> Canonical page: https://posttrainllm.com/inspiration/vllm

A note of thanks · Serving systems

# vLLM

vLLM makes scheduling, paged KV, and batching legible as serving problems. We study that architecture to understand which constraints carry over to one Mac and which belong to a cluster.

01 · The idea

## What stayed with us

Admission control, scheduling, paged KV allocation, prefill, decode, continuous batching, caching, and distributed execution explain why a serving engine behaves differently from a single-request model loop.

02 · The local translation

## What we did with it

The study maps the boundary between one-device Mac inference and cluster serving through a hand-simulated request and KV-block budget.

03 · The boundary

## Where the comparison stops

The goal is boundary mapping, not reproducing a multi-GPU serving system on one Mac or claiming equivalent throughput.

Source trail

## Follow the work

- Original project [vLLM source ↗](https://github.com/vllm-project/vllm)
- Further reading [Inside vLLM ↗](https://www.aleksagordic.com/blog/vllm)
- Our evidence · study only [Inside vLLM ↗](https://posttrainllm.com/studies/inside-vllm)

Independent appreciation. The named projects have not endorsed or affiliated with PostTrainLLM.