🔎Abstract

➡️In today's digital landscape, the demand for high-performance computer resources, notably Graphics Processing Units (GPUs), is constantly increasing. Despite the ubiquitous availability of GPUs, a large amount of their processing capability is wasted due to variables such as idle time and geographical dispersion. This underutilization creates a dilemma since it represents a missed opportunity to use these tremendous resources for beneficial purposes.

➡️Traditional centralized computer systems are unable to successfully handle this issue because of constraints in scalability, accessibility, and cost-effectiveness. Furthermore, centralized systems frequently experience bottlenecks and single points of failure, limiting their capacity to fully utilize distributed GPU resources.

➡️The rapid advancement of artificial intelligence and the increasing demand for high-performance computing have exposed significant inefficiencies in the current methods of GPU resource allocation. Traditional centralized systems often lead to underutilization of computing resources, high operational costs, and limited accessibility for smaller enterprises and individual users. Neurolov addresses these challenges by creating a decentralized platform that allows users to rent out their idle GPU resources, ensuring optimal utilization and providing a cost-effective solution for accessing computational power. Furthermore, decentralized large language models (LLMs) are essential for the future of democratic artificial general intelligence (AGI), ensuring that AI development remains transparent, inclusive, and resistant to central control and bias.