Google introduces DiffusionGemma, an experimental 26B-parameter open model that uses text diffusion for faster text generation compared to autoregressive models
Our newest open experimental model delivers up to 4x faster inference on dedicated GPUs and opens the door to exploring speed-critical, interactive local workflows.
The Keyword
Context & Ripple Effects
Google has been pursuing inference-speed improvements across the Gemma line: RecurrentGemma targeted faster inference at higher batch sizes, and newer Gemma 4 drafters use speculative decoding to accelerate generation.
DiffusionGemma extends that work by making text diffusion itself the experimental open-model approach, following Google DeepMind’s earlier Gemini Diffusion demonstration. It also fits Google’s recent emphasis on models that can run in local environments.
First-order effects
Developers and researchers can test an open, 26B-parameter text-diffusion model for speed-sensitive local interactive workloads rather than relying solely on autoregressive Gemma variants.
Google gains an additional experimental inference architecture in its open-model portfolio, with a stated speed advantage on dedicated GPUs relative to autoregressive generation.
Second-order effects
Teams evaluating local AI applications may compare diffusion generation against speculative-decoding and recurrent approaches, shifting model selection toward end-to-end latency and hardware fit rather than parameter count alone.
Competing open-model providers face added pressure to expose practical inference-speed improvements, whether through alternative architectures or decoding optimizations.
Third-order effects
If text diffusion proves broadly useful beyond experimental workloads, text-model competition could increasingly center on generation architecture and serving efficiency rather than iterative scaling of autoregressive models alone.
The Gemma family is becoming a vehicle for Google to test and distribute multiple specialized model designs; adoption will depend on whether developers can realize the claimed speed gains in real workloads and hardware configurations.
The trend: Open model ecosystems are moving from a single autoregressive design toward a mix of architectural and decoding techniques aimed at making capable AI cheaper and faster to run locally.
Diffusion is taking over the local/owned compute category by storm. DiffusionGemma architecture is significantly better for running local models. [image]
Gemma goes diffusion! DiffusionGemma with up to 1000+ tokens per second! 🌬️ - Built on Gemma 4 as a 26B MoE model. - 3.8B parameters during inference. - Generates text in 256-token blocks in parallel. - Fits within 18 GB VRAM limits when quantized. - Apache 2.0 [video]
⚡ Google DeepMind just dropped DiffusionGemma, latest experimental open model (Apache 2.0) that generates text up to 4x faster. - Uses diffusion instead of traditional next token autoregressive generation - Generates and refines 256 token blocks in parallel - Achieves up to [vide…
DiffusionGemma is an open, experimental model that brings our text diffusion research to Gemma 4. It's a racehorse 🏇achieving up to 4x faster inference by generating entire blocks of text simultaneously vs predicting token-by-token (word-by-word) output! [video]
So DiffusionGemma is a 26B uniform-state diffusion language model! Very interesting. This might be one of the first open-weight releases of this kind at this scale. (Not forgetting @dvruette's public 10B version)
Say hello to DiffusionGemma. 👋 @GoogleGemma's new open model generates text in parallel, not one token at a time, helping deliver faster, more responsive AI on NVIDIA DGX Spark & RTX GPUs. Get started: https://blogs.nvidia.com/... [image]
Google releases DiffusionGemma.✨ The new 26B-A4B diffusion text model runs locally on 18GB RAM. It supports high-speed text generation, thinking, image, video and 256K context. Run and train via Unsloth Studio. GGUF: https://huggingface.co/... Guide: https://unsloth.ai/... [image…
🎉 SGLang has Day-0 support for DiffusionGemma, a text-diffusion variant of @googlegemma 's Gemma 4 (26B A4B MoE), built for blazing low-batch generation speed! Instead of token-by-token decoding, it denoises blocks of tokens in parallel for much faster generation. 1️⃣ Discrete [i…
This is awesome! I am spending a lot of time on diffusion LLMs these days, so this is perfect timing. I feel like there are so many underexplored research questions around text diffusion. Weight available in HF. [image]
Introducing DiffusionGemma, our first exploration with open diffusion text generation models 🔥Generate blocks of text at a time 🤏26B MoE built on top of Gemma 4 ⚡️Up to 4x faster in popular consumer GPUs 🤗Apache 2.0 Excited to see what the community builds with it! [video]
Because it generates everything at once, DiffusionGemma unlocks new patterns of model behavior. ⚡ Fast: Generates up to 1,000+ tokens a second for up to 4x faster text generation. 💻 Lightweight: Runs smoothly right on 18GB consumer graphics cards. 🧠 Smart editing: Since it [image…
Most large language models predict answers by guessing the single best word to say next, then the next, and so on... 🔎 It's highly capable, but not necessarily fast. The model waits to finish one word before it can think about the next. DiffusionGemma skips the wait. It uses [vid…
Meet DiffusionGemma ⚡ Our latest experimental open model (Apache 2.0) that generates text up to 4x faster. Instead of predicting and typing just one word at a time like most language models, it drafts and refines entire blocks of text simultaneously. Here's how it works 🧵 ↓ [imag…
Meet DiffusionGemma! An experimental open model that explores a fast approach to text generation, released under an Apache 2.0 license. Moving beyond sequential, token-by-token processes to generate entire blocks of text simultaneously. Here's what's new with DiffusionGemma: 👇 [v…
Congrats to @GoogleDeepMind on DiffusionGemma 🎉 A 26B diffusion language model on the Gemma4 backbone, and the first dLLM natively supported in vLLM. It denoises 256-token blocks in parallel instead of generating one token at a time: 1200+ output tok/s at batch size 1 on a
DiffusionGemma is out 🔥 it's compute-bound so 4x faster compared to other Gemma-4 models (1k tok/s on H100) 💨 also great on coding, generate and iterate on any code from 3D generation to front-end ⤵️ [video]
Diffusion nerds are at it again — DiffusionGemma 26B-A4B — unlike previous language diffusion models, this one doesn't suck, and it's very fast — blog.google/innovation-a... [image]
DiffusionGemma refines tokens on a 256 canvas at once, rather than generating one token a time. This makes the text generation problem move from a memory-bound to a compute-bound regime. …