DiffusionGemma

DiffusionGemma is an experimental open model from Google DeepMind in the Gemma family that generates text by diffusion instead of autoregression. Rather than predicting one token at a time, it denoises whole spans in parallel, built on Gemma 4 and Gemini Diffusion research. The payoff is speed: up t

Canonical version: DiffusionGemma.

DiffusionGemma is an experimental open model from Google DeepMind in the Gemma family that generates text by diffusion instead of autoregression. Rather than predicting one token at a time, it denoises whole spans in parallel, built on Gemma 4 and Gemini Diffusion research. The payoff is speed: up to ~4× faster output, exceeding 1,000 tokens/second on a single NVIDIA H100.

How diffusion text generation differs

Standard Large Language Models (LLMs) are autoregressive: strictly sequential, token-by-token, bottlenecked on memory bandwidth. DiffusionGemma uses discrete text diffusion with bi-directional attention to generate many tokens per forward pass (a "canvas" of 256), then iteratively refines them, enabling self-correction and better global consistency, and shifting the bottleneck from memory bandwidth to raw compute.

Architecture

  • ~26B total parameters (25.2B), 3.8B active: AI Mixture of Experts (MoE) (8 of 128 experts active + 1 shared)
  • Encoder-decoder: an autoregressive encoder caches the prompt context, paired with a diffusion decoder
  • Up to 256K token Context Window; 262K vocabulary; sliding window 1024
  • 15–20 tokens generated per forward pass (>1100 tok/s on H100 in FP8)
  • Multimodal input (text, image, video → text); ~550M vision params; built-in thinking mode
  • Supports NVIDIA NVFP4 (4-bit float) on Blackwell GPUs

Performance (instruction-tuned)

  • MMLU Pro: 77.6% · GPQA Diamond: 73.2% · LiveCodeBench v6: 69.1% · MATH-Vision: 70.5%

Availability

References


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