It seems like pure management incompetence to me. They need to invest a whole lot more in software, integrating their stuff directly into pytorch/TF/XLA/etc and making sure it works on consumer cards too. The investment would be paid back tenfold. The market is crying out for more competition for Nvidia and there's huge money to be made on the datacenter side but it all needs to work on the consumer side too.
It uses DirectML on Windows: https://gist.github.com/averad/256c507baa3dcc9464203dc14610d... This is thanks to Microsoft, not AMD.
On Linux you can use ROCm: https://www.videogames.ai/2022/11/06/Stable-Diffusion-AMD-GP...
The horrible install processes and what a mess this is is all down to AMD.
Their attempts at entering the ML space so far have been failures, and they are wise to hold off on really competing with Nvidia until they have the bandwidth to go “all in”. Consciously NOT trying to compete with Nvidia is the reason they didn’t go bankrupt. Their Radeon division minted from 2016-2020 because they focused on a niche Nvidia was neglecting- low-end/eSports (also leveraging their APU expertise to win PS4/Xbox contracts).
I think Nvidia will eventually lose its monopoly on ML/AI stuff as AMD, Apple, Qualcomm, Amazon and Google chip away at their “moat” with their own accelerators/NPUs. As mentioned though, the Nvidia Edge really comes from CUDA and other software, not the hardware. I doubt that Apple, Qualcomm, Amazon or Google will be interested in selling hardware direct to consumers. They want that sweet, sweet cloud money and/or competitive advantages in their phones (like photo processing). I don’t want to be paying AWS $100/mo for a GPU I could pay $600 once for. I do think AMD/RTG will go hard on Nvidia eventually, and it will not matter whether you have an AMD or Nvidia GPU for Tensorflow or spaCy or whatever else.
Cloud/datacenter based ML is a huge growth market. Having the same software work on a consumer GPUs and enterprise ML cards is one of Nvidia's competitive advantages.
Why can't large companies tap the investment market? E.g. they could sell bonds to fund it, borrow, etc.
The idea (not aiming at you here, you didn't say this) that senior leadership at AMD is unaware of NVIDIA's lead in this space, and haven't repeatedly considered whether to invest in competing, is absurd. Likewise the idea that anyone outside of AMD understands better than AMD does what it would take in terms of investment _and opportunity cost_, is also absurd.
Senior leadership at AMD isn't dumb. The fact that they're not doing something we want doesn't make them dumb, either. Again, not aiming at you with this little rant :)
In her area. We have no real info on how proficient she is in other areas like ML.
I think it's very clear with the benefit of hindsight that not investing enough into the software side of deep learning early on was a bad decision. But it was obvious to me even at the time and I said as much to anyone who would listen (e.g. seven years ago https://news.ycombinator.com/item?id=12258027)
Lets try this with another company:
The idea that leadership at Lehmon Brothers is unaware of the fact that they are trading subprime loans is absurd! The leadership isnt dumb
The Idea that leadership at Being is unaware of safety issues with 737 Max is absurd! How could you suggest that anyone outside boesing understands better than they do the risks involved?
You are claiming that AMD leadership is infalliable based on no evidence whatsoever. Thats whats absurd
I may not know better. But I know what I like.
no, they need a product good at training and gpu compute at a reasonable price
that product doesn't need to be good at rendering, ray tracing and similar
sure students and some independent contractors probably love getting both a good graphic card and a CUDA card in one and it makes it easier for people to experiment with it but company PCs normally ban playing games on company PCs and the overlap of "needing max GPU compute" and "needing complicated 3D rendering tasks" is limited.
through having 1 product instead of two does make supply chain and pricing easier
but then 4090 is by now in a price range where students are unlikely to afford it and people will think twice about buying it just to play around with GPU compute.
So e.g. the 7900XTX having somewhat comparable GPU compute usability then a 4080 would have been good enough for the non company use case, where a dedicated compute-per-money cheaper GPU compute only card would be preferable for the company use case I think.
1) Consumer Nvidia GPU cards on custom PCs
2) Self hosted shared server
3) Cloud infrastructure.
There is no "GPU compute only card" that is widely used outside servers.
> company PCs normally ban playing games on company PCs and the overlap of "needing max GPU compute" and "needing complicated 3D rendering tasks" is limited.
The "don't play games thing" isn't a factor. Most companies just buy a 4090 or whatever, and if they have to tell staff not to play games, they say "don't play games". Fortnight runs just fine on pretty much anything anyway.
I'm aware that currently GPU compute only cards are not widly used outside of the server space.
But that's not because people need the consumer GPU features (bedsides video decode) but because the economics of availability and cost lead to consumer GPUs being the best option (and this economic effects don't just apply to customers but also Nvidia itself).
https://www.amd.com/en/graphics/servers-solutions-rocm-ml
> For this they need a product comparable to NVIDIA 4090, so that entry level researchers could use their hardware.
Why is a high end product a requirement for entry level research?
Not going to be spending $10k like the tortoise-tts guy (was looking into that project last night) but $2k might be doable for a hobby project. Plus I’d have a computer at the end.
Also, ROC-M is a bit of a mess to setup. With Nvidia i just need to install cuda, cudnn and then pip install tensorflow/pytorch.