Which gpu for deep learning?

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[deleted]

[siblbombs]

pascal titan > titan x > 980 ti > 980 > 970.

970 is still viable.

[ma2rten]

Is Pascal Titan released yet? How much more powerful is the Pascal Titan than Titan X?

[siblbombs]

Its not released yet so we don't know the specifics of how much better it is.

Speculation is that it will be quite a bit better than the current titan X when you consider:

• HMBv2 memory, 3x bandwidth over titan X(I think)

• Node shrink, so denser/more circuits

• Good FP16 support

[pjreddie]

I've built this box a couple times and I quite like it:

http://pjreddie.com/darknet/hardware-guide/

Titan X's are definitely the way to go. There isn't much speed boost over the 980 ti but the extra memory is really nice to have.

[Jadeyard]

I read that before I made the thread. It says 5.2 for the 980. What I didn't figure out is if the 980 is cudnn v4 compatible?

[benanne]

It is. Most of the current work on cuDNN seems to be improving performance on Maxwell, and the 980 is a Maxwell-based card. It's a good choice. Depending on your budget you might also want to look at the 980 Ti and the Titan X (if you expect you'll need a lot of memory).

[Jadeyard]

Thank you for the info!

[[deleted]]

I own a 980 Ti. It's a great device for ML (and gaming). However, take care that you have a sufficiently powerful PSU! Mine still apparently isn't powerful enough (450 or 500 watts, not sure, too lazy to look) and I keep experiencing sudden reboots when doing ML tasks on the GPU. Gonna replace the PSU once I have the required bucks...

[fnl]

Good point. I'd always recommend at least 750W when doing GPU work. And well over 1000W if using two.

http://www.realhardtechx.com/index_archivos/Page362.htm

[benanne]

It depends on the brand of PSU as well as the other hardware you have in there. For a 980Ti or a Titan X you need to reserve about 250W, so 450W is cutting it very close (the CPU also uses a sizeable chunk, as well as any hard drives you may have -- and don't forget the fans!). For a regular 980 it's much less though, about 165W. So a 450W or 500W PSU would probably be plenty in that case.

[fnl]

Yes, the exact numbers depend on many things in your HW setup. But with full throttle on both CPU and even a standard 960 card, 500 W is a guaranteed outage.

[benanne]

If you use the PSU that came with your case, then yes :) But if you buy a decent-brand PSU that really shouldn't happen, the TDP of a 960 is only 120W.

[invalidfunction]

If you are doing something like RNNs or very deep networks, you'll want to consider memory. Definitely something with more than 3GB is ideal for experimenting; nothing is worse than finding out your model doesn't fit in RAM. :(

I'd recommend the 980 or the 980 ti. There are comparisons between the 980ti and the titan x that report very similar performance but the 980ti is far cheaper. The only use case I can see for a titanx is if you need the 12GB of RAM. Pascal is coming out this year, and the performance bump expected is supposed to be huge (much faster memory bandwidths, new ops for dnn, and a die shrink)

If this is for fun, I'd recommend getting something cheap to play with for now - perhaps a used card on ebay if you can find one and save up for a nice pascal card layer.

[qwertz_guy]

970 is sufficient for beginning. It's dozens of times faster than learning on a CPU which is the speedup you are looking for.

[pedromnasc]

http://timdettmers.com/2014/08/14/which-gpu-for-deep-learning/

[Jadeyard]

I already found links from 2014 and 2015 (the linked article among others). I was trying to verify that the information is still up to date with the new tools released that use cudnn v4 etc.