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Yes to both.

The thing is that I can either use the q8 context, or have not enough context window, so I just live with whatever degradation there is. The same can be said about the IQ4_NL. I would not go any lower though.

As for the draft count, indeed that depends on what you do with it, but for coding, reverse engineering and that kind of stuff it does pay off in my testing, though 5 is really pushing it, but the 4090 has so much compute.

Last logline I saw scroll by right now had 47% acceptance rate for 4th and 28% for 5th, but not sure how representative that is. I think when tuning 3.6, I saw more like 33%? But not 100% sure.

by hedgehog1 hours ago|

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Same, I have one workload where on 3.6 drafting 6 tokens is the fastest setting.

by hypfer1 hours ago|

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I wonder if we could take a page out of the Solar power book and do MPPT but for draft count. Constantly sweep through possible values to find the best result for the current conditions.

Though it might turn out that that doesn't offer any benefits. Has it been tried yet?

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EDIT:

LLM pointed me towards this thing I'm not going to read

https://arxiv.org/abs/2512.11280

"AdaSD: Adaptive Speculative Decoding for Efficient Language Model Inference"

Some higher level nerd please go have a look at that. I have important meetings and golf classes to attend.

by petu36 minutes ago|

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Deepseek's DSpark does dynamically adjust speculated token count per user/completion.

https://arxiv.org/abs/2607.05147

But they do so to maximise total throughput, I don't think there's reason to do that for batch=1.

by ndriscoll58 minutes ago|

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Don't you just need to occasionally try n+1 for your current setting? Then record acceptance rates. The nature of the thing is that if you try n, you already have a good sampling of how well m works for all m<n, right?

by hedgehog33 minutes ago|

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This is all a pretty active area of research, both adaptive drafting, different draft approaches (DFlash etc), I expect to see really big improvements land in the popular runtimes this year.

by freehorse1 hours ago|

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I have found that q8 kv quantisation is really bad in anything requiring some kind of literal retrieval even at relatively shorter contexts (5-10k). Eg it would mess up names, titles of papers etc that were deeper in the context.