blockchainreporter.net
Today, Sharpe AI has announced a new partnership with io.net. Sharpe AI has integrated the io.net’s GPU cluster for its artificial intelligence (AI) fine-tuning processes. The collaboration between these two networks aims to use AI and machine learning technology to enhance the cloud computing industry.
We're excited to see @SharpeLabs leveraging our GPU cluster for their AI fine-tuning! Their smooth onboarding and seamless deployment reflect our dedication to quality service. Looking forward to supporting their growing cloud computing needs! https://t.co/ZaCnfMD27c
— io.net (@ionet) July 17, 2024
Io.net (IO) is a decentralized GPU network that was developed to provide scalable and economical processing power for applications related to machine learning (ML) and AI.
The network’s primary objective is to address the global lack of GPU capacity, which is essential for the training and operation of AI models.
Despite the ever-increasing demand for computing capacity in the field of artificial intelligence and machine learning, a considerable number of independent data centers, crypto miners, and other hardware networks such as Filecoin, Render, and others continue to be mainly inactive.
io.net has built an open network of underutilized GPUs. It makes it simple to rent GPU systems and get to their resources. You could think of it as a market for computer power. Anyone or any business can use it for their AI and Machine Learning projects.
Impact of the New GPU Cluster on Sharpe AI
Sharpe is an AI platform for the automation and execution of DeFi operations. Sharpe is the most straightforward and secure method for managing, tracking, investing, and automating your decentralized finance assets in a fully self-custodial environment, and it can be used with any wallet.
In addition to providing expert traders with comprehensive datasets and tools, it also provides market data and insights powered by artificial intelligence.
The addition of the new GPU cluster to the platform will improve the user experience and train more complex models and improve the performance and accuracy of its AI algorithms.