Comment by danpalmer
10 hours ago
Google is in a different position to others in that they're the only frontier lab with a cloud infra business. It obviously makes sense to sell GPUs on cloud infra as people want to rent them. In that respect Google buys a ton of GPUs to rent out.
What's unclear to me is how much Google uses GPUs for their own stuff. Yes Gemini runs on GPUs now, so that Google can sell Gemini on-prem boxes (recent release announced last week), but is any training or inference for Gemini really happening on GPUs? This is unclear to me. I'd have guessed not given that I thought TPUs were much cheaper to operate, but maybe I'm wrong.
Caveat, I work at Google, but not on anything to do with this. I'm only going on what's in the press for this stuff.
> Gemini on-prem boxes (recent release announced last week)
Do you have any more information on this? I only found this article about it: https://venturebeat.com/technology/googles-gemini-can-now-ru...
It mentions that Gemini can run on eight NVIDIA GPUs, but not which GPU and which Gemini model. Either way, this puts an upper bound of 288 * 8 = 2304 GB on the size of the Gemini model, which as far as I know has been a secret until now.
I have most likely outdated info, I left Google Research 4y ago. Back then, available TPU instances were plenty and GPU scarce. Nobody wanted to mess with an immature crashing compiler and very steep performance cliffs (performance was excellent only if you stayed within the guardrails, and being outside was supported and not even resulting in a warning - as it was so common in code). But I believe most of it has changed for the better for TPUs.