87
you are viewing a single comment's thread
view the rest of the comments
view the rest of the comments
this post was submitted on 30 Aug 2025
87 points (97.8% liked)
Technology
39598 readers
150 users here now
This is the official technology community of Lemmy.ml for all news related to creation and use of technology, and to facilitate civil, meaningful discussion around it.
Ask in DM before posting product reviews or ads. All such posts otherwise are subject to removal.
Rules:
1: All Lemmy rules apply
2: Do not post low effort posts
3: NEVER post naziped*gore stuff
4: Always post article URLs or their archived version URLs as sources, NOT screenshots. Help the blind users.
5: personal rants of Big Tech CEOs like Elon Musk are unwelcome (does not include posts about their companies affecting wide range of people)
6: no advertisement posts unless verified as legitimate and non-exploitative/non-consumerist
7: crypto related posts, unless essential, are disallowed
founded 6 years ago
MODERATORS
You can train or fine-tune a model on any GPU. Surely, It will be slower, but higher VRAM is better.
No. The CUDA training stuff is Nvidia only.
CUDA is not equivalent to AI training. Nvida offers useful developer tools for using their hardware, but you don't have to use them. You can train on any GPU or even CPU. The projects you've looked at (?) just chose to use CUDA because it was the best fit for what hardware they had on hand, and were able to tolerate the vendor lock-in.
CPU yes. GPU no, in my experience.
I'm not saying you can deploy these in place of Nvidia cards where the tooling is built with Nvidia in mind. I'm saying that if you're writing code you can do machine learning projects without CUDA, including training.
For sure you can work around it. But it's not optimal and requires additional work most people don't feel like putting in.
Pytorch runs on HIP now.
AMD has been lying about that every year since 2019.
Last time I checked it didn't. And it probably still doesn't.
People aren't buying NVIDIA if AMD would work too. The VRAM prices NVIDIA asks are outrageous.
I run llama.cpp and PyTorch on MI300s. It works really well.
Can you train on it too? I tried Pytorch on AMD once and it was awful. They promised mountains but delivered nothing. Newer activation functions were all broken.
llama.cpp is inference only, for which AMD works great too after converting to ONNX. But training was awful on AMD in the past.
We have trained transformers and diffusion models on AMD MI300s, yes.
Interesting. So why does NVIDIA still hold such a massive monopoly on the datacenter?
It takes a long time for large companies to change their purchases. Many of these datacenter contracts are locked in for years. You can't just change them overnight.