Eugene Cheah: commitment

Transcript context

…trillion dollars. But, I think 7 trillion is a bit too much. Like, what's going to happen to half of the world that doesn't even have a trillion dollars? Yeah, so I want AI to be accessible at scale. So, apparently ChatGPT produced, or OpenAI produced 100 billion words per day. That's 3. 4 million tokens per second. No one has the exact numbers, but it's typically 50k, H100s and above remote, like these are some old numbers, like the numbers have gone way beyond this, apparently. But, with our architecture, for a 7B model, that's just a thousand GPUs, or ten thousand GPUs for a 70B model. We're talking about one data center to handle all of OpenAI's workload. And if we want AI agents everywhere, cheaper, at a much larger scale, we need to be thinking about that fundamental shift. Because it's not just about who can it's not just about you can afford it in the US, it's about everyone else in the world. And that brings us to the second advantage of our model, which is not even architecture. Because we are accessible by language. We apparently beat Mistro and everyone else in Mountain Lingo, but that's not because our architecture is better, but because we're an open source team that came from all around the world and wanted our model to work for our mom and grandma. That was the real reason, and we We iterated and refined the data accordingly. We created a custom tokenizer that supports all languages, not just English. And sometimes in the race for the English benchmark, because one of the reasons why other models don't perform as well in multilingual, is because the truth is, if you add multilingual, you hurt your English eval. But, who are we building the AI for? Are we building it for our evals? Or are we building it for the people to use? And, and, even in evals, my frustration is, we trained on 100 languages, I only got 23 languages for evals. Like, where's everything else? So, where are we now? Just like I mentioned 1. 1 trillion, that's where we are, we are in between the 1. 5 trillion and the 1 trillion models for for all, all the, all the English models benchmarks. And, yeah, zooming in further, it just shows that we have more room to go. And, for me, like, The emphasis on English is weird because only 70 percent of the world speaks English, but we are here for the 83%. That's for us. If you all want to get the best English model, sure, it may not be true for us, but we are here for everyone else. And, yeah, and a lot, a lot, the launch of that model, I think what was the biggest feedback I had, was not that it was a linear transformer, was that it can run on their own. Laptops. Some people even ran it on a Raspberry Pi, very slowly. And it supported their language, which was more exciting because that's more important for most people. And I think the last one that I've recently like heard that was unique for us and is a lot more important is that ultimately this model is owned by everyone because We put it into the Linux Foundation. nd I think the last one that I've recently like heard that was unique for us and is a lot more important is that ultimately this model is owned by everyone because We put it into the Linux Foundation. No custom charity, no custom board structure, no weird stuff. We just put, we just train the model, put it in an open source organization. That means it's not owned exclusive to us. If I go rogue one day, you can just, the code will not disappear. The model will not disappear. Linux Foundation has already bought into it. And that is to all of you here. And so, and so what's next for us? Well, We recently started a commercial entity. I know that's weird to say after the open source stuff. But, we, and since then we managed to get more investors and sponsors that we started our next major train. So we are training the next 1 trillion token. This is 16 H100 nodes eating enough electricity for multiple homes. And by the, and by the end of next, by the next month, we'll have our 2 trillion transformer alternative. That you can do one-to-one compare with Lamar. And of course, because since we had to make a profit somehow for our investors, we are launching our platform also to host train and fine tune our models all in by March, 2024. And quick shout up to later space. We literally, the first. To cover us in, in, I guess in the AI influencer sphere, before, before beyond Transformer. It was even sexy. It was like, yeah. The first to even consider us and yeah. And we hope that a few of you get excited what this in join us along the way. Yeah. Final Frontiers had a stellar lineup of demo judges featuring CEOs and VPs of AI from LaminDex, Replit, GitHub, AMD, Meta, and Lemurian Labs. RWKV won one of two judge prizes available that night, alongside with this next startup, Pixii AI.…

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