What's this about?
A detailed technical walkthrough of vLLM — the popular open-source serving engine — explaining how it squeezes high throughput out of LLM inference.
What exactly is "inference" / "throughput"?
"Inference" is running a trained model to answer requests (as opposed to training it). "Throughput" is how many requests/tokens per second you can serve. vLLM is famous for tricks like paged attention (managing the model's memory like an OS manages RAM) that let one server handle far more traffic.
Why it matters
Serving cost dominates the economics of deploying LLMs, so how vLLM achieves throughput is directly practical for anyone running models.