this post was submitted on 15 Oct 2025
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submitted 4 days ago by blibla@slrpnk.net to c/microblogmemes@lemmy.world
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[–] HereIAm@lemmy.world 31 points 4 days ago (2 children)

Not strictly LLMs, but neural nets are really good at protein folding, something that very much directly helps understanding cancer amount other things. I know an answer doesn't magically pop out, but it's important to recognise the use cases where NN actually work well.

[–] merc@sh.itjust.works 6 points 4 days ago (1 children)

I'm trying to guess what industries might do well if the AI bubble does burst. I imagine there will be huge AI datacenters filled with so-called "GPUs" that can no longer even do graphics. They don't even do floating point calculations anymore, and I've heard their integer matrix calculations are lossy. So, basically useless for almost everything other than AI.

One of the few industries that I think might benefit is pharmaceuticals. I think maybe these GPUs can still do protein folding. If so, the pharma industry might suddenly have access to AI resources at pennies on the dollar.

[–] MotoAsh@piefed.social 6 points 3 days ago (1 children)

integer calculations are lossy because they're integers. There is nothing extra there. Those GPUs have plenty of uses.

[–] merc@sh.itjust.works 0 points 3 days ago (1 children)

I don't know too much about it, but from the people that do, these things are ultra specialized and essentially worthless for anything other than AI type work:

anything post-Volta is literally worse than worthless for any workload that isn't lossy low-precision matrix bullshit. H200's can't achieve the claimed 30TF at FP64, which is a less than 5% gain over the H100. FP32 gains are similarly abysmal. The B100 and B200? <30TF FP64.

Contrast with AMD Instinct MI200 @ 22TF FP64, and MI325X at 81.72TF for both FP32 and FP64. But 653.7TF for FP16 lossy matrix. More usable by far, but still BAD numbers. VERY bad.

https://weird.autos/@rootwyrm/115361368946190474

[–] MotoAsh@piefed.social 1 points 3 days ago* (last edited 3 days ago)

AI isn't even the first or the twentieth use case for those operations.

All the "FP" quotes are about floating point precision, which matters more for training and finely detailed models, especially FP64. Integer based matrix math comes up plenty often in optimized cases, which are becoming more and more the norm, especially with China's research on shrinking models while retaining accuracy metrics.

[–] BrioxorMorbide@lemmings.world 6 points 4 days ago

But giving all the resources to LLMs slows/prevents those useful applications of AI.