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Everyone I know who’s bought a GPU recently has gone used, including me
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 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
I bought a 7900xtx and have been VERY happy with it.
That’s what I’m running, and it’s honestly better than my partners 3090
The only thing it’s missing is dedicated video decode hardware (which is mostly a convenience) and an equivalent to shadow play. Otherwise it’s a great alternative to a 4080/S
Is amd relive not equivalent to shadow play? Can record gameplay in av1 without issue
You can even skip the whole suite if you don’t need the AMD per game driver tweaks. OBS now come with direct AMD av1 support and also can record HDR content.(which relive can’t do.)
What’s NVidia seeing in the gaming space? Or do they conflate gaming and ML sales?
Who would buy consumer grade hardware for machine learning?
Almost everyone?
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.
