# Source: https://developer.nvidia.com/nim.md

1. [Topics](https://developer.nvidia.com/topics/)

[AI](https://developer.nvidia.com/topics/ai)

[Generative AI](https://developer.nvidia.com/generative-ai)  

NVIDIA NIM  

# NVIDIA NIM for Developers

[NVIDIA NIM™](https://www.nvidia.com/en-us/ai/) provides containers to self-host GPU-accelerated inferencing microservices for pretrained and customized AI models across clouds, data centers, and RTX™ AI PCs and workstations. NIM microservices expose industry-standard APIs for simple integration into AI applications, development frameworks, and workflows and optimize response latency and throughput for each combination of foundation model and GPU.

[Try APIs](https://build.nvidia.com/explore/discover &quot;Github Repo&quot;)[Get Started With NIM](https://docs.nvidia.com/nim/large-language-models/latest/getting-started.html &quot;Download Workflows&quot;)

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## How It Works  

NVIDIA NIM simplifies the journey from experimentation to deploying enterprise AI applications by providing enthusiasts, developers, and AI builders with pre-optimized models and industry-standard APIs for building powerful AI agents, co-pilots, chatbots, and assistants. With inference engines built on leading frameworks from NVIDIA and the community, including TensorRT, TensorRT-LLM, vLLM, SGLang, and more, NIM is engineered to facilitate seamless AI inferencing for the latest AI foundation models on NVIDIA GPUs.

[Watch Video](https://www.youtube.com/watch?v=bpOvayHifNQ)

 ![NVIDIA NIM inference microservices stack diagram](https://developer.download.nvidia.com/images/nim/practitioner-nim-1920x1080.jpg)

### Introductory Blog  

Learn about NIM architecture, key features, and components.

[Read Blog](https://developer.nvidia.com/blog/nvidia-nim-offers-optimized-inference-microservices-for-deploying-ai-models-at-scale/)

### Documentation  

Access guides, reference information, and release notes for running NIM on your infrastructure.

[Read Docs](https://docs.nvidia.com/nim/)

### Introductory Video

Learn how to deploy NIM on your infrastructure using a single command.

[Watch Video (04:09)](https://www.youtube.com/watch?v=087spL8hMvM)

### Deployment Guide  

Get step-by-step instructions for self-hosting NIM on any NVIDIA accelerated infrastructure.

[Read Guide](https://docs.nvidia.com/nim/large-language-models/latest/getting-started.html)

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## Build With NVIDIA NIM

### Optimized Model Performance

Improve AI application performance and efficiency with accelerated engines from NVIDIA and the community, including TensorRT, TensorRT-LLM, vLLM, SGLang, and more—prebuilt and optimized for low-latency, high-throughput inferencing on specific NVIDIA GPU systems.

### Run AI Models Anywhere

Maintain security and control of applications and data with prebuilt microservices that can be deployed on NVIDIA GPUs anywhere—from RTX AI PCs, workstations, data centers, or the cloud. Download NIM inference microservices for self-hosted deployment, or take advantage of dedicated endpoints on Hugging Face to spin up instances in your preferred cloud.

### Choose Among Thousands of AI Models and Customizations  

Deploy a broad range of LLMs supported by vLLM, SGLang, or TensorRT-LLM, including community fine-tuned models and models fine-tuned on your data.

### Maximize Operationalization and Scale

Get detailed observability metrics for dashboarding, and access Helm charts and guides for scaling NIM on Kubernetes.

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## NVIDIA NIM Examples and Blueprints

Sample App Repo

Reference Blueprints

Agentic AI Toolkit

### Build Accelerated Generative AI Applications Including RAG, Agentic AI, and More  

Get started building AI applications powered by NIM using NVIDIA-hosted NIM API endpoints and generative AI examples from GitHub. See how easy it is to deploy retrieval-augmented generation (RAG) pipelines, agentic AI workflows, and more.

[Explore NVIDIA Generative AI Examples](https://nvidia.github.io/GenerativeAIExamples/latest/index.html)

### Jump-Start Development With Blueprints  

NVIDIA AI Blueprints are predefined, customizable AI workflows for creating and deploying AI agents and other generative AI applications. Build and operationalize custom AI applications—creating data-driven AI flywheels—using blueprints along with NVIDIA AI and Omniverse™ libraries, SDKs, and microservices. Explore blueprints [co-developed](https://build.nvidia.com/blueprints?q=partner) with leading agentic AI platform providers including CrewAI, LangChain, and more.

[Explore NVIDIA Blueprints](https://build.nvidia.com/nim/agent-blueprints)

### Simplify Development With NVIDIA AgentIQ Toolkit

Weave NIM microservices into agentic AI applications with the NVIDIA AgentIQ library, a developer toolkit for building AI agents and integrating them into custom workflows.

[Learn More](https://developer.nvidia.com/agentiq)[Try Now](http://github.com/NVIDIA/AgentIQ)

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## Get Started With NVIDIA NIM

Explore different options for experimenting, building, and deploying optimized AI applications using the latest models with NVIDIA NIM.

 ![Decorative image of building AI application with NVIDIA NIM API](https://developer.download.nvidia.com/icons/m48-nim-256px-blk.png)
### Try  

Get free access to NIM API endpoints for unlimited prototyping, powered by DGX Cloud. Your membership to the [NVIDIA Developer Program](https://developer.nvidia.com/developer-program) enables NVIDIA-hosted NIM APIs and containers for development and testing ([FAQ](https://forums.developer.nvidia.com/t/nvidia-nim-faq/300317)).

[Visit the NVIDIA API Catalog](https://build.nvidia.com/explore/discover)

 ![Decorative image of joining NVIDIA Developer Program for free access to NIM](https://developer.download.nvidia.com/icons/m48-developer-1.svg)
### Build  

Get a head start on development with sample applications built with NIM and partner microservices. [NVIDIA Blueprints](https://blogs.nvidia.com/blog/nim-agent-blueprints/) can be deployed in one click with [NVIDIA Launchables](https://developer.nvidia.com/blog/one-click-deployments-for-the-best-of-nvidia-ai-with-nvidia-launchables/), downloaded for local deployments on PCs and workstations, or for development in your datacenter or private cloud. 

[Explore NVIDIA Blueprints](https://build.nvidia.com/nim?filters=nimType%3Anim_type_run_anywhere)

 ![Decorative image of deploying with NVIDIA AI Enterprise](https://developer.download.nvidia.com/icons/m48-digital-deep-learning-institute-talks-training.svg)
### Deploy  

Deploy on your own infrastructure for development and testing. When ready for production, get the assurance of security, API stability, and support that comes with [NVIDIA AI Enterprise](https://www.nvidia.com/en-us/data-center/products/ai-enterprise/), or access dedicated enterprise-grade NIM endpoints at NVIDIA partners.

[Run NVIDIA NIM anywhere](https://build.nvidia.com/nim?filters=nimType%3Anim_type_run_anywhere)

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## NVIDIA NIM Learning Library

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## More Resources

 ![Decorative image representing forums](https://developer.download.nvidia.com/images/omniverse/m48-people-group.svg)
### Community  

 ![](https://developer.download.nvidia.com/images/isaac/m48-certification-ribbon-2-256px-blk.png)
### Training and Certification  

 ![](https://developer.download.nvidia.com/images/isaac/m48-ai-startup-256px-blk.png)
### Inception for Startups  

 ![Decorative image representing Inception for Startups](https://developer.download.nvidia.com/icons/m48-newspaper-256px-blk.png)
### Tech Blogs

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## Ethical AI

NVIDIA’s platforms and application frameworks enable developers to build a wide array of AI applications. Consider potential algorithmic bias when choosing or creating the models being deployed. Work with the model’s developer to ensure that it meets the requirements for the relevant industry and use case; that the necessary instruction and documentation are provided to understand error rates, confidence intervals, and results; and that the model is being used under the conditions and in the manner intended.

Learn about the latest NVIDIA NIM models, applications, and tools.

[Sign Up](https://www.nvidia.com/en-us/ai-data-science/generative-ai/news/)