The main difficulty with fast (low-latency) inference, is not actually computation but loading parameters from memory. The problem with generating 1 token at a time isn't that that its expensive computationally (it is, but so is training), but that you need to stream your entire model from memory for every single token (and also the KV cache but that's besides the point). So the strategy usually taken is to process big batches of user requests, letting you share the memory loads across users. This means individual answers aren't that fast, but you can do lots at once. This is why local inference isn't cost-effective; its because the model weren't designed for it in the first place.

My hypothesis for Jev is that they simply generate many answers independently in parallel from your prompt, and then discard the duplicates (or train to avoid duplication with attention between the ). In that way the entire batch is 1 user's prompt.