End of Support — \"This artifact is no longer supported. NVIDIA strongly recommends artifacts that are up to date and supported\"

What Is NVIDIA NIM?

NVIDIA NIM, part of NVIDIA AI Enterprise, is a set of easy-to-use microservices designed to speed up generative AI deployment in enterprises. Supporting a wide range of AI models, including NVIDIA AI foundation and custom models, it ensures seamless, scalable AI inferencing, on-premises or in the cloud, leveraging industry standard APIs.

Qwen2.5 is the latest series of Qwen large language models. This model is instruction-tuned language models of 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2:

Significantly more knowledge and has greatly improved capabilities in coding and mathematics, thanks to our specialized expert models in these domains.
Significant improvements in instruction following, generating long texts (over 8K tokens), understanding structured data (e.g, tables), and generating structured outputs especially JSON. More resilient to the diversity of system prompts, enhancing role-play implementation and condition-setting for chatbots.
Long-context Support up to 128K tokens and can generate up to 8K tokens.
Multilingual support for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.

NVIDIA NIM offers prebuilt containers for large language models (LLMs) that can be used to develop chatbots, content analyzers—or any application that needs to understand and generate human language. Each NIM consists of a container and a model and uses a CUDA-accelerated runtime for all NVIDIA GPUs, with special optimizations available for many configurations. Whether on-premises or in the cloud, NIM is the fastest way to achieve accelerated generative AI inference at scale.

High Performance Features

NVIDIA NIM for LLMs abstracts away model inference internals such as execution engine and runtime operations. NVIDIA NIM for LLMs provides the most performant option available whether it be with TRT-LLM, vLLM or others.

Scalable Deployment: NVIDIA NIM for LLMs is performant and can easily and seamlessly scale from a few users to millions.
Advanced Language Models: Built on cutting-edge LLM architectures, NVIDIA NIM for LLMs provides optimized and pre-generated engines for a variety of popular models.
Flexible Integration: Easily incorporate the microservice into existing workflows and applications. NVIDIA NIM for LLMs provides an OpenAI API compatible programming model and custom NVIDIA extensions for additional functionality.
Enterprise-Grade Security: Data privacy is paramount. NVIDIA NIM for LLMs emphasizes security by using safetensors, constantly monitoring and patching CVEs in our stack and conducting internal penetration tests.

Applications

Chatbots & Virtual Assistants: Empower bots with human-like language understanding and responsiveness.
Content Generation & Summarization: Generate high-quality content or distill lengthy articles into concise summaries with ease.
Sentiment Analysis: Understand user sentiments in real-time, driving better business decisions.
Language Translation: Break language barriers with efficient and accurate translation services.
And many more… The potential applications of NVIDIA NIM for LLMs are vast, spanning across various industries and use-cases.

Getting started with NVIDIA NIM

Deploying and integrating NVIDIA NIM is straightforward thanks to our industry standard APIs. Visit the NIM Container LLM page for release documentation, deployment guides and more.

Security Vulnerabilities in Open Source Packages

Please review the Security Scanning (LINK) tab 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 (LINK) tab.

Get Help

NVIDIA Developer Community Forum

For support, Visit the NVIDIA Developer Community Forum

NVIDIA NIM Documentation

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

Governing Terms

The NIM container is governed by the NVIDIA Software License Agreement; and the Product Specific Terms for AI Products; and the use of this model is governed by the [NVIDIA AI Foundation Models Community License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-ai-foundation-models-community-license-agreement/#:~:text=This%20license%20agreement%20(%E2%80%9CAgreement%E2%80%9D,algorithms%2C%20parameters%2C%20configuration%20files%2C).

You are responsible for ensuring that your use of NVIDIA AI Foundation Models complies with all applicable laws.

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NVIDIA strongly recommends artifacts that are up to date and supported\"\n\n## What Is NVIDIA NIM?\n\n\nNVIDIA NIM, part of NVIDIA AI Enterprise, is a set of easy-to-use microservices designed to speed up generative AI deployment in enterprises. Supporting a wide range of AI models, including NVIDIA AI foundation and custom models, it ensures seamless, scalable AI inferencing, on-premises or in the cloud, leveraging industry standard APIs.\n\n\nQwen2.5 is the latest series of Qwen large language models. This model is instruction-tuned language models of 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2:\n* Significantly more knowledge and has greatly improved capabilities in coding and mathematics, thanks to our specialized expert models in these domains.\n* Significant improvements in instruction following, generating long texts (over 8K tokens), understanding structured data (e.g, tables), and generating structured outputs especially JSON. More resilient to the diversity of system prompts, enhancing role-play implementation and condition-setting for chatbots.\n* Long-context Support up to 128K tokens and can generate up to 8K tokens.\n* Multilingual support for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.\n\n\nNVIDIA NIM offers prebuilt containers for large language models (LLMs) that can be used to develop chatbots, content analyzers—or any application that needs to understand and generate human language. Each NIM consists of a container and a model and uses a CUDA-accelerated runtime for all NVIDIA GPUs, with special optimizations available for many configurations. Whether on-premises or in the cloud, NIM is the fastest way to achieve accelerated generative AI inference at scale.\n\n\n### High Performance Features\n\n\nNVIDIA NIM for LLMs abstracts away model inference internals such as execution engine and runtime operations. NVIDIA NIM for LLMs provides the most performant option available whether it be with TRT-LLM, vLLM or others.\n\n- Scalable Deployment: NVIDIA NIM for LLMs is performant and can easily and seamlessly scale from a few users to millions. \n- Advanced Language Models: Built on cutting-edge LLM architectures, NVIDIA NIM for LLMs provides optimized and pre-generated engines for a variety of popular models. \n- Flexible Integration: Easily incorporate the microservice into existing workflows and applications. NVIDIA NIM for LLMs provides an OpenAI API compatible programming model and custom NVIDIA extensions for additional functionality.\n- Enterprise-Grade Security: Data privacy is paramount. NVIDIA NIM for LLMs emphasizes security by using safetensors, constantly monitoring and patching CVEs in our stack and conducting internal penetration tests.\n\n\n### Applications\n\n\n- Chatbots & Virtual Assistants: Empower bots with human-like language understanding and responsiveness. \n- Content Generation & Summarization: Generate high-quality content or distill lengthy articles into concise summaries with ease.\n- Sentiment Analysis: Understand user sentiments in real-time, driving better business decisions.\n- Language Translation: Break language barriers with efficient and accurate translation services.\n- And many more… The potential applications of NVIDIA NIM for LLMs are vast, spanning across various industries and use-cases.\n\n\n## Getting started with NVIDIA NIM\n\n\nDeploying and integrating NVIDIA NIM is straightforward thanks to our industry standard APIs. Visit 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\n## Security Vulnerabilities in Open Source Packages\n\n\nPlease review the Security Scanning [(LINK)](https://catalog.ngc.nvidia.com/orgs/nim/teams/qwen/containers/qwen-2.5-7b-instruct/security) tab to view the latest security scan results.\n\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 [(LINK)](https://catalog.ngc.nvidia.com/orgs/nim/teams/qwen/containers/qwen-2.5-7b-instruct/security) tab.\n\n\n \n\n\n## Get Help\n\n\n### NVIDIA Developer Community Forum\n\nFor support, Visit the [NVIDIA Developer Community Forum](https://forums.developer.nvidia.com/)\n\n### NVIDIA NIM Documentation\n\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\n \n\n\n## Governing Terms\n\n\nThe 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 AI Products](https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/); and the use of this model is governed by the [NVIDIA AI Foundation Models Community License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-ai-foundation-models-community-license-agreement/#:~:text=This%20license%20agreement%20(%E2%80%9CAgreement%E2%80%9D,algorithms%2C%20parameters%2C%20configuration%20files%2C). \n\n**You are responsible for ensuring that your use of NVIDIA AI Foundation Models complies with all applicable laws.**",["D",1738016866755],"Qwen",[256,325,326,327],"/api/image/nim/qwen/containers/qwen-2.5-72b-instruct",7967259557,"hasSignedTag",[319],[309,310,311],[],[305],[303],{"_128":129,"_94":130,"_91":131,"_104":132,"_133":304},[135,136,137],{"_94":306,"_149":306,"_150":307,"_152":308},"signed images",{"_154":308,"_149":306},"system",{"_94":315,"_149":316,"_150":317,"_152":153},{"_94":148,"_149":148,"_150":314,"_152":153},{"_94":312,"_149":312,"_150":313,"_152":153},"NSPECT-8GMK-32J9",{"_154":153,"_149":312},{"_154":153,"_149":148},"Automotive / Transportation","indus_automotive_transportation",{"_154":318,"_149":316},"industry",{"_94":320,"_149":321,"_150":322,"_152":323},"NVIDIA NIM","nimmcro_nvidia_nim",{"_154":324,"_149":321},"feature","nvidia_nim","1","1.0","1.0.0",{"_221":1447,"_223":1448},"ngcApps",[1400],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1381":1382,"_1383":1384,"_1385":1386,"_1387":1388,"_1389":339,"_253":365,"_1390":1391,"_1392":51,"_1393":123,"_1394":123,"_1395":1396,"_1397":123,"_1398":1399},"amplitude",{"_1378":1379,"_1002":-7,"_1380":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0yJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1372":1373,"_1374":1375,"_1376":1377},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1794440178099,"1.31.0","defaultSearchSuggestions",{"_1272":447,"_1273":1274,"_1275":1276,"_1277":1278},"announcements",[],"consentState",{"_403":1262,"_1263":26,"_1264":26,"_1265":123,"_1266":1267},"consentConfig",{"_385":386,"_387":388,"_389":390,"_391":392,"_393":394,"_395":396,"_397":398,"_399":400,"_401":402,"_403":404},"showContactAdmin",1794440178100,[],[],[379,380,381,382,383,384],{"_40":61,"_42":66,"_44":60,"_46":-5,"_48":-5,"_50":51},{"_40":61,"_42":63,"_44":60,"_46":-5,"_48":-5,"_50":51},{"_40":54,"_42":59,"_44":60,"_46":-5,"_48":-5,"_50":51},{"_40":54,"_42":55,"_44":60,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":60,"_46":-5,"_48":-5,"_50":51},{"_40":41,"_42":43,"_44":60,"_46":-5,"_48":-5,"_50":51},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. The information collected might relate to you, your preferences, or your device. We use that information to make the site work, analyze performance and traffic on our website, provide a more personalized web experience, and assist in our marketing efforts.

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