4 ms·

I will gladly download a terabyte of model data if that’s what it would take, your numbers don’t scare me!

• webmaven 5y ago
  Large models generally require beefier GPUs and more RAM. Wide (not just deep) models require more GPU cores, total model size requires more RAM. Some models require multiple GPUs to train and/or run. Some require non-consumer server GPUs. These are non-trivial hardware investments if purchased, and non-trivial cloud bills if rented, even at preemptible spot pricing.
  • brobinson 5y ago
    I'd pay money for pretrained models that can run on consumer gaming GPUs (8gb vram) for things like this. I wonder if anyone is selling these.
    • moffkalast 5y ago
      The main issue is that what neural nets are doing is just very fancy heuristic compression of vast amounts of data into a few gigs of vram (and a useful way to access it), so the smaller the model is the less it'll be able to do.
      • brobinson 4y ago
        Most of the pretrained models I see for the stuff I'm working on (Coqui, ESRGAN, etc.) are really tiny in size. I'm sure the much better models are bigger but still fit under 8gb vram. It seems like there could be a market for models that fit into 8gb vram, but I'm not familiar enough with users of ML to say for sure.