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This keeps coming up in conversation so I figured I'd start a thread.
The more I work with local setups, the more it seems like prefill/decode split decides everything downstream.
Maybe obvious to some of you, but it clicked for me this week.
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Tried to reproduce it just now. My result was mixed: first run looked bad, second run looked normal.
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Thanks, that helps. That matches what I saw on mini PC. The biggest change for me was simplifying the stack instead of adding one more optimization layer.
There are a few different conversations hiding inside this thread, so being explicit about the goal helps.
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I'd approach it in a boring order. Before rebuilding anything, I'd test one clean baseline with Mistral and compare from there. Not glamorous advice, but repeatable baselines save a lot of time.
There are a few different conversations hiding inside this thread, so being explicit about the goal helps.
trying not to rebuild again
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I think the bottleneck is somewhere else. I've seen people blame tokens/sec 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.
If you post your exact settings, people here can probably narrow it down pretty fast.
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I think the bottleneck is somewhere else. 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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My first guess would be this: If you're on TensorRT-LLM, I'd confirm the version first and then reduce variables until the behavior is obvious. A lot of these threads end up being one bad config plus a pile of conclusions drawn from it.
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Need a little more detail here. Are you testing this on 7900 XTX or something else?
small models can still be good
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A couple questions before I guess too hard. Is this local-only or are you routing anything externally?
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Need a little more detail here. Are you testing this on A6000 or something else?