PUBLISHER: QYResearch | PRODUCT CODE: 1861358
PUBLISHER: QYResearch | PRODUCT CODE: 1861358
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.
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 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 2024 as the base year, with history and forecast data for the period from 2020 to 2031. 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.
Market Segmentation
By Company
Segment by Type
Segment by Application
By Region
Chapter Outline
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.