Global AI GPU Market Strategic Research Report

Scope of the Report

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 presents a comprehensive overview of the global AI GPU market, covering market size and forecast, segmentation by product type and application, competitive landscape, leading players and regional and country-level outlook.

Segment by Type

• AI Training GPU
• AI Inference GPU
• Edge & Endpoint AI GPU

Segment by Application

• Data Center
• Enterprises
• HPC & Academia

Who Can Use This Report?

This report is written for decision-makers who need a clear, data-backed view of the global AI GPU market:

• Manufacturers, suppliers and solution providers benchmarking their position and planning product, capacity and go-to-market strategy
• Distributors, channel partners and end users in Data Center, Enterprises, HPC & Academia evaluating demand and sourcing options
• Investors, financial analysts and consultants assessing growth opportunities, competitive dynamics and M&A potential
• Government agencies, industry associations and research institutions tracking industry developments and policy impact