A decentralized platform designed for data scientists to deploy AI models on. It aims to provide a solution to the increasing demand for hardware resources in the machine learning market. By utilizing idle hardware resources from regular PCs with GPU and data centers, CryptoRat aims to provide additional workload capacity for machine learning tasks, thereby increasing efficiency.
CryptoRat aims to simplify infrastructure work by deploying models automatically and guaranteeing that the API serving the model will be available anytime, thereby saving data scientists 50% of the time necessary to set up models for work.
Additionally, CryptoRat offers a simpler interface for users willing to run a node, which is just an executable file for Windows and Linux. This means users can participate in the network without being experts in blockchain or Python environments.
Every CryptoRat user receives rewards for network participation in USD issued on Everscale. There are three main factors which determine the payment rate:
The longer the user stays in the network and the more uptime they have, the more reputation points they get. In the future, reputation will be represented by special tokens, which can be bought and sold among users. If the user doesn’t behave properly, e.g. leaves the network with unfinished tasks, their reputation stake will be slashed.
As a result, all worker nodes connected to the network will receive payment for each hour of uptime and get reputation points for every three hours of uptime. If the node stays idle, it still gets payment, and its resources are still reserved by the protocol.