Overview
diffusiongemma-26B-A4B-it is an open-weight text diffusion model from Google DeepMind, built on the Gemma 4 architecture.
- License: Apache 2.0
- Context length: 256K tokens
- Languages: Multilingual
- Minimum hardware: ~15 GB VRAM
- Strengths: reasoning and on-device inference
Highlights
- Text diffusion model — parallel, bidirectional generation built on the Gemma 4 architecture.
- Code infilling — attends to code on both sides of the cursor before writing.
- Multimodal input — accepts vision and audio alongside text.
- 256K context, A4B MoE — ~3.8B active parameters keep the footprint modest.
What it is good at
The parallel, bidirectional approach gives diffusiongemma-26B-A4B-it a structural edge on tasks where the model needs to see the whole output at once rather than guess left to right.
- Code infilling — filling a gap in the middle of a file, where the model can attend to code on both sides of the cursor before it writes.
- Inline editing — you change one sentence and it produces a local replacement quickly, drawing on its code and reasoning capabilities.
- Multimodal and multilingual work — its vision and audio inputs handle screenshots or clips, and multilingual support covers prompts across many languages with a 256K context window for long documents.
Running it locally
The model is 25.8B parameters with a 256K context length. Because only ~3.8B parameters are active at inference, a 4-bit quant of the 26B-A4B class fits in roughly 18GB of VRAM, which puts it within reach of a 24GB consumer card like an RTX 4090 or 5090. Budget extra headroom for the KV cache, since long contexts grow memory use beyond the weights alone. Pull the weights with:
huggingface-cli download google/diffusiongemma-26B-A4B-it
You can load it through Transformers or vLLM for scripted use, or open it in Atomic Chat with one click and start chatting without touching a config file.
License
diffusiongemma-26B-A4B-it is released under the apache-2.0 license. That permits commercial use, modification, and redistribution, so you can run it locally, fine-tune it, and ship it inside products without a usage fee.