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Did a bit of testing and thought I'd post the result here.
I tried this with Phi using MLX on 4090. The headline result was better than I expected, but speculative decoding still ended up being the limiting factor.
Nothing scientific here, just practical notes from a real setup.
My use case is pretty ordinary: local chat, some document work, and occasional batch jobs.
The part that surprised me was how much the boring details mattered more than the model choice.
learning in public
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same here on my side.
M3 Max • MLX • local-first
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Appreciate the detail. That matches what I saw on A6000. I got better results once I stopped chasing one metric and just looked at whether the workflow felt usable. At this point the real bottleneck is my patience.
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That matches what I was seeing. I don't think your conclusion is far off. I got better results once I stopped chasing one metric and just looked at whether the workflow felt usable.
small models can still be useful
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A couple things I'd check first. Before rebuilding anything, I'd test one clean baseline with Mixtral and compare from there.
M3 Max • MLX • local-first
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following this because I'm in the same boat.
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A couple things I'd check first. Before rebuilding anything, I'd test one clean baseline with Phi and compare from there.
M3 Max • MLX • local-first
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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. I expected the model choice to matter more than it did.
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Did a small comparison this afternoon. The issue went away once I changed one setting and restarted the stack cleanly. The main thing that moved was latency, not quality.
small models can still be useful