Which NVIDIA software component is used to manage and deploy AI models in production?

The correct answer is the NVIDIA Triton Inference Server, which is specifically designed to manage and deploy AI models in production environments. This software component provides a unified inference platform that can serve multiple models simultaneously and efficiently handle requests across different frameworks. It supports various machine learning and deep learning frameworks, enabling organizations to deploy models with flexibility and ease.

NVIDIA Triton Inference Server allows for dynamic batching, model versioning, and optimization for latency and throughput, making it a robust solution for companies looking to operationalize AI models in live environments. Its capability to integrate with existing infrastructure and its support for cloud and on-premise deployments further enhance its usability in production settings.

In comparison, while the NVIDIA NGC Catalog provides a repository for GPU-optimized software and pre-trained models, it does not serve the specific purpose of managing runtime inference processes in production. The NVIDIA TensorRT is focused on optimizing and running inference for deep learning models, but by itself, it does not encompass the broader deployment management capabilities provided by Triton. The NVIDIA CUDA Toolkit is primarily aimed at developers working on parallel computing using CUDA and does not pertain directly to the management and deployment of AI models in a production environment.