Industry: Electronics & Semiconductor
Published Date: 2025-09-10
Pages: 170 Pages
Report ld: 4810885
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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 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.
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 Includes:
This definitive report equips CEOs, marketing directors, and investors with a 360° view of the global AI GPU market, seamlessly integrating production capacity and sales performance across the value chain. It analyzes historical production, revenue, and sales data (2020–2024) and delivers forecasts through 2031, illuminating demand trends and growth drivers.
By segmenting the market by Type and by Application, the study quantifies volume and value, growth rates, technical innovations, niche opportunities, and substitution risks, and analyzes downstream customers distribution pattern.
Granular regional insights cover five major markets—North America, Europe, APAC, South America, and MEA—with in‑depth analysis of 20+ countries. Each region’s dominant products, competitive landscape, and downstream demand trends are clearly detailed.
Critical competitive intelligence profiles manufacturers—capacity, sales volume, revenue, margins, pricing strategies, and major customers—and dissects the top-player positioning across product lines, applications, and regions to reveal strategic strengths.
A concise supply‑chain overview maps upstream suppliers, manufacturing technologies, cost structures, and distribution dynamics to identify strategic gaps and unmet demand.
MARKET SEGMENTATION
CHAPTER OUTLINE
Chapter 1: Defines the AI GPU study scope, segments the market by Type and by Application, etc, highlights segment size and growth potential.
Chapter 2: Offers current market state, projects global revenue and sales to 2031, pinpointing high consumption regions and emerging market catalysts
Chapter 3: Maps global production capacity, utilization, and market share (2020–2031), identifies efficient hubs, reveals regulatory/trade policy impacts and bottlenecks.
Chapter 4: Dissects the manufacturer landscape—ranks by volume and revenue, analyzes profitability and pricing, maps production bases, details manufacturer performance by product type and evaluates concentration alongside M&A moves.
Chapter 5: Unlocks high margin product segments—compares sales, revenue, ASP, and technology differentiators, highlighting growth niches and substitution risks
Chapter 6: Targets downstream market opportunities—evaluates sales, revenue, and pricing by Application, identifies emerging use cases, and profiles leading customers by region and by Application.
Chapter 7: North America—breaks down sales and revenue by Type, by Application and country, profiles key manufacturers and assesses growth drivers and barriers.
Chapter 8: Europe—analyses regional sales, revenue and market by Type, by Application and manufacturers, flagging drivers and barriers.
Chapter 9: Asia Pacific—quantifies sales and revenue by Type, by Application, and region/country, profiles top manufacturers, and uncovers high potential expansion areas.
Chapter 10: Central & South America—measures sales and revenue by Type, by Application, and country, profiles top manufacturers, and identifies investment opportunities and challenges.
Chapter 11: Middle East and Africa—evaluates sales and revenue by Type, by Application, and country, profiles key manufacturers, and outlines investment prospects and market hurdles
Chapter 12: Profiles manufacturers in depth—details product specs, capacity, sales, revenue, margins; Top manufactures 2024 sales breakdowns by Product type, by Application, by sales region SWOT analysis, and recent strategic developments.
Chapter 13: Supply chain—analyses upstream raw materials and suppliers, manufacturing footprint and technology, cost drivers, plus downstream channels and distributor roles.
Chapter 14: Market dynamics—explores drivers, restraints, regulatory impacts, and risk mitigation strategies.
Chapter 15: Actionable conclusions and strategic recommendations.
WHY THIS REPORT
Beyond standard market data, this analysis provides a clear profitability roadmap, empowering you to:
Beyond standard market data, this analysis provides a clear profitability roadmap—empowering you to:
Allocate capital strategically to high growth regions (Chapters 7–11) and margin rich segments (Chapter 5).
Negotiate from strength with suppliers (Chapter 13) and customers (Chapter 6) using cost and demand intelligence.
Outmaneuver competitors with granular insights into their operations, margins, and strategies (Chapters 4 and 12).
Secure your supply chain against disruptions through upstream and downstream visibility (Chapters 13 and 14).
Leverage this 360° intelligence to turn market complexity into actionable competitive advantage.
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 Study Coverage
1.1 Introduction to AI GPU: Definition, Properties, and Key Attributes
1.2 Market Segmentation by Type
1.2.1 Global AI GPU Market Size by Type, 2020 VS 2024 VS 2031
1.2.2 AI Training GPU
1.2.3 AI Inference GPU
1.2.4 Edge & Endpoint AI GPU
1.3 Market Segmentation by Application
1.3.1 Global AI GPU Market Size by Application, 2020 VS 2024 VS 2031
1.3.2 Data Center
1.3.3 Enterprises
1.3.4 HPC & Academia
1.4 Assumptions and Limitations
1.5 Study Objectives
1.6 Years Considered
2 Executive Summary
2.1 Global AI GPU Revenue Estimates and Forecasts 2020-2031
2.2 Global AI GPU Revenue by Region
2.2.1 Revenue Comparison: 2020 VS 2024 VS 2031
2.2.2 Historical and Forecasted Revenue by Region (2020--2031)
2.2.3 Global Revenue Market Share by Region (2020-2031)
2.3 Global AI GPU Sales Estimates and Forecasts 2020-2031
2.4 Global AI GPU Sales by Region
2.4.1 Sales Comparison: 2020 VS 2024 VS 2031
2.4.2 Historical and Forecasted Sales by Region (2020-2031)
2.4.3 Emerging Market Focus: Growth Drivers & Investment Trends
2.4.4 Global Sales Market Share by Region (2020-2031)
3 Global Production Analysis
3.1 Global AI GPU Production Capacity and Utilization Rates (2020–2031)
3.2 Regional Production: Comparative Analysis (2020 VS 2024 VS 2031)
3.3 Regional Production Dynamics
3.3.1 Historic Production by Region (2020-2025)
3.3.2 Forecasted Production by Region (2026-2031)
3.3.3 Production Market Share by Region (2020-2031)
3.3.4 Regulatory and Trade Policy Impact on Production
3.3.5 Production Capacity Enablers and Constraints
3.4 Key Regional Production Hubs
3.4.1 North America
3.4.2 Europe
3.4.3 China
3.4.4 Japan
3.4.5 South Korea
4 Competition by Manufacturers
4.1 Global AI GPU Sales by Manufacturers
4.1.1 Global Sales Volume by Manufacturers (2020-2025)
4.1.2 Global Top 5 and Top 10 Manufacturers’Market Share by Sales Volume (2024)
4.2 Global AI GPU Manufacturer Revenue Rankings and Tiers
4.2.1 Global Revenue (Value) by Manufacturers (2020-2025)
4.2.2 Global Key Manufacturer Revenue Ranking (2023 vs. 2024)
4.2.3 Revenue-Based Tier Segmentation (Tier 1, Tier 2, and Tier 3)
4.3 Manufacturer Profitability Profiles and Pricing Strategies
4.3.1 Gross Margin by Top Manufacturer (2020 VS 2024)
4.3.2 Manufacturer-Level Price Trends (2020-2025)
4.4 Key Manufacturers Manufacturing Base and Headquarters
4.5 Main Product Type Market Size by Manufacturers
4.5.1 AI Training GPU Market Size by Manufacturers
4.5.2 AI Inference GPU Market Size by Manufacturers
4.5.3 Edge & Endpoint AI GPU Market Size by Manufacturers
4.6 Global AI GPU Market Concentration and Dynamics
4.6.1 Global Market Concentration (CR5 and HHI)
4.6.2 Entrant/Exit Impact Analysis
4.6.3 Strategic Moves: M&A, Capacity Expansion, R&D Investment
5 Global Product Segmentation Analysis
5.1 Global AI GPU Sales Performance by Type
5.1.1 Global Historical and Forecasted Sales by Type (2020-2031)
5.1.2 Global Sales Market Share by Type (2020-2031)
5.2 Global AI GPU Revenue Trends by Type
5.2.1 Global Historical and Forecasted Revenue by Type (2020-2031)
5.2.2 Global Revenue Market Share by Type (2020-2031)
5.3 Global Average Selling Price (ASP) Trends by Type (2020-2031)
5.4 Product Technology Differentiation
5.5 Subtype Dynamics: Growth Leaders, Profitability and Risk
5.5.1 High-Growth Niches and Adoption Drivers
5.5.2 Profitability Hotspots and Cost Drivers
5.5.3 Substitution Threats
6 Global Downstream Application Analysis
6.1 Global AI GPU Sales by Application
6.1.1 Global Historical and Forecasted Sales by Application (2020-2031)
6.1.2 Global Sales Market Share by Application (2020-2031)
6.1.3 High-Growth Application Identification
6.1.4 Emerging Application Case Studies
6.2 Global AI GPU Revenue by Application
6.2.1 Global Historical and Forecasted Revenue by Application (2020-2031)
6.2.2 Revenue Market Share by Application (2020-2031)
6.3 Global Pricing Dynamics by Application (2020-2031)
6.4 Downstream Customer Analysis
6.4.1 Top Customers by Region
6.4.2 Top Customers by Application
7 North America
7.1 North America Sales Volume and Revenue (2020-2031)
7.2 North America Key Manufacturers Sales Revenue in 2024
7.3 North America AI GPU Sales and Revenue by Type (2020-2031)
7.4 North America AI GPU Sales and Revenue by Application (2020-2031)
7.5 North America Growth Accelerators and Market Barriers
7.6 North America AI GPU Market Size by Country
7.6.1 North America Revenue by Country
7.6.2 North America Sales Trends by Country
7.6.3 US
7.6.4 Canada
7.6.5 Mexico
8 Europe
8.1 Europe Sales Volume and Revenue (2020-2031)
8.2 Europe Key Manufacturers Sales Revenue in 2024
8.3 Europe AI GPU Sales and Revenue by Type (2020-2031)
8.4 Europe AI GPU Sales and Revenue by Application (2020-2031)
8.5 Europe Growth Accelerators and Market Barriers
8.6 Europe AI GPU Market Size by Country
8.6.1 Europe Revenue by Country
8.6.2 Europe Sales Trends by Country
8.6.3 Germany
8.6.4 France
8.6.5 U.K.
8.6.6 Italy
8.6.7 Netherlands
9 Asia-Pacific
9.1 Asia-Pacific Sales Volume and Revenue (2020-2031)
9.2 Asia-Pacific Key Manufacturers Sales Revenue in 2024
9.3 Asia-Pacific AI GPU Sales and Revenue by Type (2020-2031)
9.4 Asia-Pacific AI GPU Sales and Revenue by Application (2020-2031)
9.5 Asia-Pacific AI GPU Market Size by Region
9.5.1 Asia-Pacific Revenue by Region
9.5.2 Asia-Pacific Sales Trends by Region
9.6 Asia-Pacific Growth Accelerators and Market Barriers
9.7 Southeast Asia
9.7.1 Southeast Asia Revenue by Country (2020 VS 2024 VS 2031)
9.7.2 Key Country Analysis: Indonesia, Vietnam, Thailand
9.8 China
9.9 Japan
9.10 South Korea
9.11 China Taiwan
9.12 India
10 Central and South America
10.1 Central and South America Sales Volume and Revenue (2020-2031)
10.2 Central and South America Key Manufacturers Sales Revenue in 2024
10.3 Central and South America AI GPU Sales and Revenue by Type (2020-2031)
10.4 Central and South America AI GPU Sales and Revenue by Application (2020-2031)
10.5 Central and South America Investment Opportunities and Key Challenges
10.6 Central and South America AI GPU Market Size by Country
10.6.1 Central and South America Revenue Trends by Country (2020 VS 2024 VS 2031)
10.6.2 Brazil
10.6.3 Argentina
11 Middle East and Africa
11.1 Middle East and Africa Sales Volume and Revenue (2020-2031)
11.2 Middle East and Africa Key Manufacturers Sales Revenue in 2024
11.3 Middle East and Africa AI GPU Sales and Revenue by Type (2020-2031)
11.4 Middle East and Africa AI GPU Sales and Revenue by Application (2020-2031)
11.5 Middle East and Africa Investment Opportunities and Key Challenges
11.6 Middle East and Africa AI GPU Market Size by Country
11.6.1 Middle East and Africa Revenue Trends by Country (2020 VS 2024 VS 2031)
11.6.2 GCC Countries
11.6.3 Turkey
11.6.4 Egypt
11.6.5 South Africa
12 Corporate Profile
12.1 NVIDIA
12.1.1 NVIDIA Corporation Information
12.1.2 NVIDIA Business Overview
12.1.3 NVIDIA AI GPU Product Models, Descriptions and Specifications
12.1.4 NVIDIA AI GPU Capacity, Sales, Price, Revenue and Gross Margin (2020-2025)
12.1.5 NVIDIA AI GPU Sales by Product in 2024
12.1.6 NVIDIA AI GPU Sales by Application in 2024
12.1.7 NVIDIA AI GPU Sales by Geographic Area in 2024
12.1.8 NVIDIA AI GPU SWOT Analysis
12.1.9 NVIDIA Recent Developments
12.2 AMD
12.2.1 AMD Corporation Information
12.2.2 AMD Business Overview
12.2.3 AMD AI GPU Product Models, Descriptions and Specifications
12.2.4 AMD AI GPU Capacity, Sales, Price, Revenue and Gross Margin (2020-2025)
12.2.5 AMD AI GPU Sales by Product in 2024
12.2.6 AMD AI GPU Sales by Application in 2024
12.2.7 AMD AI GPU Sales by Geographic Area in 2024
12.2.8 AMD AI GPU SWOT Analysis
12.2.9 AMD Recent Developments
12.3 Intel
12.3.1 Intel Corporation Information
12.3.2 Intel Business Overview
12.3.3 Intel AI GPU Product Models, Descriptions and Specifications
12.3.4 Intel AI GPU Capacity, Sales, Price, Revenue and Gross Margin (2020-2025)
12.3.5 Intel AI GPU Sales by Product in 2024
12.3.6 Intel AI GPU Sales by Application in 2024
12.3.7 Intel AI GPU Sales by Geographic Area in 2024
12.3.8 Intel AI GPU SWOT Analysis
12.3.9 Intel Recent Developments
12.4 Shanghai Denglin
12.4.1 Shanghai Denglin Corporation Information
12.4.2 Shanghai Denglin Business Overview
12.4.3 Shanghai Denglin AI GPU Product Models, Descriptions and Specifications
12.4.4 Shanghai Denglin AI GPU Capacity, Sales, Price, Revenue and Gross Margin (2020-2025)
12.4.5 Shanghai Denglin AI GPU Sales by Product in 2024
12.4.6 Shanghai Denglin AI GPU Sales by Application in 2024
12.4.7 Shanghai Denglin AI GPU Sales by Geographic Area in 2024
12.4.8 Shanghai Denglin AI GPU SWOT Analysis
12.4.9 Shanghai Denglin Recent Developments
12.5 Vastai Technologies
12.5.1 Vastai Technologies Corporation Information
12.5.2 Vastai Technologies Business Overview
12.5.3 Vastai Technologies AI GPU Product Models, Descriptions and Specifications
12.5.4 Vastai Technologies AI GPU Capacity, Sales, Price, Revenue and Gross Margin (2020-2025)
12.5.5 Vastai Technologies AI GPU Sales by Product in 2024
12.5.6 Vastai Technologies AI GPU Sales by Application in 2024
12.5.7 Vastai Technologies AI GPU Sales by Geographic Area in 2024
12.5.8 Vastai Technologies AI GPU SWOT Analysis
12.5.9 Vastai Technologies Recent Developments
12.6 Shanghai Iluvatar
12.6.1 Shanghai Iluvatar Corporation Information
12.6.2 Shanghai Iluvatar Business Overview
12.6.3 Shanghai Iluvatar AI GPU Product Models, Descriptions and Specifications
12.6.4 Shanghai Iluvatar AI GPU Capacity, Sales, Price, Revenue and Gross Margin (2020-2025)
12.6.5 Shanghai Iluvatar Recent Developments
12.7 Metax Tech
12.7.1 Metax Tech Corporation Information
12.7.2 Metax Tech Business Overview
12.7.3 Metax Tech AI GPU Product Models, Descriptions and Specifications
12.7.4 Metax Tech AI GPU Capacity, Sales, Price, Revenue and Gross Margin (2020-2025)
12.7.5 Metax Tech Recent Developments
12.8 Moore Threads
12.8.1 Moore Threads Corporation Information
12.8.2 Moore Threads Business Overview
12.8.3 Moore Threads AI GPU Product Models, Descriptions and Specifications
12.8.4 Moore Threads AI GPU Capacity, Sales, Price, Revenue and Gross Margin (2020-2025)
12.8.5 Moore Threads Recent Developments
12.9 BIRENTECH
12.9.1 BIRENTECH Corporation Information
12.9.2 BIRENTECH Business Overview
12.9.3 BIRENTECH AI GPU Product Models, Descriptions and Specifications
12.9.4 BIRENTECH AI GPU Capacity, Sales, Price, Revenue and Gross Margin (2020-2025)
12.9.5 BIRENTECH Recent Developments
12.10 Innosilicon
12.10.1 Innosilicon Corporation Information
12.10.2 Innosilicon Business Overview
12.10.3 Innosilicon AI GPU Product Models, Descriptions and Specifications
12.10.4 Innosilicon AI GPU Capacity, Sales, Price, Revenue and Gross Margin (2020-2025)
12.10.5 Innosilicon Recent Developments
12.11 Shenzhen Siroywe
12.11.1 Shenzhen Siroywe Corporation Information
12.11.2 Shenzhen Siroywe Business Overview
12.11.3 Shenzhen Siroywe AI GPU Product Models, Descriptions and Specifications
12.11.4 Shenzhen Siroywe AI GPU Capacity, Sales, Price, Revenue and Gross Margin (2020-2025)
12.11.5 Shenzhen Siroywe Recent Developments
12.12 Lisuan Technology
12.12.1 Lisuan Technology Corporation Information
12.12.2 Lisuan Technology Business Overview
12.12.3 Lisuan Technology AI GPU Product Models, Descriptions and Specifications
12.12.4 Lisuan Technology AI GPU Capacity, Sales, Price, Revenue and Gross Margin (2020-2025)
12.12.5 Lisuan Technology Recent Developments
12.13 Glenfly Tech Co., Ltd
12.13.1 Glenfly Tech Co., Ltd Corporation Information
12.13.2 Glenfly Tech Co., Ltd Business Overview
12.13.3 Glenfly Tech Co., Ltd AI GPU Product Models, Descriptions and Specifications
12.13.4 Glenfly Tech Co., Ltd AI GPU Capacity, Sales, Price, Revenue and Gross Margin (2020-2025)
12.13.5 Glenfly Tech Co., Ltd Recent Developments
12.14 Sietium
12.14.1 Sietium Corporation Information
12.14.2 Sietium Business Overview
12.14.3 Sietium AI GPU Product Models, Descriptions and Specifications
12.14.4 Sietium AI GPU Capacity, Sales, Price, Revenue and Gross Margin (2020-2025)
12.14.5 Sietium Recent Developments
12.15 Hygon Information Technology
12.15.1 Hygon Information Technology Corporation Information
12.15.2 Hygon Information Technology Business Overview
12.15.3 Hygon Information Technology AI GPU Product Models, Descriptions and Specifications
12.15.4 Hygon Information Technology AI GPU Capacity, Sales, Price, Revenue and Gross Margin (2020-2025)
12.15.5 Hygon Information Technology Recent Developments
13 Value Chain and Supply-Chain Analysis
13.1 AI GPU Industry Chain
13.2 AI GPU Upstream Materials Analysis
13.2.1 Raw Materials
13.2.2 Key Suppliers Market Share & Risk Assessment
13.3 AI GPU Integrated Production Analysis
13.3.1 Manufacturing Footprint Analysis
13.3.2 Production Technology Overview
13.3.3 Regional Cost Drivers
13.4 AI GPU Sales Channels and Distribution Networks
13.4.1 Sales Channels
13.4.2 Distributors
14 AI GPU Market Dynamics
14.1 Industry Trends and Evolution
14.2 Market Growth Drivers and Emerging Opportunities
14.3 Market Challenges, Risks, and Restraints
15 Key Findings in the Global AI GPU Study
16 Appendix
16.1 Research Methodology
16.1.1 Methodology/Research Approach
16.1.1.1 Research Programs/Design
16.1.1.2 Market Size Estimation
16.1.1.3 Market Breakdown and Data Triangulation
16.1.2 Data Source
16.1.2.1 Secondary Sources
16.1.2.2 Primary Sources
16.2 Author Details
TABLE OF FIGURES
List of Tables
List of Figures
KEY QUESTIONS ADDRESSED BY THE REPORT
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Pages: 92
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
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.
Published: 2026-01-05
Pages: 128
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
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
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
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
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
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
DESCRIPTION
OVERVIEW
MARKET SEGMENTATION
CHAPTER OUTLINE
WHY THIS REPORT
QYRESEARCH'S STRENGTHS
TABLE OF CONTENTS
TABLE OF FIGURES
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