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.
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.
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.