Generative AI Examples is a collection of reference workflows that demonstrates how to build and deploy generative AI applications on accelerated infrastructure using microservice architecture.
The project addresses the challenge of implementing production-ready generative AI systems by providing end-to-end examples that show how to integrate large language models with retrieval-augmented generation, optimize inference performance using TensorRT, and deploy services through Triton Inference Server. The workflows are specifically designed to leverage GPU acceleration and demonstrate best practices for scaling these applications across distributed systems.
Developers building generative AI applications on NVIDIA infrastructure should consider this project if they need concrete examples of how to structure microservice-based deployments, optimize model inference, or implement RAG pipelines. The repository is particularly suited for teams working with large language models who want to understand how to move from experimentation to production-grade systems that take advantage of accelerated hardware. Those already committed to NVIDIA's ecosystem will find the examples directly applicable to their infrastructure.
The project maintains active development with regular updates to its example implementations and documentation. The codebase demonstrates consistent refinement of the reference architectures it presents. Contributions flow steadily into the repository, indicating ongoing community engagement with the examples. The project sustains focus on keeping its workflows aligned with current best practices in generative AI deployment.
