market at the country level. The report also includes a precise cost, segments, trends, region, and commercial development of the major key players globally for the projected period.

The AI GPU Market report comprehensively examines market structure and competitive dynamics. Researching the AI GPU market entails a structured approach beginning with clearly defined objectives and a comprehensive literature review to understand the current landscape. Methodologies involve a mix of primary research through interviews, surveys, and secondary research from industry reports and databases. Sampling strategies ensure representation, while data analysis utilizes statistical and analytical techniques to identify trends, market sizing, and competitive landscapes. Key areas of focus include trend analysis, risk assessment, and forecasting. Findings are synthesized into a detailed report, validated through peer review or expert consultation, and disseminated to stakeholders, with ongoing monitoring to stay abreast of developments.

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