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Not sure exactly what setup you are running, in theory yes, higher temperature for both model means higher chance of overlap and thus less rejections -> faster sampling (but worse quality overall).

However, if you have higher temperature but still are operating under a top-k sampling where k is small, not sure it's going to translate to any noticeable difference, since this will make your actual distributions very much non-uniform.

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This is with Together's API via OpenRouter, running DeepSeek V3 0324 and Kimi K2 0905.

I didn't set a top-k. So it seems like Together must be doing something weird in their speculative decoding implementation.

Oh in that case there is definitely a top-k or top-p behind the scene, it might just not be exposed to the user as a param they can change through their API. I haven’t heard of anyone running a LLM in prod with actual pure sampling

I see. That's slightly unfortunate. In principle, increasing temperature flattens out the distribution but the ordering between different tokens' probabilities remain the same, so setting a top-k shouldn't break my test. Can't say the same for top-p though. And all of this is probably too deep into the provider's implementation details for me to make assumptions on.

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