The Qwen3.6-27B NIM Container is a deployable inference container for serving Qwen3.6-27B, a third-party multimodal dense model capable of processing text, image, and video inputs for text generation.
Qwen3.6-27B NIM Container Overview
Description
The Qwen3.6-27B NIM Container is a deployable inference container for serving Qwen3.6-27B, a third-party multimodal dense model capable of processing text, image, and video inputs for text generation. The container provides an OpenAI-compatible API for self-hosted deployment, powered by the SGLang backend across supported NVIDIA GPU platforms.
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 Qwen3.6-27B 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 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:
NGC 04/23/2026 via Qwen3.6-27B NIM Container on NGC
Program Classes:
The Qwen3.6-27B NIM Container includes the following model:
| Model Name & Link | Use Case | How to Pull the Model |
|---|---|---|
| Qwen3.6-27B | Multimodal text generation, agentic coding, visual understanding, reasoning | Automatic (embedded in container) |
Deployment Details
The Qwen3.6-27B NIM Container exposes an OpenAI-compatible chat completions API for seamless integration into existing applications and workflows.
API Endpoints:
/v1/chat/completions— Chat completions (streaming and non-streaming)/v1/models— List available models/health/ready— Health check
Operating System: Linux
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):
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 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 thanks to our industry standard APIs. Visit the NVIDIA NIM documentation for release documentation, deployment guides and more.
NVIDIA Developer Community Forum
For support, visit the NVIDIA Developer Community Forum.