NVIDIA/GenerativeAIExamples resource

Health v2 · maintenance only

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4160 stars · 1094 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A collection of NVIDIA reference workflows, notebooks, and example projects for building generative AI systems on accelerated infrastructure. It covers RAG pipelines, agentic workflows, fine-tuning, and guardrailing using NVIDIA NIM, NeMo, TensorRT, and Triton microservices.

Use cases

• build a RAG pipeline with NVIDIA NIM microservices
• deploy LLM inference on GPU with TensorRT and Triton
• fine-tune and evaluate LLMs with NeMo microservices
• create an agentic RAG workflow with Llama 3.1
• build knowledge graph RAG on GPU
• add guardrails and safety auditing to LLM apps
• learn to integrate NVIDIA AI endpoints with LangChain

When to choose

• you are building RAG or agentic applications on NVIDIA GPUs
• you want reference architectures using NIM, NeMo, TensorRT, or Triton
• you need worked Jupyter notebook tutorials for NVIDIA's generative AI stack

When to avoid

• you need a production-ready application rather than examples and tutorials
• you have no NVIDIA GPU or access to NVIDIA AI services
• you want a framework-agnostic stack without NVIDIA dependencies

Facets

learning-resource · maturity active

rag llm-inference agent-framework llm-training gpu-computing microservices large-language-models deep-learning developer-tools python cloud self-hosted nvidia-nim tensorrt-llm triton-inference-server nemo reference-workflows jupyter-notebooks microservice-architecture retrieval-augmented-generation ai-agents gpu docker

Member repositories

For agents

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