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AI GPU- Global Market Share and Ranking, Overall Sales and Demand Forecast 2026-2032

Industry: Electronics & Semiconductor

Published Date: 2026-01-05

Pages: 128 Pages

Report ld: 5513251

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Table of Contents
Table of Figures
Related Reports
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Industry Insights:

AI GPU Research:CAGR of 43% during the forecast period

AI GPU Market Size(US$)

CAGR 2026-2032

35.8%

Market Size,2032

USD 1,001,190

Million

Market Snapshot

Market Size in 2026 (Value)

US$ 159,630 million

Market Forecast in 2032(Value)

US$ 1,001,190 million

CAGR

35.8%

Years Considered

2021-2032

Base Year

2026

Forecast Period

2026-2032

Source: Secondary research, interviews with experts, and QYResearch analysis

The global market for AI GPU was estimated to be worth US$ 120730 million in 2025 and is projected to reach US$ 1001190 million, growing at a CAGR of 35.8% from 2026 to 2032.

The potential shifts in the 2025 U.S. tariff framework pose substantial volatility risks to global markets. This report provides a comprehensive assessment of recent tariff adjustments and international strategic countermeasures on AI GPU cross-border industrial footprints, capital allocation patterns, 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.

This report provides a comprehensive view of the global market for AI GPU, covering total sales volume, sales revenue, pricing, the market share and ranking of key companies, along with analyses by region & country, by Type, and by Application.

The AI GPU market size, estimations, and forecasts are presented in terms of sales volume (K Units) and revenue ($ millions), with 2025 as the base year and historical and forecast data from 2021 to 2032. The report combines quantitative and qualitative analysis to help readers develop growth strategies, assess the competitive landscape, evaluate their position in the current marketplace, and make informed business decisions regarding AI GPU.

MARKET SEGMENTATION

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

Consumption by Region

North America
- United States
- Canada
Asia-Pacific
- China
- Japan
- South Korea
- Southeast Asia
- India
- Australia
- Rest of Asia-Pacific
Europe
- Germany
- France
- U.K.
- Italy
- Netherlands
- Nordic Countries
- Rest of Europe
Latin America
- Mexico
- Brazil
- Rest of Latin America
Middle East & Africa
- Turkey
- Saudi Arabia
- UAE
- Rest of MEA

Segment by Type

AI Training GPU
AI Inference GPU
Edge & Endpoint AI GPU

Segment by Application

Data Center
Enterprises
HPC & Academia

biaoTi CHAPTER OUTLINE

Chapter 1: Introduces the scope of the report and the global market size (value, volume, and price). It also summarizes market dynamics and Recent Developments; identifies key drivers and restraints; outlines challenges and risks for manufacturers; reviews relevant industry policies and U.S. tariff implications.

Chapter 2: Provides a detailed analysis of the AI GPU manufacturers' competitive landscape—including pricing, sales and revenue shares, Recent Developments plans, and mergers and acquisitions (M&A).

Chapter 3: Analyzes market segmentation by Type, presenting the size and growth potential of each segment to help readers identify blue-ocean opportunities.

Chapter 4: Analyzes market segmentation by Application, presenting the size and growth potential of each downstream segment to help readers identify blue-ocean opportunities.

Chapter 5: Presents AI GPU sales and revenue at the regional level. It offers a quantitative assessment of market size and growth potential by region and summarizes market development, future prospects, addressable space, and country-level market size worldwide.

Chapter 6: Presents AI GPU sales and revenue at the country level. It provides segmented data by Type and by Application for each country/region.

Chapter 7: Profiles key players, detailing the main companies' product sales, revenue, pricing, gross margin, product portfolios, Recent Developments, etc.

Chapter 8: Analyzes the industry value chain, including upstream suppliers and downstream applications/customers.

Chapter 9: Conclusion.

biaoTi 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:

Market entry risks/opportunities by region

We identify regional market threats and growth prospects to guide your overseas layout.

Product mix optimization based on local practices

We adjust product portfolios in line with local consumption habits.

Competitor tactics in fragmented vs. consolidated markets

We unpack rivals’ operation strategies for scattered and highly concentrated industries.

Full Research Coverage

We cover competition landscape, full supply chain and quantified market size data, and deliver tailor-made customized surveys to meet your unique business demands.

19 Years Industry Expertise

We own self-owned massive exclusive databases, backed by 19 years of global market research experience across thousands of sectors.

24/7 Fast Report Delivery

Our team operates 24 hours a day, 365 days a year, enabling ultra-fast report turnaround to respond to your research needs efficiently.

Localized Strategic Analysis

We integrate regional risk assessment, localized product optimization and competitor analysis to deliver actionable market strategies.

Market entry risks/opportunities by region

All data is cross-verified from multiple industry sources to deliver thorough, precise analysis that supports reliable corporate strategic decisions.

Market entry risks/opportunities by region

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 Introduction

1.2 Global AI GPU Market Size Forecast

1.2.1 Global AI GPU Sales Value (2021–2032)

1.2.2 Global AI GPU Sales Volume (2021–2032)

1.2.3 Global AI GPU Sales Price (2021–2032)

1.3 AI GPU Market Trends & Drivers

1.3.1 AI GPU Industry Trends

1.3.2 AI GPU Market Drivers & Opportunities

1.3.3 AI GPU Market Challenges

1.3.4 AI GPU Market Restraints

1.3.5 Impact of U.S. Tariffs

1.4 Assumptions and Limitations

1.5 Study Objectives

1.6 Years Considered

2 Competitive Analysis by Company

2.1 Global AI GPU Players Revenue Ranking (2025)

2.2 Global AI GPU Revenue by Company (2021–2026)

2.3 Global AI GPU Sales Volume Ranking of Players (2025)

2.4 Global AI GPU Sales Volume by Company (2021–2026)

2.5 Global AI GPU Average Price by Company (2021–2026)

2.6 Key Manufacturers AI GPU Manufacturing Base and Headquarters

2.7 Key Manufacturers AI GPU Product Offerings

2.8 Key Manufacturers Start of Mass Production of AI GPU

2.9 AI GPU Market Competitive Analysis

2.9.1 AI GPU Market Concentration Rate (2021–2026)

2.9.2 Global 5 and 10 Largest Manufacturers by AI GPU Revenue in 2025

2.9.3 Global Companies by Tier (Tier 1, Tier 2, Tier 3), based on AI GPU revenue, 2025

2.10 Mergers & Acquisitions and Expansion

3 Segmentation AI GPU Market Classification

3.1 Introduction by Type

3.1.1 AI Training GPU

3.1.2 AI Inference GPU

3.1.3 Edge & Endpoint AI GPU

3.1.4 Global AI GPU Sales Value by Type

3.1.4.1 Global AI GPU Sales Value by Type (2021 vs 2025 vs 2032)

3.1.4.2 Global AI GPU Sales Value, by Type (2021–2032)

3.1.4.3 Global AI GPU Sales Value, by Type (%), 2021–2032

3.1.5 Global AI GPU Sales Volume by Type

3.1.5.1 Global AI GPU Sales Volume by Type (2021 vs 2025 vs 2032)

3.1.5.2 Global AI GPU Sales Volume, by Type (2021–2032)

3.1.5.3 Global AI GPU Sales Volume, by Type (%), 2021–2032

3.1.6 Global AI GPU Average Price by Type (2021–2032)

4 Segmentation by Application

4.1 Introduction by Application

4.1.1 Data Center

4.1.2 Enterprises

4.1.3 HPC & Academia

4.2 Global AI GPU Sales Value by Application

4.2.1 Global AI GPU Sales Value by Application (2021 vs 2025 vs 2032)

4.2.2 Global AI GPU Sales Value, by Application (2021–2032)

4.2.3 Global AI GPU Sales Value, by Application (%), 2021–2032

4.3 Global AI GPU Sales Volume by Application

4.3.1 Global AI GPU Sales Volume by Application (2021 vs 2025 vs 2032)

4.3.2 Global AI GPU Sales Volume, by Application (2021–2032)

4.3.3 Global AI GPU Sales Volume, by Application (%), 2021–2032

4.4 Global AI GPU Average Price by Application (2021–2032)

5 Segmentation by Region

5.1 Global AI GPU Sales Value by Region

5.1.1 Global AI GPU Sales Value by Region: 2021 vs 2025 vs 2032

5.1.2 Global AI GPU Sales Value by Region (2021–2026)

5.1.3 Global AI GPU Sales Value by Region (2027–2032)

5.1.4 Global AI GPU Sales Value by Region (%), 2021–2032

5.2 Global AI GPU Sales Volume by Region

5.2.1 Global AI GPU Sales Volume by Region: 2021 vs 2025 vs 2032

5.2.2 Global AI GPU Sales Volume by Region (2021–2026)

5.2.3 Global AI GPU Sales Volume by Region (2027–2032)

5.2.4 Global AI GPU Sales Volume by Region (%), 2021–2032

5.3 Global AI GPU Average Price by Region (2021–2032)

5.4 North America

5.4.1 North America AI GPU Sales Value, 2021–2032

5.4.2 North America AI GPU Sales Value by Country (%), 2025 vs 2032

5.5 Europe

5.5.1 Europe AI GPU Sales Value, 2021–2032

5.5.2 Europe AI GPU Sales Value by Country (%), 2025 vs 2032

5.6 Asia Pacific

5.6.1 Asia Pacific AI GPU Sales Value, 2021–2032

5.6.2 Asia Pacific AI GPU Sales Value by Region (%), 2025 vs 2032

5.7 South America

5.7.1 South America AI GPU Sales Value, 2021–2032

5.7.2 South America AI GPU Sales Value by Country (%), 2025 vs 2032

5.8 Middle East & Africa

5.8.1 Middle East & Africa AI GPU Sales Value, 2021–2032

5.8.2 Middle East & Africa AI GPU Sales Value by Country (%), 2025 vs 2032

6 Segmentation by Key Countries/Regions

6.1 Key Countries/Regions AI GPU Sales Value Growth Trends, 2021 vs 2025 vs 2032

6.2 Key Countries/Regions AI GPU Sales Value and Sales Volume

6.2.1 Key Countries/Regions AI GPU Sales Value, 2021–2032

6.2.2 Key Countries/Regions AI GPU Sales Volume, 2021–2032

6.3 United States

6.3.1 United States AI GPU Sales Value, 2021–2032

6.3.2 United States AI GPU Sales Value by Type (%), 2025 vs 2032

6.3.3 United States AI GPU Sales Value by Application, 2025 vs 2032

6.4 Europe

6.4.1 Europe AI GPU Sales Value, 2021–2032

6.4.2 Europe AI GPU Sales Value by Type (%), 2025 vs 2032

6.4.3 Europe AI GPU Sales Value by Application, 2025 vs 2032

6.5 China

6.5.1 China AI GPU Sales Value, 2021–2032

6.5.2 China AI GPU Sales Value by Type (%), 2025 vs 2032

6.5.3 China AI GPU Sales Value by Application, 2025 vs 2032

6.6 Japan

6.6.1 Japan AI GPU Sales Value, 2021–2032

6.6.2 Japan AI GPU Sales Value by Type (%), 2025 vs 2032

6.6.3 Japan AI GPU Sales Value by Application, 2025 vs 2032

6.7 South Korea

6.7.1 South Korea AI GPU Sales Value, 2021–2032

6.7.2 South Korea AI GPU Sales Value by Type (%), 2025 vs 2032

6.7.3 South Korea AI GPU Sales Value by Application, 2025 vs 2032

6.8 Southeast Asia

6.8.1 Southeast Asia AI GPU Sales Value, 2021–2032

6.8.2 Southeast Asia AI GPU Sales Value by Type (%), 2025 vs 2032

6.8.3 Southeast Asia AI GPU Sales Value by Application, 2025 vs 2032

6.9 India

6.9.1 India AI GPU Sales Value, 2021–2032

6.9.2 India AI GPU Sales Value by Type (%), 2025 vs 2032

6.9.3 India AI GPU Sales Value by Application, 2025 vs 2032

7 Company Profiles

7.1 NVIDIA

7.1.1 NVIDIA Company Information

7.1.2 NVIDIA Introduction and Business Overview

7.1.3 NVIDIA AI GPU Sales, Revenue, Price and Gross Margin (2021–2026)

7.1.4 NVIDIA AI GPU Product Offerings

7.1.5 NVIDIA Recent Developments

7.2 AMD

7.2.1 AMD Company Information

7.2.2 AMD Introduction and Business Overview

7.2.3 AMD AI GPU Sales, Revenue, Price and Gross Margin (2021–2026)

7.2.4 AMD AI GPU Product Offerings

7.2.5 AMD Recent Developments

7.3 Intel

7.3.1 Intel Company Information

7.3.2 Intel Introduction and Business Overview

7.3.3 Intel AI GPU Sales, Revenue, Price and Gross Margin (2021–2026)

7.3.4 Intel AI GPU Product Offerings

7.3.5 Intel Recent Developments

7.4 Shanghai Denglin

7.4.1 Shanghai Denglin Company Information

7.4.2 Shanghai Denglin Introduction and Business Overview

7.4.3 Shanghai Denglin AI GPU Sales, Revenue, Price and Gross Margin (2021–2026)

7.4.4 Shanghai Denglin AI GPU Product Offerings

7.4.5 Shanghai Denglin Recent Developments

7.5 Vastai Technologies

7.5.1 Vastai Technologies Company Information

7.5.2 Vastai Technologies Introduction and Business Overview

7.5.3 Vastai Technologies AI GPU Sales, Revenue, Price and Gross Margin (2021–2026)

7.5.4 Vastai Technologies AI GPU Product Offerings

7.5.5 Vastai Technologies Recent Developments

7.6 Shanghai Iluvatar

7.6.1 Shanghai Iluvatar Company Information

7.6.2 Shanghai Iluvatar Introduction and Business Overview

7.6.3 Shanghai Iluvatar AI GPU Sales, Revenue, Price and Gross Margin (2021–2026)

7.6.4 Shanghai Iluvatar AI GPU Product Offerings

7.6.5 Shanghai Iluvatar Recent Developments

7.7 Metax Tech

7.7.1 Metax Tech Company Information

7.7.2 Metax Tech Introduction and Business Overview

7.7.3 Metax Tech AI GPU Sales, Revenue, Price and Gross Margin (2021–2026)

7.7.4 Metax Tech AI GPU Product Offerings

7.7.5 Metax Tech Recent Developments

7.8 Moore Threads

7.8.1 Moore Threads Company Information

7.8.2 Moore Threads Introduction and Business Overview

7.8.3 Moore Threads AI GPU Sales, Revenue, Price and Gross Margin (2021–2026)

7.8.4 Moore Threads AI GPU Product Offerings

7.8.5 Moore Threads Recent Developments

7.9 BIRENTECH

7.9.1 BIRENTECH Company Information

7.9.2 BIRENTECH Introduction and Business Overview

7.9.3 BIRENTECH AI GPU Sales, Revenue, Price and Gross Margin (2021–2026)

7.9.4 BIRENTECH AI GPU Product Offerings

7.9.5 BIRENTECH Recent Developments

7.10 Innosilicon

7.10.1 Innosilicon Company Information

7.10.2 Innosilicon Introduction and Business Overview

7.10.3 Innosilicon AI GPU Sales, Revenue, Price and Gross Margin (2021–2026)

7.10.4 Innosilicon AI GPU Product Offerings

7.10.5 Innosilicon Recent Developments

7.11 Shenzhen Siroywe

7.11.1 Shenzhen Siroywe Company Information

7.11.2 Shenzhen Siroywe Introduction and Business Overview

7.11.3 Shenzhen Siroywe AI GPU Sales, Revenue, Price and Gross Margin (2021–2026)

7.11.4 Shenzhen Siroywe AI GPU Product Offerings

7.11.5 Shenzhen Siroywe Recent Developments

7.12 Lisuan Technology

7.12.1 Lisuan Technology Company Information

7.12.2 Lisuan Technology Introduction and Business Overview

7.12.3 Lisuan Technology AI GPU Sales, Revenue, Price and Gross Margin (2021–2026)

7.12.4 Lisuan Technology AI GPU Product Offerings

7.12.5 Lisuan Technology Recent Developments

7.13 Glenfly Tech Co., Ltd

7.13.1 Glenfly Tech Co., Ltd Company Information

7.13.2 Glenfly Tech Co., Ltd Introduction and Business Overview

7.13.3 Glenfly Tech Co., Ltd AI GPU Sales, Revenue, Price and Gross Margin (2021–2026)

7.13.4 Glenfly Tech Co., Ltd AI GPU Product Offerings

7.13.5 Glenfly Tech Co., Ltd Recent Developments

7.14 Sietium

7.14.1 Sietium Company Information

7.14.2 Sietium Introduction and Business Overview

7.14.3 Sietium AI GPU Sales, Revenue, Price and Gross Margin (2021–2026)

7.14.4 Sietium AI GPU Product Offerings

7.14.5 Sietium Recent Developments

7.15 Hygon Information Technology

7.15.1 Hygon Information Technology Company Information

7.15.2 Hygon Information Technology Introduction and Business Overview

7.15.3 Hygon Information Technology AI GPU Sales, Revenue, Price and Gross Margin (2021–2026)

7.15.4 Hygon Information Technology AI GPU Product Offerings

7.15.5 Hygon Information Technology Recent Developments

8 Industry Chain Analysis

8.1 AI GPU Industrial Chain

8.2 AI GPU Upstream Analysis

8.2.1 Key Raw Materials

8.2.2 Key Suppliers of Raw Materials

8.2.3 Manufacturing Cost Structure

8.3 Midstream Analysis

8.4 Downstream Analysis (Customer Analysis)

8.5 Sales Model and Sales Channelss

8.5.1 AI GPU Sales Model

8.5.2 Sales Channels

8.5.3 AI GPU Distributors

9 Research Findings and Conclusion

10 Appendix

10.1 Research Methodology

10.1.1 Methodology/Research Approach

10.1.1.1 Research Programs/Design

10.1.1.2 Market Size Estimation

10.1.1.3 Market Breakdown and Data Triangulation

10.1.2 Data Source

10.1.2.1 Secondary Sources

10.1.2.2 Primary Sources

10.2 Author Details

10.3 Disclaimer

TABLE OF FIGURES

List of Tables

Table 1. AI GPU Market Trends

Table 2. AI GPU Market Drivers & Opportunities

Table 3. AI GPU Market Challenges

Table 4. AI GPU Market Restraints

List of Figures

Figure 1. AI GPU Product Picture

Figure 2. Global AI GPU Sales Value, 2021 vs 2025 vs 2032 (US$ Million)

Figure 3. Global AI GPU Sales Value (US$ Million), 2021–2032

Figure 4. Global AI GPU Sales Volume (K Units), 2021–2032

Figure 5. Global AI GPU Sales Price (US$/Unit), 2021–2032

Figure 6. AI GPU Report Years Considered

Figure 7. Global AI GPU Players Revenue Ranking (US$ Million), 2025

Figure 8. Global AI GPU Sales Volume Ranking of Players (K Units), 2025

Figure 9. The 5 and 10 Largest Manufacturers in the World: Market Share by AI GPU Revenue in 2025

Figure 10. AI GPU Market Share by Company Type (Tier 1, Tier 2, and Tier 3): 2021 vs 2025

Figure 11. AI Training GPU Picture

Figure 12. AI Inference GPU Picture

Figure 13. Edge & Endpoint AI GPU Picture

Figure 14. Global AI GPU Sales Value by Type (US$ Million), 2021 vs 2025 vs 2032

Figure 15. Global AI GPU Sales Value Market Share by Type, 2025 & 2032

Figure 16. Global AI GPU Sales Volume by Type (K Units), 2021 vs 2025 vs 2032

Figure 17. Global AI GPU Sales Volume Market Share by Type, 2025 & 2032

Figure 18. Global AI GPU Price by Type (US$/Unit), 2021–2032

Figure 19. Product Picture of Data Center

Figure 20. Product Picture of Enterprises

Figure 21. Product Picture of HPC & Academia

Figure 22. Global AI GPU Sales Value by Application (US$ Million), 2021 vs 2025 vs 2032

Figure 23. Global AI GPU Sales Value Market Share by Application, 2025 & 2032

Figure 24. Global AI GPU Sales Volume by Application (K Units), 2021 vs 2025 vs 2032

Figure 25. Global AI GPU Sales Volume Market Share by Application, 2025 & 2032

Figure 26. Global AI GPU Price by Application (US$/Unit), 2021–2032

Figure 27. North America AI GPU Sales Value (US$ Million), 2021–2032

Figure 28. North America AI GPU Sales Value by Country (%), 2025 vs 2032

Figure 29. Europe AI GPU Sales Value (US$ Million), 2021–2032

Figure 30. Europe AI GPU Sales Value by Country (%), 2025 vs 2032

Figure 31. Asia Pacific AI GPU Sales Value (US$ Million), 2021–2032

Figure 32. Asia Pacific AI GPU Sales Value by Region (%), 2025 vs 2032

Figure 33. South America AI GPU Sales Value (US$ Million), 2021–2032

Figure 34. South America AI GPU Sales Value by Country (%), 2025 vs 2032

Figure 35. Middle East & Africa AI GPU Sales Value (US$ Million), 2021–2032

Figure 36. Middle East & Africa AI GPU Sales Value by Country (%), 2025 vs 2032

Figure 37. Key Countries/Regions AI GPU Sales Value (%), 2021–2032

Figure 38. Key Countries/Regions AI GPU Sales Volume (%), 2021–2032

Figure 39. United States AI GPU Sales Value (US$ Million), 2021–2032

Figure 40. United States AI GPU Sales Value by Type (%), 2025 vs 2032

Figure 41. United States AI GPU Sales Value by Application (%), 2025 vs 2032

Figure 42. Europe AI GPU Sales Value (US$ Million), 2021–2032

Figure 43. Europe AI GPU Sales Value by Type (%), 2025 vs 2032

Figure 44. Europe AI GPU Sales Value by Application (%), 2025 vs 2032

Figure 45. China AI GPU Sales Value (US$ Million), 2021–2032

Figure 46. China AI GPU Sales Value by Type (%), 2025 vs 2032

Figure 47. China AI GPU Sales Value by Application (%), 2025 vs 2032

Figure 48. Japan AI GPU Sales Value (US$ Million), 2021–2032

Figure 49. Japan AI GPU Sales Value by Type (%), 2025 vs 2032

Figure 50. Japan AI GPU Sales Value by Application (%), 2025 vs 2032

Figure 51. South Korea AI GPU Sales Value (US$ Million), 2021–2032

Figure 52. South Korea AI GPU Sales Value by Type (%), 2025 vs 2032

Figure 53. South Korea AI GPU Sales Value by Application (%), 2025 vs 2032

Figure 54. Southeast Asia AI GPU Sales Value (US$ Million), 2021–2032

Figure 55. Southeast Asia AI GPU Sales Value by Type (%), 2025 vs 2032

Figure 56. Southeast Asia AI GPU Sales Value by Application (%), 2025 vs 2032

Figure 57. India AI GPU Sales Value (US$ Million), 2021–2032

Figure 58. India AI GPU Sales Value by Type (%), 2025 vs 2032

Figure 59. India AI GPU Sales Value by Application (%), 2025 vs 2032

Figure 60. AI GPU Industrial Chain

Figure 61. AI GPU Manufacturing Cost Structure

Figure 62. Channels of Distribution (Direct Sales, and Distribution)

Figure 63. Bottom-up and Top-down Approaches for This Report

Figure 64. Data Triangulation

Figure 65. Key Executives Interviewed

KEY QUESTIONS ADDRESSED BY THE REPORT

Which companies rank high in the global AI GPU market? zhanKai

The top companies in the global AI GPU market are NVIDIA、AMD、Intel.

What is the annual compound growth rate of the global AI GPU market size from 2026 to 2032? shouQi

What was the global market size of AI GPU in 2026? shouQi

Which region is expected to have the highest market share? shouQi

What was the global market size of AI GPU in 2032? shouQi

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USD 4350.00
(Single User License)
AI GPU- Global Market Share and Ranking, Overall Sales and Demand Forecast 2024-2030
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 Date: 2024-05-17
Pages: 97
USD 3950.00
(Single User License)
Global AI GPU Market Research Report 2024
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 Date: 2024-05-17
Pages: 86
USD 2900.00
(Single User License)

Global AI GPU Sales Market Report, Competitive Analysis and Regional Opportunities 2026-2032
The global AI GPU market size was US$ 120730 million in 2025 and is forecast to reach a readjusted size of US$ 1001190 million by 2032 with a CAGR of 35.8% during the forecast period 2026-2032.
Published: 2026-01-05
Pages: 92
USD 4250.00 (Single User License)
Global AI GPU Market Research Report 2026
The global AI GPU market was valued at US$ 120730 million in 2025 and is anticipated to reach US$ 1001190 million by 2032, at a CAGR of 35.8% from 2026 to 2032.
Published: 2026-01-05
Pages: 145
USD 2900.00 (Single User License)
AI GPU - Global Market Share and Ranking, Overall Sales and Demand Forecast 2025-2031
The global market for AI GPU was estimated to be worth 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.
Published: 2025-10-15
Pages: 115
USD 3950.00 (Single User License)
Global AI GPU Sales Market Report, Competitive Analysis and Regional Opportunities 2025-2031
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.
Published: 2025-09-10
Pages: 100
USD 4250.00 (Single User License)
Global AI GPU Market Outlook, In‑Depth Analysis & Forecast to 2031
The global AI GPU market is projected to grow from US$ 85625 million in 2024 to US$ 757212 million by 2031, at a CAGR of 35.8% (2025-2031), driven by critical product segments and diverse end‑use applications, while evolving U.S. tariff policies introduce trade‑cost volatility and supply‑chain uncertainty.
Published: 2025-09-10
Pages: 170
USD 4900.00 (Single User License)
Global AI GPU Market Research Report 2025
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.
Published: 2025-09-10
Pages: 102
USD 2900.00 (Single User License)
AI GPU- Global Market Share and Ranking, Overall Sales and Demand Forecast 2025-2031
The global market for AI GPU was estimated to be worth US$ 74970 million in 2024 and is forecast to a readjusted size of US$ 476220 million by 2031 with a CAGR of 30.7% during the forecast period 2025-2031.
Published: 2025-01-19
Pages: 107
USD 3950.00 (Single User License)
Global and China AI GPU Market Report & Forecast 2024-2030
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: 109
USD 4350.00 (Single User License)
AI GPU- Global Market Share and Ranking, Overall Sales and Demand Forecast 2024-2030
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
USD 3950.00 (Single User License)
Global AI GPU Market Research Report 2024
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
USD 2900.00 (Single User License)