Ministral 3 14B Instruct 2512 FP8 model is an efficient language model with vision capabilities, this instruct post-trained version in **FP8 precision** is fine-tuned for instruction tasks, making it ideal for chat and instruction-based use cases.
Ministral 3 14B Instruct 2512 Container Overview
Description:
This container houses the Ministral 3 14B Instruct 2512 FP8 model, which is the largest model in the Ministral 3 family, offering frontier capabilities and performance comparable to its larger Mistral Small 3.2 24B counterpart. A powerful and efficient language model with vision capabilities, this instruct post-trained version in FP8 precision is fine-tuned for instruction tasks, making it ideal for chat and instruction-based use cases.
The FP8 quantization enables deployment with reduced memory requirements while maintaining model quality, capable of fitting in 24GB of VRAM, and less if further quantized.
The container components are ready for commercial use.
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 2.0 License.
You are responsible for ensuring that your use of NVIDIA provided models complies with all applicable laws.
Deployment Geography:
Global
Release Date:
Ministral 3 14B Instruct 2512 NIM:
The Ministral 3 14B Instruct 2512 Container includes the Ministral 3 14B Instruct 2512 model.
| Model Name & Link | Use Case | How to Pull the Model |
|---|---|---|
| Ministral 3 14B Instruct 2512 https://build.nvidia.com/mistralai/ministral-14b-instruct-2512 | Ideal for chat and instruction-based use cases. | 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.
Container Version(s):
nvcr.io/nim/mistralai/ministral-14b-instruct-2512:1.7.0
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. When downloaded or used in accordance with our terms of service, 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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Getting started with the NIM
Deploying and integrating the NIM is straightforward thanks to our industry standard APIs. Visit the VLM NIM page for release documentation, deployment guides and more.
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