The upcoming Volta-based consumer GPUs are going to be your best value for machine learning, not V100.
[1] https://www.amazon.com/NVIDIA-Tesla-P100-computing-processor... [2] http://accessories.us.dell.com/sna/productdetail.aspx?c=us&l...
http://images.nvidia.com/content/pdf/quadro/data-sheets/3020...
Agreed re consumer GPUs for machine learning though.
I'm not sure how consumer GPUs that don't exist can be better, though. That seems highly presumptive, especially given that most machine learning setups are heat, cooling and space limited.
You forgot "cost". V100, like P100, will cost literally more than 10 times what consumer cards do, for a likely speedup of less than 2 times. For the vast majority of people, that will not be worth it.
Of course eventually maybe they'll come out with a cheap but powerful deep learning GPU (though with the V100 having a 800mm2 die, inexpensive is going to be relative), and it's impossible for me to compare with future products, but there's a good reason these cost as much as they do.
The gap between V100 and 1180 is yet to be determined and depends mostly on what Nvidia does with the tensor units. We shall see. But I am extremely confident in saying that the performance gap will be nowhere near 60x, let alone higher than that. And despite the early announcement of V100, 1180 is likely to be available to most people long before V100, just like P100 and 1080 before.
I said that FP16 performance is at least 60x worse, which it absolutely is. The cores on the GT102 do not natively support FP16, so each FP16 operand has to be converted to FP32 and then processed, with significant overhead. The P100 can use FP16 directly yielding not only double the processing speed (because the FP32 cores are really dual FP16 cores, like registers on an x86), but a major memory savings. The 1080(Ti) is crippled at FP64 as well, again dramatically slower than the P100. It's a consumer GPU and FP16 and FP64 just aren't usually a consumer need.
These aren't just the same thing, with one having a sucker price. They are dramatically different chips, and many applications require the P100.
just like P100 and 1080 before.
Comparing the P100 and 1080Ti as if the latter is the consumer version of the former is not useful. They are profoundly different chips.
OK, I didn't think that's what you meant because it's a rather silly benchmark to use as it artificially disadvantages the 1080. FP32 deep learning performance on 1080 Ti will usually be ~half of P100's FP16 performance. FP16 is an advantage, but not a 60x advantage, and the 15+ 1080 Tis that you can buy for the price of one P100 are going to be far faster in most deep learning scenarios (admittedly at a higher power/etc cost).
> Comparing the P100 and 1080Ti as if the latter is the consumer version of the former is not useful. They are profoundly different chips.
I couldn't disagree more. The chips are similar enough that most deep learning applications could run on either. Really the only reason to choose P100 for deep learning with the ridiculous prices Nvidia is charging would be memory capacity and bandwidth with the FP16 advantage.
> I couldn't disagree more. The chips are similar enough that most deep learning applications could run on either. Really the only reason to choose P100 for deep learning with the ridiculous prices Nvidia is charging would be memory capacity and bandwidth with the FP16 advantage.
Sure, but there are more applications than just deep learning which is where things get fuzzy. The P100 and 1080TI are fundamentally different for anything relating to FP64. I think the point of the GP is, or would hope it is, not to collapse comparisons to the case of deep learning when things like scientific computing make the comparisons necessarily more nuanced.