How to self-host and hyperscale AI with Nvidia NIM

Introduces the concept of self-hosting and scaling AI agents using Nim, discusses the evolution of the workforce with AI models like llama 3, mistal, and stable diffusion, and explores the potential future where robots replace human jobs.

Discusses the challenges of running AI models, the role of GPUs in parallel computing, and introduces Nvidia Nim as a solution for deploying AI models at scale using inference microservices.

Explains the features of Nvidia Nims such as packaging popular AI models with APIs, containerization on Kubernetes, deployment options to cloud or on-premises, and the availability of a playground to interact with AI models.

Explores how Nims can be used to replace human roles in various industries such as customer service, warehouse operations, product management, web development, and mental well-being, emphasizing the collaboration between AI and human workforce.

Illustrates the role of Nims in scaling AI capabilities for individuals, reducing development time, and enabling the deployment of AI tools. Emphasizes the augmentation of human work instead of replacing it, with a personal goal of creating a billion-dollar business as a solo developer using Nims.

Demonstrates the technical setup of Nvidia Nims on a server with an h100 GPU, including Docker image deployment, monitoring GPU status, operation on Kubernetes, running Python scripts to access AI models, and making HTTP requests for AI responses.

Shows the practical usage of Nims by sending HTTP requests to access AI models like llama 3, retrieving responses with context, configuring model parameters, and monitoring performance metrics such as latency, GPU temperature, CPU, and memory usage.