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Nvidia Announces H100 NVL – Max Memory Server Card for Large Language Models
- neilmovva 4y agoA bit underwhelming - H100 was announced at GTC 2022, and represented a huge stride over A100. But a year later, H100 is still not generally available at any public cloud I can find, and I haven't yet seen ML researchers reporting any use of H100. The new "NVL" variant adds ~20% more memory per GPU by enabling the sixth HBM stack (previously only five out of six were used). Additionally, GPUs now come in pairs with 600GB/s bandwidth between the paired devices. However, the pair then uses PCIe as the sole interface to the rest of the system. This topology is an interesting hybrid of the previous DGX (put all GPUs onto a unified NVLink graph), and the more traditional PCIe accelerator cards (star topology of PCIe links, host CPU is the root node). Probably not an issue, I think PCIe 5.0 x16 is already fast enough to not bottleneck multi-GPU training too much.
- binarymax 4y agoIt is interesting that hopper isn’t widely available yet. I have seen some benchmarks from academia but nothing in the private sector. I wonder if they thought they were moving too fast and wanted to milk amphere/ada as long as possible. Not having any competition whatsoever means Nvidia can release what they like when they like.
- TylerE 4y agoWhy bother when you can get cryptobros paying way over MSRP for 3090s?
- binarymax 4y agoNot just cryptobros. A100s are the current top of the line and it’s hard to find them available on AWS and Lambda. Vast.AI has plenty if you trust renting from a stranger. AMD really needs to pick up the pace and make a solid competitive offering in deep learning. They’re slowly getting there but they are at least 2 generations out.
- fbdab103 4y agoI would take a huge performance hit to just not deal with Nvidia drivers. Unless things have changed, it is still not really possible to operate on AMD hardware without a list of gotchas.
- brucethemoose2 4y agoIts still basically impossible to find MI200s in the cloud. On desktops, only the 7000 series is kinda competitive for AI in particular, and you have to go out of your way to get it running quick in PyTorch. The 6000 and 5000 series just weren't designed for AI.
- breatheoften 4y agoIt's crazy to me that no other hardware company has sought to compete for the deep learning training/inference market yet ... The existing ecosystems (cuda, pytorch etc) are all pretty garbage anyway -- aside from the massive number of tutorials it doesn't seem like it would actually be hard to build a vertically integrated competitor ecosystem ... it feels a little like the rise of rails to me -- is a million articles about how to build a blog engine really that deep a moat ..?
- wmf 4y agoThere are tons of companies trying; they just aren't succeeding.
- KeplerBoy 4y agoHow could their moat possibly be deeper? First of all you need hardware with cutting-edge chips. Chips which can only be supplied by TSMC and Samsung. Then you need the software ranging all the way from the firmware and driver over something analogous to CUDA with libraries like cuDNN, cuBLAS and many others to integrations into pytorch and tensorflow. And none of that will come for free, like it came to Nvidia. Nvidia built CUDA and people built their DL frameworks around it in the last decade, but nobody will invest their time into doing the same for a competitor, when they could just do their research on Nvidia hardware instead. Realistically it's up to AMD or Intel.
- andy81 4y agoGPU mining died last year. There's so little liquidity post-merge that it's only worth mining as a way to launder stolen electricity. The bitcoin people still waste raw materials, and prices are relatively sticky with so few suppliers and a backlog of demand, but we've already seen prices drop heavily since then.
- TylerE 4y agoRight, that's why NVidia is acutally trying again. The money printer has run out of ink.
- pixl97 4y agoThe question is, do they not have much production, or is OpenAI and Microsoft buying every single one they produce?
- rerx 4y agoYou can also join a pair of regular PCIe H100 GPUs with an NVLink bridge. So that topology is not so new either.
- __anon-2023__ 4y agoYes, I was expecting a RAM-doubled edition of the H100, this is just a higher-binned version of the same part. I got an email from vultr, saying that they're "officially taking reservations for the NVIDIA HGX H100", so I guess all public clouds are going to get those soon.
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- ksec 4y ago>H100 was announced at GTC 2022, and represented a huge stride over A100. But a year later, H100 is still not generally available at any public cloud I can find You can safely assume an entity bought as many as they could.
- brucethemoose2 4y agoThe really interesting upcoming LLM products are from AMD and Intel... with catches. - The Intel Falcon Shores XPU is basically a big GPU that can use DDR5 DIMMS directly, hence it can fit absolutely enormous models into a single pool. But it has been delayed to 2025 :/ - AMD have not mentioned anything about the (not delayed) MI300 supporting DIMMs. If it doesn't, its capped to 128GB, and its being marketed as an HPC product like the MI200 anyway (which you basically cannot find on cloud services). Nvidia also has some DDR5 grace CPUs, but the memory is embedded and I'm not sure how much of a GPU they have. Other startups (Tenstorrent, Cerebras, Graphcore and such) seemed to have underestimated the memory requirements of future models.
- virtuallynathan 4y agoGrace can be paired with Hopper via a 900GB/s NVLINK bus (500GB/s memory bandwidth), 1TB of LPDDR5 on the CPU and 80-94GB of HBM3 on the GPU.
- brucethemoose2 4y agoThat does sound pretty good, but its still going chip to chip over NVLink.
- YetAnotherNick 4y ago> DDR5 DIMMS directly That's the problem. Good DDR5 RAM's memory speed is <100GB/s, while nvidia could has up to 2TB/s, and still the bottleneck lies on memory speed for most applications.
- brucethemoose2 4y agoNot if the bus is wide enough :P. EPYC Genoa is already ~450GB/s, and the M2 max is 400GB/s. Anyway, what I was implying is that simply fitting a trillion parameter model into a single pool is probably more efficient than splitting it up over a power hungry interconnect. Bandwidth is much lower, but latency is also slower, you are shuffling much less data around.
- tromp 4y agoThe TDP row in the comparison table must be in error. It shows the card with dual GH100 GPUs at 700W and the one with a single GH100 GPU at 700-800W ?!
- rerx 4y agoThat's the SXM version, used for instance in servers like the DGX. It's also faster than the PCIe variation.
- metadat 4y agoHow is this card (which is really two physical cards occupying 2 PCIe slots) exposed to the OS? Does it show up as a single /dev/gfx0 device, or is the unification a driver trick?
- rerx 4y agoThe two cards show as two distinct GPUs to the host, connected via NVLink. Unification / load balancing happens via software.
- sva_ 4y agoKinda depressing if you consider how they removed NVLink in the 4090, stating the following reason: > “The reason we took [NVLink] off is that we need I/O for other things, so we’re using that area to cram in as many AI processors as possible,” Jen-Hsun Huang explained of the reason for axing NVLink.[0] "NVLink is bad for your games and AI, trust me bro." But then this card, actually aimed at ML applications, uses it. 0. https://www.techgoing.com/nvidia-rtx-4090-no-longer-supports-nvlink/ https://www.techgoing.com/nvidia-rtx-4090-no-longer-supports...
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- rerx 4y agoMarket segmentation. Back when the Pascal architecture was the latest thing, it didn't make much sense to buy expensive Tesla P100 GPUs for many professional applications when consumer GeForce 1080 Ti cards gave you much more bang for the buck with few drawbacks. From the corporation's perspective it makes so much sense to differentiate the product lines more, now that their customers are deeply entrenched.
- int_19h 4y agoI wonder how soon we'll see something tailored specifically for local applications. Basically just tons of VRAM to be able to load large models, but not bleeding edge perf. And eGPU form factor, ideally.
- pixl97 4y agoI'm not a ML scientist my any means, but Perf seems as important as RAM from what I'm reading. Running prompts in internal chain of thought (eating up more TPU time) appears to give much better output.
- int_19h 4y agoIt's not that perf is not important, but not having enough VRAM means you can't load the model of a given size at all. I'm not saying they shouldn't bother with RAM at all, mind you. But given some target price, it's a balance thing between compute and RAM, and right now it seems that RAM is the bigger hurdle.
- frankchn 4y agoThe Apple M-series CPUs with unified RAM is interesting in this regard. You can get an 16-inch MBP with an M2 Max 96GB of RAM for $4300 today, and I expect the M2 Ultra go to 192GB.
- enlyth 4y agoPlease give us consumer cards with more than 24GB VRAM, Nvidia. It was a slap in the face when the 4090 had the same memory capacity as the 3090. A6000 is 5000 dollars, ain't no hobbyist at home paying for that.
- andrewstuart 4y agoNvidia don't want consumers using consumer GPUs for business. If you are a business user then you must pay Nvidia gargantuan amounts of money. This is the outcome of a market leader with no real competition - you pay much more for lower power than the consumer GPUs and you are forced into ujsing their business GPUs through software license restrictions on the drivers.
- Melatonic 4y agoThat was always why the Titan line was so great - they typically unlocked features in between Quadro and Gaming cards. Sometimes it was subtle (like very good FP32 AND FP16 performance) or adding full 10 bit colour support if you had a Titan only. Now it seems like they have opened up even more of those features to consumer cards (at least the creative ones) with the studio drivers.
- koheripbal 4y agoIsn't a new Titan RTX 4090 coming out soon?
- enlyth 4y agoAn alleged photo of an engineering sample was spotted in the wild a while ago, but no one knows if it's actually going to end up being a thing you can buy.
- andrewstuart 4y agoHmmm ... "Studio Drivers" ... how are these tangibly different to gaming drivers? According to this, the difference seems to be that Studio Drivers are older and better tested, nothing else. https://nvidia.custhelp.com/app/answers/detail/a_id/4931/~/nvidia-studio-faqs https://nvidia.custhelp.com/app/answers/detail/a_id/4931/~/n... What am I missing in my understanding of Studio Drivers? """ How do Studio Drivers differ from Game Ready Drivers (GRD)? In 2014, NVIDIA created the Game Ready Driver program to provide the best day-0 gaming experience. In order to accomplish this, the release cadence for Game Ready Drivers is driven by the release of major new game content giving our driver team as much time as possible to work on a given title. In similar fashion, NVIDIA now offers the Studio Driver program. Designed to provide the ultimate in functionality and stability for creative applications, Studio Drivers provide extensive testing against top creative applications and workflows for the best performance possible, and support any major creative app updates to ensure that you are ready to update any apps on Day 1. ""
- 0xbadc0de5 4y agoSo it's essentially two H100's in a trenchcoat? (plus a sprinkling of "latest")
- ecshafer 4y agoI was wondering today if we would start to see the reverse of this. Small ASICS or some kind of optimized for LLM Gpu for desktop / or maybe even laptops of mobile. It is evident I think that LLM are here to stay and will be a major part of computing for a while. Getting this local, so we aren't reliant on clouds would be a huge boon for personal computing. Even if its a "worse" experience, being able to load up an LLM into our computer, tell it to only look at this directory and help out would be cool.
- 01100011 4y agoA couple of the big players are already looking at developing their own chips.
- JonChesterfield 4y agoHave been for years. Maybe lots of years. It's expensive to have a go (many engineers plus cost of making the things) and it's difficult to beat the established players unless you see something they're doing wrong or your particular niche really cares about something the off the shelf hardware doesn't.
- ethbr0 4y agoSoftware/hardware co-evolution. Wouldn't be the first time we went down that road to good effect. For anything that can be run remotely, it'll always be deployed and optimized server-side first. Higher utilization means more economy. Then trickle down to local and end user devices if it makes sense.
- wyldfire 4y agoIn fact, Qualcomm has announced a "Cloud AI" PCIe card designed for inference (as opposed to training & inference) [1, 2]. It's populated with NSPs like the ones in mobile SoCs. [1] https://www.qualcomm.com/products/technology/processors/cloud-artificial-intelligence/cloud-ai-100 https://www.qualcomm.com/products/technology/processors/clou... [2] https://github.com/quic/software-kit-for-qualcomm-cloud-ai-100-cc https://github.com/quic/software-kit-for-qualcomm-cloud-ai-1...
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- tpmx 4y agoDoes AMD have a chance here in the short term (say 24 months)?
- Symmetry 4y agoAMD seems to be focusing on traditional HPC, they've got a ton of 64 bit flops in their recent commercial model. I expect their server GPUs are mostly for chasing supercomputer contracts, which can be pretty lucrative, while they cede model training to NVidia.
- shubham-rawat5 4y agoFor now nvidia is a very dominant player for sure but in long run do you see it changing, with competition from Amd-xilinx, intel or potential AI hardware startups,why have the startups or other big players failed to make dent in nvidia's dominance ? considering how big this market will be in coming years there should have been significant investment made by other players but they seem to be incompetent in making even a competitive chip and nvidia which is already so ahead is running even more faster expanding its software ecosystem across various industries.
- aliljet 4y agoI'm super duper curious if there are ways to glob together VRAM between consumer-grade hardware to make this whole market more accessible to the common hacker?
- rerx 4y agoYou can, for instance, connect two RTX 3090 with an NVLink bridge. That gives you 48 GB in total. The 4090 doesn't support NVLink anymore.
- koheripbal 4y ago> The 4090 doesn't support NVLink anymore. Are you sure about that?
- homarp 4y agothat's what the press said: https://www.tomshardware.com/news/gigabyte-leaves-nvlink-traces-on-geforce-rtx-4090 https://www.tomshardware.com/news/gigabyte-leaves-nvlink-tra...
- mk_stjames 4y agoYou actually can split a model [0] onto multiple GPUs even without NVLink, just using the PCIe for the transfers. Depending on the model the performance is sometimes not all that different. I believe for solely inference on some models the speed difference may barely be noticeable, where for other training activities it may make 10+% difference [1] [0] https://pytorch.org/tutorials/intermediate/model_parallel_tutorial.html#single-machine-model-parallel-best-practices https://pytorch.org/tutorials/intermediate/model_parallel_tu... [1] https://huggingface.co/transformers/v4.9.2/performance.html https://huggingface.co/transformers/v4.9.2/performance.html
- bick_nyers 4y agoI remember reading about a guy who soldered 2GB VRAM modules on his 3060 12GB (replacing the 1GB modules) and was able to attain 24GB on that card. Or something along those lines.
- andrewstuart 4y agoGPUs are going to be weird, underconfigured and overpriced until there is real competition. Whether or not there is real competition depends entirely on whether Intels Arc line of GPUs stays in the market. AMD strangely has decided not to compete. Its newest GPU the 7900 XTX is an extremely powerful card, close to the top of the line Nvidia RTX 4090 in raster performance. If AMD had introduced it with an aggressively low price then then they could have wedged Nvidia, which is determinbed to exploit it's market dominance by squeezing the maximum money out of buyers. Instead, AMD has decided to simply follow Nvidia in squeezing for maximum prices, with AM prices slightly behind Nvidia. It's a strange decision from AMD who is well behind in market and apparently seems disinterested in increasing that market share by competing aggressively. So a third player is needed - Intel - it's alot harder for three companies to sit on outrageously high prices for years rather than compete with each other for market share.
- JonChesterfield 4y agoGPUs strike me as absurdly cheap given the performance they can offer. I'd just like them to be easier to program.
- andrewstuart 4y agoDepends on the GPU of course but at the top end of the market AUD$3000 / USD$1,600 is not cheap and certainly not absurdly cheap. Much less powerful GPUs represent better value but the market is ridiculously overpriced at the moment.
- dragontamer 4y agoThe root cause is that TSMC raised prices in everyone. Since Intel GPUs are again TSMC manufactured, you really aren't going to see price improvements unless Intel subsidizes all of this.
- andrewstuart 4y ago>> The root cause is that TSMC raised prices in everyone. This is not correct.
- sargun 4y agoWhat exactly is an SXM5 socket? It sounds like a PCIe competitor, but proprietary to nvidia. Looking at it, it seems specific to nvidia DGX (mother?)boards. Is this just a "better" alternative to PCIe (with power delivery, and such), or fundamentally a new technology?
- 0xbadc0de5 4y agoIt's one of those /If you have to ask, you can't afford it/ scenarios.
- koheripbal 4y agoYes to all your questions. It's specifically designed for commercial compute servers. It provides significantly more bandwidth and speed over PCIe. It's also enormously more expensive and I'm not sure if you can buy it new without getting the nvidia compute server.
- g42gregory 4y agoI wonder how this compares to AMD Instinct MI300 128GB HBM3 cards?
- ipsum2 4y agoI would sell a kidney for one of these. It's basically impossible to train language models on a consumer 24GB card. The jump up is the A6000 ADA, at 48GB for $8,000. This one will probably be priced somewhere in the $100k+ range.
- solarmist 4y agoYou think? It’s double 48 GB (per card) so why wouldn’t it be in the $20k range?
- YetAnotherNick 4y agoUse 4 consumer grade 4090 then. It would be much cheaper and better in almost every aspect. Also even with this, forget about training foundational models. Meta spent 82k GPU hours on the smallest llama and 1M hours on largest.
- throwaway743 4y agoGo with 2x 3090s instead. 4000 series doesn't support SLI, so you're stuck with the max of whatever one card you get.
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- garbagecoder 4y agoSarah Connor is totally coming for NVIDIA.
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- eliben 4y agoNVIDIA is selling shovels in a gold rush. Good for them. Their P/E of 150 is frightening, though.
- jiggawatts 4y agoI was just saying to a colleague the day before this announcement that the inevitable consequence of the popularity of large language models will be GPUs with more memory. Previously, GPUs were designed for gamers, and no game really "needs" more than 16 GB of VRAM. I've seen reviews of the A100 and H100 cards saying that the 80GB is ample for even the most demanding usage. Now? Suddenly GPUs with 1 TB of memory could be immediately used, at scale, by deep-pocket customers happy to throw their entire wallets at NVIDIA. This new H100 NVL model is a Frankenstein's monster stitched together from whatever they had lying around. It's a desperate move to corner the market early as possible. It's just the beginning, a preview of the times to come. There will be a new digital moat, a new capitalist's empire, built upon on the scarcity of cards "big enough" to run models that nobody but a handful of megacorps can afford to train. In fact, it won't be enough to restrict access by making the models expensive to train. The real moat will be models too expensive to run. Users will have to sign up, get API keys, and stand in line. "Safe use of AI" my ass. Safe profits, more like. Safe monopolies, safe from competition.