If Google’s TPUs were really substantially superior, don’t you think that would result in at least short term market advantages for Gemini? Where are they?
Seemingly fast interconnects benefit training more than inference since training can have more parallel communication between nodes. Inference for users is more embarrassingly parallel (requires less communication) than updating and merging network weights.
My point: cool benchmark, what does it matter? The original post says Nvidia doesn’t have anything to compete with massively interconnected TPUs. It didn’t merely say Google’s TPUs were better. It said that Nvidia can’t compete. That’s clearly bullshit and wishful thinking, right? There is no evidence in the market to support that, and no actual technical points have been presented in this thread either. OpenAI, Anthropic, etc are certainly competing with Google, right?
And then people explained why the effects are smoothed over right now but will matter eventually and you rejected them as if they didn't understand your question. They answered it, take the answer.
> It didn’t merely say Google’s TPUs were better. It said that Nvidia can’t compete.
Can't compete at clusters of a certain size. The argument is that anyone on nVidia simply isn't building clusters that big.