Model-Free NIM

Description:

Model-Free NIM is a NIM microservice container designed to support a broad range of LLMs. It includes a production-ready inference runtime with optimized inference engines from NVIDIA and the community—such as vLLM. When provided with an LLM at container runtime, stored in a Hugging Face format, the NIM identifies the model’s architecture and quantization, selects the appropriate inference backend, applies preconfigured settings for the model and backend, and begins serving the model for inference. For more information, see the NIM documentation and supported model architectures.

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

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.

The use of any models in this container is governed by the NVIDIA Open Model Agreement in addition to any terms which govern the specific models used.

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

Deployment Geography:

Global

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.

Documentation

Visit the NIM Container LLM page for release documentation, deployment guides, and more.

Program Classes:

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

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

Inference Engine Name	Use Case	How to Pull the Inference Engine
vLLM Inference Engine	Execution for building custom large language models	Automatic

Security Common Vulnerabilities and Exposures (CVEs)

Please review the Security Scanning tab on NGC to view the latest security scan results.\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.

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 Container page for release documentation, deployment guides and more NIM Container LLM.

Enterprise Support

Get access to knowledge base articles and support cases or submit a ticket.

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When provided with an LLM at container runtime, stored in a Hugging Face format, the NIM identifies the model’s architecture and quantization, selects the appropriate inference backend, applies preconfigured settings for the model and backend, and begins serving the model for inference. For more information, see the NIM documentation and supported model architectures.\n\nThe container components are ready for commercial/non-commercial use.\n\n## **License/Terms of Use:**\n\n**GOVERNING TERMS:** The NIM container is governed by the [NVIDIA Software License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/) and the [Product-Specific Terms for NVIDIA AI Products](https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/).\n\nThe use of any models in this container is governed by the [NVIDIA Open Model Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-agreement/) in addition to any terms which govern the specific models used.\n\nYou are responsible for ensuring that your use of any provided models complies with all applicable laws.\n\n\n## **Deployment Geography:**\n\nGlobal \n\n## **Deployment Details:**\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**Documentation**\n\nVisit the [NIM Container LLM](https://docs.nvidia.com/nim/large-language-models/latest/introduction.html) page for release documentation, deployment guides, and more.\n\n## Program Classes:\n| Inference Engine Name | Use Case | How to Pull the Inference Engine|\n| :--- | :--- | :--- |\n| vLLM Inference Engine | Execution for building custom large language models | Automatic |\n\n## Security Common Vulnerabilities and Exposures (CVEs)\nPlease review the Security Scanning tab on NGC to view the latest security scan results.\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\n## **Ethical Considerations:**\n\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\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 Container page for release documentation, deployment guides and more [NIM Container LLM](https://docs.nvidia.com/nim/large-language-models/latest/introduction.html).\n\n\n## Enterprise Support\n\nGet access to knowledge base articles and support cases or [submit a ticket](https://www.nvidia.com/en-us/data-center/products/ai-enterprise-suite/support/).",["D",1787182858764],[275,273,271,269,265,263,261,258,256,277,253],"/api/image/nim/nvidia/containers/model-free-nim",13501634040,{"_310":311,"_312":313,"_314":315,"_316":26,"_317":318},"hasSignedTag",[301,302],[299],[],[295],[],{"_91":296,"_123":296,"_124":297,"_126":298},"signed images",{"_128":298,"_123":296},"system",{"_91":122,"_123":122,"_124":300,"_126":127},{"_128":127,"_123":122},{"_91":308,"_123":308,"_124":309,"_126":306},{"_91":303,"_123":304,"_124":305,"_126":306},"NVIDIA NIM","nimmcro_nvidia_nim",{"_128":307,"_123":304},"feature","nvidia_nim","NVIDIA AI Enterprise Supported",{"_128":127,"_123":308},"governingTerms","

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

The use of any models in this container is governed by the NVIDIA Open Model Agreement.

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

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

The use of any models in this container is governed by the NVIDIA Open Model Agreement.

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

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It helps track your activity on the platform, including how you interact with their embedded services on other websites.","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",4,[653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668,669,670,671,672,673,674,675,676,677],[624,625,626,627,628],{"_462":537,"_91":537,"_90":104,"_435":648},{"_462":511,"_91":511,"_90":104,"_435":643},{"_462":638,"_91":638,"_90":104,"_435":639},{"_462":633,"_91":633,"_90":104,"_435":634},{"_462":629,"_91":629,"_90":104,"_435":630},"nvidia.partners",[631],{"_254":632,"_468":629,"_469":26,"_90":505},"edge","app.hushly.com",[635],{"_254":636,"_468":633,"_469":26,"_90":637},"*CORS","This domain is associated with Hushly, a platform that provides lead conversion solutions for content marketing by using personalization and content engagement strategies.","github.com",[640],{"_254":641,"_468":638,"_469":26,"_90":642},"_octo","This domain is owned by Github. 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These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website performance.",[736,737,738,739,740,741,742,743,744,745,746,747,748,749,750,751,752,753,754,755,756,757,758,759,760,761,762,763,764],[716,717,718,719],{"_462":731,"_91":731,"_90":104,"_435":732},{"_462":726,"_91":726,"_90":104,"_435":727},{"_462":524,"_91":524,"_90":104,"_435":723},{"_462":638,"_91":638,"_90":104,"_435":720},[721],{"_254":722,"_468":638,"_469":134,"_90":642},"_gh_sess",[724],{"_254":725,"_468":524,"_469":134,"_90":531},"YSC","bizible.com",[728],{"_254":729,"_468":726,"_469":26,"_90":730},"_BUID","Bizible is a marketing attribution software that tracks the performance of marketing efforts and connects them to revenue, primarily used for B2B environments.","a.usbrowserspeed.com",[733],{"_254":734,"_468":731,"_469":26,"_90":735},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_254":806,"_468":583,"_469":26,"_90":807},{"_254":791,"_468":805,"_469":134,"_90":792},{"_254":774,"_468":586,"_469":26,"_90":699},{"_254":774,"_468":687,"_469":26,"_90":699},{"_254":803,"_468":583,"_469":26,"_90":804},{"_254":791,"_468":589,"_469":134,"_90":792},{"_254":801,"_468":583,"_469":26,"_90":802},{"_254":791,"_468":586,"_469":134,"_90":792},{"_254":799,"_468":686,"_469":26,"_90":800},{"_254":791,"_468":708,"_469":134,"_90":792},{"_254":771,"_468":586,"_469":26,"_90":773},{"_254":797,"_468":583,"_469":134,"_90":798},{"_254":795,"_468":583,"_469":26,"_90":796},{"_254":793,"_468":583,"_469":134,"_90":794},{"_254":791,"_468":682,"_469":134,"_90":792},{"_254":789,"_468":583,"_469":26,"_90":790},{"_254":787,"_468":583,"_469":26,"_90":788},{"_254":785,"_468":583,"_469":134,"_90":786},{"_254":783,"_468":691,"_469":26,"_90":784},{"_254":771,"_468":682,"_469":26,"_90":773},{"_254":781,"_468":583,"_469":26,"_90":782},{"_254":779,"_468":583,"_469":134,"_90":780},{"_254":777,"_468":583,"_469":26,"_90":778},{"_254":775,"_468":583,"_469":26,"_90":776},{"_254":774,"_468":583,"_469":26,"_90":699},{"_254":771,"_468":772,"_469":26,"_90":773},{"_254":769,"_468":583,"_469":26,"_90":770},{"_254":767,"_468":583,"_469":134,"_90":768},{"_254":765,"_468":583,"_469":26,"_90":766},"_ga_xxxxxxxxxx","Used by Google Analytics to identify and track an individual session with your device.","s_ppvl","This cookie is set by Adobe Analytics to store information on the percentage of the page displayed to the end user.","coveo_visitorId","This cookie name is associated with Coveo which is used to store a unique user ID for marketing purposes and to track the number of distinct users who submitted a query or clicked an item, counting them only once even if they did the same query or clicked the same item over the course of many visits.","_dd_s","marketplace.nvidia.com","Datadog Cookie used to group all events generated from a unique user session across multiple pages. 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