In the previous part of this series, I introduced the architecture of vLLM and how it is optimized for serving large language models (LLMs). In this installment, we will take a behind-the-scenes look at vLLM to understand the end-to-end workflow, from accepting the prompt to generating the response.
vLLM’s architecture is optimized for high throughput and low latency. It efficiently manages GPU memory and scheduling, allowing many requests to be served in parallel. In the sections below, we’ll dive into each stage in detail, using simple…
