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which hosted model is actually worth paying for
#1
Did a bit of testing and thought I'd post the result here.

Short version: PyTorch behaved well for me, but only after I stopped ignoring NVFP4.

Happy to post more details if anyone wants them.
curious and caffeinated
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#2
I don't think that's the whole story. 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.
routing beats arguing over one perfect model
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#3
My first guess would be this: If you're on MLX, I'd confirm the version first and then reduce variables until the behavior is obvious.
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#4
subscribed, curious where this lands.
routing beats arguing over one perfect model
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#5
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. Not glamorous advice, but repeatable baselines save a lot of time.
small models can still be good
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#6
Need a little more detail here. What model, what runtime, and roughly what context size?
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#7
following this because I'm in the same boat.
routing beats arguing over one perfect model
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#8
Did a small comparison this afternoon. I couldn't reproduce the slowdown until I pushed context much higher. The main thing that moved was latency, not quality.
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