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GOVERNING DOWNLOAD TERMS: Use of this container is governed by the NVIDIA Software License Agreement and Product-Specific Terms for NVIDIA AI Products.

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Muse Glimmer NIM Overview

Description:

A deployable inference container for serving Muse Glimmer, a 30B vision-language model (VLM) with a dense multimodal causal language architecture and a dedicated perception encoder, through industry-standard APIs. It supports local, always-on agentic workflows on NVIDIA GPU-accelerated systems, accepting interleaved text and image inputs to produce text output without requiring cloud infrastructure or network access. For video workflows, applications should submit individual video frames as image inputs; the embedded VLM is not explicitly optimized for native temporal video understanding.

The container components are ready for commercial or non-commercial use.

This container image is classified as a Pre-Release candidate (NVIDIA Software License Agreement).

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 Muse Glimmer Model Card.

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.

You are responsible for ensuring that your use of NVIDIA provided models complies with all applicable laws.

Deployment Geography:

Global

Release Date:

NGC: 08/10/2026

Program Classes:

The NIM container includes the following model:

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Model Name & Link	Use Case	How to Pull the Model
Muse Glimmer	Local agentic workflows with multimodal input and text output	Automatic (embedded in container)

Deployment Details:

The NIM container exposes industry-standard inference APIs for integration into applications and workflows.

API Endpoints:

/v1/chat/completions — Chat completions
/v1/models — List available models
/health/ready — Health check

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.

References:

Container Version(s)

2.1.2-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 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 NIM documentation for release documentation, deployment guides and more.

NVIDIA Developer Community Forum

Get access to community knowledge base articles and support cases (NVIDIA Developer Forums).

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It supports local, always-on agentic workflows on NVIDIA GPU-accelerated systems, accepting interleaved text and image inputs to produce text output without requiring cloud infrastructure or network access. For video workflows, applications should submit individual video frames as image inputs; the embedded VLM is not explicitly optimized for native temporal video understanding.\n\n*The container components are ready for commercial or non-commercial use.*\n\n*This container image is classified as a Pre-Release candidate ([NVIDIA Software License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/)).*\n\n## Third-Party Community Consideration\nThe 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 [Muse Glimmer Model Card](https://huggingface.co/meta-models/Muse-Glimmer-30B).\n\n## License/Terms of Use:\n**GOVERNING DOWNLOAD TERMS:** Use of this container is governed by the [NVIDIA Software License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/) and [Product-Specific Terms for NVIDIA AI Products](https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/).\n\nYou are responsible for ensuring that your use of NVIDIA provided models complies with all applicable laws.\n\n## Deployment Geography:\nGlobal\n\n## Release Date:\n**NGC:** 08/10/2026\n\n## Program Classes:\nThe NIM container includes the following model:\n\n| Model Name & Link | Use Case | How to Pull the Model |\n|:------------------|:---------|:---------------------|\n| Muse Glimmer | Local agentic workflows with multimodal input and text output | Automatic (embedded in container) |\n\n## Deployment Details:\nThe NIM container exposes industry-standard inference APIs for integration into applications and workflows.\n\n**API Endpoints:**\n* `/v1/chat/completions` — Chat completions\n* `/v1/models` — List available models\n* `/health/ready` — Health check\n\nOur 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.\n\n## References:\n* [Muse Glimmer Model Card](https://huggingface.co/meta-models/Muse-Glimmer-30B)\n* [NVIDIA NIM for Vision Language Models Documentation](https://docs.nvidia.com/nim/vision-language-models/latest/introduction.html)\n\n## Container Version(s)\n2.1.2-variant\n\n## Security Common Vulnerabilities and Exposures (CVEs)\nPlease review the Security Scanning tab on NGC to view the latest security scan results.\n\nFor 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.\n\n## Ethical Considerations:\nNVIDIA 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.\n\nPlease report quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).\n\n## Get Help\n\n### Getting started with the NIM\nDeploying and integrating the NIM is straightforward thanks to our industry standard APIs. Visit the [NIM documentation](https://docs.nvidia.com/nim/vision-language-models/latest/introduction.html) for release documentation, deployment guides and more.\n\n### NVIDIA Developer Community Forum\nGet access to community knowledge base articles and support cases ([NVIDIA Developer Forums](https://forums.developer.nvidia.com/)).","displayName","Muse Glimmer",["D",1787617904602],"publisher","Meta","latestVersion","versions",[205,201,203,137],"imageUrl","/api/image/nim/meta/containers/muse-glimmer","rawImageUrl","canGuestDownload","latestVersionSizeInBytes",9091965953,"licenseTerms",{"_264":265,"_266":16,"_267":268,"_269":20,"_270":271},"isMultinodeEnabled","isMultiArchitecture","hasSignedTag","featureLabels",[258],"displayLabels",[255],"policyLabels",[],"systemLabels",[248],"products",[],"hasNimMetadata",{"_218":249,"_250":249,"_251":252,"_72":253},"signed images","value","filter",{"_254":253,"_250":249},"system","field",{"_218":256,"_250":256,"_251":257,"_72":78},"NSPECT-Y5NF-UYZK",{"_254":78,"_250":256},{"_218":259,"_250":260,"_251":261,"_72":262},"NVIDIA NIM","nimmcro_nvidia_nim",{"_254":263,"_250":260},"feature","nvidia_nim","governingTerms","

GOVERNING DOWNLOAD TERMS: Use of this container is governed by the NVIDIA Software License Agreement and Product-Specific Terms for NVIDIA AI Products.

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Under certain privacy laws, you have the right to direct us not to \"sell\" or \"share\" your personal information for targeted advertising. To opt-out of the \"sale\" and \"sharing\" of personal information through cookies, you must opt-out of optional cookies using the toggles below. To opt out of the \"sale\" and \"sharing\" of data collected by other means (e.g., online forms) you must also update your data sharing preferences through the NVIDIA Preference Center.

Click on the different category headings below to find out more and change the settings according to your preference. You cannot opt out of Required Cookies as they are deployed to ensure the proper functioning of our website (such as prompting the cookie banner and remembering your settings, etc.). By clicking \"Save and Accept\" or \"Decline All\" at the bottom, you consent to the use of cookies and other tools as described in our Cookie Policy in accordance with your settings and accept our Terms of Service (which contains important waivers). 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These cookies may store a unique ID so that our system will remember you when you return. 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