And isn't your second paragraph the obvious reason for why this product (A3) exists? It's something they expect to sell to cloud customers who have an existing GPU-based workflow, and just want to run it as-is as fast/cheap/scalable as possible, without worrying about compatibility, and making sure they can always move the workload to some other cloud provider or on-prem if needed.
It's like suggesting Sony releasing some of their games on the PC means they're deprecating Playstation.
(Maybe there would be more details in the IO talk. Does anyone know which one this announcement is from?)
A sentence that reads "I am going to eat nothing but vegetables from now on" doesn't mention meat, but you can infer that I won't eat meat again from the sentence.
A sentence that says Google are going all in on nVidea GPUs for AI doesn't need to mention TPUs to convey information about their future either.
Where are you reading that Google is going “all in” on nVidia GPUs? I don’t see that in the linked article at all.
These are clearly targeted at their cloud customers who have workloads tailored to GPUs. They’re supplying demand, as cloud providers do.
Companies can do more than thing at a time.
TBF, there is no mention of anything remotely similar to "I am going to eat nothing but vegetables from now on".
Google is huge.
Just a few H100 doesn't represent anything huge in Google scale.
I also tried to find your analogy in that article and google announcement and it's not there.
If anything, this just reinforces the point I was making. There is nothing at all in the article supporting this narrative. So, where is this coming from? Why are you so intent on this idea that you're reduced to fabricating support for it?
Andnfrom.all that we're meant to say it infers nothing about TPUs?
Offering more options to customers is always better especially when Nvidia has great market share in this area. This is probably the reason why Microsoft is trying to help AMD catch up so their is more competition. AI GPU prices are insane compared to standard GPU because of the lack of competition.
There were articles that Microsoft was helping AMD, but the denied it.
https://arstechnica.com/gadgets/2023/05/microsoft-and-amd-ar...
https://arxiv.org/abs/2304.01433 from April 4 of this year.
> I wonder why they didn't try to polish the rough edges with Pytorch, et al.
It's always funny to me when people have this blindspot - because TPUs aren't for you, they're for the ads org. Neither are PyTorch nor TF for that matter. They're more than happy to get external bug fixers but trust me those individual teams dgaf about external customers. They're not in the least bit community driven projects.
TPUs were supposed to be their unfair advantage in the cloud ML/DL space. But from what I've experienced, and have heard from other engineers, there's always some subtle incompatibility with TPUs that requires modifying the training/eval scripts. I wonder why they didn't try to polish the rough edges with Pytorch, et al.
If they're admitting TPUs aren't their competitive advantage, then why not sell it to other hosting providers, or hell, even directly to ML scientists and enthusiasts? They'll finally get economies of scale, and take business (and mind share) away from NVidia's monopoly.