Disaggregated Prefill-Decode: The Architecture Behind Meta's LLM Serving
· 11 min read
Why I'm Writing This Series
I've been deep in research mode lately, studying how to optimize LLM inference. The goal is to eventually integrate these techniques into JarvisLabs - making it easier for our users to serve models efficiently without having to become infrastructure experts themselves.
As I learn, I want to share what I find. This series is part research notes, part explainer. If you're trying to understand LLM serving optimization, hopefully my journey saves you some time.
This first post covers disaggregated prefill-decode - a pattern I discovered while reading through the vLLM router repository. Meta's team has been working closely with vLLM on this, and it solves a fundamental problem that's been on my mind.