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Spent some time on this today and wanted to compare notes.
I'm comparing two approaches here: keep it simple on B200, or squeeze more out of the stack and deal with the complexity.
Interested in tradeoffs more than fan loyalty.
For context, I'm not chasing leaderboard numbers. I mostly want the system to feel consistent day to day.
still benchmarking everything
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Maybe, but I had the opposite result. I wouldn't optimize around that first. On my side the bigger swing came from the runtime and not the model. Could be a workload difference, but I wouldn't generalize too fast from one setup.
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A couple things I'd check first. On mini PC, the easiest win is often to back off the aggressive settings and see whether the problem survives.
M3 Max • MLX • local-first
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following this because I'm in the same boat.
If you post your exact settings, people here can probably narrow it down pretty fast.
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Appreciate the detail. 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.
still benchmarking everything
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Thanks, that helps. I ended up in about the same place using llama.cpp.
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What exact setup are you running? Is this local-only or are you routing anything externally?
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My first guess would be this: Start by verifying the driver/runtime combo, then test with a smaller prompt, then look at quant drift. Not glamorous advice, but repeatable baselines save a lot of time.