Sovereign AI's Battlefield: Compute, Storage, & Running On The Edge | Crypto x AI Event
Episode publishedPodcast: The Delphi PodcastThe Delphi Podcast
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Hello, everyone. Welcome to DAI Month at Delphi. Today, we are talking all things infrastructure. From the frontiers of decentralized compute to storage to pushing the limits of what can be done at the edge. This is pondering Durian from Delphi, and I am joined by a stacked lineup of panelists today. So we have Greg, founder of Akash. We have Mohammed, founder of Exo Labs, and we have Michael, cofounder and CEO of Zero g Labs. Gentlemen, welcome to the Delphi podcast. Nice to be here. Thanks
for having us. Yeah.
Hi, buddy.
Yeah. So so maybe we just get started with kinda quick intros. And then for people that might not be familiar, just a, you know, a 10, 22nd download on your project, what you guys are working on, and then we'll dive in. So, Greg, maybe we start with you. My
name is Greg. I'm the CEO for Oracle Labs and the cofounder for Akash. Akash is, the ones for supercloud. A supercloud, like the name suggests, is a supercluster that sits on cloud capable computer and offers a cloud like interface, while, providing a market driven price point and a decentralized, mechanism to access, hard to reach resources, particularly, you know, high density GPUs. Awesome.
Michael. Hey,
everyone. I'm, Michael, cofounder and CEO of Zero g, also Zero Gravity in the long form. What we're building is the 1st decentralized AI operating system, and we started at the data layer. So we made sure that we created a decentralized storage network that's fully available, has fast data ingestion and retrieval rates at costs up to 80% off, centralized systems specifically made for AI, workloads. On top of that, we built a data availability layer, which, can get throughput of up to 50 gigabytes per second per consensus layer so that we can eventually do fully on chain trading for large scale
Mohammed. I work at ExoLabs. What we work on is essentially allowing you to run any AI models yourself on your own hardware. So there are many great efforts to the market as AI, like Meta, creating these best models in the world, like for LAMA for 5 b. But to use these models, you still need to have, like, multiple like, in the case of llama 45, you would need, like, multiple h one 100s. What we do with XO, which is fully open source, you can, like, upgrade any compute you have, like, a few MacBooks and run any model fully privately on your own. And that's it. It's just like your own little pr
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