# Problem and Solution The production of next-generation media, which demands exponentially more processing power than what is available in the centralized GPU cloud, is hampered by computational infrastructure. The centralized GPU cloud is experiencing rivalry for resources, ranging from GPU Delivering and cloud streaming to AI training. This has resulted in limited availability and unaffordable prices for numerous artists. These computational limitations are being made worse by augmented and mixed reality material, which demands orders of magnitude more Delivering power than current HD or 4K images. As a result, in order to produce immersive visuals, artists are frequently faced with time-consuming or expensive Delivering procedures, which reduces the possibility of next-generation media production becoming widely accessible. All of the new AI solutions are vying for the same GPUs and are not scalable. In the meantime, the majority of artists' GPUs sit idle when not Delivering their own work or become unusable after updating to new models, which lowers the potential productivity of the current local GPU infrastructure. Furthermore, an arms race wherein more and more computational resources are devoted to mining fixed (or regressive) block rewards has resulted from an excess GPU supply from proof-of-work cryptocurrency mining. As a result, the productivity per watt of GPUs used for proof-of-work blockchain mining has decreased, making many GPU models unsustainable due to energy consumption costs that exceed marginal revenue. There is a chance to make better use of latent GPU compute resources as Proof of Stake blockchain protocols become more computationally efficient. By linking creators in need of computing resources for Delivering their scenes or other compute requirements with providers with accessible GPU capacity, the Sibyl Network makes advantage of these underutilized GPU cycles. --- # 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://sibylnetwork.gitbook.io/docs/problem-and-solution.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.