On June 10 Google AI Blog launched DiffusionGemma — a 26B-total / 3.8B-active MoE experimental model, open-sourced under Apache 2.0. It is not a routine iteration of the Gemma 4 family, but a paradigm leap: the diffusion mechanism from image/video is brought into text generation, decoding 256 tokens in parallel in a single forward pass, claiming 1000+ t/s on H100 and 700+ t/s on RTX 5090, a maximum 4× speedup over the Gemma 4 autoregressive version.
Technical core. DiffusionGemma takes a "placeholder-token canvas + multi-iteration denoising" path, fundamentally different from autoregressive "left-to-right" token-by-token inference. All tokens during generation can attend to the full text — naturally friendly to non-linear tasks like online editing, code infill, math graphs, and amino acid sequences. The MoE design + quantization lets it fit in 18GB of VRAM, runnable on a single RTX 4090/5090. The cost is that overall output quality is lower than standard Gemma 4, and Google explicitly positions it as "experimental" and "born for local, low-concurrency, interactive scenarios."
Ecosystem synergy. Google shipped a full engineering stack at the same time: Hugging Face, vLLM (Red Hat integration), MLX, Unsloth, NVIDIA NeMo all support it on day-0, with llama.cpp coming soon. Native NVFP4 4-bit floating point gives Hopper/Blackwell an additional throughput bump. Unsloth has already released Sudoku and 3D SVG fine-tuning demos.
Commentary. This path continues the research line of Gemini Diffusion, but the more notable thing isn't whether it will replace the GPT/Gemini main line, but that together with Nemotron-Labs Diffusion and DFlash it forms the 2026 inflection point where "diffusion language models go from papers to usable tools." In high-QPS cloud scenarios, autoregressive models still win by batching; but for individual developers, laptop users, and single-GPU workstation players, 4× speedup is a real qualitative change. Text diffusion may not become mainstream, but it will become a powerful complement for edge, real-time coding, and local IDE scenarios.