Industry: Electronics & Semiconductor
Published Date: 2025-09-10
Pages: 100 Pages
Report ld: 4932375
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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 AI GPU market size was US$ 85625 million in 2024 and is forecast to a readjusted size of US$ 757212 million by 2031 with a CAGR of 35.8% during the forecast period 2025-2031.
By 2025, the evolving U.S. tariff policy is poised to inject considerable uncertainty into the global economic landscape. This report delves into the latest U.S. tariff measures and the corresponding policy responses across the globe, evaluating their impacts on AI GPU market competitiveness, regional economic performance, and supply chain configurations.
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.
The global AI GPU market is strategically segmented by company, region (country), by Type, and by Application. This report empowers stakeholders to capitalize on emerging opportunities, optimize product strategies, and outperform competitors through data-driven insights on sales, revenue, and forecasts across regions, by Type, and by Application for 2020-2031.
MARKET SEGMENTATION
CHAPTER OUTLINE
Chapter 1: Report scope, executive summary, and market evolution scenarios (short/mid/long term).
Chapter 2: Quantitative analysis of AI GPU market size and growth potential at global, regional, and country levels.
Chapter 3: Competitive benchmarking of manufacturers (revenue, market share, M&A, R&D focus).
Chapter 4: Type-based segmentation analysis – Uncovering blue ocean markets (e.g., AI Inference GPU in China).
Chapter 5: Application-based segmentation analysis – High-growth downstream opportunities (e.g., Enterprises in India).
Chapter 6: Regional sales and revenue breakdown by company, type, application and customer.
Chapter 7: Key manufacturer profiles – Financials, product portfolios, and strategic developments.
Chapter 8: Market dynamics – Drivers, restraints, regulatory impacts, and risk mitigation strategies.
Chapter 9: Actionable conclusions and strategic recommendations.
WHY THIS REPORT
Beyond standard market data, this analysis provides a clear profitability roadmap, empowering you to:
Unlike generic global market reports, this study combines macro-level industry trends with hyper-local operational intelligence, empowering data-driven decisions across the AI GPU value chain, addressing:
- Market entry risks/opportunities by region
- Product mix optimization based on local practices
- Competitor tactics in fragmented vs. consolidated markets
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 Market Overview
1.1 AI GPU Product Scope
1.2 AI GPU by Type
1.2.1 Global AI GPU Sales by Type (2020 & 2024 & 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 Sales Comparison by Application (2020 & 2024 & 2031)
1.3.2 Data Center
1.3.3 Enterprises
1.3.4 HPC & Academia
1.4 Global AI GPU Market Estimates and Forecasts (2020-2031)
1.4.1 Global AI GPU Market Size in Value Growth Rate (2020-2031)
1.4.2 Global AI GPU Market Size in Volume Growth Rate (2020-2031)
1.4.3 Global AI GPU Price Trends (2020-2031)
1.5 Assumptions and Limitations
2 Market Size and Prospective by Region
2.1 Global AI GPU Market Size by Region: 2020 VS 2024 VS 2031
2.2 Global AI GPU Retrospective Market Scenario by Region (2020-2025)
2.2.1 Global AI GPU Sales Market Share by Region (2020-2025)
2.2.2 Global AI GPU Revenue Market Share by Region (2020-2025)
2.3 Global AI GPU Market Estimates and Forecasts by Region (2026-2031)
2.3.1 Global AI GPU Sales Estimates and Forecasts by Region (2026-2031)
2.3.2 Global AI GPU Revenue Forecast by Region (2026-2031)
2.4 Major Region and Emerging Market Analysis
2.4.1 North America AI GPU Market Size and Prospective (2020-2031)
2.4.2 Europe AI GPU Market Size and Prospective (2020-2031)
2.4.3 China AI GPU Market Size and Prospective (2020-2031)
2.4.4 Japan AI GPU Market Size and Prospective (2020-2031)
2.4.5 South Korea AI GPU Market Size and Prospective (2020-2031)
3 Global Market Size by Type
3.1 Global AI GPU Historic Market Review by Type (2020-2025)
3.1.1 Global AI GPU Sales by Type (2020-2025)
3.1.2 Global AI GPU Revenue by Type (2020-2025)
3.1.3 Global AI GPU Price by Type (2020-2025)
3.2 Global AI GPU Market Estimates and Forecasts by Type (2026-2031)
3.2.1 Global AI GPU Sales Forecast by Type (2026-2031)
3.2.2 Global AI GPU Revenue Forecast by Type (2026-2031)
3.2.3 Global AI GPU Price Forecast by Type (2026-2031)
3.3 Different Types AI GPU Representative Players
4 Global Market Size by Application
4.1 Global AI GPU Historic Market Review by Application (2020-2025)
4.1.1 Global AI GPU Sales by Application (2020-2025)
4.1.2 Global AI GPU Revenue by Application (2020-2025)
4.1.3 Global AI GPU Price by Application (2020-2025)
4.2 Global AI GPU Market Estimates and Forecasts by Application (2026-2031)
4.2.1 Global AI GPU Sales Forecast by Application (2026-2031)
4.2.2 Global AI GPU Revenue Forecast by Application (2026-2031)
4.2.3 Global AI GPU Price Forecast by Application (2026-2031)
4.3 New Sources of Growth in AI GPU Application
5 Competition Landscape by Players
5.1 Global AI GPU Sales by Players (2020-2025)
5.2 Global Top AI GPU Players by Revenue (2020-2025)
5.3 Global AI GPU Market Share by Company Type (Tier 1, Tier 2, and Tier 3) & (based on the Revenue in AI GPU as of 2024)
5.4 Global AI GPU Average Price by Company (2020-2025)
5.5 Global Key Manufacturers of AI GPU, Manufacturing Sites & Headquarters
5.6 Global Key Manufacturers of AI GPU, Product Type & Application
5.7 Global Key Manufacturers of AI GPU, Date of Enter into This Industry
5.8 Manufacturers Mergers & Acquisitions, Expansion Plans
6 Region Analysis
6.1 North America Market: Players, Segments, Downstream and Major Customers
6.1.1 North America AI GPU Sales by Company
6.1.1.1 North America AI GPU Sales by Company (2020-2025)
6.1.1.2 North America AI GPU Revenue by Company (2020-2025)
6.1.2 North America AI GPU Sales Breakdown by Type (2020-2025)
6.1.3 North America AI GPU Sales Breakdown by Application (2020-2025)
6.1.4 North America AI GPU Major Customer
6.1.5 North America Market Trend and Opportunities
6.2 Europe Market: Players, Segments, Downstream and Major Customers
6.2.1 Europe AI GPU Sales by Company
6.2.1.1 Europe AI GPU Sales by Company (2020-2025)
6.2.1.2 Europe AI GPU Revenue by Company (2020-2025)
6.2.2 Europe AI GPU Sales Breakdown by Type (2020-2025)
6.2.3 Europe AI GPU Sales Breakdown by Application (2020-2025)
6.2.4 Europe AI GPU Major Customer
6.2.5 Europe Market Trend and Opportunities
6.3 China Market: Players, Segments, Downstream and Major Customers
6.3.1 China AI GPU Sales by Company
6.3.1.1 China AI GPU Sales by Company (2020-2025)
6.3.1.2 China AI GPU Revenue by Company (2020-2025)
6.3.2 China AI GPU Sales Breakdown by Type (2020-2025)
6.3.3 China AI GPU Sales Breakdown by Application (2020-2025)
6.3.4 China AI GPU Major Customer
6.3.5 China Market Trend and Opportunities
6.4 Japan Market: Players, Segments, Downstream and Major Customers
6.4.1 Japan AI GPU Sales by Company
6.4.1.1 Japan AI GPU Sales by Company (2020-2025)
6.4.1.2 Japan AI GPU Revenue by Company (2020-2025)
6.4.2 Japan AI GPU Sales Breakdown by Type (2020-2025)
6.4.3 Japan AI GPU Sales Breakdown by Application (2020-2025)
6.4.4 Japan AI GPU Major Customer
6.4.5 Japan Market Trend and Opportunities
6.5 South Korea Market: Players, Segments, Downstream and Major Customers
6.5.1 South Korea AI GPU Sales by Company
6.5.1.1 South Korea AI GPU Sales by Company (2020-2025)
6.5.1.2 South Korea AI GPU Revenue by Company (2020-2025)
6.5.2 South Korea AI GPU Sales Breakdown by Type (2020-2025)
6.5.3 South Korea AI GPU Sales Breakdown by Application (2020-2025)
6.5.4 South Korea AI GPU Major Customer
6.5.5 South Korea Market Trend and Opportunities
7 Company Profiles and Key Figures
7.1 NVIDIA
7.1.1 NVIDIA Company Information
7.1.2 NVIDIA Business Overview
7.1.3 NVIDIA AI GPU Sales, Revenue and Gross Margin (2020-2025)
7.1.4 NVIDIA AI GPU Products Offered
7.1.5 NVIDIA Recent Development
7.2 AMD
7.2.1 AMD Company Information
7.2.2 AMD Business Overview
7.2.3 AMD AI GPU Sales, Revenue and Gross Margin (2020-2025)
7.2.4 AMD AI GPU Products Offered
7.2.5 AMD Recent Development
7.3 Intel
7.3.1 Intel Company Information
7.3.2 Intel Business Overview
7.3.3 Intel AI GPU Sales, Revenue and Gross Margin (2020-2025)
7.3.4 Intel AI GPU Products Offered
7.3.5 Intel Recent Development
7.4 Shanghai Denglin
7.4.1 Shanghai Denglin Company Information
7.4.2 Shanghai Denglin Business Overview
7.4.3 Shanghai Denglin AI GPU Sales, Revenue and Gross Margin (2020-2025)
7.4.4 Shanghai Denglin AI GPU Products Offered
7.4.5 Shanghai Denglin Recent Development
7.5 Vastai Technologies
7.5.1 Vastai Technologies Company Information
7.5.2 Vastai Technologies Business Overview
7.5.3 Vastai Technologies AI GPU Sales, Revenue and Gross Margin (2020-2025)
7.5.4 Vastai Technologies AI GPU Products Offered
7.5.5 Vastai Technologies Recent Development
7.6 Shanghai Iluvatar
7.6.1 Shanghai Iluvatar Company Information
7.6.2 Shanghai Iluvatar Business Overview
7.6.3 Shanghai Iluvatar AI GPU Sales, Revenue and Gross Margin (2020-2025)
7.6.4 Shanghai Iluvatar AI GPU Products Offered
7.6.5 Shanghai Iluvatar Recent Development
7.7 Metax Tech
7.7.1 Metax Tech Company Information
7.7.2 Metax Tech Business Overview
7.7.3 Metax Tech AI GPU Sales, Revenue and Gross Margin (2020-2025)
7.7.4 Metax Tech AI GPU Products Offered
7.7.5 Metax Tech Recent Development
7.8 Moore Threads
7.8.1 Moore Threads Company Information
7.8.2 Moore Threads Business Overview
7.8.3 Moore Threads AI GPU Sales, Revenue and Gross Margin (2020-2025)
7.8.4 Moore Threads AI GPU Products Offered
7.8.5 Moore Threads Recent Development
7.9 BIRENTECH
7.9.1 BIRENTECH Company Information
7.9.2 BIRENTECH Business Overview
7.9.3 BIRENTECH AI GPU Sales, Revenue and Gross Margin (2020-2025)
7.9.4 BIRENTECH AI GPU Products Offered
7.9.5 BIRENTECH Recent Development
7.10 Innosilicon
7.10.1 Innosilicon Company Information
7.10.2 Innosilicon Business Overview
7.10.3 Innosilicon AI GPU Sales, Revenue and Gross Margin (2020-2025)
7.10.4 Innosilicon AI GPU Products Offered
7.10.5 Innosilicon Recent Development
7.11 Shenzhen Siroywe
7.11.1 Shenzhen Siroywe Company Information
7.11.2 Shenzhen Siroywe Business Overview
7.11.3 Shenzhen Siroywe AI GPU Sales, Revenue and Gross Margin (2020-2025)
7.11.4 Shenzhen Siroywe AI GPU Products Offered
7.11.5 Shenzhen Siroywe Recent Development
7.12 Lisuan Technology
7.12.1 Lisuan Technology Company Information
7.12.2 Lisuan Technology Business Overview
7.12.3 Lisuan Technology AI GPU Sales, Revenue and Gross Margin (2020-2025)
7.12.4 Lisuan Technology AI GPU Products Offered
7.12.5 Lisuan Technology Recent Development
7.13 Glenfly Tech Co., Ltd
7.13.1 Glenfly Tech Co., Ltd Company Information
7.13.2 Glenfly Tech Co., Ltd Business Overview
7.13.3 Glenfly Tech Co., Ltd AI GPU Sales, Revenue and Gross Margin (2020-2025)
7.13.4 Glenfly Tech Co., Ltd AI GPU Products Offered
7.13.5 Glenfly Tech Co., Ltd Recent Development
7.14 Sietium
7.14.1 Sietium Company Information
7.14.2 Sietium Business Overview
7.14.3 Sietium AI GPU Sales, Revenue and Gross Margin (2020-2025)
7.14.4 Sietium AI GPU Products Offered
7.14.5 Sietium Recent Development
7.15 Hygon Information Technology
7.15.1 Hygon Information Technology Company Information
7.15.2 Hygon Information Technology Business Overview
7.15.3 Hygon Information Technology AI GPU Sales, Revenue and Gross Margin (2020-2025)
7.15.4 Hygon Information Technology AI GPU Products Offered
7.15.5 Hygon Information Technology Recent Development
8 AI GPU Manufacturing Cost Analysis
8.1 AI GPU Key Raw Materials Analysis
8.1.1 Key Raw Materials
8.1.2 Key Suppliers of Raw Materials
8.2 Proportion of Manufacturing Cost Structure
8.3 Manufacturing Process Analysis of AI GPU
8.4 AI GPU Industrial Chain Analysis
9 Marketing Channel, Distributors and Customers
9.1 Marketing Channel
9.2 AI GPU Distributors List
9.3 AI GPU Customers
10 AI GPU Market Dynamics
10.1 AI GPU Industry Trends
10.2 AI GPU Market Drivers
10.3 AI GPU Market Challenges
10.4 AI GPU Market Restraints
11 Research Findings and Conclusion
12 Appendix
12.1 Research Methodology
12.1.1 Methodology/Research Approach
12.1.1.1 Research Programs/Design
12.1.1.2 Market Size Estimation
12.1.1.3 Market Breakdown and Data Triangulation
12.1.2 Data Source
12.1.2.1 Secondary Sources
12.1.2.2 Primary Sources
12.2 Author Details
12.3 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: 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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QYRESEARCH'S STRENGTHS
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