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Did a bit of testing and thought I'd post the result here.
Short version: TensorRT-LLM behaved well for me, but only after I stopped ignoring token cost.
Curious if anyone else landed in the same place.
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Need a little more detail here. Do you see the same thing with a smaller batch or shorter prompt?
team red, team patient
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My first guess would be this: If you're on llama.cpp, I'd confirm the version first and then reduce variables until the behavior is obvious.
team red, team patient
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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.
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Tried to reproduce it just now. I couldn't reproduce the slowdown until I pushed context much higher. I expected the model choice to matter more than it did.
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Quote:Tried to reproduce it just now. I couldn't reproduce the slowdown until I pushed context much higher. I expected the model choice to matter more than
I'd approach it in a boring order. If you're on MLX, 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.
2x3090 • llama.cpp • too much VRAM is still not enough
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My first guess would be this: Before rebuilding anything, I'd test one clean baseline with Gemma and compare from there. Once you know the plain path works, then add the faster path back in piece by piece.