Global Graphics Cards for AI Market Growth 2026-2032
Description
The global Graphics Cards for AI market size is predicted to grow from US$ 5390 million in 2025 to US$ 37380 million in 2032; it is expected to grow at a CAGR of 33.1% from 2026 to 2032.
Graphics Cards for AI, often referred to as AI Accelerators or GPUs for AI, are specialized hardware components designed to efficiently process the complex mathematical calculations involved in artificial intelligence tasks. These cards leverage parallel processing architectures to handle the large datasets and iterative computations common in machine learning, deep learning, and other AI applications.
Unlike traditional CPUs, which are designed for sequential tasks, GPUs excel at handling numerous simultaneous operations, making them ideal for tasks like training neural networks, inferencing models, and processing large volumes of data.
In 2024, global Graphics Cards for AI production reached approximately 571 k units, with an average global market price of around US$ 7110 per unit.
The upstream of the Graphics Cards for AI market is highly concentrated and capital-intensive. Core inputs include advanced semiconductor fabrication (primarily at leading foundries), high-bandwidth memory (HBM), advanced packaging technologies such as CoWoS or similar 2.5D/3D integration, and high-end substrates and interposers. Key upstream suppliers include semiconductor foundries, memory manufacturers, packaging and testing providers, and substrate vendors. Supply tightness in advanced nodes and HBM capacity has become a structural constraint influencing both production volumes and pricing.
Downstream demand is driven mainly by hyperscale cloud service providers, enterprise AI infrastructure operators, research institutions, and national computing centers. Cloud AI training, large language models, recommendation systems, and generative AI applications represent the dominant demand drivers. While training workloads generate the highest per-unit value, inference deployment across data centers and edge environments increasingly contributes to shipment growth. OEM server manufacturers and system integrators serve as important intermediaries between GPU vendors and end users.
The cost structure of Graphics Cards for AI is dominated by silicon fabrication, HBM memory, advanced packaging, and board-level components, followed by testing, logistics, and warranty support. Compared with consumer GPUs, AI graphics cards exhibit significantly higher bill-of-materials costs but also benefit from strong pricing power. Gross margins for leading vendors are structurally high, supported by differentiated architectures, software ecosystems, and long-term supply agreements, while operating margins reflect substantial ongoing R&D investment.
LP Information, Inc. (LPI) ' newest research report, the “Graphics Cards for AI Industry Forecast” looks at past sales and reviews total world Graphics Cards for AI sales in 2025, providing a comprehensive analysis by region and market sector of projected Graphics Cards for AI sales for 2026 through 2032. With Graphics Cards for AI sales broken down by region, market sector and sub-sector, this report provides a detailed analysis in US$ millions of the world Graphics Cards for AI industry.
This Insight Report provides a comprehensive analysis of the global Graphics Cards for AI landscape and highlights key trends related to product segmentation, company formation, revenue, and market share, latest development, and M&A activity. This report also analyzes the strategies of leading global companies with a focus on Graphics Cards for AI portfolios and capabilities, market entry strategies, market positions, and geographic footprints, to better understand these firms’ unique position in an accelerating global Graphics Cards for AI market.
This Insight Report evaluates the key market trends, drivers, and affecting factors shaping the global outlook for Graphics Cards for AI and breaks down the forecast by Type, by Application, geography, and market size to highlight emerging pockets of opportunity. With a transparent methodology based on hundreds of bottom-up qualitative and quantitative market inputs, this study forecast offers a highly nuanced view of the current state and future trajectory in the global Graphics Cards for AI.
This report presents a comprehensive overview, market shares, and growth opportunities of Graphics Cards for AI market by product type, application, key manufacturers and key regions and countries.
Segmentation by Type:
AI Training Graphics Cards
AI Inference Graphics Cards
Unified Training & Inference Cards
Segmentation by Form Factor:
PCIe Add-in Cards
SXM / OAM Modules
MXM / Embedded AI Graphics Cards
Others
Segmentation by Memory Configuration:
HBM-based AI Graphics Cards
GDDR-based AI Graphics Cards
Others
Segmentation by Application:
Data Center
Enterprise
Others
This report also splits the market by region:
Americas
United States
Canada
Mexico
Brazil
APAC
China
Japan
Korea
Southeast Asia
India
Australia
Europe
Germany
France
UK
Italy
Russia
Middle East & Africa
Egypt
South Africa
Israel
Turkey
GCC Countries
The below companies that are profiled have been selected based on inputs gathered from primary experts and analysing the company's coverage, product portfolio, its market penetration.
Nvidia
AMD
Intel
Moore Threads
Biren Intelligent Technology
Key Questions Addressed in this Report
What is the 10-year outlook for the global Graphics Cards for AI market?
What factors are driving Graphics Cards for AI market growth, globally and by region?
Which technologies are poised for the fastest growth by market and region?
How do Graphics Cards for AI market opportunities vary by end market size?
How does Graphics Cards for AI break out by Type, by Application?
Please note: The report will take approximately 2 business days to prepare and deliver.
Graphics Cards for AI, often referred to as AI Accelerators or GPUs for AI, are specialized hardware components designed to efficiently process the complex mathematical calculations involved in artificial intelligence tasks. These cards leverage parallel processing architectures to handle the large datasets and iterative computations common in machine learning, deep learning, and other AI applications.
Unlike traditional CPUs, which are designed for sequential tasks, GPUs excel at handling numerous simultaneous operations, making them ideal for tasks like training neural networks, inferencing models, and processing large volumes of data.
In 2024, global Graphics Cards for AI production reached approximately 571 k units, with an average global market price of around US$ 7110 per unit.
The upstream of the Graphics Cards for AI market is highly concentrated and capital-intensive. Core inputs include advanced semiconductor fabrication (primarily at leading foundries), high-bandwidth memory (HBM), advanced packaging technologies such as CoWoS or similar 2.5D/3D integration, and high-end substrates and interposers. Key upstream suppliers include semiconductor foundries, memory manufacturers, packaging and testing providers, and substrate vendors. Supply tightness in advanced nodes and HBM capacity has become a structural constraint influencing both production volumes and pricing.
Downstream demand is driven mainly by hyperscale cloud service providers, enterprise AI infrastructure operators, research institutions, and national computing centers. Cloud AI training, large language models, recommendation systems, and generative AI applications represent the dominant demand drivers. While training workloads generate the highest per-unit value, inference deployment across data centers and edge environments increasingly contributes to shipment growth. OEM server manufacturers and system integrators serve as important intermediaries between GPU vendors and end users.
The cost structure of Graphics Cards for AI is dominated by silicon fabrication, HBM memory, advanced packaging, and board-level components, followed by testing, logistics, and warranty support. Compared with consumer GPUs, AI graphics cards exhibit significantly higher bill-of-materials costs but also benefit from strong pricing power. Gross margins for leading vendors are structurally high, supported by differentiated architectures, software ecosystems, and long-term supply agreements, while operating margins reflect substantial ongoing R&D investment.
LP Information, Inc. (LPI) ' newest research report, the “Graphics Cards for AI Industry Forecast” looks at past sales and reviews total world Graphics Cards for AI sales in 2025, providing a comprehensive analysis by region and market sector of projected Graphics Cards for AI sales for 2026 through 2032. With Graphics Cards for AI sales broken down by region, market sector and sub-sector, this report provides a detailed analysis in US$ millions of the world Graphics Cards for AI industry.
This Insight Report provides a comprehensive analysis of the global Graphics Cards for AI landscape and highlights key trends related to product segmentation, company formation, revenue, and market share, latest development, and M&A activity. This report also analyzes the strategies of leading global companies with a focus on Graphics Cards for AI portfolios and capabilities, market entry strategies, market positions, and geographic footprints, to better understand these firms’ unique position in an accelerating global Graphics Cards for AI market.
This Insight Report evaluates the key market trends, drivers, and affecting factors shaping the global outlook for Graphics Cards for AI and breaks down the forecast by Type, by Application, geography, and market size to highlight emerging pockets of opportunity. With a transparent methodology based on hundreds of bottom-up qualitative and quantitative market inputs, this study forecast offers a highly nuanced view of the current state and future trajectory in the global Graphics Cards for AI.
This report presents a comprehensive overview, market shares, and growth opportunities of Graphics Cards for AI market by product type, application, key manufacturers and key regions and countries.
Segmentation by Type:
AI Training Graphics Cards
AI Inference Graphics Cards
Unified Training & Inference Cards
Segmentation by Form Factor:
PCIe Add-in Cards
SXM / OAM Modules
MXM / Embedded AI Graphics Cards
Others
Segmentation by Memory Configuration:
HBM-based AI Graphics Cards
GDDR-based AI Graphics Cards
Others
Segmentation by Application:
Data Center
Enterprise
Others
This report also splits the market by region:
Americas
United States
Canada
Mexico
Brazil
APAC
China
Japan
Korea
Southeast Asia
India
Australia
Europe
Germany
France
UK
Italy
Russia
Middle East & Africa
Egypt
South Africa
Israel
Turkey
GCC Countries
The below companies that are profiled have been selected based on inputs gathered from primary experts and analysing the company's coverage, product portfolio, its market penetration.
Nvidia
AMD
Intel
Moore Threads
Biren Intelligent Technology
Key Questions Addressed in this Report
What is the 10-year outlook for the global Graphics Cards for AI market?
What factors are driving Graphics Cards for AI market growth, globally and by region?
Which technologies are poised for the fastest growth by market and region?
How do Graphics Cards for AI market opportunities vary by end market size?
How does Graphics Cards for AI break out by Type, by Application?
Please note: The report will take approximately 2 business days to prepare and deliver.
Table of Contents
78 Pages
- *This is a tentative TOC and the final deliverable is subject to change.*
- 1 Scope of the Report
- 2 Executive Summary
- 3 Global by Company
- 4 World Historic Review for Graphics Cards for AI by Geographic Region
- 5 Americas
- 6 APAC
- 7 Europe
- 8 Middle East & Africa
- 9 Market Drivers, Challenges and Trends
- 10 Manufacturing Cost Structure Analysis
- 11 Marketing, Distributors and Customer
- 12 World Forecast Review for Graphics Cards for AI by Geographic Region
- 13 Key Players Analysis
- 14 Research Findings and Conclusion
Pricing
Currency Rates
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