This may be the future of local models.

The thing is, diffusion models perform somewhat worse than autoregressive on text. So you lose some performance.

Speed is the big advantage. Autoregressive when doing local inference is mostly memory bound; you're doing one token at a time, for each token you need to load all weights. MTP helps a bit by allowing you to draft tokens in a smaller model and then verify them in parallel with the larger model, allowing you to do a few computations for every memory load, but because you're still doing tokens sequentially and need to discard invalid drafted tokens, you can only get so much speedup.

For hosted models, however, you can batch many token generations together, fully utilizing all of the compute while no longer being bottlenecked on memory bandwidth. So they are already operating at close to max efficiency.

So, diffusion kind of loses its beneifit in hosted models. Sure, maybe you could pay more to have slightly lower latency responses by doing diffusion for one user at a time instead of autoregressive for many in parallel. But given that it also reduces accuracy, it's hard to see where you'd really want that. Unless they're able to bring it up to par with autoregressive, it seems like it's a bit of a dead out outside of local models where you're generally just doing one thing at a time.

Almost certainly not if things remain as they are. The reason there's been little traction is the quality gap between diffusion and autoregressive models is pretty stark. I mean just look at the benchmarks here. Large dropoffs, with the hardest benchmarks seeing the largest drops. On top of that, almost all the speed benefits of diffusion models become negated at scale. So this is only attractive for local model development and almost everyone training local models still care about pound for pound quality and inference efficiency at scale.