8 ms·
The TL;DR on this is that the 2080 Ti is the most cost effective GPU on the market today for deep learning. It is: - 37% faster than the 1080 Ti with FP32, 62
by sabalaba 8y ago
The TL;DR on this is that the 2080 Ti is the most cost effective GPU on the market today for deep learning.
It is:
- 37% faster than the 1080 Ti with FP32, 62% faster with FP16, and 25% more expensive.
- 35% faster than the 2080 with FP32, 47% faster with FP16, and 25% more expensive.
- 96% as fast as the Titan V with FP32, 3% faster with FP16, and ~1/2 of the cost.
- 80% as fast as the Tesla V100 with FP32, 82% as fast with FP16, and ~1/5 of the cost.
- bitL 8y ago> 37% faster than the 1080 Ti with FP32, 62% faster with FP16, and 25% more expensive. Maybe in theory, but you can get a new 1080Ti for $700 while 2080Ti is impossible to get under $1200, which makes it 70% more expensive. At that point 2x 1080Ti sounds way better than 1x 2080Ti for Deep Learning to me (up to 22TFlops and 22GB of RAM).
- sabalaba 8y agoNot in theory, in practice, these are actual benchmarks. Your central argument is that prices within the first few weeks of a new product launch are higher than MSRP. That point will likely not hold true 30 days from now. It makes perfect sense to compare the products based on their MSRPs.
- guiomie 8y agoUnless crypto mining picks up again.
- knicholes 8y agoYou mean it MAY not hold true 30 days from now. Last year around this time the cryptocurrencies started spiking. Perhaps demand for graphics cards will go up again, and this time, maybe the demand for the 1080 Tis won't match the demand for the newer, shinier 2080 Tis.
- sabalaba 8y agoFair feedback, I've edited parent. 1080 Tis are back under MSRP right now and have been for some time. We decided to assume that 2080 Tis would exhibit similar behavior.
- p1necone 8y agoHe's not talking about the benchmarks, he's talking about the prices - which as you pointed out are theoretical. It's been a long time since GPU prices have been anywhere near MSRP, it seems dishonest to assume that they will be in the near future.
- zeroname 8y agoThe 1080Ti is back at MSRP and the cryptocurrency demand has vanished, so what's left is the demand displaced by the high prices earlier. In the past, it has been common that new hardware with low initial supply fetched premiums that lasted until supply met demand. It's not dishonest to assume that it's the same case here.
- sabalaba 8y agoExactly. We're sort of between a rock and a hard place in terms of choosing a price. While we could decide to write some software that could pull today's market prices and update tables and graphs dynamically, we just decided to settle on a single number. If you are settling on a single number, the choice is either market price on date of publication or MSRP. Given that other GPUs tend towards their MSRP as time goes on, we decided to choose MSRP.
- freediver 8y agoYour article still states that 2080ti is 25% more expensive, but lists 1080ti at $700 and 2080ti at $1,200 (which are correct prices from nvidia website) which is 70% more. Which one is right?
- zeroname 8y agoIt states that the "typical GPU system" (estimated at ~1300$ without GPU) will be 25% more expensive. By the way, the MSRP for a regular 2080Ti is 999$, the 1200$ is the Founder's Edition price.
- 8y ago
- sabalaba 8y agoHey bitL, thanks a lot for pointing this out. I think you hit the nail on the head in terms of the biggest weakness of our post: choosing the price. We've decided to make the spreadsheet that generated our graphs and (performance / $) tables public. You can view it here: https://docs.google.com/spreadsheets/d/1La55B-AVHSv9LiQcs6GMhUyackNdhJ4WHprUnWfQOxI/edit?usp=sharing https://docs.google.com/spreadsheets/d/1La55B-AVHSv9LiQcs6GM... You can copy that spreadsheet and insert whatever system price (in kilodollars) you want into B15:F15. Hope this makes everybody's decision making easier. As a system builder and AI research company, we're trying to make benchmarks that are scientific, reproducible, correlate with real world training scenarios, and have accurate prices.
- vaidhy 8y agoAre 2 1080TI better than a single 2080Ti?
- ydau 8y agoYes. 1080 Ti do not scale linearly, but in most of the benchmarks I’ve seen, two 1080 Ti are at least 1.75x faster than one when doing multi-GPU training. 1.75 / 1.36 (speed up of 2080 Ti over a single 1080 Ti) = 1.28. So expect 2x 1080 Ti to be about 30% faster. You can see how multi-GPU training works with Titan V benchmarks in the link below. 1080 Ti have similar scaling profile. https://deeptalk.lambdalabs.com/t/benchmarking-the-titan-v-volta-gpu-with-tensorflow/108 https://deeptalk.lambdalabs.com/t/benchmarking-the-titan-v-v...
- The_rationalist 8y agoActually AMD gpus are the most cost effective gpus and have supported full FP16/8 since 2 years. But people are locked in the Nvidia proprietary jail and no one seems to care... That is really sad and against the consummer interest, but also, deep learning will never become mainstream if it can only run on 5% of the world hardware (Nvidia).
- keldaris 8y agoI agree with your broader point, but stating that Nvidia comprises a mere 5% of the world's hardware is disingenous in a post where you've only talked about GPUs. In actual fact, Nvidia has the overwhelming majority of the high end GPU market, especially in HPC and ML. And as much as I dislike the proprietary lock-in, they have by far the superior software ecosystem.
- The_rationalist 8y agoActually nvidia isn't the high end leader if you account console APUs (almost all being from AMD). And most gpus in the world are from igpus, apus or ARM SoCs. Dgpu IS a small market, High end Dgpu is a few percents max.
- gradys 8y agoDeep learning generally doesn't run on AMD GPUs, but it runs on a lot more than only Nvidia GPUs. If we're only counting model training, it runs on CPUs, Google's TPUs, FPGAs, whatever other secret datacenter ASICs are out there, various DL-specific mobile chips, etc. Way more than 5% of the world's hardware can run inference with deep neural nets, which is the important thing for mass adoption, and definitely more than only Nvidia GPUs can run training.
- ratsimihah 8y agoI haven't had a great experience using anything else than CUDA, e.g. OpenCL. Then again maybe I just lack the skills :x
- TTPrograms 8y agoIf AMD developed CUDA transpilers / DL backends / CUDNN equivalents people would switch over in a heartbeat. It's fully their fault for not investing in the software. If they did that and had a card that got 2x performance / $ or more I would switch in a heartbeat.
- p1esk 8y agoNote that these results are only for convnets. Has anyone posted RNN results?