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According to our (Global Info Research) latest study, the global AI GPU market size was valued at US$ 77140 million in 2024 and is forecast to a readjusted size of USD 474030 million by 2031 with a CAGR of 29.9% during review period.

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According to our (Global Info Research) latest study, the global AI GPU Servers market size was valued at US$ 7397 million in 2024 and is forecast to a readjusted size of USD 19690 million by 2031 with a CAGR of 16.2% during review period.

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The global AI GPU Servers market size is expected to reach $ 19690 million by 2031, rising at a market growth of 16.2% CAGR during the forecast period (2025-2031).

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The global Edge AI GPU Servers market size is expected to reach $ 12310 million by 2031, rising at a market growth of 12.8% CAGR during the forecast period (2025-2031).

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According to our (Global Info Research) latest study, the global Edge AI GPU Servers market size was valued at US$ 5463 million in 2024 and is forecast to a readjusted size of USD 12310 million by 2031 with a CAGR of 12.8% during review period.

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According to our (Global Info Research) latest study, the global AI GPU Server System market size was valued at US$ 7546 million in 2024 and is forecast to a readjusted size of USD 12940 million by 2031 with a CAGR of 9.0% during review period.

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The global AI GPU Server System market size is expected to reach $ 12940 million by 2031, rising at a market growth of 9.0% CAGR during the forecast period (2025-2031).

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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.

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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.

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The global AI GPU market size is expected to reach $ million by 2029, rising at a market growth of % CAGR during the forecast period (2023-2029).

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