Deploying and Integrating LLMs on Fly.io

This video, narrated by Chris McCord, walks through the process of deploying a large language model (LLM) on a Fly.io GPU and integrating it into an application. The aim is to enhance a to-do application by generating intelligent to-dos. Chris explains how to set up the LLM using olama library, run it locally on different OS, and then deploy it using Docker on Fly.io. He highlights the cost-effectiveness of using Fly.io GPUs due to their auto-scaling capabilities and private networking features. The video provides practical steps for building the Docker image, configuring the Fly.toml file, and deploying the application. Finally, Chris demonstrates how to test the deployment locally using WireGuard, showcasing the usefulness and efficiency of integrating LLMs in applications.