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These specifications rather look like some AI acceleration ICs, and not like GPUs (recall that "GPU" stands for Graphics Processing Unit).
by aleph_minus_one 3y ago
These specifications rather look like some AI acceleration ICs, and not like GPUs (recall that "GPU" stands for Graphics Processing Unit).
- KeplerBoy 3y agoSure, no one actually cares about the graphics part anymore in these contexts. Many Nvidia Datacenter GPUs are also no longer GPUs in that sense.
- mise_en_place 3y agoYep just need to load the model. It can and probably should be headless.
- bee_rider 3y agoI know it isn’t the what you mean, but as an aside (rather than a contradiction), the gaming marked is still much bigger than the ML model training one, right? So presumably people care about the graphics part in the sense that development there funds the development of the cards, haha. I always assumed that was what killed Xeon Phi. Hard to justify a GEMM card in and of itself. However clever you get, NVIDIA will be back next year with twice as much bandwidth.
- KeplerBoy 3y agoNo, datacenter GPU revenue has surpassed gaming revenue some time ago. At least for Nvidia and I fully expect this trend to continue.
- krapht 3y agoI'll bet against that. NVIDIA's margin is so large that Google/Microsoft and others can fund / are funding large teams to make TPUs and other dedicated hardware competitive. I also believe LLMs are part of the hype cycle just like crypto. In ten years the gaming market will return to being NVIDIA's primary market.
- CuriouslyC 3y agoIf by "LLMs are part of the hype cycle" you mean "people are hyping them more than their current capabilities entirely warrant" maybe. If you mean "LLMs are a hype technology that is going to peter out without leaving a major impact on society" as kindly as I can phrase it, what on earth are you smoking, or are you Amish?
- hnfong 3y agoTo be fair, GP compared the LLM hype with crypto... And as niche crypto may be in the future, I don't think it will also "peter out without leaving a major impact on society"...
- KetoManx64 3y agoCrypto will not be niche in the future. Bitcoin has a market cap of 1,349,125,982,176 (https://coinmarketcap.com/currencies/bitcoin/ https://coinmarketcap.com/currencies/bitcoin/) and recently surpassed the market cap of gold itself and has large investors from Black Rock to Apple, ans recently got approved for ETFs which means the general population's 401k's will have bitcoin in them starting this year. Other crypto currencies may die out, but bitcoin is here permanantly. If you haven't yet, buy some bitcoin and stop letting the government steal your hard earned money through inflation.
- hnfong 3y agoA couple factors to consider: 1. The hardware isn't nVidia's primary moat. CUDA is. Currently all machine learning tooling assumes you have a CUDA device. 2. The hype cycle may be real, but nobody really knows how much better AI will become. The uncertainty is driving the hype (which is obviously on the optimistic side), but it could well be that models become so good that everyone "needs" an AI chip like how we "need" a smartphone today. 3. The gaming/graphics market is probably going to slowly shrink. There's only so many pixels you can push to the eye. There's very real diminishing returns there. At some point people literally won't be able to tell the difference between the newer/better/faster graphics and the older generation, at which point the GPU vendor's margins drop significantly.
- SonOfLilit 3y agoAccording to my googling, data center has been bigger than gaming for nvidia since 2022, and almost x2 in Q2 2023 following a crazy nosedive in gaming sales.
- bee_rider 3y agoWoah, that’s really something
- FridgeSeal 3y agoNvidia burnt quite a lot of goodwill with the 40xx series cards and pricing. Melting connectors, obscene pricing, marginal performance increase, zero availability; then when hardly any of that was addressed, Ti versions release which are equally expensive, and equally lacklustre. Combine that with patchy support for frame generation and 30xx series cards still going really strong, and you’ve got not a lot of incentive to upgrade.
- weebull 3y agoIn dollars, for sure. In units sold?
- bee_rider 3y agoI think dollars was the right figure to defeat my point. My thought was that they would be impossible for their more datacenter type competition to beat, because their R&D budget came from a much larger gaming segment. But, if they are around the same size, or the data center type users actually pay more, it is a level playing field. I’m sure they don’t mind having both revenue streams of course! But on the other hand, they have to serve two sets of interests.
- gleenn 3y agoMany of them don't even have video out ports, they are purely for the compute power.
- weebull 3y agoThe hardware is still on the chip.
- fyrn_ 3y agoThey are "inferrence only" according to the article
- camel-cdr 3y agograyskull is "inference only", but wormhole will also "support" inference. I put those in quotes, because theoretically both can do training, it's just that grayskull isn't well suited for it, because of the internal fp19 format, and no good support for scaling to multiple cards. wormhole will have internal fp32, and is designed to scale out with across multiple cards, and servers. At least that's how I understood it.
- buildbot 3y agoFP19 is perfectly fine for training - llama is trained in fp16, many train in bf16, with microscaling exponents, you can do 6 bit training : https://arxiv.org/abs/2310.10537 https://arxiv.org/abs/2310.10537
- smallnamespace 3y agoI think the broader context here is that it's "fine for training" in the sense that you can successfully train a model, but it's not "fine for training" in the sense that it can only train small models due to the lack of scaling across cards, which directly cuts against where ML has been trending over the last several years. In LLM-land we've rapidly gone from training bespoke models to doing fine tuning to RLHF to zero-shot prompting. The better the underlying model the more you can do without additional training, so hardware that fails to scale up to the largest training runs will have limited practical utility despite technically supporting training.
- buildbot 3y agoWormhole does support scale out though via it's built in networking? And there seem to be links for something on top of the board, NVLINK style. And yes, I know, I've been working on LLM pretraining for about 4 years now, since 2020. The number formats themselves so far are mostly scale invariant or improve with larger scale - you can quantize a larger model and see less performance drop than a smaller model.
- RobotToaster 3y agoObviously they are graphicless processing units.
- karmakaze 3y agoGPGPU is a thing.
- aleph_minus_one 3y ago> GPGPU is a thing. Indeed. GPGPU means "using a GPU for things that are General Purpose (GPGPU)". The aforementioned IC is not a GPU that is used for general-purpose computations, but a specialized chip for AI computations.
- Almondsetat 3y agoIsn't a GPGPU just a PPU?
- threecheese 3y agoI wonder, is the dual-use of GPUs a significant engineering bottleneck for Nvidia et al? My understanding is that for libraries like Tensorflow and PyTorch, much of the complexity is supporting the many-to-many mapping of model structures to GPU silicon in a way that reduces “compile time”, with all the tradeoff landmines you might expect there. I would imagine that abstracting this at the hardware layer is really valuable. Are these non-GPU architectures the future of machine learning, and can we expect the big vendors to start competing here or is this a business risk for them? (robbing Peter to pay Paul, like Google investing in AI that cannibalizes their search revenue)
- dannyw 3y agoI doubt it. Toolkits like TensorRT are specifically optimised, and NVIDIA already segregated their HPC vs gaming dies (and corresponding, die space). The H100s feature a minuscule amount of ROPs and TMUs (mainly for graphics); and doesn’t come with NVENC/DEC, etc. DirectX, Vulkan is not supported. You’ll just see more and more tensor cores, optimised CUDA cores for bfloat, etc.
- paulmd 3y agoH100 does have NVDEC and jpeg ingest accelerators https://www.servethehome.com/wp-content/uploads/2023/10/NVIDIA-H100-SXM-PCIe-and-NVL-Spec-Table.jpg https://www.servethehome.com/wp-content/uploads/2023/10/NVID... Tbh it’s mildly surprising they even removed NVENC considering the overall size of the chip (in the absolute we are only talking about low-single-digit mm2 savings) and then H100 is still advertised and has features targeting VM graphics/visualization still… remember they also still put a full graphics pipeline with ROPs/TMUs on the chip, just no actual display hardware.
- lobochrome 3y agoIt's a tradeoff with verification and validation, not so much die-space. If you include the IP, you need to run an expensive (time-consuming) simulation and emulation for the IP. Then, you need to validate it on first silicon and pray that it doesn't impact yield in HVM. These days, simulation runs basically nonstop on all the FPGAs the company is able to throw at it until tape out. Then, in post-silicon validation, you have engineering teams working 24/7 to validate the device on the bench. Finally, test engineering tries to achieve yield in HVM. Anything that you don't need on the spec sheet or the designer insists on is cut to save time to market.
- tiahura 3y agoMatrix-Math Coprocessors.
- weebull 3y ago8087 - vector edition.
- layer8 3y agoThat’s why the article characterizes them as an “alternative to GPUs […] for AI development“. That being said, it’s a Grayskull Processing Unit. ;)