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

QY Research Inc. (Global Market Report Research Publisher) announces the release of 2024 latest report “AI GPU- Global Market Share and Ranking, Overall Sales and Demand Forecast 2024-2030”. Based on current situation and impact historical analysis (2019-2023) and forecast calculations (2024-2030), this report provides a comprehensive analysis of the global Wire Drawing Dies market, including market size, share, demand, industry development status, and forecasts for the next few years.

 

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.

 

【Get a free sample PDF of this report (Including Full TOC, List of Tables & Figures, Chart)】 

https://www.qyresearch.com/reports/2680654/ai-gpu

 

 

AI GPU Market Summary

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.

 

According to the new market research report "Global AI GPU Market Report 2023-2029", published by QYResearch, the global AI GPU market size is projected to reach USD 313644 million by 2029, at a CAGR of 43% during the forecast period.

 

• Global AI GPU MarketSize(US$ Million), 2018-2029

 

Above data is based on report from QYResearch: Global AI GPU Market Report 2023-2029 (published in 2023). If you need the latest data, plaese contact QYResearch.

 

• Global AI GPU Top6 Players Rankingand Market Share (Ranking is based on the revenue of 2023, continually updated)

 

Above data is based on report from QYResearch: Global AI GPU Market Report 2023-2029 (published in 2023). If you need the latest data, plaese contact QYResearch.

According to QYResearch Top Players Research Center, the global key manufacturers of AI GPU include NVIDIA, Intel, etc. In 2022, the global top three players had a share approximately 99.0% in terms of revenue. Among them, AMD AI GPU will be shipped in batches in 2024.

• AI GPU,Global Market Size, Split by Product Segment

 

 

Based on or includes research from QYResearch: Global AI GPU Market Report 2023-2029.

In terms of product type, currently 32-80GB is the largest segment, hold a share of 73%.

In terms of product application, currently Machine Learning is the largest segment, hold a share of 86.2%.

 

 

 

 

Market Drivers:

1. Policy support and promotion are the core driving force in the AI field.
2. The continuous introduction of AI large Language Models/NLP and the increasing number of parameters have driven the growth of model GPU demand to a certain extent.
3. With the increasing demand for 3D image processing/machine vision, AI machine learning, etc., it is expected to drive the rapid growth of the GPU market.
4. Advances/upgrades in process node technology can improve the performance of GPU products and meet the diverse needs of the market. The scope of GPU applications continues to expand, driving the continuous development of GPU market demand.
5. GPU servers account for more than 90% of the artificial intelligence infrastructure. In the future, the growth of the server market will drive the rapid growth of the AI GPU market.

Market Barriers:

1. Technical barriers: GPU consists of hardware architecture, algorithms, software ecosystem, etc. The hardware architecture is complex and sophisticated and requires constant iterative upgrades. Algorithms and software ecology require high-tech talents, involving a variety of knowledge such as mathematics and physics, and require technical personnel to have certain strength and R&D capabilities.
2. Financial barriers: Entering the GPU market requires a large amount of capital investment, from research and development to production to promotion and sales. New entrants need sufficient financial support at every step to survive in the market.

Trend:

1. As deep learning models become more complex, performance requirements such as computing power continue to increase. It is expected that the demand for AI GPUs in the field of machine learning will increase as the model size increases in the future.
2. In the future, in order to meet diverse needs, AI GPU performance will continue to improve, including computing, GPU core, GPU memory, etc.
3. Application scenarios continue to expand from machine learning to other fields, including cloud computing, Internet of Things, Language Models/NLP, etc.

 

 

 

The report provides a detailed analysis of the market size, growth potential, and key trends for each segment. Through detailed analysis, industry players can identify profit opportunities, develop strategies for specific customer segments, and allocate resources effectively.

 

The AI GPU market is segmented as below:

By Company

 

Segment by Type

    ≤16GB
    32-80GB
    Above 80GB

 

Segment by Application

    Machine Learning
    Language Models/NLP
    Computer Vision
    Others

 

Each chapter of the report provides detailed information for readers to further understand the AI GPU market:

Chapter 1: Introduces the report scope of the AI GPU report, global total market size (valve, volume and price). This chapter also provides the market dynamics, latest developments of the market, the driving factors and restrictive factors of the market, the challenges and risks faced by manufacturers in the industry, and the analysis of relevant policies in the industry. (2019-2030)

Chapter 2: Detailed analysis of AI GPU manufacturers competitive landscape, price, sales and revenue market share, latest development plan, merger, and acquisition information, etc. (2019-2024)

Chapter 3: Provides the analysis of various AI GPU market segments by Type, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different market segments. (2019-2030)

Chapter 4: Provides the analysis of various market segments by Application, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different downstream markets.(2019-2030)

Chapter 5:  Sales, revenue of AI GPU in regional level. It provides a quantitative analysis of the market size and development potential of each region and introduces the market development, future development prospects, market space, and market size of each country in the world..(2019-2030)

Chapter 6:  Sales, revenue of AI GPU in country level. It provides sigmate data by Type, and by Application for each country/region.(2019-2030)

Chapter 7: Provides profiles of key players, introducing the basic situation of the main companies in the market in detail, including product sales, revenue, price, gross margin, product introduction, recent development, etc. (2019-2024)

Chapter 8: Analysis of industrial chain, including the upstream and downstream of the industry.

Chapter 9: Conclusion.

 

Benefits of purchasing QYResearch report:

Competitive Analysis: QYResearch provides in-depth AI GPU competitive analysis, including information on key company profiles, new entrants, acquisitions, mergers, large market shear, opportunities, and challenges. These analyses provide clients with a comprehensive understanding of market conditions and competitive dynamics, enabling them to develop effective market strategies and maintain their competitive edge.

Industry Analysis: QYResearch provides AI GPU comprehensive industry data and trend analysis, including raw material analysis, market application analysis, product type analysis, market demand analysis, market supply analysis, downstream market analysis, and supply chain analysis.

and trend analysis. These analyses help clients understand the direction of industry development and make informed business decisions.

Market Size: QYResearch provides AI GPU market size analysis, including capacity, production, sales, production value, price, cost, and profit analysis. This data helps clients understand market size and development potential, and is an important reference for business development.

 

Other relevant reports of QYResearch:

Global and China AI GPU Market Report & Forecast 2024-2030
Global AI GPU Market Research Report 2024
Global AI GPU Market Insights, Forecast to 2030
AI GPU - Global Market Insights and Sales Trends 2024
Global AI GPU Market Insights, Forecast to 2029
Global AI GPU Market Research Report 2023
Global and United States AI GPU Market Report & Forecast 2023-2029
Global AI GPU Market Report, History and Forecast 2018-2029, Breakdown Data by Manufacturers, Key Regions, Types and Application

 

 

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