Speakers in the public record
Claim mix
evaluation 5belief 3uncertainty 2prediction 2recommendation 1preference 1
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The useful parts, with receipts.
14 published records
“I think the startup's point is the most interesting, right? You mentioned companies that are GPU poor, they raise a lot of money, and there's a lot of startups out there that are GPU poor and did not raise a lot of money.”
- Publisher
- Latent Space
“Like, you look at what Tsinghua University is doing in China, actually, they open sourced their model to I think the largest like by parameter count, at least open source models.”
- Publisher
- Latent Space
“I don't know. You went into a lot more detail and I'd love to dig into some of that.”
- Publisher
- Latent Space
“Like it has the lowest scaling laws, the lowest Moore's laws, and I don't know what to do about that because no one else has any solutions.”
- Publisher
- Latent Space
“I think with AI, especially like how scaling laws are going, it's like incredibly important for infrastructure is like so much more important. And then like when you just think about software cost, right, like the cost structure of it, there was always a bigger component of R&D and like SAS businesses, you know, all over SF, all these SAS businesses did crazy good because, you know, they just start as they grow and then all of a sudden they're so freaking profitable for each incremental new customer.”
- Publisher
- Latent Space
“Building a bigger and better model every, you know, every few months. And I don't know how Apple gets on that train, but you know, at the same time, there's no company that has more powerful distribution, right?”
- Publisher
- Latent Space
“I don't even think like, I expect in the next few years that the OpenAI Microsoft probably falls apart too.”
- Publisher
- Latent Space
“The supercomputer, it's, it's, oh, it's slightly more, but yeah, I think the 500 million is a fair enough number.”
- Publisher
- Latent Space
“Their chips are not as good, or if they are, even though, you know, I mentioned Intel and AMD's chips are better, that's only because they're throwing more money at the problem kind of, right?”
- Publisher
- Latent Space
“I mean, it was good. They were doing good, and NVIDIA bought them, you know, in 19, I believe, or 18, but Broadcom has been number one in networking for a decade plus, and Google partnered with them on making the TPU, right?”
- Publisher
- Latent Space
“Unless you're fine tuning for on-device use, I think fine tuning current existing models, especially the smaller ones is a useless waste of time because the cost of inference is actually much cheaper than you think once you achieve good MBU and you batch at a decent size, which any successful business in the cloud is going to achieve, you know, and then two, fine tuning like people like, oh, you know, this 7 billion parameter model, if you fine tune it on a data set is almost as good as 3.”
- Publisher
- Latent Space
“Some people would argue lower, right? But at the very least, you need to achieve human reading level speeds and probably a little bit faster, because we like their skin, to have a usable model for chatbot style applications.”
- Publisher
- Latent Space
“I think that one's really critical because it explains Google's infrastructure quite a bit from networking through chips, through all that sort of history of the TPU a little bit.”
- Publisher
- Latent Space
“So in training, everyone just talks about MFU, right? But then on inference, right, which I think is one LLM inference will be bigger than training or multimodal, whatever, bubble inference will be bigger than training, probably next year, in fact, at least in terms of GPUs deployed.”
- Publisher
- Latent Space