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0 pointsthrowaway3141551y ago0 comments

> There should be no expectation that consumer grade gaming cards will be optimal for ML use.

And yet it just so happens they work effectively the same. I've done research on an RTX 2070 with just 8 GB VRAM. That card consistently met or got close to the performance of a V100 albeit with less vram.

Why indicate people shouldn't use consumer cards? It's dramatically (like 10x-50x) cheaper. Is machine learning only for those who can afford 10k-50k USD workstation GPU's? That's lame and frankly comes across as gate keeping.

Honestly I can't really imagine how a person could reasonably have this stance. Just let folks buy hardware and use it however they want. Sure if may be less than optimal but it's important to remember that not everyone in the world has the money to afford an H100.

Perhaps you can explain some other better reason for why people shouldn't use consumer cards for ML? It's frankly kind of a rude suggestion in the absence of a better explanation.

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vonneumannstan1y ago

If you can do research on a mid tier consumer card then more power to you. I'm specifically referencing the people who are complaining that the specs on consumer video game GPUs are not good for ML work. Like theres just no reasonable expectation that they will be.

throwaway314155OP1y ago

Ah, I see what you mean. Yeah I think it comes from a place of viewing increase in VRAM as relatively low cost and therefore an artificial limitation of sorts used to differentiate between consumer and workstation products (and the respective price disparities).

Which may be true although there are more differences than just VRAM and I assume those market segments have different perceptions of the real value Gamers want it cheaper/faster, institutions want it closer to state of the art, more robust to lengthy workloads (as in year long training sessions), and better support from nvidia. Among other things.

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