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DiffusionGemma Technical Report

arXiv:2608.00146CLAI31 jul 20261 min readHN ×4PDF

Google turned an autoregressive Gemma 4 model into a diffusion model that writes 256-token blocks at once.

1456

tokens/sec

Average decoding speed on one H100

About 7x faster than the same model writing one token per pass

How it works

  1. One token per pass

    Standard LLMs emit one token per forward pass. At batch size 1 the GPU mostly waits on memory transfers.

  2. Fine-tune into diffusion

    Start from Gemma 4 26B A4B. Teach it to denoise 256-token blocks, then use RL to cut denoising steps.

  3. 20 tokens per pass

    About 12 denoising steps per block give roughly 1,500 tokens/sec. Benchmark scores are somewhat below the original model.

encodecontextrefineclean blocknext blockend tokenPromptCausalencoder cacheRandom256-token canvasBidirectionaldenoisingAppendto cacheResponsetextencodecontextrefineclean blocknext blockend tokenPromptCausalencoder cacheRandom256-token canvasBidirectionaldenoisingAppendto cacheResponsetext
From the paper