• @firadin
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    31 month ago

    What’s NVidia seeing in the gaming space? Or do they conflate gaming and ML sales?

      • @[email protected]
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        61 month ago

        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.

      • @[email protected]
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        21 month ago

        If you want to get started in machine learning cheap and want something faster than cpu training, a 1080ti goes for $120 or so on ebay.