upvote
It's not Googles fault. It's our fault, llama.cpp doesn't support it, vllm can't really offload to host ram and while huggingface transformers supports it, it only can do f16 quant for MoEs like Diffusion Gemma. Unusable for low spec machines where diffusion really could speed up things.

We're sleeping on a amazing model right in front of our eyes. With diffusion models you can properly ban phrases. You can decensor and deslopiffy the model output without touching a single weight or doing a single backwards pass.

reply
Their preprint showed it worked well with small batch size, but most prod deployments right now have rather large batch sizes from serving multiple users
reply
That's a great answer, thanks. I'd missed that detail.
reply
Honestly, a lot of those issues could be remedied with more optimized kernels, which LLMs could definitely write quite well now. I wouldn't be surprised if some deployments of Gemini Flash switched to diffusion for cases where there is low utilization, since Google showed you don't need to retrain an autoregressive model to rework it for diffusion inference.
reply
> since Google showed you don't need to retrain an autoregressive model to rework it for diffusion inference

IIRC you do need to retrain the model, just closer to the level of finetuning rather than e.g. pretraining from scratch. Which also means that the model will perform differently with a different capability profile.

reply
If that's true, that's the real answer, or more like anty-answer because you're saying basically: hey, nothing should stop them.
reply
Google's gonna Google...
reply
I wonder if it'd work better as a local model
reply
This talk serves as sort on update on where they are with it: https://youtu.be/r305-aQTaU0?si=j3IqG9BAT0uumPeA

At a high level: It doesn't benefit from batching to reduce memory bandwidth in the same way that a "standard" autoregressive model does, which ends up meaning that it's great at reducing latency for individual/low-concurrency users and delivers better outcomes on certain tasks but much more expensive per token at scale.

reply
Difussion Gemma performed a lot worse than its equivalent autoregressive counterpart (it was particularly bad because rather than a sort of constant-ish gap you might see between 2 model classes, the gap seemed to worsen the harder the task/benchmark) and the speed benefits got negated at scale. Not really surprising we haven't heard much.
reply
They probably did not get it to work well?
reply
Good at research, terrible at product?
reply