Industry: Electronics & Semiconductor
Published Date: 2025-09-10
Pages: 102 Pages
Report ld: 3526558
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AI GPU Market Size(US$)

CAGR 2025-2031
35.8%
Market Size,2031
USD 757,212
Million
Market Snapshot
Source: Secondary research, interviews with experts, and QYResearch analysis
The global market for AI GPU was valued at US$ 85625 million in the year 2024 and is projected to reach a revised size of US$ 757212 million by 2031, growing at a CAGR of 35.8% during the forecast period.
The 2025 U.S. tariff policies introduce profound uncertainty into the global economic landscape. This report critically examines the implications of recent tariff adjustments and international strategic countermeasures on AI GPU competitive dynamics, regional economic interdependencies, and supply chain reconfigurations.
In 2024, the global AI GPU production will be around 10.442 million units, with an average price of US$8,200 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.
REPORT SCOPE
This report aims to provide a comprehensive presentation of the global market for AI GPU, with both quantitative and qualitative analysis, to help readers develop business/growth strategies, assess the market competitive situation, analyze their position in the current marketplace, and make informed business decisions regarding AI GPU.
The AI GPU market size, estimations, and forecasts are provided in terms of output/shipments (K Units) and revenue ($ millions), considering 2024 as the base year, with history and forecast data for the period from 2020 to 2031. This report segments the global AI GPU market comprehensively. Regional market sizes, concerning products by Type, by Application, and by players, are also provided.
For a more in-depth understanding of the market, the report provides profiles of the competitive landscape, key competitors, and their respective market ranks. The report also discusses technological trends and new product developments.
The report will help the AI GPU manufacturers, new entrants, and industry chain related companies in this market with information on the revenues, production, and average price for the overall market and the sub-segments across the different segments, by company, by Type, by Application, and by regions.
By Company
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
Segment by Type
AI Training GPU
AI Inference GPU
Edge & Endpoint AI GPU
Segment by Application
Data Center
Enterprises
HPC & Academia
Production by Region
North America
Europe
China
Japan
South Korea
Consumption by Region
North America
United States
Canada
Asia-Pacific
China
Japan
South Korea
India
Australia
China Taiwan
Southeast Asia
Europe
Germany
France
U.K.
Italy
Netherlands
Latin America
Mexico
Brazil
Argentina
Middle East and Africa
Turkey
Saudi Arabia
UAE
CHAPTER OUTLINE
Chapter 1: Introduces the report scope of the report, executive summary of different market segments (by region, by Type, by Application, etc), including the market size of each market segment, future development potential, and so on. It offers a high-level view of the current state of the market and its likely evolution in the short to mid-term, and long term.
Chapter 2: Detailed analysis of AI GPU manufacturers competitive landscape, price, production and value market share, latest development plan, merger, and acquisition information, etc.
Chapter 3: Production/output, value of AI GPU by region/country. It provides a quantitative analysis of the market size and development potential of each region in the next six years.
Chapter 4: Consumption of AI GPU in regional level and country level. It provides a quantitative analysis of the market size and development potential of each region and its main countries and introduces the market development, future development prospects, market space, and production of each country in the world.
Chapter 5: Provides the analysis of various market segments by Type, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different market segments.
Chapter 6: Provides the analysis of various market segments by Application, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different downstream markets.
Chapter 7: Provides profiles of key players, introducing the basic situation of the main companies in the market in detail, including product production/output, value, price, gross margin, product introduction, recent development, etc.
Chapter 8: Analysis of industrial chain, including the upstream and downstream of the industry.
Chapter 9: Introduces the market dynamics, latest developments of the market, the driving factors and restrictive factors of the market, the challenges and risks faced by manufacturers in the industry, and the analysis of relevant policies in the industry.
Chapter 10: The main points and conclusions of the report.
QYRESEARCH'S STRENGTHS
Unlike generic global market reports, this study combines macro-level industry trends with hyper-local operational intelligence, empowering data-driven decisions across the Compound Chocolate value chain, addressing:
We identify regional market threats and growth prospects to guide your overseas layout.
We adjust product portfolios in line with local consumption habits.
We unpack rivals’ operation strategies for scattered and highly concentrated industries.
We cover competition landscape, full supply chain and quantified market size data, and deliver tailor-made customized surveys to meet your unique business demands.
We own self-owned massive exclusive databases, backed by 19 years of global market research experience across thousands of sectors.
Our team operates 24 hours a day, 365 days a year, enabling ultra-fast report turnaround to respond to your research needs efficiently.
We integrate regional risk assessment, localized product optimization and competitor analysis to deliver actionable market strategies.
All data is cross-verified from multiple industry sources to deliver thorough, precise analysis that supports reliable corporate strategic decisions.
We provide responsive, dedicated after-sales support to resolve all follow-up inquiries about reports, data and industry interpretation.
TABLE OF CONTENTS
1 AI GPU Market Overview
1.1 Product Definition
1.2 AI GPU by Type
1.2.1 Global AI GPU Market Value Growth Rate Analysis by Type: 2024 VS 2031
1.2.2 AI Training GPU
1.2.3 AI Inference GPU
1.2.4 Edge & Endpoint AI GPU
1.3 AI GPU by Application
1.3.1 Global AI GPU Market Value Growth Rate Analysis by Application: 2024 VS 2031
1.3.2 Data Center
1.3.3 Enterprises
1.3.4 HPC & Academia
1.4 Global Market Growth Prospects
1.4.1 Global AI GPU Production Value Estimates and Forecasts (2020-2031)
1.4.2 Global AI GPU Production Capacity Estimates and Forecasts (2020-2031)
1.4.3 Global AI GPU Production Estimates and Forecasts (2020-2031)
1.4.4 Global AI GPU Market Average Price Estimates and Forecasts (2020-2031)
1.5 Assumptions and Limitations
2 Market Competition by Manufacturers
2.1 Global AI GPU Production Market Share by Manufacturers (2020-2025)
2.2 Global AI GPU Production Value Market Share by Manufacturers (2020-2025)
2.3 Global Key Players of AI GPU, Industry Ranking, 2023 VS 2024
2.4 Global AI GPU Company Type and Market Share by Company Type (Tier 1, Tier 2, and Tier 3)
2.5 Global AI GPU Average Price by Manufacturers (2020-2025)
2.6 Global Key Manufacturers of AI GPU, Manufacturing Base Distribution and Headquarters
2.7 Global Key Manufacturers of AI GPU, Product Offered and Application
2.8 Global Key Manufacturers of AI GPU, Date of Enter into This Industry
2.9 AI GPU Market Competitive Situation and Trends
2.9.1 AI GPU Market Concentration Rate
2.9.2 Global 5 and 10 Largest AI GPU Players Market Share by Revenue
2.10 Mergers & Acquisitions, Expansion
3 AI GPU Production by Region
3.1 Global AI GPU Production Value Estimates and Forecasts by Region: 2020 VS 2024 VS 2031
3.2 Global AI GPU Production Value by Region (2020-2031)
3.2.1 Global AI GPU Production Value by Region (2020-2025)
3.2.2 Global Forecasted Production Value of AI GPU by Region (2026-2031)
3.3 Global AI GPU Production Estimates and Forecasts by Region: 2020 VS 2024 VS 2031
3.4 Global AI GPU Production Volume by Region (2020-2031)
3.4.1 Global AI GPU Production by Region (2020-2025)
3.4.2 Global Forecasted Production of AI GPU by Region (2026-2031)
3.5 Global AI GPU Market Price Analysis by Region (2020-2025)
3.6 Global AI GPU Production and Value, Year-over-Year Growth
3.6.1 North America AI GPU Production Value Estimates and Forecasts (2020-2031)
3.6.2 Europe AI GPU Production Value Estimates and Forecasts (2020-2031)
3.6.3 China AI GPU Production Value Estimates and Forecasts (2020-2031)
3.6.4 Japan AI GPU Production Value Estimates and Forecasts (2020-2031)
3.6.5 South Korea AI GPU Production Value Estimates and Forecasts (2020-2031)
4 AI GPU Consumption by Region
4.1 Global AI GPU Consumption Estimates and Forecasts by Region: 2020 VS 2024 VS 2031
4.2 Global AI GPU Consumption by Region (2020-2031)
4.2.1 Global AI GPU Consumption by Region (2020-2025)
4.2.2 Global AI GPU Forecasted Consumption by Region (2026-2031)
4.3 North America
4.3.1 North America AI GPU Consumption Growth Rate by Country: 2020 VS 2024 VS 2031
4.3.2 North America AI GPU Consumption by Country (2020-2031)
4.3.3 U.S.
4.3.4 Canada
4.4 Europe
4.4.1 Europe AI GPU Consumption Growth Rate by Country: 2020 VS 2024 VS 2031
4.4.2 Europe AI GPU Consumption by Country (2020-2031)
4.4.3 Germany
4.4.4 France
4.4.5 U.K.
4.4.6 Italy
4.4.7 Russia
4.5 Asia Pacific
4.5.1 Asia Pacific AI GPU Consumption Growth Rate by Region: 2020 VS 2024 VS 2031
4.5.2 Asia Pacific AI GPU Consumption by Region (2020-2031)
4.5.3 China
4.5.4 Japan
4.5.5 South Korea
4.5.6 China Taiwan
4.5.7 Southeast Asia
4.5.8 India
4.6 Latin America, Middle East & Africa
4.6.1 Latin America, Middle East & Africa AI GPU Consumption Growth Rate by Country: 2020 VS 2024 VS 2031
4.6.2 Latin America, Middle East & Africa AI GPU Consumption by Country (2020-2031)
4.6.3 Mexico
4.6.4 Brazil
4.6.5 Turkey
4.6.6 GCC Countries
5 Segment by Type
5.1 Global AI GPU Production by Type (2020-2031)
5.1.1 Global AI GPU Production by Type (2020-2025)
5.1.2 Global AI GPU Production by Type (2026-2031)
5.1.3 Global AI GPU Production Market Share by Type (2020-2031)
5.2 Global AI GPU Production Value by Type (2020-2031)
5.2.1 Global AI GPU Production Value by Type (2020-2025)
5.2.2 Global AI GPU Production Value by Type (2026-2031)
5.2.3 Global AI GPU Production Value Market Share by Type (2020-2031)
5.3 Global AI GPU Price by Type (2020-2031)
6 Segment by Application
6.1 Global AI GPU Production by Application (2020-2031)
6.1.1 Global AI GPU Production by Application (2020-2025)
6.1.2 Global AI GPU Production by Application (2026-2031)
6.1.3 Global AI GPU Production Market Share by Application (2020-2031)
6.2 Global AI GPU Production Value by Application (2020-2031)
6.2.1 Global AI GPU Production Value by Application (2020-2025)
6.2.2 Global AI GPU Production Value by Application (2026-2031)
6.2.3 Global AI GPU Production Value Market Share by Application (2020-2031)
6.3 Global AI GPU Price by Application (2020-2031)
7 Key Companies Profiled
7.1 NVIDIA
7.1.1 NVIDIA AI GPU Company Information
7.1.2 NVIDIA AI GPU Product Portfolio
7.1.3 NVIDIA AI GPU Production, Value, Price and Gross Margin (2020-2025)
7.1.4 NVIDIA Main Business and Markets Served
7.1.5 NVIDIA Recent Developments/Updates
7.2 AMD
7.2.1 AMD AI GPU Company Information
7.2.2 AMD AI GPU Product Portfolio
7.2.3 AMD AI GPU Production, Value, Price and Gross Margin (2020-2025)
7.2.4 AMD Main Business and Markets Served
7.2.5 AMD Recent Developments/Updates
7.3 Intel
7.3.1 Intel AI GPU Company Information
7.3.2 Intel AI GPU Product Portfolio
7.3.3 Intel AI GPU Production, Value, Price and Gross Margin (2020-2025)
7.3.4 Intel Main Business and Markets Served
7.3.5 Intel Recent Developments/Updates
7.4 Shanghai Denglin
7.4.1 Shanghai Denglin AI GPU Company Information
7.4.2 Shanghai Denglin AI GPU Product Portfolio
7.4.3 Shanghai Denglin AI GPU Production, Value, Price and Gross Margin (2020-2025)
7.4.4 Shanghai Denglin Main Business and Markets Served
7.4.5 Shanghai Denglin Recent Developments/Updates
7.5 Vastai Technologies
7.5.1 Vastai Technologies AI GPU Company Information
7.5.2 Vastai Technologies AI GPU Product Portfolio
7.5.3 Vastai Technologies AI GPU Production, Value, Price and Gross Margin (2020-2025)
7.5.4 Vastai Technologies Main Business and Markets Served
7.5.5 Vastai Technologies Recent Developments/Updates
7.6 Shanghai Iluvatar
7.6.1 Shanghai Iluvatar AI GPU Company Information
7.6.2 Shanghai Iluvatar AI GPU Product Portfolio
7.6.3 Shanghai Iluvatar AI GPU Production, Value, Price and Gross Margin (2020-2025)
7.6.4 Shanghai Iluvatar Main Business and Markets Served
7.6.5 Shanghai Iluvatar Recent Developments/Updates
7.7 Metax Tech
7.7.1 Metax Tech AI GPU Company Information
7.7.2 Metax Tech AI GPU Product Portfolio
7.7.3 Metax Tech AI GPU Production, Value, Price and Gross Margin (2020-2025)
7.7.4 Metax Tech Main Business and Markets Served
7.7.5 Metax Tech Recent Developments/Updates
7.8 Moore Threads
7.8.1 Moore Threads AI GPU Company Information
7.8.2 Moore Threads AI GPU Product Portfolio
7.8.3 Moore Threads AI GPU Production, Value, Price and Gross Margin (2020-2025)
7.8.4 Moore Threads Main Business and Markets Served
7.8.5 Moore Threads Recent Developments/Updates
7.9 BIRENTECH
7.9.1 BIRENTECH AI GPU Company Information
7.9.2 BIRENTECH AI GPU Product Portfolio
7.9.3 BIRENTECH AI GPU Production, Value, Price and Gross Margin (2020-2025)
7.9.4 BIRENTECH Main Business and Markets Served
7.9.5 BIRENTECH Recent Developments/Updates
7.10 Innosilicon
7.10.1 Innosilicon AI GPU Company Information
7.10.2 Innosilicon AI GPU Product Portfolio
7.10.3 Innosilicon AI GPU Production, Value, Price and Gross Margin (2020-2025)
7.10.4 Innosilicon Main Business and Markets Served
7.10.5 Innosilicon Recent Developments/Updates
7.11 Shenzhen Siroywe
7.11.1 Shenzhen Siroywe AI GPU Company Information
7.11.2 Shenzhen Siroywe AI GPU Product Portfolio
7.11.3 Shenzhen Siroywe AI GPU Production, Value, Price and Gross Margin (2020-2025)
7.11.4 Shenzhen Siroywe Main Business and Markets Served
7.11.5 Shenzhen Siroywe Recent Developments/Updates
7.12 Lisuan Technology
7.12.1 Lisuan Technology AI GPU Company Information
7.12.2 Lisuan Technology AI GPU Product Portfolio
7.12.3 Lisuan Technology AI GPU Production, Value, Price and Gross Margin (2020-2025)
7.12.4 Lisuan Technology Main Business and Markets Served
7.12.5 Lisuan Technology Recent Developments/Updates
7.13 Glenfly Tech Co., Ltd
7.13.1 Glenfly Tech Co., Ltd AI GPU Company Information
7.13.2 Glenfly Tech Co., Ltd AI GPU Product Portfolio
7.13.3 Glenfly Tech Co., Ltd AI GPU Production, Value, Price and Gross Margin (2020-2025)
7.13.4 Glenfly Tech Co., Ltd Main Business and Markets Served
7.13.5 Glenfly Tech Co., Ltd Recent Developments/Updates
7.14 Sietium
7.14.1 Sietium AI GPU Company Information
7.14.2 Sietium AI GPU Product Portfolio
7.14.3 Sietium AI GPU Production, Value, Price and Gross Margin (2020-2025)
7.14.4 Sietium Main Business and Markets Served
7.14.5 Sietium Recent Developments/Updates
7.15 Hygon Information Technology
7.15.1 Hygon Information Technology AI GPU Company Information
7.15.2 Hygon Information Technology AI GPU Product Portfolio
7.15.3 Hygon Information Technology AI GPU Production, Value, Price and Gross Margin (2020-2025)
7.15.4 Hygon Information Technology Main Business and Markets Served
7.15.5 Hygon Information Technology Recent Developments/Updates
8 Industry Chain and Sales Channels Analysis
8.1 AI GPU Industry Chain Analysis
8.2 AI GPU Raw Material Supply Analysis
8.2.1 Key Raw Materials
8.2.2 Raw Materials Key Suppliers
8.3 AI GPU Production Mode & Process Analysis
8.4 AI GPU Sales and Marketing
8.4.1 AI GPU Sales Channels
8.4.2 AI GPU Distributors
8.5 AI GPU Customer Analysis
9 AI GPU Market Dynamics
9.1 AI GPU Industry Trends
9.2 AI GPU Market Drivers
9.3 AI GPU Market Challenges
9.4 AI GPU Market Restraints
10 Research Findings and Conclusion
11 Methodology and Data Source
11.1 Methodology/Research Approach
11.1.1 Research Programs/Design
11.1.2 Market Size Estimation
11.1.3 Market Breakdown and Data Triangulation
11.2 Data Source
11.2.1 Secondary Sources
11.2.2 Primary Sources
11.3 Author List
11.4 Disclaimer
TABLE OF FIGURES
List of Tables
List of Figures
KEY QUESTIONS ADDRESSED BY THE REPORT
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Published: 2024-05-17
Pages: 109
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. Some of the most recent applications of GPU-powered deep learning include recommendation systems, which are AI algorithms trained to understand the preferences, previous decisions, and characteristics of people and products using data gathered about their interactions, large Language Models/NLP, which can recognize, summarize, translate, predict and generate text and other content based on knowledge gained from massive datasets. Generative AI, which uses algorithms that create new content, including audio, code, images, text, simulations, and videos, based on the data they have been trained on.
Published: 2024-05-17
Pages: 97
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. Some of the most recent applications of GPU-powered deep learning include recommendation systems, which are AI algorithms trained to understand the preferences, previous decisions, and characteristics of people and products using data gathered about their interactions, large Language Models/NLP, which can recognize, summarize, translate, predict and generate text and other content based on knowledge gained from massive datasets. Generative AI, which uses algorithms that create new content, including audio, code, images, text, simulations, and videos, based on the data they have been trained on.
Published: 2024-05-17
Pages: 86
REPORT COVERAGE
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CHAPTER OUTLINE
QYRESEARCH'S STRENGTHS
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