Hungary 🇭🇺🇪🇺

Developer behind the Eternity for Lemmy android app.

@bazsalanszky@lemmy.ml is my old account, migrated to my own instance in 2023.

  • 34 Posts
  • 218 Comments
Joined 1 year ago
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Cake day: July 2nd, 2023

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  • From what I’ve seen, it’s definitely worth quantizing. I’ve used llama 3 8B (fp16) and llama 3 70B (q2_XS). The 70B version was way better, even with this quantization and it fits perfectly in 24 GB of VRAM. There’s also this comparison showing the quantization option and their benchmark scores:

    1000029570

    Source

    To run this particular model though, you would need about 45GB of RAM just for the q2_K quant according to Ollama. I think I could run this with my GPU and offload the rest of the layers to the CPU, but the performance wouldn’t be that great(e.g. less than 1 t/s).