The GPT-OSS-120b-Turbo NIM container packages OpenAI's GPT-OSS-120b large language model, a sparse Mixture of Experts (MoE) architecture with 120B total parameters and 5.1B active parameters, as an NVIDIA NIM microservice.
GPT-OSS-120B-Turbo NIM Container Overview
Description:
The GPT-OSS-120b-Turbo NIM container packages OpenAI's GPT-OSS-120b large language model — a sparse Mixture of Experts (MoE) architecture with 120B total parameters and 5.1B active parameters — as an NVIDIA NIM microservice. The container delivers an OpenAI-compatible API for high-reasoning tasks including agentic workflows, function calling, code execution, RAG pipelines, and structured output generation, with configurable reasoning effort levels (low, medium, high).
The container components are ready for 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 GPT-OSS-120b Model Card.
License/Terms of Use:
GOVERNING TERMS: The NIM container is governed by the NVIDIA Software License Agreement and the Product-Specific Terms for NVIDIA AI Products; and the use of the model is governed by the NVIDIA Open Model License Agreement.
Additional Information: Apache License 2.0.
You are responsible for ensuring that your use of NVIDIA provided models complies with all applicable laws.
Deployment Geography:
Global
Release Date:
NGC 5/28/2026 via link
Huggingface 08/07/2025 via link
Program Classes:
The GPT-OSS-120B-Turbo NIM Container includes the following model:
| Model Name & Link | Use Case | How to Pull the Model |
|---|---|---|
| GPT-OSS-120b-Turbo | General-purpose reasoning, agentic workflows, function calling, code execution, and structured output generation | Automatic |
Deployment Details:
Visit the NIM Container LLM page for release documentation, deployment guides, and more.
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. 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.
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 NIM Container page for release documentation, deployment guides and more.
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