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2080 Ti TensorFlow GPU Benchmarks
- std_throwawayay 8y agoFor comparison the Phoronix benchmarks: https://www.phoronix.com/scan.php?page=article&item=nvidia-rtx2080ti-tensorflow&num=1 https://www.phoronix.com/scan.php?page=article&item=nvidia-r... this one is more oriented to the lower end.
- sabalaba 8y agoThe 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.
- 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?
- londons_explore 8y agoIt would be good to see it compared to a TPU... I feel like thats becoming tensorflows 'native platform'...
- zitterbewegung 8y agoTPUv1 were made to have good performance per watt for inference. [1] TPUv2 were benchmarked against NVIDIa's K80 at 30 times the performance and had a peak of 92 TOPS [2] while the 2080 TI is at 440 TOPS [3] [1] https://www.anandtech.com/show/11749/hot-chips-google-tpu-performance-analysis-live-blog-3pm-pt-10pm-utc https://www.anandtech.com/show/11749/hot-chips-google-tpu-pe... [2] https://www.tomshardware.com/news/tpu-v2-google-machine-learning,35370.html https://www.tomshardware.com/news/tpu-v2-google-machine-lear... [3] https://www.microway.com/knowledge-center-articles/comparison-of-nvidia-geforce-gpus-and-nvidia-tesla-gpus/ https://www.microway.com/knowledge-center-articles/compariso...
- shaklee3 8y agoWhy would anyone compare a modern tpu against an Nvidia card that is four generations old? The tpu didn't even exist when the K80 came out.
- deepnotderp 8y agoMarketing. nVidia is destroying the TPUs right now and Google is desperate to keep their perception in the public eye as the king of AI (which tbf, they probably are, compute capabilities aside)
- matt4077 8y agoYou should try the assumption of good faith some time. It generally leads to a happier place. In this case the data was from the time Google introduced the TPU internally, when the K80 was very much up-to-date. It also makes sense because the K80 was the only GPU offered in GCP.
- shaklee3 8y agoThe fp16 results versus the 1080ti are somewhat surprising. The author specifically pointed out that they are using tensor cores were possible. I would have expected fp16 would be more than 100% faster than the 1080ti if they were using tensor cores. Can anyone explain that?
- pavanky 8y agoNot everything in the compute pipeline is going to be converted to fp16 operations. Anytime you are doing accumulation or exponentials, you would have to have it in fp32. There was a good talk from NVIDIA at last years GTC: http://on-demand.gputechconf.com/gtc/2018/presentation/s8923-training-neural-networks-with-mixed-precision-theory-and-practice.pdf http://on-demand.gputechconf.com/gtc/2018/presentation/s8923... Here is another relevant blog post: https://devblogs.nvidia.com/mixed-precision-training-deep-neural-networks/ https://devblogs.nvidia.com/mixed-precision-training-deep-ne... EDIT: Also not everything in the training loop is a matrix multiplication where tensor cores are useful.
- shaklee3 8y agoRight, but the tensor cores should be about 10x faster on the compute side, and about 2x the memory bandwidth. GEMM is usually constrained by compute, which is why the tensor cores exist. These benchmarks are for training, so the expectation is that they are running them in fp16 all the way through. Also, tensor cores can accumulate in fp32 registers with a slight hit to performance.
- KaoruAoiShiho 8y agoTuring tensor cores are not as fast as Volta ones. You won't see the 10x number repeated on any turing marketing material.
- shaklee3 8y agoWhat's your source for that? They should be the same speed, but more capable. You absolutely see the 8x in their marketing: https://devblogs.nvidia.com/nvidia-turing-architecture-in-depth/ https://devblogs.nvidia.com/nvidia-turing-architecture-in-de...
- jaytaylor 8y agoThe RTX 2080 Ti, on the other hand, is like a Porsche 911. It's very fast, handles well, expensive but not ostentatious Since when is a Porsche 911 not ostentatious? It's a sports car in $100K+ territory and functionally impractical / limited. Only compared to a car which costs millions could it be considered reasonable and not ostentatious. Guess I'll be sticking to my "poor mans" 970 GTX :p
- zdw 8y agoTo extend the analogy, things are going to get even more interesting when this kind of hardware hits the Honda Civic Si or VW GTI (ie 25-30% of the price of the Porsche) pricing levels, but with the same tiny6 performance delta.
- Analemma_ 8y agoCompared to e.g. a Lamborghini, it's not nearly as loud in appearance and can almost pass for a normal car in a grocery store parking lot, as long as it's not fire-engine red or something. But I think this analogy breaks down a little when applied to graphics cards.
- dragonwriter 8y ago> Compared to e.g. a Lamborghini, it's not nearly as loud in appearance and can almost pass for a normal car in a grocery store parking lot Kinda depends on which Lamborghini; an Urus probably does a better job at paying for normal in a grocery store parking lot (and is better for actually carrying groceries) than a 911.
- dahart 8y ago> Since when is a Porsche 911 not ostentatious? I feel like he answered that question directly, by comparing it to an ostentatious computer. ;) “And if you think I'm going overboard with the Porsche analogy, you can buy a DGX-1 8x V100 for $120,000 or a Lambda Blade 8x 2080 Ti for $28,000 and have enough left over for a real Porsche 911. Your pick.“
- mychael 8y ago
- sp332 8y agoFor one GPU, sure. But the cooling design is going to be awful if you try to pack a bunch of cards in a 4U or 5U box.
- moonrobin 8y agoIs this because of the reference dual axial open style cooler design? If so there are blower style cards available from AIBs for a marginal discount.
- sp332 8y agoYup, just puts the hot air back into the box. I was hoping that someone would put a blower on it but hadn't seen one yet, good to know.
- ydau 8y agoAs others have stated, blower cards resolve this by pushing air directly outside of the case. 2080 Ti blowers are in the works: https://www.pny.com/GeForce-RTX-2080-Ti-11GB-Blower https://www.pny.com/GeForce-RTX-2080-Ti-11GB-Blower
- _Wintermute 8y agoPretty sure the ASUS TURBO models are already out, which are blower style cards.
- sabalaba 8y agoLower in this thread, bitL pointed out that the prices we used in our analysis is not exactly in line with current market prices. bitL hit the nail on the head in terms of the biggest weakness of our post: choosing the price. So, 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#gid=0 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.
- howscrewedami 8y agoWhat about int8 performance? Would it be somehow improved by using tensor cores?