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Nvidia CEO Reveals New TITAN X at Stanford Deep Learning Meetup
- steve19 10y agoAnnoying Apple style branding "new Titan X" and "previous Titan X". X2 would have been easier all round.
- anoother 10y agoI agree, but X2 already holds the connotation of a dual-GPU card.
- setrofim_ 10y agoCould have gone with "TITAN XX" then. The added advantage is that the next one would be "TITAN XXX", and think of the marketing opportunity there!
- earthnail 10y agoThe Titan XXX will be a breakthrough in VR.
- LeoPanthera 10y agoTitan 2 X?
- kyriakos 10y agohow about Tita X V2 or Titan X 2017 etc
- neurostimulant 10y agoTitan XL
- gambiting 10y agoWhat was the last dual-gpu card which had the name ending with X2? GeForce 9800 GX2? That was released in....2008. 8 years ago. I think by now it's safe to use X2 for other things, no?
- freeone3000 10y agoTitan P, for Pascal.
- justinclift 10y agoTITAN Z :)
- jesperhh 10y agoInteresting that this does not have HBM2 memory. Apparently this will only be for the Tesla GPUs on pascal GPUs unless they are going to put it on the 1080 ti, which does not seem likely when the Titan does not have it.
- gbrown_ 10y agoI don't see this showing up in consumer parts soon. It seems Nvidia can squeeze out GDDR5(X) outside of the HPC space for this generation. Which is good for cost but also reduces the risk in terms of reliability of throwing lots of new technology into a single product. HPC obviously has different requirement but Nvidia can work with interrogators and customers with less of a backlash when fixing issues in this segment.
- 314 10y agoAbout 50-60% faster than a 980-ti / old titan. Quite pedestrian for a generation of cards.
- zeroer 10y agoDo you think we're in the mid-2000s?
- 314 10y agoDo you think this is any different from the trend in the past five generations from Nvidia?
- dogma1138 10y agoYes, gen to gen was 15-20% overall improvement (which is also questionable because when a new gen arrives all of a sudden the previous gen starts losing performance in games, it was really bad between Kepler and Maxwell where the 780/780ti all of a sudden lost 5-15% performance in certain games with initial "Maxwell Drivers").
- 314 10y agoDepends on which figures you look at. http://www.techspot.com/article/928-five-generations-nvidia-geforce-graphics-compared/ http://www.techspot.com/article/928-five-generations-nvidia-... has a reasonable comparison. Varies between 20-50% over the range of that sample. The increase in performance between the 980/1080 or the titans does not seem out of the "ordinary".
- dogma1138 10y agoLook at the final trend chart 780ti > 980 = ~15% increase 780 > 780ti = ~12% increase 680 (potentially should've used the 690 as a reference even tho it was a dGPU card but it was the) > 780 = ~27 increase. For the most part in generation that there wasn't a near ~2 year gap, and in generations where there wasn't a huge change in GPU memory type or a major architecture change (like dropping the hardware scheduler in Fermi for Kepler in order to save silicon space for things that actually matter(ed) for game at the time) there isn't a 50% increase gen to gen. 15-20% increase gen to gen with comparable price point cards (nvidia has been making it harder now by charging 150-200$ more per card and effectively bumping a price point lately) is what you should be expecting.
- tylerwhipple 10y agoAs great as this product may be, I went to the Stanford Deep Learning Meetup to learn more about how Baidu Research/Andrew Ng are solving large scale deep learning problems. I am disappointed by how much (unannounced) time was dedicated to the keynote/sales pitch.
- argonaut 10y agoI'm pretty sure Andrew Ng was hired by Baidu as a recruiting tool / evangelist. He hasn't done much research recently.
- tudorw 10y agoA moment to champion the importance of a great evangelist, research is important, though without propagating the results and helping with 'real world's deployment, it might be effort wasted :)
- nabla9 10y agoIf his job as a Chief Scientst at Baidu is similar to the Director of Research at Google (Peter Norvig) he is too busy to be part of individual research projects. He is probably one of the guys who decides where the direction of company research is heading and the one who supervises those projects.
- nl 10y agoto learn more about how Baidu Research/Andrew Ng are solving large scale deep learning problems. Throwing NVidia cards at it. Like everyone else. https://github.com/baidu-research/persistent-rnn https://github.com/baidu-research/persistent-rnn is a pretty interesting way to use all those NVidia cards though.
- stephanheijl 10y agoI'm looking forward to seeing this in stores, as I've wanted to build a new machine learning rig for some time. The GTX 1080 just didn't seem like it would do the trick, with ostensibly limited software support and all. I'm specifically wondering about FP16 handling though. Single precision FLOPS are never mentioned in the blog, nor on the NVidia page. It would be a shame if the FP16 units on this card were gimped in the same way as the GTX 1080...
- xiphias 10y agoNVIDIA is always publishing just the numbers that it wants to publish. There are still improvements, but they are slowing down.. I'm sure the engineers did their best. The announcement skipped energy usage as well.
- deleted 10y ago[deleted]
- nitrogen 10y agoThe GTX1080 still isn't available anyway, unless you pay an exorbitant sum to a scalper.
- rdl 10y agoI've been able to get 1070 and 1080s at retail price by backordering through B&H; they took 2 extra days to ship but NBD.
- Jach 10y agoFunny, my friend had no issue getting two of them. The only impossible to get model is gigabyte's xtreme version and it's not even the 'best'.
- nitrogen 10y agoFrom which vendor, may I ask?
- deleted 10y ago[deleted]
- dbalan 10y agoHN hug of death. Google cache here: https://webcache.googleusercontent.com/search?q=cache:https%3A%2F%2Fblogs.nvidia.com%2Fblog%2F2016%2F07%2F21%2Ftitan%2Dx%2F https://webcache.googleusercontent.com/search?q=cache:https%...
- dharma1 10y agoFp16?
- gbrown_ 10y agoI'm guessing FP32 as other announcements for the FLOP counts of GTX1080 and GTX1070 were single precision.
- quantumhobbit 10y agoWhile we're at it, Fp64?
- deleted 10y ago[deleted]
- p1esk 10y agoAnyone knows what is this new INT8 instruction does?
- svennek 10y agoIt is most likely just to show, that it is even faster working with 64-bit ints than floats... So my guess, all integer ops...
- rys 10y agoIt's not all integer ops. The peak throughput comes from a 4-wide accumulated dot product. dp4a if you want to look it up.
- nabla9 10y agoNVIDIA says: 44 TOPS INT8 (new deep learning inferencing instruction) I think it's a related to storing float arrays as arrays of 8-bit integers in memory and converting them into floats just before using. It's 2x more space efficient than fp16 http://www.kanadas.com/program-e/2015/11/method_for_packing_int8_arrays.html http://www.kanadas.com/program-e/2015/11/method_for_packing_...
- astrodust 10y agoYou can get away with 8-bit floats on many neural network type applications so that might be the idea here.
- bitfiddler0 10y agoIt's an 8-bit int type though. Perhaps even that's sufficient for inference?
- astrodust 10y agoFor a lot of network types that will do the job. 2x the performance is often better than 2x the fidelity. You can get higher accuracy with more nodes.
- bcaulfield 10y agoMore details from the event: https://blogs.nvidia.com/blog/2016/07/21/titan-x-deep-learning/ https://blogs.nvidia.com/blog/2016/07/21/titan-x-deep-learni... “This is insane, I’m actually speechless, I didn’t expect something like that,” one of the event’s organizers stammered after the TITAN X was unveiled. “This is one of the craziest product launches ever in the history of technology.”
- mtgx 10y ago> 3,584 CUDA cores at 1.53GHz (versus 3,072 cores at 1.08GHz in previous TITAN X) Isn't this chip made on 14nm FinFET compared to 28nm for the previous one? Why only 20% more cores? Is the chip smaller this time?
- creshal 10y agoThe chip is 50% bigger (12bn transistors vs. 8bn), I assume (there's no details on the card yet) the additional budget went into things like caches to give the cores an additional performance boost.
- ethbro 10y agoBigger in transistor count != bigger in physical size given a process shrink at the same time, which might be what the parent comment was asking.
- mpg33 10y ago> Isn't this chip made on 14nm FinFET 16nm FinFET. AMD's current gen of cards are on 14nm FinFET.
- jsheard 10y agoI'm guessing that GP102 is actually a 3840 core part (50% bigger than GP104), but this initial release is being cut down to improve yields on the immature 16nm process. They did something similar with the original GK110 Titans - the first Titan had 2688 cores enabled, and a fully unlocked 2880 core Titan Black came later.
- ucaetano 10y agoIt will be interesting to see how this compares to Google's custom TPU: https://cloudplatform.googleblog.com/2016/05/Google-supercharges-machine-learning-tasks-with-custom-chip.html https://cloudplatform.googleblog.com/2016/05/Google-supercha... NVidia is still taking the one-size-fits-all approach to AI and graphics, maybe it is time to develop AI-specific hardware.
- melling 10y agoIsn't this simple economics? The R&D cost is amortized over a larger market. You can always try to build specialized chips but the market might not be nearly as big so there's a lot less money. The general purpose market still moves faster.
- ucaetano 10y agoGiven that the market is big enough for a single player to develop a chip for internal usage, minimum efficient scale is probably not an issue. I'd guess NVidia sells far more cards than Google will produce TPUs.
- nl 10y agoThis is so great. NVidia actually care about research, researchers and the scientific computing market. Next time someone complains about the lack of OpenCL support, again, in another framework remember how much work NVidia puts into supporting people who use their cards for scientific computing, and how they listen to them.
- raverbashing 10y agoThis While ATI was always about gaming, gaming, gaming, NVidia always worried about the Pro market (Quadro, Linux support, even with proprietary modules, etc) and now with Deep Learning And now they can sell their deep learning processor for embedded applications (equals $$$)
- astrodust 10y agoThere's a lot of promise here, but revenue? Where's the sales figures on those processors?
- nl 10y agoIs a significant source of revenue There are some numbers for 2015 at [1]. In the Enterprise, HPC and Auto markets they had a bit more than $1B in revenue. I believe the Auto market includes some Tegra numbers, but that is "only" $180M. Gaming is a little more than $2B, and "OEM & IP" is $1B. [1] http://www.nextplatform.com/2015/05/08/tesla-gpu-accelerator-grows-fast-for-nvidia/ http://www.nextplatform.com/2015/05/08/tesla-gpu-accelerator...
- Cybiote 10y agoMicrosoft also carefully listened to developers while building DirectX and by versions 8 and especially 9, it really showed. But only Windows benefited from this. Having the control of important GPU tech so strongly centered about a single company is never a good idea, it sets up a conflict of interest. Something like OpenCL does not face the same conflict nVidia would if porting core APIs across a wide set of competing technologies. With CUDA, nVidia prioritizes themselves above AMD, intel, FPGAs and whatever parallel compute technology the future holds.
- bryanlarsen 10y agoFor gaming, it's an estimated 24% faster than a 1080 for twice the price. http://www.anandtech.com/show/10325/the-nvidia-geforce-gtx-1080-and-1070-founders-edition-review http://www.anandtech.com/show/10325/the-nvidia-geforce-gtx-1...
- baq 10y agodoes it have a place where you can plug a display into...?
- pjmlp 10y agoSurely, after all it supports DX 12, Vulkan and OpenGL 4.5.
- Vexs 10y agoNot surely- the old titan X didn't have any video ports on the back. It's a computing card, not a video card.
- Meegul 10y agoThere has never been a titan card that did not have any video outs. Perhaps you're thinking of their Tesla cards?
- Vexs 10y agoWhoops, I was.
- selectodude 10y agoTitan X Maxwell definitely had video outputs on it. http://www.anandtech.com/show/9059/the-nvidia-geforce-gtx-titan-x-review http://www.anandtech.com/show/9059/the-nvidia-geforce-gtx-ti...
- jfb 10y agoI have two in the box under my desk, and they most assuredly do. Perhaps you're thinking of the Teslas?
- cs702 10y agoGreat news all-around for deep learning practitioners. Nvidia says memory bandwidth is "460GB/s," which will probably have the most impact on deep learning applications (lots of giant matrices must be fetched from memory, repeatedly, for extended periods of time). For comparison, the GTX 1080's memory bandwidth is quoted as "320GB/s." The new Titan X also has 3,584 CUDA cores (1.3x more than GTX 1080) and 12GB of RAM (1.5x more than GTX 1080). We'll have to wait for benchmarks, but based on specs, this new Titan X looks like the best single GPU card you can buy for deep learning today. For certain deep learning applications, if properly configured, two GTX 1080's might outperform the Titan X and cost about the same, but that's not an apples-to-apples comparison. A beefy desktop computer with four of these Titan X's will have 44 Teraflops of raw computing power, about "one fifth" of the raw computing power of the world's current 500th most powerful supercomputer.[1] While those 44 Teraflops are usable only for certain kinds of applications (involving 32-bit floating point linear algebra operations), the figure is still kind of incredible. [1] https://www.top500.org/list/2015/06/?page=5 https://www.top500.org/list/2015/06/?page=5
- pavlov 10y agoA beefy desktop computer with four of these Titan X's will have 44 Teraflops of raw computing power, about "one fifth" of the raw computing power of the world's current 500th most powerful supercomputer. There have been calls that Moore's Law is dead for at least 10 years, and yet desktops keep catching up to supercomputing. I know this increase is not in single-threaded general-purpose computing power in the fashion of the old gigahertz race... But on the other hand, the scope of what's considered "general purpose" keeps expanding too. Machine learning may be part of mainstream consumer applications in 5 years.
- joelschw 10y agoIt already is - SwiftKey has been doing it right on my phone for a while!
- fnord123 10y ago>Machine learning may be part of mainstream consumer applications in 5 years. It doesn't seem likely since the Titan X pulls 250 watts. Maybe if we get asics for deep learning which consume less power; and a lot of research is done on deforesting trained networks so users can compute classifications more cost effectively, then sure.
- imtringued 10y agoIntel already surpassed the old Titan X with their Xeon Phi Knights Landing CPUs which have a peak performance of up to 7TFLOPS. It was about time that they release a new Titan X.
- vegabook 10y agoyeah but where can you actually buy the Xeon Phi KL? I only see dev-programme versions. I would love to pick one up.
- raverbashing 10y agoUntil this is out there, this can be safely ignored Also, it needs a Xeon processor. You can plug the nVidia card wherever you want
- gbrown_ 10y ago> Also, it needs a Xeon processor. You can plug the nVidia card wherever you want What? The first KNL parts will be host CPUs rather than PCI add-on cards.
- raverbashing 10y agoAh it seems you're right http://www.extremetech.com/extreme/226604-intels-next-generation-xeon-phi-knights-landing-now-shipping-to-developers http://www.extremetech.com/extreme/226604-intels-next-genera...
- ipunchghosts 10y agoWhat is the FP64 performance???
- ipunchghosts 10y agohttps://en.wikipedia.org/wiki/List_of_Nvidia_graphics_processing_units https://en.wikipedia.org/wiki/List_of_Nvidia_graphics_proces... Looks like its still crap :( Long live the titan black!
- gabiruh 10y ago/me counting down to when "Can it run Crysis?" appears in thread
- ihuman 10y agoI would expect that the card can run it. Crysis came out about 9 years ago.
- xchaotic 10y agoBut can it run at 4k 60fps, VSync on, which seems to be the sweet spot for PC gamers with big budget (and big 4 screens/projectors)?
- binarycrusader 10y agoWell, since the 1080 Founder's Edition can only run Crysis 3 at ~35fps, I doubt it, considering the Titan X is supposed to be roughly ~25% faster for gaming applications: http://www.anandtech.com/show/10325/the-nvidia-geforce-gtx-1080-and-1070-founders-edition-review/23 http://www.anandtech.com/show/10325/the-nvidia-geforce-gtx-1... But it will certainly be closer; my guess is that we're still another iteration out from running Crysis 3 at 60fps at 4k. If you're talking about the original Crysis, I suppose that's possible -- I don't have any benchmarks handy though.
- balls187 10y agoThat was the first thought as well. But HN shuns memes. Looking forward to seeing VR Performance with this card.
- kartD 10y agoSince this question has come up so many times in the thread, my take is that FP64 and FP16 won't be as good at the GP100. If TITAN X is based on the consumer parts, it misses out on the GP100's FP improvements. From Anandtech's GTX1080 review page2: As a result while GP100 has some notable feature/design elements for HPC – things such faster FP64 & FP16 performance, ECC, and significantly greater amounts of shared memory and register file capacity per CUDA core – these elements aren’t present in GP104 (and presumably, future Pascal consumer-focused GPUs). This requires confirmation though, it depends on whether it uses the consumer chip or the HPC chip. Edit: AT has an article up http://www.anandtech.com/show/10510/nvidia-announces-nvidia-titan-x-video-card-1200-available-august-2nd http://www.anandtech.com/show/10510/nvidia-announces-nvidia-... They think it's likely a consumer card, so lower FP16 and FP64 perf. Should be a gaming monster though
- mrb 10y agoWell the Titan X is a new chip: GP102. So they could have picked and chosen features from either GP100 (professional Tesla) or GP104 (consumer GeForce). We know almost certainly it has lower FP64 perf than GP100 because it has 22% fewer transistors and the FP64 units take a lot of transistors count. However it is less clear about what the FP16 performance is. Nvidia could have decided to match GP100 on that regard.
- kartD 10y agoDoubt it, why have three versions of a chip (one with fp16 improv, one with fp16/fp64 improv and one without them)? Especially when the Titan X has the same number of CUDA cores as the GP100.
- jhj 10y ago"CUDA cores" is a misleading term. When they quote "CUDA cores", they've been counting float32 fma functional units; e.g., Tesla K40 has 192 float32 fma units per SM x 15 SMs => 2880 "CUDA cores". http://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#arithmetic-instructions__throughput-native-arithmetic-instructions http://docs.nvidia.com/cuda/cuda-c-programming-guide/index.h... fp16 and fp64 are likely different functional units with different issue rates, as is the case with old hardware; unless they've managed to share the same hardware (since for P100 the quoted fp64 rate is exactly half the fp32 rate, and the fp16 is exactly double the fp32 rate).
- mrb 10y agoSlightly more tech details at http://wccftech.com/nvidia-geforce-gtx-titan-x-pascal-unleashed/ http://wccftech.com/nvidia-geforce-gtx-titan-x-pascal-unleas... Notably: 1.53GHz is the boost clock, while the base clock is 1.41GHz. Does anyone knows the performance of the half-precision units (16-bit floating point)? It's probably 1/64 the FP32 rate, but Nvidia may have been generous and uncapped it at 2× FP32 like GP100, which would be a big difference (128× factor!)
- hkhall 10y agoAs this thread is filled with people that know way, way more about CUDA and OpenCL than myself I hope that you will indulge me a serious question: I get that graphics cards are great for floating point operations and that bitwise binary operations are supported by these libraries, but are they similarly efficient at it? Some background: I occasionally find myself doing FPGA design for my doctoral work and am realizing that the job market for when I get done may be better for me if I was fluent in GPGPU programming as it is easier to build, manage, and deploy a cluster of such machines than the same for FPGAs. My current problem has huge numbers of XOR operations on large vectors and if OpenCL or CUDA could be learned and spun up quickly (I have a CS background) I may be inclined to jump aboard this train vs buying another FPGA for my problem.
- Svenstaro 10y agoI think both of these can be learned reasonably quickly if you know a bit about C programming. I'd also tend to agree that GPGPU is probably a better bet than FPGAs these days.
- ak217 10y agohttp://docs.nvidia.com/cuda/cuda-c-programming-guide/#arithmetic-instructions http://docs.nvidia.com/cuda/cuda-c-programming-guide/#arithm... Throughput of integer operations ranges between 25% and 100% of floating point FMA performance. 32-bit bitwise AND, OR, XOR throughput is equal to 32-bit FMA throughput.
- ldargin 10y agoAMD GPUs have a reputation for speedy integer operations, which are essentially bit-wise operations, so they are often chosen for bitcoin mining. So you might want to consider learning OpenCL, since CUDA runs only on NVidia cards.
- sounds 10y agoI've spent a lot of time using both OpenCL and CUDA, and I would recommend CUDA not because I like NVidia as a company, but because your productivity will be so much higher. NVidia has really invested into their developer resources. Of course, if your time to write code and debug driver issues isn't that important, then an AMD card using OpenCL might be the right choice. (I'll try to be honest about my bias against NVidia, so you can more accurately interpret my suggestions. I think along the lines of Linus Torvalds with regard to NVidia... http://www.wired.com/2012/06/nvidia-linus-torvald/ http://www.wired.com/2012/06/nvidia-linus-torvald/ )
- zk00006 10y agoCan we expect the price of "old" Titan X to drop after this announcement? I would like to upgrade 4x SLI.
- dogma1138 10y agoIf you have a 40 PCIE lane CPU (and not NVME drives) was the price the thing that actually held you back? The scaling past 2 cards is also pretty horrible, depending on how much of a performance increase it would most likely still be cheaper to get 2 new Titan X's instead of upgrading your maxwell cards if you can offload them at 400-500$.
- akiselev 10y agoIt would probably be pretty useless for gaming over 2x or 3x SLI but depending on whether any CUDA work you need to do is compute or memory (not bandwidth) bound, it can still make a difference worth the cost.
- dogma1138 10y agoWell SLI has nothing to do with CUDA, you can have as many CUDA supported cards as you want and use all of them they don't even have to be from the same model/generation and you can use them all. Since he mentioned SLI i assumed it was for gaming since it's the only thing that actually limits you. But that said the number of PCIE lanes is still a problem this is why people that do work on compute opt out (well are forced to) use Xeon parts with QPI to get enough PCIE lanes because the amount of lanes available for standard desktop parts (including the PCH) is pretty pathetic even if you are going with the full 40 PCIE lanes E series of CPU's. This can be even worse if you want to use SATA Express or NVME drives since they also use your PCIe lanes, as well as a few other things like M.2 wireless network cards (pretty common these days on the upper midrange and high end motherboards), Thunderbolt and a few other things.
- akiselev 10y agoTo repeat what I just said woth slightly different wording: Yes, my point was specifically referring to the difference between users who want the Titan X for supercomputing versus those who want it for gaming. You can have as many CUDA cards as you want but you face the same problem of limited PCIe bandwidth just like you do with gamers who want SLI. If your use case is CUDA and not gaming, then the usefulness of four Titans depends entirely on whether your algorithms are limited by memory, memory bandwidth, or computing power.
- akhilcacharya 10y ago>$1200 Well I'm glad I got my 1080 now.
- epaulson 10y agoFor comparison, the first supercomputer that was in the Teraflops range and that was available outside of the nuclear labs was the Pittsburgh Terascale machine. http://www.psc.edu/publicinfo/news/2001/terascale-10-01-01.html http://www.psc.edu/publicinfo/news/2001/terascale-10-01-01.h... It cost $45M, and peaked at 6 Teraflops. (I think that was on 32 bit floats, but I can't find the specs. It might have been 64 bit floats) "Total TCS floor space is roughly that of a basketball court. It uses 14 miles of high-bandwidth interconnect cable to maintain communication among its 3,000 processors. Another seven miles of serial, copper cable and a mile of fiber-optic cable provide for data handling. The TCS requires 664 kilowatts of power, enough to power 500 homes. It produces heat equivalent to burning 169 pounds of coal an hour, much of which is used in heating the Westinghouse Energy Center. To cool the computer room, more than 600 feet of eight-inch cooling pipe, weighing 12 tons, circulate up to 900 gallons of water per minute, and twelve 30-ton air-handling units provide cooling capacity equivalent to 375 room air conditioners."