Varun Mohan: recommendation

Transcript context

…teams. Under utilizing their hardware, how should they think about what to own? You know, like should they own the appearance architecture? Like should they use Xlo to get it to production? How do you think about it? So I think one thing that has proven to be true over the last year and a half is companies, for the most part, should not be trying to figure out what the optimal ML architecture is or training architecture is. Especially with a lot of these large language models. We have generic models and transformer architecture that are solving a lot of distinct problems. I'll caveat that with most companies. Some of our customers, which are autonomous vehicle companies, have extremely strict requirements like they need to be able to run a model at very low latency, extremely high precision recall. You know, GBT three is great, but the Precision Recall, you wouldn't trust someone's life with that, right? So because of that, they need to innovate new kinds of model architectures. For a vast majority of enterprises, they should probably be using something off the shelf, fine tuning Bert models. If it's vision, they should be fine tuning, resonant or using something like clip like the less work they can do, the better. And I guess that was a key turning point for us, which is like we start to build more and more infrastructure for the architectures that. The most popular and the most popular architecture was the transformer architecture. We had a lot of L L M companies explicitly reach out to us and ask us, wow, our GT three bill is high. Is there a way to serve G P T three or some open source model much more cheaply? And that's sort of what we viewed as why we were maybe prepared for when we internally needed to deploy transform models our. And so the next step was, Hey, we have this amazing infrastructure. We can build kind of consumer facing products, so to speak, at with much better unit economics, much better performance. And that's how code kind…

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