Gemma 4 31B IT model which, is an open multimodal model built by Google DeepMind that handles text and image inputs, can process video as sequences of frames, and generates text output.
Gemma 4 31B IT Container Overview
Description:
The Gemma 4 31B IT NIM container packages the Google Gemma 4 31B IT model for deployment through NVIDIA NGC as a Downloadable NIM. Gemma 4 31B IT is a dense multimodal decoder-only transformer in the Eevee / Gemma 4 family designed for reasoning, coding assistance, multimodal understanding, and agentic workflows.
The container components are ready for commercial or 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 gemma-4-31B-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 this model is governed by the NVIDIA Open Model License Agreement. Additional Information:Apache License, Version 2.0.
You are responsible for ensuring that your use of NVIDIA provided models complies with all applicable laws.
Deployment Geography:
Global
Release Date:
Build.NVIDIA.com: 04/02/2026 via link
NGC via link: For the most recent update, refer to the right-hand side of the NGC catalog page.
Gemma 4 31B IT NIM:
The Gemma 4 31B IT Container includes the Gemma 4 31B IT model.
| Model Name & Link | Use Case | How to Pull the Model |
|---|---|---|
| Gemma 4 31B IT https://build.nvidia.com/google/gemma-4-31b-it | Designed for reasoning, coding assistance, multimodal understanding, and agentic workflows. | Automatic |
| Gemma 4 31B IT NGC model | Designed for reasoning, coding assistance, multimodal understanding, and agentic workflows. | Automatic |
Deployment Details:
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.
Security Common Vulnerabilities and Exposures (CVEs)
Please review the Security Scanning tab on NGC to view the latest security scan results. For certain open-source vulnerabilities listed in the scan results, NVIDIA provides a response in the form of a Vulnerability Exploitability eXchange (VEX) document. The VEX information can be reviewed and downloaded from the Security Scanning tab.
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.
Users are responsible for model inputs and outputs. Users are responsible for ensuring safe integration of this model, including implementing guardrails as well as other safety mechanisms, prior to deployment.
Please report quality, risk, security vulnerabilities or NVIDIA AI Concerns here.
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