I know it's a small set, but for gaming and is honestly king. Unless you want the absolute "I'm willing to pay double the cost for 5% more performance" top of the line, amd is just great.
For AI and compute.... They're far behind. CUDA just wins. I hope a joint standard will be coming up soon, but until then Nvidia wins
For AI and compute… They’re far behind. CUDA just wins. I hope a joint standard will be coming up soon, but until then Nvidia wins
I got a W6800 recently. I know a nvidia model of the same generation would be faster for AI - but that thing is fast enough to run stable diffusion variants with high resolution pictures locally without getting too annoyed.
The completely different software stack is a killer. It's not that you can't find versions of a model to run, but almost everything that hits the GPU for compute is going to be targeting CUDA, not RocM. From a compatibility standpoint alone this killed AMD for me. I just do not want to spend my time fighting the stack to get these models running.
That and there just hasn't been much gains in performance in recent years, so it makes sense to not upgrade for a while. And a lot of people upgraded all at once during the pandemic, so there are less people on the market for a new GPU.
I got a prebuilt like a couple years ago after getting a chunk of money and it still does me ok. There's a 6800xt in it and it still handles current games ok. I'm in no rush, the only thing I would like is better ray tracing but that's not enough of a reason for me to spend £700+ on a new card.
GPU's aren't in a shortage like they were. The majority of new GPUs are just regular people selling them. I wouldn't personally call it scalping if it's below MSRP.
At this point, emulating or using a wrapper for 3dfx is not gonna make any game that needs it run bad. Don't really need the "full speed" of native support anymore.
There are many different niches of ML. 99% of hobbyist would use consumer grade hardware. It's quite frankly more than good enough.
Even in commercial usage, consumer GPUs provide better value unless you need to do something that very specifically require a huge vram pool. Like connecting multiple A100 GPUs to have hundreds or tens of thousands of gigabyte vram. Those use cases only come up if you're making base models for general purpose.
If you're using it for single person use case, something like 4090 is actually the best hardware. Enough ram to run almost anything and it's higher clock speed than enterprise GPU means your results come back faster.
Even training doesn't require that much vram. Chat models are generally more vram heavy but if you're doing specific image training like stable diffusion for how to render your face, or some specific fetish porn, you only really need like 12GB of vram to do it. There are ways to even do it at lower like 8GB but 12 is sweet value spot where even 3060 or 4060ti can do. Consumer GPUs will get that trained in like 30min to 24hrs depending on settings and model.