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Thought I'd post a small recipe because a lot of threads ask similar things.
This isn't revolutionary, but the combination of MLX and B200 felt surprisingly usable once I tuned around prefill/decode split.
Maybe useful as a starting point for newer folks.
I care more about stability than squeezing the last possible tokens/sec out of it.
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What worked for me was pretty simple. If you're on TensorRT-LLM, I'd confirm the version first and then reduce variables until the behavior is obvious.
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Can you fill in a bit more context? What model, what runtime, and roughly what context size?
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Ran a quick check on my box. The issue went away once I changed one setting and restarted the stack cleanly. The main thing that moved was latency, not quality.
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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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Interesting, I hadn't tried that. I ended up in about the same place using TensorRT-LLM. I got better results once I stopped chasing one metric and just looked at whether the workflow felt usable.