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Posting this in case it saves someone else a little time.
I'm helping a small team set up a local workflow. We don't need a giant stack, but we do care about MoE serving.
Would rather learn from your scars than collect my own all at once.
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Can you fill in a bit more context? Do you see the same thing with a smaller batch or shorter prompt?
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Maybe, but I had the opposite result. I've seen people blame speculative decoding when the real issue was much more mundane.
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I'd start with the simpler stack.
small models can still be useful
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I tested this tonight. I couldn't reproduce the slowdown until I pushed context much higher. The surprise was that memory pressure showed up before throughput collapsed.
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Need a little more detail here. Do you see the same thing with a smaller batch or shorter prompt?
If you post your exact settings, people here can probably narrow it down pretty fast.
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What worked for me was pretty simple. On M3 Max, the easiest win is often to back off the aggressive settings and see whether the problem survives.
mostly local
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Appreciate the detail. I ended up in about the same place using vLLM.
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I'd start with the simpler stack.