Thread

1. 0xbadcafebee · 1791121892 · focus · HN ↗

   Lol, sure, if you quant it to hell (Q2) it'll go real fast...

   They even link to a Q1 quant (Qwen3.8-Flash-Next-GSQ-RCO-Coder-GGUF) with half the experts ripped out. The idea is it'll go much faster and supposedly benches to not-terrible results. But the problem is you can't rely on it for real world long-horizon coding because that's where reasoning comes in, which is why you want the other layers.

   It turns out there's still no free lunch. Either get enough VRAM for a Q4, or use a much smaller model. Lobotomizing a larger model just to say you can run it fast isn't useful.

   1. sigbottle · 1791123622 · focus · HN ↗

      It's interesting though that Q4 seems to be enough, is there a reason that 4 bit floats are good enough for inference?

      1. MaxikCZ · 1791123940 · focus · HN ↗

         New models are trained with 8/4bit quantization in mind. Going from "native" 8 to 4 isnt as big of a step as going from 8 to 4 if native is full bf16.

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