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15 results for “hardware scaling”
Topic4 mentions across episodes
hardware
Concept2 mentions across episodes
hardware
...scaling loss and the validation loss actually translate into quality improvements for text to image synthesis. We saw similar results also in different modality other modalities like video synthesis. And overall, it makes us confident that further sc
4:11:57 / 4:13:03
from transcript4:11:57 – 4:13:03
...scaling test time compute more generally, is the way to go to have, smarter models, without having to keep scaling, the training, compute by, you know, 10 x to get sort of diminishing returns in terms of the the the performance. Then again, the the t
34:20 / 35:36
from transcript34:20 – 35:36
...scaling law for it, which is effectively for how much compute you put in, the architecture will get to different levels of performance at test tasks. And mixture of experts is one of the ones at training time, even if you don't consider the inference
38:57 / 40:02
from transcript38:57 – 40:02
...scaling. So people are definitely familiar with qubits in the quantum case. Yeah. Explain for people what a p bit is and how it might relate,
43:29 / 44:36
from transcript43:29 – 44:36
sort of working to scale the system in multiple dimensions. So one is we wanted to make our index bigger so we could retrieve from a larger index, which always helps your quality in general. Because if you don't have the page in your index, you're go
24:43 / 25:50
from transcript24:43 – 25:50
...that scaling improves the performance, but it's also important to note that blindly following this is not is quickly becoming inefficient. One of those cases is if we again consider a time slip distribution, we actually have to adjust that to differe
4:12:42 / 4:13:46
from transcript4:12:42 – 4:13:46
...about the scaling law behind it. So if you take, like, Gusto, for example, or Rippling or an HR service like this, when they're buying from an AWS or a GCP, they're buying CPUs and they're running web servers. Those web servers, they kind of buy up t
2:17 / 3:23
from transcript2:17 – 3:23
...hardware. And so your customers are buying, at much higher volumes than you'd otherwise expect. And it's also smaller customers who are buying at higher months of volume, so relative to the what they're spending in general. But in GPUs in particular,
1:35 / 2:44
from transcript1:35 – 2:44
...on, a set of hardware, is gonna get too much traffic that it cannot handle. And at that point, you need to horizontally scale it. And that's not an ML problem. That's not a PyTorch problem. That is, an infrastructure problem to ensure that you can ho
54:45 / 55:47
from transcript54:45 – 55:47
...the next scaling paradigm. All analogies are imperfect. What is one way in which thinking fast and slow or system one, system two kinda doesn't transfer to how we actually scale these things?
8:08 / 9:22
from transcript8:08 – 9:22
...scaling in context length. So this can mean just having more text inputs for for your models, but it can also mean things like taking a lot of visual token inputs, image inputs to your models, or generating lots of outputs. And one thing that's been
2:56 / 4:01
from transcript2:56 – 4:01
...scaling. And I think less famously for also showing that you can scale reinforcement learning training and get kind of this log x axis and then a linear increase in performance on y axis. So there's kind of these three axes now where the traditional
1:03:00 / 1:04:11
from transcript1:03:00 – 1:04:11
...of the next scaling paradigm. All analogies are imperfect. What is one way in which thinking fast and slow or system one, system two kinda doesn't transfer to how we actually scale these things? One thing that I think is underappreciated is that the
9:03 / 10:09
from transcript9:03 – 10:09
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