DiffusionGemma in Python: Generate Text 4x Faster

Every large language model you have used until now writes the way you read a sentence out loud: one token, then the next, then the next, left to right. That single design choice is why long answers feel slow. DiffusionGemma, which Google DeepMind released on June 9, 2026, throws that assumption out. Instead of predicting one token at a time, it starts from a block of masked noise and denoises the whole block in parallel, refining many positions at once over a handful of diffusion steps. The result is text generation that Google measures at roughly four times the throughput of a comparable autoregressive model on the same hardware.

It blew up fast. Within days the weights crossed 300K downloads on Hugging Face, demo Spaces for code generation and OCR correction appeared, and r/LocalLLaMA filled with people watching the model 'paint' an answer into place rather than typing it. The model is a 26B-parameter Mixture-of-Experts built on the Gemma 4 architecture, but it only activates about 3.8B parameters per step, and it ships under a permissive Apache 2.0 license. Quantized to NVFP4 it fits in roughly 18GB of VRAM, so it runs on a single RTX 4090 or 5090.