io.net transforms idle GPUs worldwide into a powerful decentralized computing network, creating an "Airbnb for GPUs" that spans data centers, GPU farms, and individual devices. In this session, Aline Almeida explores how Ray orchestrates this diverse ecosystem of over 300,000 verified GPUs to power scalable AI workloads.
Almeida dives into the technical challenges of managing a heterogeneous, globally distributed GPU network, showcasing how io.net customizes Ray to optimize resource allocation, node selection, and communication protocols. From graph theory applications in node selection to advanced scheduling algorithms for distributed task execution, she explores the cutting-edge solutions driving this innovative approach to decentralized AI computation.
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