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: / venelin-valkov Follow me on X: / venelin_valkov Discord: / discord Subscribe: http://bit.ly/venelin-subscribe GitHub repository: https://github.com/curiousily/AI-Boot... 👍 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: / @venelin_valkov
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