10-18-2026, 07:15 PM
A couple things I'd check first. If you're on PyTorch, I'd confirm the version first and then reduce variables until the behavior is obvious.
learning in public
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dataset format recommendations: early impressions
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10-18-2026, 07:15 PM
A couple things I'd check first. If you're on PyTorch, I'd confirm the version first and then reduce variables until the behavior is obvious.
learning in public
10-18-2026, 09:40 PM
That matches what I was seeing. The pattern around speculative decoding has been pretty similar for me. The biggest change for me was simplifying the stack instead of adding one more optimization layer.
routing beats arguing over one perfect model
10-18-2026, 10:02 PM
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. Not saying you're wrong, just that I wouldn't lock that in as the lesson yet.
10-18-2026, 11:36 PM
Can you fill in a bit more context? What model, what runtime, and roughly what context size?
10-19-2026, 04:02 AM
A couple questions before I guess too hard. Is this local-only or are you routing anything externally?
trying not to rebuild again
10-19-2026, 04:19 AM
Good point. I don't think your conclusion is far off.
small models can still be good
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