Somebody who has almost no money isn't going to be able to equip a desktop with a GTX 1050 Ti ($175), fast disk ($50), and RAM ($50) on an entry level cpu/motherboard/power supply/case/monitor/peripherals ($300) and pay for the electricity used during training. Colab can be accessed from a free public computer or a cheap Chromebook ($200).
However, since you are probably eagerly reading this to see how fast the new RTX cards are, so you should know upfront that the numbers he has so far are just estimates based on specs:
> Note that the numbers for the RTX 2080 and RTX 2080 Ti should be taken with a grain of salt since no hard performance numbers existed. I estimated performance according to a roofline model of matrix multiplication and convolution under this hardware together with Tensor Core benchmarks from the V100 and Titan V.
I guess the click baiting is needed / the best option, but I hate that's it's what most web resources are like now.
Lower value would indicate lower cost per unit level of performance.
It should be "Lower is better" or the plot needs to say "Performance/Cost". Am I missing something?
Its performance/cost and not cost/performance.
Or maybe the author fixed a typo?
We’ve benched the 1080Ti vs the Titan V and the Titan V is nowhere near 2x faster at training than the 1080Ti as suggested in that graph. We observed a 30% to 40% speedup during our benchmarking:
https://deeptalk.lambdalabs.com/t/benchmarking-the-titan-v-v...
This is consistent with the 32% increase in FP32 flops from 11.3TFlops for the 1080Ti to 15TFlops for the Titan V. Additional speedups can be explained by the increase in memory bandwidth for HBM2 and the mixed precision fused multiply adds provided by the TensorCores.
Thus, given the quoted 13Tflop numbers for the 2080Ti, I would expect the 2080Ti to present something more like a 15-20% speedup over the 1080Ti. So 2080Ti is less bang for your buck. But benchmarking is the only way to tell what’s better on a FLOPS/$ basis.
You also do not benchmark LSTMs: https://www.xcelerit.com/computing-benchmarks/insights/bench...
If you put both of those benchmarks together my conclusion is quite reasonable. But I see that you could also come to your conclusion with your benchmarks. It is just a question which benchmarks are less biased and that is too difficult to evaluate.
I guess we have to wait for real data, but thanks for putting your data out there to get a discussion going.
https://web.archive.org/web/20180821173206/http://timdettmer...
