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quant support gaps: what are you seeing?
#1
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.
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#2
Appreciate the detail. I don't think your conclusion is far off.
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#3
Quote:A couple questions before I guess too hard. Are you testing this on B200 or something else?

Appreciate the detail. I don't think your conclusion is far off. Worth rechecking your defaults though, because one bad assumption can make the rest of the thread look confusing.
Mostly CUDA, sometimes wrong, usually measured
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#4
I think the bottleneck is somewhere else. I've seen people blame reranking when the real issue was much more mundane. Not saying you're wrong, just that I wouldn't lock that in as the lesson yet.
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#5
Quote:Can you fill in a bit more context? Are you testing this on 5090 or something else?

My first guess would be this: If you're on llama.cpp, I'd confirm the version first and then reduce variables until the behavior is obvious. Not glamorous advice, but repeatable baselines save a lot of time. The logs were somehow calmer than I was.
Mostly CUDA, sometimes wrong, usually measured
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#6
Quote:Appreciate the detail. I don't think your conclusion is far off.

following this because I'm in the same boat.
Mostly CUDA, sometimes wrong, usually measured
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#7
A couple things I'd check first. Start by verifying the driver/runtime combo, then test with a smaller prompt, then look at prefill/decode split. A lot of these threads end up being one bad config plus a pile of conclusions drawn from it.
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#8
Need a little more detail here. Is this local-only or are you routing anything externally?
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#9
I hit almost the same issue last night.
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#10
Can you fill in a bit more context? Are you testing this on 5090 or something else?

If you post your exact settings, people here can probably narrow it down pretty fast.
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