It runs four times faster on consumer GPUs
Chubby♨️#955
DiffusionGemma is our new experimental open model with up to 4x faster output on dedicated GPUs.
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.
Users are excited about DiffusionGemma's parallel text generation and up to 4x faster inference via bi-directional attention, praising it as a profound architectural leap and banger from Google.
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!
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/unsloth/diffusiongemma-26B-A4B-it-GGUF Guide: https://unsloth.ai/docs/models/diffusiongemma
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: 👇
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 🧵 ↓
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!
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
Want 4x faster local inference on dedicated GPUs for your interactive apps? DiffusionGemma is an experimental, open 26B MoE model that generates entire blocks of text simultaneously instead of token-by-token.
By shifting the local decoding bottleneck from memory-bandwidth to compute, it hits speeds over 700 tokens/sec on a single NVIDIA RTX 5090 GPU. This diffusion unlocks unique local workflows like real-time inline editing, code infilling, and instant self-correction.
📥 Download the Apache 2.0 weights on @HuggingFace: https://goo.gle/4xqzKTA
📖 Read the full technical announcement on the blog: https://goo.gle/4ursgwI
@googlegemma Google Deepmind once again delivering when it comes to open-source! 🙏🥰
You can run DiffusionGemma locally on 18GB RAM via our GGUFs: https://huggingface.co/unsloth/diffusiongemma-26B-A4B-it-GGUF
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: 👇
We're releasing DiffusionGemma as an open model under an Apache 2.0 license for anyone to experiment with.
Download the model weights on @huggingface, and learn more about DiffusionGemma → http://goo.gle/3Sy0Is7
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 processes larger amounts of information at once, it can easily fill in blanks, format code, and fix its own errors in real time.
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 processes larger amounts of information at once, it can easily fill in blanks, format code, and fix its own errors in real time.
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 single H200 (FP8).
Built on model runner v2's ModelState plus the existing speculative decoding path, with minimal scheduler or runner changes. FP8 and NVFP4 checkpoints are on the @RedHat_AI hub. Thanks to the @GoogleDeepMind, @RedHat_AI, and @NVIDIAAI teams!
🔗 http://vllm.ai/blog/2026-06-10-diffusion-gemma
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: 👇
Model weights available on Hugging Face under Apache 2.0 license, read more here: https://blog.google/innovation-and-ai/technology/developers-tools/diffusion-gemma-faster-text-generation/
DiffusionGemma, our experimental open model released under an Apache 2.0 license, explores text diffusion, an exceptionally fast approach to text generation.
Here’s how DiffusionGemma accelerates development:
+ Faster token output: By shifting the bottleneck from memory bandwidth to raw compute, the model generates up to 4x faster token output on dedicated GPUs + Accessible hardware footprint: Activates just 3.8B parameters during inference, fitting comfortably within 24GB-VRAM high-end consumer GPUs when quantized + Novel workflows: Parallel token generation enables self-correction, making it ideal for code infilling, in-line editing, and non-linear structures
DiffusionGemma prioritizes speed over raw quality and accelerates best on compute-bound hardware (like @NVIDIAAI GPUs). Standard @GoogleGemma 4 remains recommended for production quality and memory-bound devices.
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.
DiffusionGemma is our new experimental open model with up to 4x faster output on dedicated GPUs.
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.
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 ⤵️
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 "diffusion" to generate text by refining noise step by step — drafting and error-correcting whole blocks simultaneously. This makes it incredibly fast, and helpful for editing complex math and code.
DiffusionGemma is our new experimental open model with up to 4x faster output on dedicated GPUs.
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.
Local models can't benefit from batch parallelism as easily, but you can still parallelise over the token axis. So here's an open text diffusion model! >1000 tokens/sec for accelerated tokenmaxxing, yay!🫨
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: 👇
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: 👇
Meet DiffusionGemma: 4x faster text generation, released under Apache 2.0 license🔥
https://huggingface.co/google/diffusiongemma-26B-A4B-it
DiffusionGemma brings high intelligence and lightning fast ⚡️ inference to local developers (>1100 tok/s on a single H100)!
I'm excited to see what people will do with this model - and what improvements people can build on top (better samplers maybe??).
So unbelievably proud of the hard work the team put in to get this out!🪐🪐🪐
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!
It runs four times faster on consumer GPUs
Chubby♨️#955DiffusionGemma is our new experimental open model with up to 4x faster output on dedicated GPUs.
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.
