Comment by coppsilgold
13 hours ago
Nvidia went through a lot of effort to make CUDA operational on their entire lineup, and they did it before deep learning even took off.
You do this thing not because you expect consumers with 5 year old hardware to provide meaningful utilization but as a demo ("let me grab my old gaming machine and do some supercomputing real quick") and a signal that you intend to stay the course. AMD management hasn't realized this even after various Nvidia people said that this was exactly why they did it, at some point the absence of that signal is a signal that the AMD compute ecosystem is an unreliable investment, no?
You got it right I think. I’m sitting with two “AI Ready Radeon AI Pro 9700 workstation cards, which are RDNA4 not CDNA. My experience is that my cards are not a priority. Individual engineers at AMD may care, the company doesn’t. I have been trying since February to get ahold of anyone responsible for shipping tuned Tensile gfx1201 kernels in rocm-libs, which is used by Ollama.its been three weeks since I raised enough hell on the discord to get a response, but they still can’t find “who” is responsible for Tensile tuning, and “if” they are even going to do it for the gfx12* cards.
Don’t get me started with vLLM and AITER.
Yeah I own an AMD Instinct MI50 and i need to patch all of my applications to work, like PyTorch, bitsandbytes, blender etc, while Nvidia cards from the same generation are still mostly supported. But the better value and hardware are worth it
I agree, and think AMD and Nvidia philosophy diverged way before Cuda.
I can't count how many times over the last 30 years I've had AMD drivers crash the OS (Linux and Windows). Nvidia have been mostly rock solid.
The thing is, the die isn't much use without a stable driver (and AI stack).