The DiffusionGemma-4-26B-A4B-IT model is an open-weights multimodal generative model developed by Google DeepMind that processes text, image, and video inputs to produce text output via discrete diffusion.
DiffusionGemma-4-26B-A4B-IT Overview
Description:
The DiffusionGemma-4-26B-A4B-IT NIM container houses the DiffusionGemma-4-26B-A4B-IT model for deployment through NVIDIA NGC as a Downloadable NIM. The DiffusionGemma-4-26B-A4B-IT model is an open-weights multimodal generative model developed by Google DeepMind that processes text, image, and video inputs to produce text output via discrete diffusion.
The container components are ready for commercial and non-commercial use.
Third-Party Community Consideration
The model embedded in the container is not owned or developed by NVIDIA. This model has been developed and built to a third-party's requirements for this application and use case; see link to Non-NVIDIA diffusiongemma-4-26B-A4B-it Model Card.
License/Terms of Use:
GOVERNING TERMS: The NIM container is governed by the NVIDIA Software License Agreement and Product-Specific Terms for NVIDIA AI Products. Use of the model is governed by the NVIDIA Open Model Agreement. Additional Information: Apache 2.0. Gemma Terms of Use and Gemma Prohibited Use Policy.
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Deployment Geography:
Global
Release Date:
NGC 06/10/2026 via link
Build.nvidia.com 06/10/2026 via link
Program Classes:
The DiffusionGemma-4-26B-A4B-IT NIM Container includes the following model:
| Model Name & Link | Use Case | How to Pull the Model |
|---|---|---|
| diffusiongemma-4-26B-A4B-it | Multimodal reasoning, coding, function calling, long-context analysis, image understanding, video understanding, multilingual conversation, and multi-turn conversation. | Automatic |
Deployment Details:
This Downloadable NIM provides an OpenAI-compatible inference service for DiffusionGemma-4-26B-A4B-IT.
API Endpoints:
/v1/chat/completions- Chat completions/v1/models- List available models/health/ready- Health check
Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.
Ethical Considerations:
NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. Developers should work with their internal developer team to ensure these software components meet requirements for the relevant industry and use case and address unforeseen product misuse.
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