Powering the AI-driven science revolution with Lilypad
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About This Episode
In this episode of DevNTell Narb welcomes Allison Haire (CEO) and Stanley Bishop (Head of Research) from Lilypad Network back to the podcast. Lilypad is a decentralized compute network that enables AI and science use cases, such as DeSci, to utilize decentralized hardware. The guests discuss their backgrounds and why they are passionate about distributed compute. They also detail Lilypad's infrastructure, which is a three-sided marketplace comprising hardware providers, model developers, and end users. Finally, they provide some updates on the project's roadmap and upcoming mainnet launch.
Key Takeaways
Lilypad is a decentralized compute network designed for high-performance AI and scientific workflows, addressing the high costs and accessibility issues of traditional cloud compute.
Lilypad operates as a three-sided marketplace connecting hardware providers, model developers (who can monetize their models), and end-users who need reliable and scalable compute resources.
Lilypad aims to create a new standard for decentralized AI compute using 'modules,' which are dockerized jobs with Lilypad specifications that ensure reliable and interoperable AI execution.
The project is heavily focused on real-world scientific applications, like marine science and rare disease research, through decentralized science (DeSci) primitives.
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