NVIDIA Generative AI Examples

Introduction

State-of-the-art Generative AI examples that are easy to deploy, test, and extend. All examples run on the high performance NVIDIA CUDA-X software stack and NVIDIA GPUs.

NVIDIA NGC

Generative AI Examples uses resources from the NVIDIA NGC AI Development Catalog.

Sign up for a free NGC developer account to access:

The GPU-optimized NVIDIA containers, models, scripts, and tools used in these examples
The latest NVIDIA upstream contributions to the respective programming frameworks
The latest NVIDIA Deep Learning and LLM software libraries
Release notes for each of the NVIDIA optimized containers
Links to developer documentation

Retrieval Augmented Generation (RAG)

A RAG pipeline embeds multimodal data -- such as documents, images, and video -- into a database connected to a Large Language Model. RAG lets users use an LLM to chat with their own data.

Name	Description	LLM	Framework	Multi-GPU	Multi-node	Embedding	TRT-LLM	Triton	VectorDB	K8s
Linux developer RAG	Single VM, single GPU	llama2-13b	Langchain + Llama Index	No	No	e5-large-v2	Yes	Yes	Milvus	No
Windows developer RAG	RAG on Windows	llama2-13b	Llama Index	No	No	NA	Yes	No	FAISS	NA

Large Language Models

NVIDIA LLMs are optimized for building enterprise generative AI applications.

Name	Description	Type	Context Length	Example	License
nemotron-3-8b-qa-4k	Q&A LLM customized on knowledge bases	Text Generation	4096	No	NVIDIA AI Foundation Models Community License Agreement
nemotron-3-8b-chat-4k-steerlm	Best out-of-the-box chat model with flexible alignment at inference	Text Generation	4096	No	NVIDIA AI Foundation Models Community License Agreement
nemotron-3-8b-chat-4k-rlhf	Best out-of-the-box chat model performance	Text Generation	4096	No	NVIDIA AI Foundation Models Community License Agreement

Integration Examples

NVIDIA support

In each of the READMEs, we indicate the level of support provided.

Feedback / Contributions

We're posting these examples on GitHub to better support the community, facilitate feedback, as well as collect and implement contributions using GitHub Issues and pull requests. We welcome all contributions!

Known issues

In each of the READMEs, we indicate any known issues and encourage the community to provide feedback.
The datasets provided as part of this project is under a different license for research and evaluation purposes.
This project will download and install additional third-party open source software projects. Review the license terms of these open source projects before use.

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