# Overview The rapid advancement of AI technology has ushered in a new era, one that is driving technological and societal changes at an unprecedented rate. As deep learning, machine learning, and other AI technologies mature, the global demand for the computational resources that support these technologies—known as AI compute power—has sharply increased. AI compute power is fundamental for training and running AI models, requiring significant processing power to handle and analyze massive amounts of data. This growing demand has introduced a range of challenges, including a shortage of computational resources, high costs, long deployment cycles, and geographical limitations. These challenges have spurred the development of new solutions, such as the use of distributed computing resources to meet global AI compute needs more efficiently and economically. Distributed AI computing platforms, like Distributed Super Computing (DSC), gather computational resources from around the world, providing AI developers and researchers with the necessary computing power regardless of their location. Such platforms not only enhance resource utilization but also significantly reduce costs and barriers, promoting the widespread adoption and application of AI technology. --- # 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://dsc-4.gitbook.io/dsc/overview.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.