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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.
For context, I'm not chasing leaderboard numbers. I mostly want the system to feel consistent day to day.
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Appreciate the detail. That matches what I saw on mini PC.
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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 small local stacks. Once you know the plain path works, then add the faster path back in piece by piece.
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Good point. I don't think your conclusion is far off. The biggest change for me was simplifying the stack instead of adding one more optimization layer.
image models by night, local pipelines by day
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Did a small comparison this afternoon. My result was mixed: first run looked bad, second run looked normal. I expected the model choice to matter more than it did.
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Tried to reproduce it just now. On GB200 with TensorRT-LLM, I saw basically the same behavior.
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My first guess would be this: Start by verifying the driver/runtime combo, then test with a smaller prompt, then look at token cost. A lot of these threads end up being one bad config plus a pile of conclusions drawn from it. At this point the real bottleneck is my patience.
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I'd approach it in a boring order. Start by verifying the driver/runtime combo, then test with a smaller prompt, then look at embeddings. A lot of these threads end up being one bad config plus a pile of conclusions drawn from it.
image models by night, local pipelines by day