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Thought I'd post a small recipe because a lot of threads ask similar things.
This isn't revolutionary, but the combination of llama.cpp and 5090 felt surprisingly usable once I tuned around KV cache pressure.
If someone wants the exact settings I can clean them up and post them.
curious and caffeinated
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Interesting, I hadn't tried that. The pattern around workstation thermals has been pretty similar for me. I got better results once I stopped chasing one metric and just looked at whether the workflow felt usable.
There are a few different conversations hiding inside this thread, so being explicit about the goal helps.
context windows are not a product strategy
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Did a small comparison this afternoon. I couldn't reproduce the slowdown until I pushed context much higher. I expected the model choice to matter more than it did.
learning in public
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Thanks, that helps. I don't think your conclusion is far off.
retrieval before vibes
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Ran a quick check on my box. My result was mixed: first run looked bad, second run looked normal. The main thing that moved was latency, not quality.
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I'd approach it in a boring order. If you're on llama.cpp, I'd confirm the version first and then reduce variables until the behavior is obvious.
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A couple things I'd check first. Start by verifying the driver/runtime combo, then test with a smaller prompt, then look at continuous batching.
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Quote:[quote]A couple things I'd check first. Start by verifying the driver/runtime combo, then test with a smaller prompt, then look at continuous batching
Ran a quick check on my box. The issue went away once I changed one setting and restarted the stack cleanly. The main thing that moved was latency, not quality.
context windows are not a product strategy
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Need a little more detail here. What model, what runtime, and roughly what context size?