Q a lot of sense in the, in the age of AI. Do you know more about, um, kind of like early use cases, obviously like the frontier insane, like, you know, gigawatt scale that's going to come in the future. What are, what are some of the more exciting use cases in like Like the near term, is it just like fine tuning stuff? Do you have any insight there?
A Yeah, yeah. So I mean, they're, right now, they're basically working on just like scaling this up to like state of the art level models. Like they did the 1.1 billion parameter, you know, training run. Like a few months later, they completed like a ten billion parameter training run. And then, you know, basically just serving as like the, the peer to peer marketplace between the supply and demand side. So like on the demand side, you have like AI startups that need extra compute, like labs, like, you know, random independent developers, that sort of thing. And then on a supply side, it's like data centers and then individuals and also startups with like idle compute. Like you have like hugging face, like semi-analysis that want to just like earn extra for, for their idle compute. So it's basically just like, like the way we think about it is just enabling like the AI market to like progress at a much faster rate because it's just like enabling all this idle compute to be To be put to work. And then on, yeah, on their end, it's like basically what they did is Google deep mind released a paper called like D loco, um, which stands for like distributed low communications framework. Um, and they implemented that, but in like an open source way, like they called it open D loco, um, such that like you can train across like many different continents. And that was like a huge step funct…
AI assessment note: “they're basically working on just like scaling this up to like state of the art level models”