The rising interest in AI has significantly increased the demand for computing power, driving the need for GPUs due to their ability to efficiently handle deep learning operations. However, not all AI startups can afford the vast number of GPUs required, leading to the emergence of GPU-as-a-Service (GPUaaS). This service provides a cost-effective alternative by pooling unused processing power from various sources like idle computers and servers. Companies like Kinesis have developed technology to efficiently repurpose these resources into a unified computing platform, allowing businesses to pay only for the computing power they utilize and thereby economize and operate sustainably. This growing sector, valued at over $4 billion in 2024, addresses AI's computational barriers and environmental concerns by optimizing existing resources rather than adding new servers, marking a shift towards more sustainable and efficient AI model development.