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.
It will be a niche product with poor sales.
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.
>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.
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...
It is just that there's a limit to how much you can compress the models.
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?")
Thank you. Now I feel a log better for dropping $700 on the 32MB of RAM when I built my first rig.
The people training 70B parameter models from scratch need ~600GB of VRAM to do it!
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.
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.
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.
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.
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.
It doesn't even matter if that's your primary goal or not.
Frustrated AMD customers willing to put their money where their mouth is?
>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.
It's not like they don't have a monopoly on pre-installed OSes.
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.
However, server solutions could have some traction.