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Raven Protocol is a decentralized and distributed deep-learning training protocol designed to revolutionize AI development by providing cost-efficient, faster computations through the blockchain. It harnesses a global network of users' idle computing power, rewarding them with RAVEN tokens. The protocol, operating on the BNB Beacon Chain (BEP2), offers significant advantages in training times, reducing them drastically from weeks to hours. It supports a range of applications and fosters a self-sustaining ecosystem where compute power is rented using RAVEN tokens. The protocol incorporates the Proof-of-Calculation consensus mechanism to ensure data integrity and incentivize speed and redundancy. With a total supply of 10 billion RAVEN tokens, none in circulation, and active trading across 18 markets, Raven Protocol leverages robust technical expertise to make high-performance computing accessible to a broader audience.
Raven Protocol is a decentralized and distributed deep-learning training protocol designed to revolutionize AI development by providing cost-efficient, faster computations through the blockchain. It harnesses a global network of users' idle computing power, rewarding them with RAVEN tokens. The protocol, operating on the BNB Beacon Chain (BEP2), offers significant advantages in training times, reducing them drastically from weeks to hours. It supports a range of applications and fosters a self-sustaining ecosystem where compute power is rented using RAVEN tokens. The protocol incorporates the Proof-of-Calculation consensus mechanism to ensure data integrity and incentivize speed and redundancy. With a total supply of 10 billion RAVEN tokens, none in circulation, and active trading across 18 markets, Raven Protocol leverages robust technical expertise to make high-performance computing accessible to a broader audience.
Raven Protocol aims to revolutionize deep-learning training by providing a decentralized and distributed network that offers cost-efficient and faster computations. The platform is designed for AI developers seeking enhanced training speeds for deep neural networks by leveraging the distributed compute power of a network of users.
Users can benefit from Raven Protocol by contributing idle computing power to the network and earning RAVEN tokens as a reward. This setup democratizes access to high-performance computing for AI tasks, allowing users to train deep-learning models without requiring expensive hardware, while contributing to a collective decentralized network.
Raven Protocol offers significant time and cost advantages over traditional platforms like AWS. It reduces the training time for a dataset of 1 million images from 2-3 weeks to just 2-3 hours. This faster processing time caters to AI companies looking for efficient model training and a scalable alternative to conventional cloud computing services.
RAVEN tokens are central to Raven Protocol's operations, facilitating transactions, rewards, and governance within the network. The tokens incentivize contributors by rewarding them for sharing computing resources and enable enterprise clients to rent compute power for AI training, embodying the decentralized nature of the platform.
Raven Protocol employs a Proof-of-Calculation consensus mechanism. This system incentivizes compute nodes based on their calculation speed and redundancy, rewarding nodes that perform gradient calculations quickly and reliably. This approach ensures data integrity and high-quality, optimized outputs, maintaining an efficient and secure network.
Common issues in Raven Protocol could involve connectivity and syncing challenges within the decentralized network, such as delays in returning gradient calculations or securing RAVEN token transactions. Users should ensure their system's compatibility and connectivity for optimal performance and refer to Raven Protocol's detailed resources for resolution support.
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