14 ms·
Nvidia Announces “Titan V” Video Card: GV100 for $3000
- modeless 9y agoUgh, this company desperately needs some competition to keep prices in check. AMD really has no one to blame but themselves for their lack of success in ML. They could have been a real competitor years ago with a relatively small investment in software but they didn't even start seriously trying to compete with CuDNN until this year. Edit: I didn't originally see that the die size of this chip is nearly double that of the older Titan XP. Perhaps the price isn't as outrageous as I first thought! I'm actually glad that there's a big enough market now to support this kind of ultra high end product. It's certainly a beast and I want one.
- TheArcane 9y ago> needs some competition to keep prices in check. Prices as well as the tech itself. $3000 and still just 12GB. I wonder why this isn't in the range of ~64/128GB yet... too expensive for the type of memory?
- sliken 9y agoHigh bandwidth GDDR5 is more expensive than DDR4. HBM is more expensive again. On top of that the high bandwidth chips are lower density, and the complexity of higher bandwidth links gets more complicated as you get more chips involved. Low end servers use normal dimm, but typically not many dimms per channel, typically 1-3. But to hit the higher memory configurations you end up putting buffer chips on the dimms which makes them more expensive and also slightly higher latency. So 500GB/sec to 128GB ram using HBM2 would be difficult, expensive, and likely wouldn't fit in today's GPU form factors... yet. GPUs generally seem to be mostly hungry for more bandwidth and not that many cases (percentage wise) is 16GB a major limitation. However in those cases check out the power9, the nvlink that x86's uses for GPU<->GPU communication can be used for CPU <-> GPU communication and avoid the high latency/low bandwidth pci-e links.
- gbrown_ 9y agoNo-one is manufacturing larger stacks of HBM yet so far as I am aware. If there are it's only a small amount.
- andars 9y agoLarger (higher capacity) HBM2 stacks do not yet exist and the manufacturing process is pretty complicated/expensive. It's worth noting that all the stacks end up on the same piece of silicon as the GPU itself. Don't assume that they are holding back tech due to lack of competition. 650 GB/s is pretty impressive.
- krautsourced 9y agoEven if it just were DDR4 (which of course it is not) - have you looked at RAM prices recently? 128GB will cost you upwards of 1000$ alone. I don't even want to think how much 128gb DDR5 or HBM2 (if it did exist) would cost you... about the current price of the card, probably.
- theandrewbailey 9y agoAMD GPUs might not be good at ML, but they are good at cryptocurrencies.
- llukas 9y agoPretty soon BTC price will justify buying one ;)
- CoryG89 9y agoWould be all but worthless for BTC from my understanding, need an ASIC to even have a chance. Might be worth it for Ethereum though.
- philtar 9y agoCurious what the hash rate on this is.
- rybosome 9y agoRumors of a near-future switch to proof of stake would discourage me from spending large amounts of money on Ethereum mining.
- jrs95 9y agoThere are so many other coins that use the same GPU friendly algorithm as ETH that your hardware wouldn’t become useless even if that did happen.
- pknerd 9y agoCare to guide more?
- jrs95 9y agoI don’t know specifics on what these coins are honestly but there are a few clients like Nicehash which basically group a lot of different mining tasks by algorithm for you. It has an idea as to which will be most profitable for your hardware at any given time, and mines that currency for you.
- 9y ago
- buildbot 9y agoSomewhat surprising they didn't release this card as a Quadro GPU, given its high price tag even by Titan standards. Still for single GPU machine learning, this is almost exactly a V100 unless you need the extra 4GB of memory. You could also buy ~4 1080Ti cards, but unless you can't use the FP16 tensor cores, you'd still lose heavily to a single V100.
- Namidairo 9y agoBetween the 4GB of memory (+wider memory bus) and the driver allowing you to use it in a hypervisor, you are paying a $5000 premium for the V100. A price premium that will really stack up when certain cloud providers want to buy some of these in bulk. Edit: The *999 pricing confused me, it's actually $5000 more.
- deleted 9y ago[deleted]
- tromp 9y agoFor the Alpha Zero fans among us, how many of these does it take to match the performance of four 2nd gen Google TPUs?
- bitL 9y agoI think one Titan V should be around the same performance as 4xTPUv2, which is their basic module.
- jacksmith21006 9y agoBased on what Google has shared a second generation TPU which includes four chips is capable of 180 Tflops which would be a decent amount more powerful than this new Nvidia option.
- Quequau 9y agoIs it possible for a regular human to even buy four 2nd gen Google TPUs?
- samfriedman 9y agoGoogle is beginning to offer them on their Cloud services: https://www.blog.google/topics/google-cloud/google-cloud-offer-tpus-machine-learning/ https://www.blog.google/topics/google-cloud/google-cloud-off...
- farresito 9y agoSpeaking about buying TPUs, yesterday I vaguely remembered that the company that sells mining ASICs like the Antminer was planning to start selling AI chips ala Google's TPU. I checked their website and they have actually started selling them already https://www.sophon.ai/ https://www.sophon.ai/ I think it's pretty exciting seeing how we are getting increasingly more specific chips for AI tasks. I wonder what their performance is like compared to GPUs.
- DiThi 9y ago
- sabalaba 9y agoWanted to note that Lambda Labs now offers Titan Vs on our Lambda Quad workstations. They're available here: https://lambdal.com/products/quad https://lambdal.com/products/quad. We're excited to see Volta come down below $5,000 / GPU. We're also looking forward to AMD, Intel, Graphcore, Rex, Bitmain, Vathys.ai, Wave Computing, Tesla or Cerebras providing competitive alternatives.
- ryanlol 9y agoWhat's up with the hidden $857 "Labor/Shipping/Handling" charge? It seems a little out of place given that you guys already seem to be charging huge premiums on all the hardware. This all seems really expensive compared to the local PC shops that seem to offer most of the components for less, while only charging ~100 euros for assembly and testing. Do you guys have some really fancy cooling solution that you forgot to mention on the site?
- analognoise 9y agoI think you were looking for "outrageously overpriced".
- ryanlol 9y agoPretty much, yeah. Though I'm still curious if getting your computers "Built by Researchers" has some novel advantages over "Built by Lawyers" or "Built by the guys at the local computer shop" Perhaps the presence of smart Researchers while the computer is being born will make the machine better at learning, improving tensorflow performance?
- lninyo 9y ago“Built by Researchers” is about as reassuring and genuine sounding as “natural and wholesome” on the side of every box of sugary cereal. JTFC!
- 659087 9y agoIt has me wondering if people are actually buying these from them. If so, undercutting their pricing would be trivial - and maybe an easy idea for a side business.
- bryanlarsen 9y agoThis is targeted at ML, which means this is targeted at Linux. Which means us system administrators have to deal with the piece of shit that is the Nvidia binary driver. - it doesn't work with Wayland - it doesn't work in UEFI mode with CSM enabled. There are motherboards available that won't boot in UEFI mode with CSM disabled without a monitor attached. - it doesn't work with Secure Boot - about half of their releases fail to work with at least one of the pieces of software we try to use it with. The current release 384.90-0ubuntu0.16.04.2 in particular crashes our software. And for some reason Nvidia has the reputation for being good at drivers. FirePro was also really bad, but I haven't had to deal with that for years now...
- bryanlarsen 9y agoAnd in case this is getting visibility at Nvidia, this is also another major source of frustration: https://github.com/NVIDIA/nvidia-docker/issues/11 https://github.com/NVIDIA/nvidia-docker/issues/11 Some of the software we use is linked against GL to display results when run on the desktop. It doesn't actually use GL when run on the server, but it's still linked against GL so we need it on the server.
- angry_octet 9y agoCan't you just bundle some GL libs in a library search path on the server? Or does it init the GL context?
- bryanlarsen 9y agoWhat we do is we install the nVidia binary driver package inside the docker container, then delete some files that overlap with those that nvidia-docker injects.
- bhouston 9y agoAnd the last major stable release for Ubuntu broke egl completely.
- blakesterz 9y ago
- TazeTSchnitzel 9y agoGV100 implies it's the full card, not cut down in any way. Huh.
- gehwartzen 9y agoIt's been a while since I've built a PC; when did graphics cards physically start looking like they were designed by a five year old?
- rocqua 9y agoThat's been the case for at least the last few years, and it's been quite common the last 5 years. It's part of the 'gamer aesthetic'. If I could speculate, I'd suggest they might intentionally do this to push businesses away from consumer-level stuff.
- freeone3000 9y agoBusinesses shove them in opaque chassis. Businesses don't care. The primary means of pushing businesses away from consumer-level stuff is SKU-ing out nvlink and putting the power connector parallel to the PCI slot instead of perpendicular (makes it a 3.5U server instead of a 3U)
- opencl 9y agoAMD's reference designs still don't look too weird at least. http://images.bit-tech.net/content_images/2016/06/amd-radeon-rx-480-8gb-review/rx480-16b.jpg http://images.bit-tech.net/content_images/2016/06/amd-radeon...
- gehwartzen 9y agoNice clean design. I get that its largely subjective and comes down to personal preference but I remember being a teen and thinking a graphic card looked cool in their natural state (i.e seeing the PCB, chips and heatsinks/pipes)
- potatolicious 9y agoSadly this is no longer possible - the plastic shroud actually serves a cooling purpose by directing air out the back of the card rather than blower-style coolers that direct hot air into the rest of the case. But I agree with you, "gamer" hardware aesthetic is grating. It's as if a F-22 and a Nerf Gun decided to have a child together.
- lucaspiller 9y agoAnd of course, somebody has already ordered one for cryptocurrency mining: https://twitter.com/BitsBeTrippin/status/939031211233525760 https://twitter.com/BitsBeTrippin/status/939031211233525760
- huffpopo 9y agoI do deep learning for a day job, this card is for me. Going on the specs it'll be worth it upgrading my 1080 TIs but I'll wait for a proper review before committing $12K.
- alvern 9y agoWhat are the use cases for the Titan V vs. Radeon Pro SSG? Just ML? Higher memory bandwidth? https://pro.radeon.com/en/product/pro-series/radeon-pro-ssg/ https://pro.radeon.com/en/product/pro-series/radeon-pro-ssg/
- vosper 9y agoThat Radeon card has 2TB of onboard memory, and seems to be aimed at very heavy content creation workloads (8k VR). I think it's in a different (and ultra-niche) league to this Nvidia card. I would guess this is aimed at heavy ML workloads?
- freeone3000 9y agoIt's nvidia. You get CUDA and CuDNN, which means the standard suite of ML programs. AMD has no equivalent offering.
- lovelearning 9y agoROCm and MIOpen are AMD's equivalent offerings. They've developed their own ports for some of the popular DL frameworks. https://rocm.github.io/dl.html https://rocm.github.io/dl.html
- aseipp 9y agoVery few people who do DL professionally or as casual hobbyists have the time to sort out ports of various frameworks to work with in-progress compute drivers/toolchain. Most engineers aren't cash strapped, they'll just buy what they need instead of waste time, especially when the field and the tools move so quickly. It's just not important enough when NVidia cards work well out of the box and can be acquired at both the hobbyist low end (GTX 1060) and professional high end (Tesla series), have proven track records, and every DL framework works more-or-less immediately. They need the patches to be upstream and widely maintained by the developers (and be involved themselves) before people 'at large' will really consider using them. The code is there, but practically doesn't exist for anyone yet, outside of AMD developers and a minuscule amount of AMD users. This is also true of all of Intel's ports of various DL frameworks, too. One thing NVidia has learned is that sticking their engineers directly into the pipeline works -- from everything from game development studios, and now to deep learning frameworks (which have nvidia engineers). That said, AMD cards do have some excellent advantages like unlocked FP16 that Nvidia does not offer outside of the Tesla series, and bringing card price down would rock. So I'm hopeful that they'll sort all this out and make themselves competitive. I'm hoping to see MIOpen support (and if we want ponies, an upstream ROCm driver) sometime in the next ~year for at least a few frameworks (Caffe2 or PyTorch would be good.) But until then, CUDA, CuDNN and Nvidia technology are really the only name in town for like +90% of people, for better or worse.
- bischofs 9y agoThis seems like a shameless money grab. Even the historical "enterprise" GPUs were never this expensive. Nvidia seems like they are just taking their new GPU design which is legitimate (Volta) and trying to cash in on the ML mania. GPUS should be general purpose and with Nvidia throwing all their eggs in the ML basket and charging a fortune it might risk alienating their core users (gamers).
- was_boring 9y agoI don't know much about the market for this type of GPU, but the article does note that the Tesla model (priced at about 10,000) is selling out as soon as they make them. It seems like the demand is there but the supply isn't. It is true that Nvidia could be artificially reducing supply.
- potatolicious 9y agoThe Titan cards were not intended for the gaming market, though some gamers who wanted every last ounce of (severely diminished return) performance certainly tried nonetheless. I really don't see the issue. Sometime early next year Nvidia will announce the Volta enthusiast cards (GTX 1170, 1180?), priced in line with the current-gen enthusiast cards (GTX 1070, 1080). If you want the new shiny right now, you can, but as always you can get 85% of the same gaming performance for 25% of the price by waiting a few months.
- sp332 9y agoThis has 13 teraflops of single-precision performance, which is a small bump over the 12 of Titan Xp. But it also adds 110 teraflops of matrix-multiplication power with the dedicated tensor hardware. That means it can outperform 8 Titan Xp cards for certain increasingly-popular workloads, and still fit in the same physical and power budget as one. That's why they can charge $3k.
- mathgeek 9y ago> GPUS should be general purpose Not all GPU's need to be good at all things. There are plenty of general-purpose chipsets out there for those that want one, and I'd say there's room in the market for highly specialized GPU's and if those are priced incorrectly, the market will correct it. The irony in your statement is that this is designed to be a cheaper version of a very popular card.
- c1505 9y agoI don't get the price complaints. This is targeted for deep learning and looks to be similar in performance to a card that currently costs 3x the amount. This is a huge price cut for the compute performance and should make cloud GPU time cheaper and/or more efficient.
- bllguo 9y agoI'd hazard to guess that most people complaining about price are misguided gamers who think Nvidia is now charging them 5x more for a card.
- deleted 9y ago[deleted]
- slizard 9y agoresearch != deep learning > should make cloud GPU time cheaper and/or more efficient. No way. These will be not put in data centers of cloud providers. I'm not even sure many regular data centers will have anything like this -- especially if NVIDIA keeps pushing and blackmailing server vendors to stop selling servers packed with GeForce cards if they don't want to loose big [1,2]. [1] https://www.pcgamesn.com/nvidia-geforce-server https://www.pcgamesn.com/nvidia-geforce-server [2] https://www.digitimes.com/news/a20171027PD200.html https://www.digitimes.com/news/a20171027PD200.html
- slizard 9y ago"With TITAN V, we are putting Volta into the hands of researchers and scientists all over the world" Screw you NVIDIA. I've been vocal about this for a while but this is now getting utterly ridiculous, and frankly insuting to the research community. I don't mind that NVIDIA is ripping of the big derplearning -- and more recently "data" -- companies (Goog, FB, Baidu, etc.), but this greedy bullshit attitude led to the Tesla cards all of a sudden going from expensive, with a 2-2.5x price jump, to the ridiculously priced. In the meantime, they've been "enabling" research by throwing peanuts at the science community with cancer stunts [1]. However, that was just the facade, the real story was that they'd found the cash-cow and the enthusiastic HPC/sci-comp crowd that helped NVIDIA grow out of their sexy-witch-on-the-front-panel-of-the-GTX company box and step into the "big compute boys' league" is no more than just another means to convert peanuts to marketing material. After years of complaints that there are no sensibly prices _compute_ cards devs and independent researchers can afford, constant battles with NVIDIA to stop crippling their management tools on GTX cards, to allow cheap cards to be used productively for compute and development, etc. they step up their game and release a $3k "affordable" card for "researchers and scientists". This claim is beyond ridiculous, revolting and, as a researcher (working in HPC on a large FOSS simulation project) this is insulting. Which researchers are they talking about, the ones employed by the big ad, finance, companies? Definitely not me, not us! Time for us to start showing NVIDIA the middle finger for good! Get porting people, support AMD, test ROCm, their openness and attitude receptive to public feedback and criticism has impressed me recently. At the same time, we need to push NVIDIA too: file bug reports against NVIDIA's shitty OpenCL support, against their consumer card crippling drivers and management tools, and resist their efforts to blackmail OEMs to stop selling servers with GeForce. [1] https://www.nextplatform.com/2016/11/15/deep-learning-supercomputer-approach-cancer-research/ https://www.nextplatform.com/2016/11/15/deep-learning-superc...
- robotresearcher 9y agoIf you can't afford the $3K card, buy the $800 card. The $800 card that outperforms the fastest SIMD computer any amount of money could buy just a few years ago. NVIDIA makes fast computers and sells them for market prices. I'm not sure we need to storm their castle with pitchforks.
- juicyfroot 9y agoThe real question is... how many hashes can it get for ETH mining?