[vegabook]

The company that did 4-cores-forever, has the opportunity to redeem itself, in its next consumer GPU release, by disrupting the "8-16GB VRAM forever" that AMD and Nvidia have been imposing on us for a decade. It would be poetic to see 32-48GB at a non-eye-watering price point.

Intel definitely seems to be doing all the right things on software support.

[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.

[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.

[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.

[chessgecko]

Going above 24GB is probably not going to be cheap until gddr7 is out, and even that will only push it to 36gb. The fancier stacked gddr6 stuff is probably pretty expensive and you can’t just add more dies because of signal integrity issues.

[frognumber]

Assuming you want to maintain full bandwidth.

Which I don't care too much about.

However, even 16->24GB is a big step, since a lot of the model are developed for 3090/4090-class hardware. 36GB would place it lose to the class of the fancy 40GB data center cards.

If Intel decided to push VRAM, it will definitely have a market. Critically, a lot of folks will also be incentivized to make software compatible, since it will be the cheapest way to run models.

[0cf8612b2e1e]

At this point, I cannot run an entire class of models without OOM. I will take a performance hit if it lets me run it at all.

I want a consumer card that can do some number of tokens per second. I do not need a monster that can serve as the basis for a startup.

[hnfong]

A maxed out Mac Studio probably fits your requirements as stated.

[0cf8612b2e1e]

If I were willing to drop $4k on that setup, I might as well get the real NVidia offering.

The hobbyist market needs something priced well under $1k to make it accessible.

[rnewme]

How comes you don't care about full bandwidth?

[frognumber]

Mostly because I use this for development.

If a model takes twice as long to run.... I'll live. Worst-case, it will be mildly annoying.

If I can't run a model, that's a critical failure.

There's a huge step up CPU->GPU which I need, but 3060 versus 4090 isn't a big deal at all. Indeed, the 24GB versus 16GB is a bigger difference than the number of CUDA cores.

[Dalewyn]

The thing about RAM speed (aka bandwidth) is that it becomes irrelevant if you run out and have to page out to slower tiers of storage.

[sitkack]

What is obvious to us, is an industry standard to Product Managers. When is the last time you have seen an industry player upset the status quo? Intel has not changed that much.

[zoobab]

"It would be poetic to see 32-48GB at a non-eye-watering price point."

I heard some Asrock motherboard BIOSes could set the VRAM up to 64GB on Ryzen5.

Doing some investigations with different AMD hardware atm.

[stefanka]

That would be an interesting information. Which MB works with with which APU with 32 or more GB of VRAM. Can you post your findings please?

[LoganDark]

When has an APU ever been as fast as a GPU? How much cache does it have, a few hundred megabytes? That can't possibly be enough for matmul, no matter how much slow DDR4/5 is technically addressable.

[zoobab]

"APU ever been as fast as a GPU"

Ryzen5 has both CPU+GPU on one chip, the BIOS allows you set the amount of VRAM. They share the same RAM bank, you can set 16GB of VRAM and 16GB for the OS if you use a 32GB RAM bank.

[LoganDark]

What I'm saying is that GPUs rely on having the memory close to the die so that it actually has enough bandwidth to saturate the cores. System memory is not very close to the CPU (compared to GPUs), so I have doubts about whether an APU would be able to reach GPU levels of performance over gigabytes of model weights.

[belter]

AMD making drivers of high quality? I would pay to see that :-)

[haunter]

First crypto then AI, I wish GPUs were left alone for gaming.

[talldayo]

Are there actually gamers out there that are still struggling to source GPUs? Even at the height of the mining craze, it was still possible to backorder cards at MSRP if you're patient.

The serious crypto and AI nuts are all using custom hardware. Crypto moved onto ASICs for anything power-efficient, and Nvidia's DGX systems aren't being cannibalized from the gaming market.

[azinman2]

Didn’t nvidia try to block this in software by slowing down mining?

Seems like we just need consumer matrix math cards with literally no video out, and then a different set of requirements for those with a video out.

[wongarsu]

But Nvidia doesn't want to make consumer compute cards because those might steal market share from the datacenter compute cards they are selling at 5x markup.

[talldayo]

Nvidia does sell consumer compute cards, they're just sold at datacenter compute prices: https://www.newegg.com/p/pl?d=nvidia+quadro

Nvidia's approach to software certainly deserves scrutiny, but their hardware lineup is so robust that I find it hard to complain. Jetson already exists for low-wattage solutions, and gaming cards can run Nvidia datacenter drivers on headless Linux without issue. The consumer compute cards are already here, you just aren't using them.

[dragonwriter]

What we need is more real competition for NVidia cards at all levels, so rather than avoiding competing with themselves, they are worried about actually competing with the competition.

But capital doesn't want to invest in competition when it can instead invest in a chance at a moat that allows charging monopoly rents.

[baq]

They were.

But then those pesky researchers and hackers figured out how to use the matmul hardware for non-gaming.

[OkayPhysicist]

The issue from the manufacturer's perspective is that they've got two different customer bases with wildly different willingness to pay, but not substantially different needs from their product. If Nvidia and AMD didn't split the two markets somehow, then there would be no cards available to the PC market, since the AI companies with much deeper pockets would buy up the lot. This is undesirable from the manufacturer's perspective for a couple reasons, but I suspect a big one is worries that the next AI winter would cause their entire business to crater out, whereas the PC market is pretty reliable for the foreseeable future.

Right now, the best discriminator they have is that PC users are willing to put up with much smaller amounts of VRAM.

[UncleOxidant]

> Intel definitely seems to be doing all the right things on software support.

Can you elaborate on this? Intel's reputation for software support hasn't been stellar, what's changed?

[whalesalad]

still wondering why we can't have gpu's with sodimm slots so you can crank the vram

[amir_karbasi]

I believe that the issue is that graphic cards require really fast memory. This requires close memory placement (that's why the memory is so close to the core on the board). expandable memory will not be able to provide the required bandwidth here.

[frognumber]

The universe used to have hierarchies. Fast memory close, slow memory far. Registers. L1. L2. L3. RAM. Swap.

The same thing would make a lot of sense here. Super-fast memory close, with overflow into classic DDR slots.

As a footnote, going parallel also helps. 8 sticks of RAM at 1/8 the bandwidth each is the same as one stick of RAM at 8x the bandwidth, if you don't multiplex onto the same traces.

[riskable]

It's not so simple... The way GPU architecture works is that it needs as-fast-as-possible access to its VRAM. The concept of "overflow memory" for a GPU is your PC's RAM. Adding a secondary memory controller and equivalent DRAM to the card itself would only provide a trivial improvement over, "just using the PC RAM".

Point of fact: GPUs don't even use all the PCI Express lanes they have available to them! Most GPUs (even top of the line ones like Nvidia's 4090) only use about 8 lanes of bandwidth. This is why some newer GPUs are being offered with M.2 slots so you can add an SSD (https://press.asus.com/news/asus-dual-geforce-rtx-4060-ti-ss... ).

[wongarsu]

GPUs have memory hierarchies too. A 4090 has about 16MB of L1 cache and 72MB of L2 cache, followed by the 24GB of GDDR6 RAM, followed by host ram that can be accessed via PCIe.

The issue is that GPUs are massively parallel. A 4090 has 128 streaming multiprocessors, each executing 128 "threads" or "lanes" in parallel. If each "thread" works on a different part of memory that leaves you with 1kB of L1 cache per thread, and 4.5kB of L2 cache each. For each clock cycle you might be issuing thousands of request to your memory controller for cache misses and prefetching. That's why you want insanely fast RAM.

You can write CUDA code that directly accesses your host memory as a layer beyond that, but usually you want to transfer that data in bigger chunks. You probably could make a card that adds DDR4 slots as an additional level of hierarchy. It's the kind of weird stuff Intel might do (the Phi had some interesting memory layout ideas).

[magicalhippo]

Isn't part of the problem that the connectors add too much inductance, making the lines difficult to drive at high speed? Similar issue to distance I suppose but more severe.

[riskable]

You can do this sort of thing but you can't use SODIMM slots because that places the actual memory chips too far away from the GPU. Instead what you need is something like BGA sockets (https://www.nxp.com/design/design-center/development-boards/... ) which are stupidly expensive (e.g. $600 per socket).

[monocasa]

You could probably use something like CAMM which solved a similar problem for lpddr.

https://en.wikipedia.org/wiki/CAMM_(memory_module)

[justsomehnguy]

Look at the motherboards with >2 Memory channels. That would require a lot of physical space, which is quite restricted on a 50 y/o standard for the expansion cards.

[chessgecko]

You could, but the memory bandwidth wouldn’t be amazing unless you had a lot of sticks and it would end up getting pretty expensive