Running an LLM on your laptop is one thing.
Serving that LLM efficiently to hundreds or thousands of users is a completely different challenge.
In Day 17 of 100 Days of GenAI for DevOps, we explore vLLM, an open-source LLM serving framework designed for fast and efficient inference.
In this session, we cover:
🔹 Why we need a dedicated LLM serving framework
🔹 What happens when multiple users send requests to an LLM
🔹 How vLLM improves LLM inference and throughput
🔹 Running an LLM using vLLM
🔹 Exposing the model through an OpenAI-compatible API
🔹 Why all of this matters for DevOps and AI infrastructure engineers
The goal of this series is not just to learn GenAI concepts.
It is to understand the infrastructure behind GenAI applications: GPUs, inference, model serving, Kubernetes, observability, scalability, and eventually how all these pieces fit together in production.
🚀 Want to learn GenAI from a DevOps Engineer's perspective?
📅 New batch starting soon!
🔗 https://ideaweaver.ai/#courses/genai-for-devops-engineers
🎥 Day 17 (English): https://www.ideaweaver.ai/courses/100-days-of-genai-for-devops-english/lectures/66527584
Day 17(Hindi): https://www.ideaweaver.ai/courses/100-days-of-genai-for-devops-hindi/lectures/66527587
If you're a DevOps, SRE, Platform, Cloud, or Infrastructure Engineer trying to understand how the GenAI infrastructure stack actually works, I hope this series helps you along the way.
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