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Wanted to compare notes with people doing similar things.
My current setup is PyTorch with B200, and I keep running into workstation thermals.
Would appreciate practical advice rather than theory.
The part that surprised me was how much the boring details mattered more than the model choice. At this point the real bottleneck is my patience.
mostly local
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I'd approach it in a boring order. If you're on vLLM, I'd confirm the version first and then reduce variables until the behavior is obvious. Once you know the plain path works, then add the faster path back in piece by piece.
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A couple questions before I guess too hard. What model, what runtime, and roughly what context size?
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subscribed, curious where this lands.
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Quote:Quote:subscribed, curious where this lands.
Good point. I ended up in about the same place using PyTorch. 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.
Apple Silicon • MLX mostly
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Good point. The pattern around structured output has been pretty similar for me.
post logs or it didn't happen
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Maybe, but I had the opposite result. 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.
curious and caffeinated
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that fixed it for me too.
