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15 years of FP64 segmentation, and why the Blackwell Ultra breaks the pattern
- wtallis 8mo agoIt's amazing to step back and look at how much of NVIDIA's success has come from unforeseen directions. For their original purpose of making graphics chips, the consumer vs pro divide was all about CAD support and optional OpenGL features that games didn't use. Programmable shaders were added for the sake of graphics rendering needs, but ended up spawning the whole GPGPU concept, which NVIDIA reacted to very well with the creation and promotion of CUDA. GPUs have FP64 capabilities in the first place because back when GPGPU first started happening, it was all about traditional HPC workloads like numerical solutions to PDEs. Fast forward several years, and the cryptocurrency craze drove up GPU prices for many years without even touching the floating-point capabilities. Now, FP64 is out because of ML, a field that's almost unrecognizable compared to where it was during the first few years of CUDA's existence. NVIDIA has been very lucky over the course of their history, but have also done a great job of reacting to new workloads and use cases. But those shifts have definitely created some awkward moments where their existing strategies and roadmaps have been upturned.
- rustyhancock 8mo agoThey were also bailed out by Sega. When they couldn't deliver the console GPU they promised for the Dreamcast (the NV2), Shoichiro Irimajiri, the Sega CEO at the time let them keep the cash in exchange for stock [0]. Without it Nvidia would have gone bankrupt months before Riva 128 changed things. Sega console arm went bust not that it mattered. But they sold the stock for about $15mn (3x). Had they held it, Jensen Huang ,estimated itd be worth a trillion[1]. Obviously Sega and especially it's console arm wasn't really into VC but... My wet dream has always been what if Sega and Nvidia stuck together and we had a Sega tegra shield instead of a Nintendo switch? Or even what if Sega licensed itself to the Steam Deck? You can tell I'm a sega fan boy but I can't help that the Mega Drive was the first console I owned and loved! [0] https://www.gamespot.com/articles/a-5-million-gift-from-sega-saved-nvidia/1100-6536736/ https://www.gamespot.com/articles/a-5-million-gift-from-sega... [1] https://youtu.be/3hptKYix4X8?t=5483&si=h0sBmIiaduuJiem_ https://youtu.be/3hptKYix4X8?t=5483&si=h0sBmIiaduuJiem_
- readitalready 8mo agoThe whole GPU history is off and being driven by finance bros as well. Everyone believes Nvidia kicked off the GPU AI craze when Ilya Sutskever cleaned up on AlexNet with an Nvidia GPU back in 2012, or when Andrew Ng and team at Stanford published their "Large Scale Deep Unsupervised Learning using Graphics Processors" in 2009, but in 2004, a couple of Korean researches were the first to implement neural networks on a GPU, using ATI Radeons (now AMD): https://www.sciencedirect.com/science/article/abs/pii/S0031320304000524 https://www.sciencedirect.com/science/article/abs/pii/S00313... I remember ATI and Nvidia were neck-and-neck to launch the first GPUs around 2000. Just so much happening so fast. I'd also say Nvidia had the benefit of AMD going after and focusing on Intel both at the server level as well as the integrated laptop processors, which was the reason they bought ATI.
- gdiamos 8mo agoMost people don't appreciate how many dead end applications NVIDIA explored before finding deep learning. It took a very long time, and it wasn't luck.
- wtallis 8mo agoIt was luck that a viable non-graphics application like deep learning existed which was well-suited to the architecture NVIDIA already had on hand. I certainly don't mean to diminish the work NVIDIA did to build their CUDA ecosystem, but without the benefit of hindsight I think it would have been very plausible that GPU architectures would not have been amenable to any use cases that would end up dwarfing graphics itself. There are plenty of architectures in the history of computing which never found a killer application, let alone three or four.
- oivey 8mo agoEven that is arguably not lucky, it just followed a non-obvious trajectory. Graphics uses a fair amount of linear algebra, so people with large scale physical modeling needs (among many) became interested. To an extent the deep learning craze kicked off because of developments in computation on GPUs enabled economical training.
- imtringued 8mo agoNvidia started their GPGPU adventure by acquiring a physics engine and porting it over to run on their GPUs. Supporting linear algebra operations was pretty much the goal from the start.
- adrian_b 8mo agoThey were also full of lies when they have started their GPGPU adventure (like also today). For a few years they have repeated continuously how GPGPU can provide about 100 times more speed than CPUs. This has always been false. GPUs are really much faster, but their performance per watt has oscillated during most of the time around 3 times and sometimes up to 4 times greater in comparison with CPUs. This is impressive, but very far from the "100" factor originally claimed by NVIDIA. Far more annoying than the exaggerated performance claims, is how the NVIDIA CEO was talking during the first GPGPU years about how their GPUs will cause a democratization of computing, giving access for everyone to high-throughput computing. After a few years, these optimistic prophecies have stopped and NVIDIA has promptly removed FP64 support from their price-acceptable GPUs. A few years later, AMD has followed the NVIDIA example. Now, only Intel has made an attempt to revive GPUs as "GPGPUs", but there seems to be little conviction behind this attempt, as they do not even advertise the capabilities of their GPUs. If Intel will also abandon this market, than the "general-purpose" in GPGPUs will really become dead.
- pmontra 8mo agoI don't think it was luck. I think it was inevitable. They positioned the company on high performance computing, even if maybe they didn't think they were a HPC company, and something was bound to happen in that market because everybody was doing more and more computing. Then they executed well with the usual amount of greed that every company has. The only risk for well positioned companies is being too ahead of times: being in the right market but not surviving long enough to see a killer app happen.
- brookst 8mo agoMaybe some luck. But there’s also a principle that if you optimize the hell out of something and follow customer demand, there’s money to be made. Nvidia did a great job of avoiding the “oh we’re not in that market” trap that sunk Intel (phones, GPUs, efficient CPUs). Where Intel was too big and profitable to cultivate adjacent markets, Nvidia did everything they could to serve them and increase demand.
- pjc50 8mo agoThe counter question is: why have AMD been so bad by comparison?
- Eisenstein 8mo agoBecause GPUs require a lot on the software side, and AMD sucks at software. They are a CPU company that bought a GPU company. ATI should have been left alone.
- 0x457 8mo agoBecause AMD only every had one strategy for software: Look at it as, we're David against the Goliath, we're going to utilize open-source because that's how you win. Then make zero effecting trying to increase adoption, which then result in reducing support for it and eventually giving up.
- jjmarr 8mo agoFP64 performance is limited on consumer because the US government deems it important to nuclear weapons research. Past a certain threshold of FP64 throughput, your chip goes in a separate category and is subject to more regulation about who you can sell to and know-your-customer. FP32 does not matter for this threshold. https://en.wikipedia.org/wiki/Adjusted_Peak_Performance https://en.wikipedia.org/wiki/Adjusted_Peak_Performance It is not a market segmentation tactic and has been around since 2006. It's part of the mind-numbing annual export control training I get to take.
- latentsea 8mo agoCan't wait until they update this to also include export controls around FP8 and FP4 etc in order to combat deepfakes, and then all of a sudden not be able to buy increasingly powerful consumer GPUs.
- fp64enjoyer 8mo agoThis is so interesting, especially given that it is in theory possible to emulate FP64 using FP32 operations. I do think though that Nvidia generally didn't see much need for more FP64 in consumer GPUs since they wrote in the Ampere (RTX3090) white paper: "The small number of FP64 hardware units are included to ensure any programs with FP64 code operate correctly, including FP64 Tensor Core code." I'll try adding an additional graph where I plot the APP values for all consumer GPUs up to 2023 (when the export control regime changed) to see if the argument of Adjusted Peak Performance for FP64 has merit. Do you happen to know though if GPUs count as vector processors or not under these regulations since the weighing factor changes depending on the definition? https://www.federalregister.gov/documents/2018/10/24/2018-22163/wassenaar-arrangement-2017-plenary-agreements-implementation https://www.federalregister.gov/documents/2018/10/24/2018-22... What I found so far is that under Note 7 it says: "A ‘vector processor’ is defined as a processor with built-in instructions that perform multiple calculations on floating-point vectors (one-dimensional arrays of 64-bit or larger numbers) simultaneously, having at least 2 vector functional units and at least 8 vector registers of at least 64 elements each." Nvidia GPUs have only 32 threads per warp, so I suppose they don't count as a vector processor (which seems a bit weird but who knows)?
- throwaway81523 8mo agoNo mention of the Radeon VII from 2019 where for some unfathomable reason AMD forgot about the segmentation scam and put real FP64 into a gaming GPU. From this 2023 list, it's still faster at FP64 than any other consumer GPU by a wide margin (enterprise GPU's aren't in the list). Scroll all the way to the end. https://www.eatyourbytes.com/list-of-gpus-by-processing-power-double-precision/ https://www.eatyourbytes.com/list-of-gpus-by-processing-powe...
- Azantys 8mo agoThats because Radeon VIIs were just AMD Instinct MI50 server gpus which didn't make the cut or were left over.
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- adrian_b 8mo agoThey did a mild segmentation with that one, by reducing the throughput from 1:2 to 1:4 in the consumer variant, with the hope of forcing people to buy the "professional" version. Even with the throughput reduction, Radeon VII had a performance somewhat better than the previous best FP64 product, AMD Hawaii, due to the large and fast memory. Most later consumer GPUs from NVIDIA and AMD have never approached again such a high memory interface throughput. Radeon VII has remained for many years the champion of FP64 performance per dollar. I am still using one bought in 2019, 7 years ago. Last year was the first time when a GPU with good FP64 performance per dollar has appeared again after Radeon VII: Intel Battlemage B580. Unfortunately it is a small GPU, but nonetheless the performance per dollar is excellent.
- throwaway81523 8mo agoLol I just looked up the Battlemage B580. Seems it was introduced in 2024 at $250 and I looked on Newegg expecting for it to be less by now. Instead it's $425. Damn AI.
- gdiamos 8mo agoI'm not sure why the article dismisses cost. Let's say X=10% of the GPU area (~75mm^2) is dedicated to FP32 SIMD units. Assume FP64 units are ~2-4x bigger. That would be 150-300mm^2, a huge amount of area that would increase the price per GPU. You may not agree with these assumptions. Feel free to change them. It is an overhead that is replicated per core. Why would gamers want to pay for any features they don't use? Not to say there isn't market segmentation going on, but FP64 cost is higher for massively parallel processors than it was in the days of high frequency single core CPUs.
- wmf 8mo agoWhy would gamers want to pay for any features they don't use? Obviously they don't want to. Now flip it around and ask why HPC people would want to force gamers to pay for something that benefits the HPC people... Suddenly the blog post makes perfect sense.
- rustyhancock 8mo agoSimilar to when Nvidia released LHR GPUs that nerfed performance for Ethereum mining. NVIDIA GeForce RTX 3060 LHR which tried to hinder mining at the bios level. The point wasn't to make the average person lose out by preventing them mining on their gaming GPU. But to make miners less inclined to buy gaming GPUs. They also released a series of crypto mining GPUs around the same time. So fairly typical market segregation. https://videocardz.com/newz/nvidia-geforce-rtx-3060-anti-mining-algoritm-is-not-just-a-driver-thing-technology-could-expand-to-more-skus https://videocardz.com/newz/nvidia-geforce-rtx-3060-anti-min...
- adrian_b 8mo agoNVIDIA could make 2 separate products, a GPU for gamers and a FP accelerator for HPC. Thus everybody would pay for what they want. The problem is that both NVIDIA and AMD do not want to make, like AMD did until a decade ago and NVIDIA stopped doing a few years earlier, a FP accelerator of reasonable size and which would be sold at a similar profit margin with their consumer GPUs. Instead of this, they want to sell only very big FP accelerators and at huge profit margins, preferably at 5-digit prices. This makes impossible for small businesses and individual users to use such FP accelerators. Those are accessible only for big companies, who can buy them in bulk and negotiate lower prices than the retail prices, and who will also be able to keep them busy for close to 24/7, in order to be able to amortize the excessive profit margins of the "datacenter" GPU vendors. One decade and a half ago, the market segmentation was not yet excessive, so I was happy to buy "professional" GPUs, with unlocked FP64 throughput, at a price about twice greater in comparison with consumer GPUs. Nowadays, I can no longer afford such a thing, because the similar GPUs are no longer 2 times more expensive, but 20 to 50 times more expensive. So during the last 2 decades, first I shifted much of my computations from CPUs to GPUs, but then I had to shift them back to CPUs, because there are no upgrades for my old GPUs, any newer GPU being slower, not faster.
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- SubiculumCode 8mo agoTo me it is crazy that NVIDIA somehow got away with telling owners of consumer grade hardware.that they cannot be used in datacenters.
- justincormack 8mo agoMy understanding is this was not enforceable in Europe, and maybe elsewhere
- lelanthran 8mo ago> My understanding is this was not enforceable in Europe, and maybe elsewhere "Not enforceable" just means they can't sue you. It doesn't mean they can't say "We won't sell to you anymore".
- YetAnotherNick 8mo agoThey did it by limiting the supply of cards. Even if you are ready to pay 4x of MSRP, you can't buy 100 of the card at once. Many consumers bought 1 GPU at 2-4x of MSRP.
- BoredPositron 8mo agoCode 43 was a worldwide thing before we found a workaround.
- soulofmischief 8mo agoYep, I do GPU passthrough to virtual machines because I would not let Windows touch my bare metal. You have to patch your ROM headers and hide the fact that you're in a VM from the OS. So even as an end-user, a single person, I cannot naturally use my card how I please without significant technical investment. Imagine buying a $1000 piece of equipment and then being told what you can and can't do with it.
- botusaurus 8mo agothis article is so dumb. NVIDIA delivered what the market wanted - gamers dont need FP64, they dont waste silicon on it. now enterprise doesnt want FP64 anymore and they are reducing silicon for it too weird way to frame delivering exactly what the consumer wants as a big market segmentation fuck the user conspiracy
- wtallis 8mo agoYour framing is what's backwards. NVIDIA artificially nerfed FP64 for a long time before they started making multiple specialized variants of their architectures. It's not a conspiracy theory; it's historical fact that they shipped the same die with drastically different levels of FP64 capability. In a very real way, consumers were paying for transistors they couldn't use, subsidizing the pro parts.
- zokier 8mo ago> subsidizing the pro parts. You got this wrong way around. It's the high margin (pro) products subsidizing low margin (consumer) products.
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- wtallis 8mo agoIn general, yes, but when consumer parts are spending silicon area on features they can't use, it is happening in the other direction too.
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- kbolino 8mo agoThis isn't really true, and it wouldn't be a big deal even if it was. Die areas for consumer card chips are smaller than die areas for datacenter card chips, and this has held for a few generations now. They can't possibly be the same chips, because they are physically different sizes. The lowest-end consumer dies are less than 1/4 the area of datacenter dies, and even the highest-end consumer dies are only like 80% the area of datacenter dies. This implies there must be some nontrivial differentiation going on at the silicon level. Secondly, you are not paying for the die area anyway. Whether a chip is obtained from being specially made for that exact model of GPU, or it is obtained from being binned after possibly defective areas get fused off, you are paying for the end-result product. If that product meets the expected performance, it is doing its job. This is not a subsidy (at least, not in that direction), the die is just one small part of what makes a usable GPU card, and excess die area left dark isn't even pure waste, as it helps with heat dissipation. The fact that nVidia excludes decent FP64 from all of its prosumer offerings (*) can still be called "artificial" insofar as it was indeed done on purpose for market segmentation purposes, but it's not some trivial trick. They really are just not putting it into the silicon. This has been the case for longer than it wasn't by now, even. * = The Quadro line of "professional" workstation cards nowadays are just consumer cards with ECC RAM and special drivers
- benreesman 8mo ago[flagged]
- benreesman 8mo ago[flagged]
- juleiie 8mo agoI hope for their fall. I invest in their success
- numbers_guy 8mo agoA question that has been bugging me for a while is what will NVIDIA do with its HPC business? By HPC I mean clusters intended for non-AI related workloads. Are they going to cater to them separetely, or are they going to tell them to just emulate FP64?
- arcanus 8mo agoHopper had 60 TF FP64, Blackwell has 45 TF, and Rubin has 33 TF. It is pretty clear that Nvidia is sunsetting FP64 support, and they are selling a story that no serious computational scientist I know believes, namely that you can use low precision operations to emulate higher precision. See for example, https://www.theregister.com/2026/01/18/nvidia_fp64_emulation/ https://www.theregister.com/2026/01/18/nvidia_fp64_emulation... It seems the emulation approach is slower, has more errors, and doesn't apply to FP64 vector, only matrix operations.
- dgacmu 8mo agoThis is kind of amazing - I still have a bunch of Titan V's (2017-2018) that do 7 TF FP64. 8 years old and managing 1/4 of what Rubin does, and the numbers are probably closer if you divide by the power draw. (Needless to say, the FP32 / int8 / etc. numbers are rather different.)
- adrian_b 8mo agoFor a long time AMD has been offering much better FP64 performance than NVIDIA, in their CDNA GPUs (which continue the older AMD GCN ISA, instead of resembling the RDNA used in gaming GPUs). Nevertheless, the AMD GPUs continue to have their old problems, weak software support, so-and-so documentation, software incompatibility with the cheap GPUs that could be used directly by a programmer for developing applications. There is a promise that AMD will eventually unify the ISA of their "datacenter" and gaming GPUs, like NVIDIA has always done, but it in unclear when this will happen. Thus they are a solution only for big companies or government agencies.
- wmf 8mo agoAMD MI430X is taking that market.
- kittbuilds 8mo ago[dead]
- adrian_b 8mo agoWhile implementing double-precision by double-single may be a solution in some cases, the article fails to mention the overflow/underflow problem, which is critical in scientific/technical computing (a.k.a. HPC). With the method from the article, the exponent range remains the same as in single precision, instead of being increased to that of double precision. There are a lot of applications for which such an exponent range would cause far too frequent overflows and underflows. This could be avoided by introducing a lot of carefully-chosen scaling factors in all formulae, but this tedious work would remove the main advantage of floating-point arithmetic, i.e. the reason why computations are not done in fixed-point. The general solution of this problem is to emulate double-precision with 3 numbers, 2 FP32 for the significand and a third number for the exponent, either a FP number or an integer number, depending on which format is more convenient for a given GPU. This is possible, but it lowers considerably the achievable ratio between emulated FP64 throughput and hardware FP32 throughput, but the ratio is still better than the vendor-enforced 1:64 ratio. Nevertheless, for now any small business or individual user can achieve a much better performance per dollar for FP64 throughput by buying Intel Battlemage GPUs, which have a 1:8 FP64/FP32 throughput ratio. This is much better than you can achieve by emulating FP64 on NVIDIA or AMD GPUs. Intel B580 is a small GPU, so it has only a FP64 throughput about equal to a Ryzen 9 9900X and smaller than a Ryzen 9 9950X. However it provides that throughput at a much lower price. Thus if you start with a PC with a 9900X/9950X, you can double or almost double the FP64 throughput for a low additional price with an Intel GPU. Multiple GPUs will proportionally multiply the throughput. The sad part is that with the current Intel CEO and with NVIDIA being a shareholder of Intel, it is unclear whether Intel will continue to compete in the GPU market, or they will abandon it, leaving us at the mercy of NVIDIA and AMD, which both refuse to provide products with good FP64 support to small businesses and individual users.
- nsajko 8mo agoYeah, double-word floating-point loses many of the desirable properties of the usual floating-point.
- fp64enjoyer 8mo agoYeah fair enough. The exponent of an FP32 has only 8 bits instead of 11 bits. I'll make an edit to make this explicit. It's also fairly interesting how Nvidia handles this for the Ozaki scheme: https://docs.nvidia.com/cuda/cublas/#floating-point-emulation https://docs.nvidia.com/cuda/cublas/#floating-point-emulatio.... They generally need to align all numbers in a matrix row to the maximum exponent (of a number in the row) but depending on scale difference of two numbers this might not be feasible without extending the number of mantissa bits significantly. So they dynamically (Dynamic Mantissa Control) decide if they use Ozaki's scheme or execute on native FP64 hardware. Or they let the user decide on the number of mantissa bits (Fixed Mantissa Control) which is faster but has no longer the guarantees for FP64 precision.
- Sweepi 8mo agoTable to compare Blackwell U300 to U200 (-97% FP64 performance): https://www.forum-3dcenter.org/vbulletin/showpost.php?p=13803349&postcount=3 https://www.forum-3dcenter.org/vbulletin/showpost.php?p=1380...