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Been looking at Titan X the last few days. Here is one article I came across on the topic: https://timdettmers.wordpress.com/2014/08/14/which-gpu-for-deep-lear

Been looking at Titan X the last few days. Here is one article I came across on the topic: https://timdettmers.wordpress.com/2014/08/14/which-gpu-for-deep-learning/ https://timdettmers.wordpress.com/2014/08/14/which-gpu-for-d... tl;dr: GTX Titan X = 0.35 GTX 680 = 0.35 AWS GPU instance (g2.2 and g2.8) = 0.33 GTX 960 GTX Titan X = 0.66 GTX 980 = 0.6 GTX 970 = 0.5 GTX Titan = 0.40 GTX 580 also: I was under the impression single precision was fine for most deep learning applications and double precision doesen't even have good support in most libraries but I guess it depends on the use case.

mdda 11y ago
FLOP-wise that makes sense. But for deep learning, the big deal is in the 12Gb GPU-local memory, which has enormous bandwidth (and can store more of your dataset / parameters at once). The largest concern with GPU processing is keeping the GPU adequately fed with data - and avoiding round-trips of blobs of data with the CPU helps a lot.
- johanneskanybal 11y ago
  Oh I agree and the article talks plenty about that topic as well. For me the temptation with Titan X is primarily the "laziness" of a) not manualy having to try to parallelize AWS units and b) not needing to try to squeeze in models into 4-6gb. Rather than a speedup factor of 2-3.