GPU

“We already have a line of sight to 18,000 GPUs if we need”

“I can tell you that the level of complexity with GPUs and with AI infrastructure, given how latency sensitive and how you know, the number of parameters that you have to worry about is much, much an order of magnitude more complex.”

“Scale in terms of number of employees, 65 in terms of GPUs. We've already deployed 1200 GPUs.”

“Look there, Meta will spend well north of fifty billion, I think some number between 50 and seventy billion dollars on the underlying data set, on GPUs, essentially.”

“And media chips are generally very bad for voice inference. Like if you have 40 gigabytes of RAM in a NVIDIA GPU, you can generally, if you want to do real time, like a hundred milliseconds, you can use maybe two or four gigabytes of it for our models. And bey…”