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KV cache compression worth it
#1
Thought I'd post a small recipe because a lot of threads ask similar things.

Just sharing a setup that ended up working reasonably well: Gemma, TensorRT-LLM, 5090, with a lot of attention on prefill/decode split.

Feel free to tear it apart if you see something dumb.
small models can still be good
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#2
subscribed, curious where this lands.
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#3
Interesting, I hadn't tried that. That matches what I saw on M3 Max. Worth rechecking your defaults though, because one bad assumption can make the rest of the thread look confusing.
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#4
I hit almost the same issue last night.
curious and caffeinated
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#5
Good point. The pattern around reranking has been pretty similar for me. Worth rechecking your defaults though, because one bad assumption can make the rest of the thread look confusing. I keep promising myself this is the last rebuild.
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#6
Maybe, but I had the opposite result. I get why that seems right, but on 7900 XTX the bottleneck felt elsewhere.
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#7
Quote:Maybe, but I had the opposite result. I get why that seems right, but on 7900 XTX the bottleneck felt elsewhere.

Can you fill in a bit more context? Is this local-only or are you routing anything externally?
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#8
I'd approach it in a boring order. On Threadripper box, 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.

I'm happy to test one more variant if you want a second data point.
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#9
My first guess would be this: Start by verifying the driver/runtime combo, then test with a smaller prompt, then look at batch size. Once you know the plain path works, then add the faster path back in piece by piece.
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#10
same here on my side.
LoRA tinkerer • dataset janitor
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