quant choices for 70B local

[homelabhenry]

What worked for me was pretty simple. On 5090, the easiest win is often to back off the aggressive settings and see whether the problem survives. A lot of these threads end up being one bad config plus a pile of conclusions drawn from it.

[turboscope]

Interesting, I hadn't tried that. The pattern around tokens/sec has been pretty similar for me. Worth rechecking your defaults though, because one bad assumption can make the rest of the thread look confusing. The logs were somehow calmer than I was.

[cacheworks]

Thanks, that helps. I ended up in about the same place using PyTorch. Worth rechecking your defaults though, because one bad assumption can make the rest of the thread look confusing.

[relaynest82]

Did a small comparison this afternoon. On M3 Max with llama.cpp, I saw basically the same behavior. The main thing that moved was latency, not quality.

[cacheworks]

I think the bottleneck is somewhere else. I've seen people blame VRAM headroom when the real issue was much more mundane. Could be a workload difference, but I wouldn't generalize too fast from one setup.

[vrambandit]

Interesting, I hadn't tried that. That matches what I saw on dual 3090. I got better results once I stopped chasing one metric and just looked at whether the workflow felt usable.