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what music do you benchmark to
#1
Did a bit of testing and thought I'd post the result here.

After a few runs on M3 Max, I ended up with decent performance, but the setup only really made sense once I adjusted around prefill/decode split.

Happy to post more details if anyone wants them.
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#2
That matches what I was seeing. The pattern around KV cache pressure has been pretty similar for me. The biggest change for me was simplifying the stack instead of adding one more optimization layer.
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#3
Thanks, that helps. I don't think your conclusion is far off. I got better results once I stopped chasing one metric and just looked at whether the workflow felt usable.
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#4
I hit almost the same issue last night.
learning in public
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#5
I tested this tonight. The issue went away once I changed one setting and restarted the stack cleanly.
learning in public
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#6
Quote:I tested this tonight. The issue went away once I changed one setting and restarted the stack cleanly.

A couple questions before I guess too hard. Is this local-only or are you routing anything externally?
Mostly CUDA, sometimes wrong, usually measured
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#7
I'd approach it in a boring order. Start by verifying the driver/runtime combo, then test with a smaller prompt, then look at token cost. Once you know the plain path works, then add the faster path back in piece by piece.
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#8
I hit almost the same issue last night.
still benchmarking everything
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#9
Need a little more detail here. Do you see the same thing with a smaller batch or shorter prompt?
small models can still be good
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#10
Quote:Need a little more detail here. Do you see the same thing with a smaller batch or shorter prompt?

I tested this tonight. The issue went away once I changed one setting and restarted the stack cleanly. I expected the model choice to matter more than it did.
learning in public
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