# Computational Power and the Role of GPUs The rapid advancements in AIGC, and AI more generally, rely heavily on computational power. Modern generative AI models, especially large language models (LLMs) and deep neural networks (DNNs), require vast amounts of processing capability to train and operate efficiently. Graphics Processing Units (GPUs), which are optimized for parallel processing tasks, have become indispensable in the AI domain. GPUs outperform traditional Central Processing Units (CPUs) in training deep learning models, making them a critical component for the scalability of AIGC technologies. In recent years, the demand for GPUs has surged, driven by the increasing deployment of AI applications, including AIGC, across industries. The market for GPUs used in AI is expected to grow significantly, with [reports](https://www.marketsandmarkets.com/Market-Reports/artificial-intelligence-market-74851580.html) predicting that the global AI chip market will reach over USD 120 billion by 2030, expanding at a CAGR of more than 40%. Nvidia, AMD, and Intel are some of the key players in this market, with Nvidia leading the way, particularly with its A100 and H100 GPU series, which are optimized for AI workloads. Despite the strong growth in the GPU market, challenges related to supply chain disruptions and the rising cost of high-performance computing resources persist. As AI models become more complex, the need for increasingly powerful and efficient GPUs will continue to rise, placing additional pressure on both the production and distribution of these components. Furthermore, the environmental impact of the energy consumption associated with AI training and GPU-intensive tasks is a growing concern. As AI models become more energy-hungry, there is an urgent need for innovations in energy-efficient computing technologies and solutions. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://zkagent.gitbook.io/whitepaper/background/computational-power-and-the-role-of-gpus.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.