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 building the Gemma line around specialized and deployable variants: CodeGemma and RecurrentGemma targeted coding and higher-throughput inference, while TranslateGemma broadened task-specific open models. More recently, Google also released Gemma 4 drafters using speculative decoding to speed generation.
DiffusionGemma extends a parallel track first visible in Gemini Diffusion, moving the diffusion approach from a waitlisted demonstration toward an experimental open model. Its relevance is therefore less a one-off model release than a test of whether a non-autoregressive design can be practical for local, latency-sensitive work.
First-order effects
Developers and researchers can evaluate a 26B open diffusion model for interactive text-generation workloads where inference speed is the immediate differentiator.
Google expands the Gemma ecosystem beyond autoregressive generation and speculative-decoding add-ons, while keeping the release explicitly experimental.
Second-order effects
Teams choosing local-model stacks gain another route to lower response latency on dedicated GPUs, potentially changing model selection from a primarily quality-and-size trade-off to one that weighs generation architecture.
The release raises pressure on autoregressive open-model providers to improve serving efficiency through techniques such as multi-token prediction or to demonstrate why their quality, tooling, or hardware compatibility offsets slower generation.
Third-order effects
If diffusion text models sustain competitive output quality and operational reliability, text-model deployment could diversify into architecture-specific tiers rather than defaulting to autoregressive models for nearly every generative task.
Open releases make performance claims easier for the ecosystem to test across hardware and workloads; that evaluation will determine whether diffusion becomes a durable local-inference option or remains a specialized experimental path.
The trend: This is part of a broader shift from scaling model parameters alone toward architecture and inference innovations that make capable models faster and more usable in local interactive applications.
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]
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 singl…
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…
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]
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…
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]
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]
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…
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]
Google released DiffusionGemma, a new open model with up to 4x faster output! > Instead of predicting word-by-word, it generates entire blocks of text simultaneously. This lets the model self-correct and format complex markdown in real time. Same performance as Gemma 4 is a big […
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)
⚡ 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…
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…
🎉 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 …
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]
Diffusion is taking over the local/owned compute category by storm. DiffusionGemma architecture is significantly better for running local models. [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. …
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]