How to Deploy LLMs | LLMOps Stack with vLLM, Docker, Grafana & MLflow

Running LLMs on localhost is easy. Deploying them to production without going insane is hard. Most developers wrap a Python script in a Docker container and call it a day. This leads to high latency, security vulnerabilities, and zero visibility when things break. In this video, I'll show you how to build a production-level inference stack using consumer GPUs. AI Academy: https://www.mlexpert.io/ LinkedIn: https://www.linkedin.com/in/venelin-valkov/ Follow me on X: https://twitter.com/venelin_valkov Discord: https://discord.gg/UaNPxVD6tv Subscribe: http://bit.ly/venelin-subscribe GitHub repository: https://github.com/curiousily/AI-Bootcamp 👍 Don't Forget to Like, Comment, and Subscribe for More Tutorials! 00:00 - Why Python script fail in production 01:47 - The stack architecture (vLLM, nginx, Grafana) 04:42 - Docker compose definition 08:35 - Nginx config 09:08 - Monitoring with Prometheus and Grafana config 10:13 - Virtual instance setup 13:54 - Live load test with LangChain client Join this channel to get access to the perks and support my work: https://www.youtube.com/channel/UCoW_WzQNJVAjxo4osNAxd_g/join