The Qwen3.8-27B NIM Container is a model-specific inference container for serving Qwen3.8-27B, a third-party dense vision-language model that accepts text, image, and video inputs and produces text.
Qwen3.8-27B NIM Overview
Description
The Qwen3.8-27B NIM Container is a model-specific inference container for serving Qwen3.8-27B, a third-party dense vision-language model that accepts text, image, and video inputs and produces text. The container exposes industry-standard APIs for self-hosted deployment and uses an SGLang-based serving backend.
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 Qwen3.8-27B Model Card from Qwen.
License/Terms of Use:
GOVERNING DOWNLOAD TERMS: Use of this 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 Agreement.
ADDITIONAL INFORMATION: Apache 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: 08/24/2026 via Qwen3.8-27B NIM Container on NGC
Program Classes:
The Qwen3.8-27B NIM Container includes the following model:
| Model Name & Link | Use Case | How to Pull the Model |
|---|---|---|
| Qwen3.8-27B | Multimodal text generation, coding, visual understanding, video understanding, and reasoning | Automatic (embedded in container) |
Deployment Details:
The container exposes OpenAI-compatible inference and health endpoints:
/v1/chat/completions— Chat completions, including streaming responses/v1/models— Available model information/health/ready— Service readiness
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.
Reference(s):
Container Version(s):
Container Version: 2.1.1-variant
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.
Please make sure you have proper rights and permissions for all input image and video content; if image or video includes people, personal health information, or intellectual property, the image or video generated will not blur or maintain proportions of image subjects included.
Please report quality, risk, security vulnerabilities or NVIDIA AI Concerns here.
Get Help
Getting started with the NIM
Deploying and integrating the NIM is straightforward through industry-standard APIs. Visit the NVIDIA NIM for Vision Language Models documentation for deployment and release guidance.
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