Floating point operations (TFlop) != Tensor Operations (TOp)
I think it's fair to say that 92T/s 8-bit arithmetic ops is much less than 25x the 27T/s half-float operations of a GPU.
I don't think that accusation is justified.
The part about float operations being better is only a side note. The core of the comment is that they are not inferior. If you needed to, you could snip wires to turn that half-float unit into an 8 bit unit. So treat the numbers as if they were the same thing. 27 vs. 92. That's not a 25x increase. Not even close. Something about this comparison seems either unfair or misleading. For example if a GPU doesn't engage most of its ALUs for certain sizes of input (cough GP10x cough), that's not a point in favor of the google design, that's just the GPU being broken.
But the actually important part here is that the register and bypass networks to pass 4 bits of int8 data around are way more complicated than those required to pass a single float32 around and that's where Google's decision to restrict the flexibility of its TPU pays big dividends. NVidia's GPUs do not have broken designs. They're just making compromises based on the need to handle a wider variety of use cases.
Yeah but there wasn't a suggestion to do so. Just by raw count there are issues with 25x.
> NVidia's GPUs do not have broken designs.
The part where the current generation sticks in a single FP16x2 unit per 128 FP32 units, so that if your code triggers them it runs 64x slower on FP16 while leaving all the FP32 units idle? That's broken as far as I can see, there to upsell you the pro cards.
Anything that would make 8 bit math slower than 32 bit math is just a fundamental lack of forethought. It's not preferred by GPU design, and shouldn't be used as a point against GPUs in general.