Market Executive Summary

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.

The global market for AI GPU was estimated to be worth US$ 36800 million in 2023 and is forecast to a readjusted size of US$ 373210 million by 2030 with a CAGR of 30.7% during the forecast period 2024-2030.

Global key players of AI GPU include NVIDIA, Intel, Shanghai Denglin, etc. The top three players hold a share over 99%. China is the largest market, and has a share about 68%, followed by America and Europe with share 24% and 4%, separately. In terms of product type, 32-80GB is the largest segment, occupied for a share of 73%. In terms of application, Machine Learning has a share about 82 percent.

This report aims to provide a comprehensive presentation of the global market for AI GPU, focusing on the total sales volume, sales revenue, price, key companies market share and ranking, together with an analysis of AI GPU by region & country, by Type, and by Application.

The AI GPU market size, estimations, and forecasts are provided in terms of sales volume (K Units) and sales revenue ($ millions), considering 2023 as the base year, with history and forecast data for the period from 2019 to 2030. With both quantitative and qualitative analysis, to help readers develop business/growth strategies, assess the market competitive situation, analyze their position in the current marketplace, and make informed business decisions regarding AI GPU.

Global AI GPU Market, by Region

North America (U.S., Canada, Mexico)

Europe (Germany, France, UK, Italy, etc.)

Asia Pacific (China, Japan, South Korea, Southeast Asia, India, etc.)

South America (Brazil, etc.)

Middle East and Africa (Turkey, GCC Countries, Africa, etc.)

Global AI GPU Market, Segment by Type

?16GB

32-80GB

Above 80GB

Global AI GPU Market, Segment by Application

Machine Learning

Language Models/NLP

Computer Vision

Others

Chapter 1: Introduces the report scope of the report, global total market size (value, 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.

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

Chapter 3: Provides the analysis of various 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.

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.

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.

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

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.

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

Chapter 9: Conclusion.