GTX 2080/2080Ti RTX for Deep Learning?

We have the current DAWNBench record. We’re seeing it about twice as fast. Try it with fastai’s fp16 support.


Does it work for all kinds of tasks? I mean, it should give a speedup for various kinds of models, and not only to image recognition architectures, right?

Is that the case? I’ve read somewhere that it will not double the memory if you have two cards even for 2080ti

Well, it should if you train two separate models in parallel, I guess =)

It won’t help as much for RNNs, although possibly QRNNs it will - I haven’t tested. I also haven’t tried localization tasks.

If you use nn.DataParallel then effectively you get double the memory. You should use NVLink to get good performance, of course (which requires a 2080 or better IIRC).


Hm, understood. Thank you for the response!

I guess it is worth to think more carefully about what GPU to purchase than =)

NVLink is available on 2080 as well, but not 2070.

1 Like

Do you have some kind of comparison table? Or some kind of benchmark to test?
I knew that you had nice record there, but I missed details of the implementation, if you somehow make fp16 twice as fast as fp32 than it really changes my video-cards comparison

I guess the source code used to compete in that benchmark is available via this repo.

1080Ti would be my recommendation here. Memory is one of the more important aspects to deep learning, and while there are ways around the limitation now it’s still complex. 11gigs of memory is a much better footprint over the 8gigs of the 2080, especially when you’re also running the graphics off that card as well.


1 Like

This is the updated imagenet training repo


I still got to understand how much memory a rtx card spares when it runs computations in mixed precision, both for vision and for structured data. That is, how much of that mixed precision computation is actually done in FP16.

I’m seeing 2070 as low as $499 at EVGA so you could get 2 8gb fp16 gpus cheaper than the single 11gb 2080ti. The 2070s do not have NVlink, but I don’t know if that would add enough benefit to offset the increase in power/memory at a lower price.

Hm, I see. Yes, I am thinking right now what is more beneficial: GPU with fp16 computations support, or 11GB of memory. Haven’t thought that the answer would be so difficult :sweat_smile:

I’m interested in this answer as well. @sgugger has an excellent post on single precision vs mixed precision. I just posted a question on the Mixed Precision thread asking him what memory usage he’s seen in practice on the mixed precision work he’s done.

1 Like

Hi Ilia (hope name is ok). I bought one last week in preparation for Jeremy’s new course tomorrow. I HATE games (grumpy old man). I will be looking to advise you numerically, as the machine that I am ‘uograding’ is old i7-9?? in lga1366, x58 board. I’ll be incrementally plugging in Gigabyte 2080Ti oc, 970evo vnand ssd, and 2Tb 860 vnand pro: should be fun and instructive. Will advise.
Cheers, peter kelly.

1 Like

I’d have missed that !! Thanks Jeremy.

1 Like

Hi Peter!

That’s great! Would really appreciate if you could carry out a benchmark. As I can understand, you definitely should get a speedup with conv nets, and probably the possibility to use bigger mini-batches.

Hi Ilia (is 'Ilia’ok? Or should I call you Devforfu -I am not good at yhis and too old to learn, please advise). Thanks for reply. Yep, the motherboard is also old -PCIe 2.0 - rs, but the new spec (4.0) ones are due in 3-4 months, AND 5.0 spec is about to be released ! So when first quality boards AND micros to suit??
I decided to stick with old motherboard and take each step with it for gpu 2080Ti, nvme, nvmessd, then keap to major expense of motherboard, ram,cpu and more stuff in next $warp.
Perhaps I’m naieve, but at least I can watch!
Cheers & stay cool and faid (new term of trade - FastAI’d),