TL;DR: Google released DiffusionGemma, an open Apache 2.0 diffusion-based LLM that generates text up to 4x faster than autoregressive models, hitting 1,000+ tokens/sec on a single H100 and fitting in 18 GB VRAM. It trades some accuracy for speed. Here is what that means in practice.
What DiffusionGemma Actually Is
Google DeepMind released DiffusionGemma, the first production-grade open-weight model that applies discrete diffusion to text generation. The same family of techniques behind image generators like Stable Diffusion, now applied to language.
Instead of predicting one token at a time left-to-right, DiffusionGemma fills a 256-token block with noise and iteratively refines the entire block across multiple denoising passes until confidence thresholds are met. It commits roughly 15-20 tokens per forward pass on average, not one.
This is a fundamentally different compute pattern from everything shipping in production today.
