[riskable]

No kidding... Intel is playing catch-up with Nvidia in the AI space and a big reason for that is their offerings aren't competitive. You can get an Intel Arc A770 with 16GB of VRAM (which was released in October, 2022) for about $300 or an Nvidia 4060 Ti with 16GB of VRAM for ~$500 which is twice as fast for AI workloads in reality (see: https://cdn.mos.cms.futurecdn.net/FtXkrY6AD8YypMiHrZuy4K-120... )

This is a huge problem because in theory the Arc A770 is faster! It's theoretical performance (TFLOPS) is more than twice as fast as an Nvidia 4060 (see: https://cdn.mos.cms.futurecdn.net/Q7WgNxqfgyjCJ5kk8apUQE-120... ). So why does it perform so poorly? Because everything AI-related has been developed and optimized to run on Nvidia's CUDA.

Mostly, this is a mindshare issue. If Intel offered a workstation GPU (i.e. not a ridiculously expensive "enterprise" monster) that developers could use that had something like 32GB or 64GB of VRAM it would sell! They'd sell zillions of them! In fact, I'd wager that they'd be so popular it'd be hard for consumers to even get their hands on one because it would sell out everywhere.

It doesn't even need to be the fastest card. It just needs to offer more VRAM than the competition. Right now, if you want to do things like training or video generation the lack of VRAM is a bigger bottleneck than the speed of the GPU. How does Intel not see this‽ They have the power to step up and take over a huge section of the market but instead they're just copying (poorly) what everyone else is doing.

[Workaccount2]

Based on leaks, it looks like intel somehow missed an easy opportunity here. There is an insane demand for high VRAM cards now, and it seems the next intel cards will be 12GB.

Intel, screw everything else, just pack as much VRAM in those as you can. Build it and they will come.

[dheera]

Exactly, I'd love to have 1TB of RAM that can be accessed at 6000 MT/s.

[talldayo]

Optane is crying and punching the walls right now.

[yjftsjthsd-h]

Does optane have an advantage over RAM here?

[watersb]

Optane products were sold as DIMMS with single-DIMM capacity as high as 512 GB. With an Intel memory controller that could make it look like DRAM.

512 GB.

It was slower than conventional DRAM.

But for AI models, Optane may have an advantage: it's bit-addressable.

I'm not aware of any memory controllers that exposed that single-bit granularity; Optane was fighting to create a niche for itself, between DRAM and NAND Flash: pretending to be both, when it was neither.

Bit-level operations, computational units in the same device as massive storage, is an architecture that has yet to be developed.

AI GPUs try to be such an architecture by plopping 16GB of HBM next to a sea of little dot-product engines.

[wtallis]

> But for AI models, Optane may have an advantage: it's bit-addressable.

That's an advantage over NAND but not over DRAM. Fundamentally, DRAM is also bit-addressable, but everybody uses DRAM parts with memory cells organized into a hierarchy of groupings for reasons that mostly apply to 3D XPoint memory.

[ponector]

I don't agree. Who will buy it? A few enthusiasts who wants to run LLM locally but cannot afford M3 or 4090?

It will be a niche product with poor sales.

[bt1a]

I think there's more than a few enthusiasts who would be very interesting in buying 1 or more of these cards (if they had 32+ GB of memory), but I don't have any data to back that opinion up. It is not only those who can't afford a 4090 though.

While the 4090 can run models that use less than 24GB of memory at blistering speeds, models are going to continue to scale up and 24GB is fairly limiting. Because LLM inference can take advantage of splitting the layers among multiple GPUs, high memory GPUs that aren't super expensive are desirable.

To share a personal perspective, I have a desktop with a 3090 and an M1 Max Studio with 64GB of memory. I use the M1 for local LLMs because I can use up to 57~GB of memory, even though the output (in terms of tok/s) is much slower than ones I can fit on a 3090.

[michaelbrave]

Right now I have a 3090TI so it's not worth it for me to upgrade to a 4090, but I do run into Vram constraints a lot, especially with merging stable diffusion models, especially as the models get larger (XL-Cascade-etc). As I move toward running multiple LLMs at a time I run into similar problems.

I would gladly buy a card that ran a touch slower but had massive Vram, especially if it was affordable, but I guess that puts me into that camp of enthusiasts you mentioned.

[Dalewyn]

>models are going to continue to scale up and 24GB is fairly limiting

>24GB is fairly limiting

Can I take a moment to suggest that maybe we're very spoiled?

24GB of VRAM is more than most peoples' system RAM, and that is "fairly limiting"?

To think Bill once said 640KB would be enough.

[hnfong]

It doesn't matter whether anyone is "spoiled" or not.

The fact is large language models require a lot of VRAM, and the more interesting ones need more than 24GB to run.

The people who are able to afford systems with more than 24GB VRAM will go buy hardware that gives them that, and when GPU vendors release products with insufficient VRAM they limit their market.

I mean inequality is definitely increasing at a worrying rate these days, but let's keep the discussion on topic...

[Dalewyn]

I'm just fascinated that the response/demand to running out of RAM is "Just sell us more RAM, god damn!" instead of engineering a solution to make due with what is practically (and realistically) available.

[dekhn]

I would say that increasing RAM to avoid engineering a solution has long been a successful strategy.

i learned my RAM lesson when I bought my first real linux PC. it had 4MB of RAM, which was enough to run X, bash, xterm, and emacs. But once I ran all that and also wanted to compile with g++, it would start swapping, which in the days of slow hard drives, was death to productivity.

I spent $200 to double to 8MB, and then another $200 to double to 16MB, and then finally, $200 to max out the RAM on my machine-- 32MB! And once I did that everything flew.

Rather than attempting to solve the problem by making emacs (eight megs and constantly swapping) use less RAM, or find a way to hack without X, I deployed money to max out my machine (which was practical, but not realistically available to me unless I gave up other things in life for the short term). Not only was I more productive, I used that time to work on other engineering problems which helped build my career, while also learning an important lesson about swapping/paging.

People demand RAM and what was not practically available is often available 2 years later as standard. Seems like a great approach to me, especially if you don't have enough smart engineers to work around problems like that (see "How would you sort 4M integers in 2M of RAM?")

[nl]

While saying "we want more efficiency" is great there is a trade off between size and accuracy here.

It is possible that compressing and using all of human knowledge takes a lot of memory and in some cases the accuracy is more important than reducing memory usage.

For example [1] shows how Gemma 2B using AVX512 instructions could solve problems it couldn't solve using AVX2 because of rounding issues with the lower-memory instructions. It's likely that most quantization (and other memory reduction schemes) have similar problems.

As we develop more multi-modal models that can do things like understand 3D video in better than real time it's likely memory requirements will increase, not decrease.

[1] https://github.com/google/gemma.cpp/issues/23

[xoranth]

People have engineered solutions to make what is available practical (see all the various quantization schemes that have come out).

It is just that there's a limit to how much you can compress the models.

[michaelt]

There has in fact been a great deal of careful engineering to allow 70 billion parameter models to run on just 48GB of VRAM

The people training 70B parameter models from scratch need ~600GB of VRAM to do it!

[dragonwriter]

Quantization and CPU mode and hybrid mode where the model is split between CPU and GPU exist and work well for LLMs, but in the end more VRAM is a massive quality of life improvement for running (and probably more for training, which has higher RAM needs and forbwhich quantization isn't useful, AFAIK) them, even ifbyou technically can do them on CPU alone or hybrid with no/lower VRAM requirements.

[whiplash451]

By the same logic, we’d still be writing assembly code on 640KB RAM machines in 2024.

[hnfong]

What makes you think people aren't trying to engineer a solution that uses less RAM?

There are millions (billions?) of dollars at stake here, and obviously the best minds are already tackling the problem. Only plebs like us who don't have the skills to do so bicker on an internet forum... It's not like we could realistically spend the time inventing ways to run inference with fewer resources and make significant headway.

[loudmax]

I tend to agree that it would be niche. The machine learning enthusiast market is far smaller than the gamer market.

But selling to machine learning enthusiasts is not a bad place to be. A lot of these enthusiasts are going to go on to work at places that are deploying enterprise AI at scale. Right now, almost all of their experience is CUDA and they're likely to recommend hardware they're familiar with. By making consumer Intel GPUs attractive to ML enthusiasts, Intel would make their enterprise GPUs much more interesting for enterprise.

[mysteria]

The problem is that this now becomes a long term investment, which doesn't work out when we have CEOs chasing quarterly profits and all that. Meanwhile Nvidia stuck with CUDA all those years back (while ensuring that it worked well on both the consumer and enterprise line) and now they reap the rewards.

[Wytwwww]

Current Intel and its leadership seems to be much more focused on long term goals/growth than before, or so they claim.

[resource_waste]

I need offline LLMs for work.

It doesnt need to be consumer grade, it doesnt need to be ultra high either.

It needs to be cheap enough for my department to expensive it via petty cash.

[antupis]

It would be same playbook that NVIDIA did CUDA where was market 2010 when it was research labs and hobbyists doing vector calculations.

[Aerroon]

It's about mindshare. Random people using your product to do AI means that the tooling is going to improve because people will try to use them. But as it stands right now if you think there's any chance you want to use AI in the next 5 years, then why would you buy anything other than Nvidia?

It doesn't even matter if that's your primary goal or not.

[talldayo]

> Who will buy it?

Frustrated AMD customers willing to put their money where their mouth is?

[resource_waste]

>M3

>4090

These are noob hardware. A6000 is my choice.

Which really only further emphesizes your point.

>CPU based is a waste of everyone's time/effort

>GPU based is 100% limited by VRAM, and is what you are realistically going to use.

[jmward01]

Microsoft got where they are because the developed tools that everyone used. The got the developers and the consumers followed. Intel (or AMD) could do the same thing. Get a big card with lost of ram so that the developers get used to your ecosystem and then sell the enterprise GPUs to make the $$$. It is a clear path with a lot of history and it blows my mind Intel and AMD aren't doing it.

[zoobab]

"Microsoft got where they are because the developed tools that everyone used."

It's not like they don't have a monopoly on pre-installed OSes.

[alecco]

AFAIK, unless you are a huge American corp with orders above $100m Nvidia will only sell you old and expensive server cards like the crappy A40 PCIe 4.0 48GB GDDR6 at $5,000. Good luck getting SXM H100s or GH200.

If Intel sells a stackable kit with a lot of RAM and a reasonable interconnect a lot of corporate customers will buy. It doesn't even have to be that good, just half way between PCIe 5.0 and NVLink.

But it seems they are still too stuck in their old ways. I wouldn't count on them waking up. Nor AMD. It's sad.

[ponector]

Parent comment requested non-enterprise, consumer grade GPU with tons of memory. I'm sure there is no market for this.

However, server solutions could have some traction.

[alecco]

Hobbyists are stacking 3090s with NVLink.

[glitchc]

I think the answer to that is fairly straightforward. Intel isn't in the business of producing RAM. They would have to buy and integrate a third-party product which is likely not something their business side has ever contemplated as a viable strategy.

[monocasa]

Their GPUs as sold already include RAM.

[glitchc]

Yes, but they don't fab their own RAM. It's a cost center for them.

[monocasa]

If they can sell the board with more RAM for more than their extra RAM costs, or can sell more GPUs total but the RAM itself is priced essentially at cost, then it's not a cost center.

[RussianCow]

That's not what a cost center is. There is an opportunity for them to make more money by putting more RAM into their GPUs and exposing themselves to a different market. Whether they physically manufacture that RAM doesn't matter in the slightest.

[glitchc]

I agree with you, it makes good business sense. No doubt. I'm merely positing that Intel executives are not used to doing business as integrators. Their core business has always been that of a supplier.

[actionfromafar]

I wonder what the market dynamics for NVidia would be if Intel bought as much VRAM as it could.