The really interesting thing to me was that they didn’t need to train this model from scratch they just used their existing MOE checkpoint:
“To convert a decoder-only model (Gemma 4 26B A4B) into a denoiser, we can make use of something it is not directly using when generating tokens, namely the logits of all tokens!”
What makes me hopeful about this release is that possibly this same conversion can be applied to other open models and we might see a bunch of diffusion versions of existing local models. It’s exciting stuff!
Appealing results... do we think there is scope to close the accuracy gap against AR models? or even leverage the "Bidirectional Reasoning and Self-Correction" into an overall advantage?
I'm very interested in Diffusion text models. The concept of taking noise and adding words starting randomly all over the response, and filling in the noise from there on breaks my brain.
I'm sure I have a fundamental misunderstanding of the technology, though.
Diffusion text models for me is the more interesting type of LLMs for local usage, as it really makes good use of single GPUs for single responses, rather than auto-regressive ones, and is a lot faster! Probably the fastest model I've been able to run so far, ending up doing ~670 tok/s (depending on the type of text) on a Pro 6000
Normally people have feelings about things before they are able to put them into words, I would imagine if you were asked a question like "what city would you most like to visit" then unless you've already thought about it a lot, then you would have to do substantial non-verbal thinking before you can come up with an answer, and once you have the answer you may respond "my favorite city is X" and you decided what X would be before you started the sentence.
(to finish your thought) Which is important for consumer hardware to be better suited to running these models. Cloud providers are already able to batch as many requests as they want together to improve resource utilization, so they will not see a big benefit from diffusion models.
The really interesting thing to me was that they didn’t need to train this model from scratch they just used their existing MOE checkpoint:
“To convert a decoder-only model (Gemma 4 26B A4B) into a denoiser, we can make use of something it is not directly using when generating tokens, namely the logits of all tokens!”
What makes me hopeful about this release is that possibly this same conversion can be applied to other open models and we might see a bunch of diffusion versions of existing local models. It’s exciting stuff!
I'm sure I have a fundamental misunderstanding of the technology, though.
Diffusion text models for me is the more interesting type of LLMs for local usage, as it really makes good use of single GPUs for single responses, rather than auto-regressive ones, and is a lot faster! Probably the fastest model I've been able to run so far, ending up doing ~670 tok/s (depending on the type of text) on a Pro 6000
My entire brain runs on sentences and words since I have no inner eye or whatever. So my thinking and writing both work kind of forward only.
I wouldn’t have thought that was too unique. But maybe it is?
Would DiffusionGemma be suitable candidate for DFlash 2?