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Spent some time on this today and wanted to compare notes.
I'm comparing two approaches here: keep it simple on M3 Max, or squeeze more out of the stack and deal with the complexity.
What would you pick if you had to live with it for a few months?
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I think the bottleneck is somewhere else. The reported numbers can look compelling, but in ordinary usage I didn't see the same benefit. Not saying you're wrong, just that I wouldn't lock that in as the lesson yet.
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Not sure I agree there. I wouldn't optimize around that first. On my side the bigger swing came from the runtime and not the model.
M3 Max • MLX • local-first
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Interesting, I hadn't tried that. 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.
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Need a little more detail here. Is this local-only or are you routing anything externally?
learning in public
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Interesting, I hadn't tried that. That matches what I saw on mini PC.
M3 Max • MLX • local-first
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I'd approach it in a boring order. Start by verifying the driver/runtime combo, then test with a smaller prompt, then look at quant drift. A lot of these threads end up being one bad config plus a pile of conclusions drawn from it.
I'm happy to test one more variant if you want a second data point.
learning in public
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Interesting, I hadn't tried that. I don't think your conclusion is far off.
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Quote:[quote]I'd approach it in a boring order. Start by verifying the driver/runtime combo, then test with a smaller prompt, then look at quant drift. A lo
Interesting, I hadn't tried that. 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.
If you post your exact settings, people here can probably narrow it down pretty fast.
still benchmarking everything