2 posts tagged with "Optimization"

Running large language models efficiently can be challenging. You want good performance without overloading your servers or exceeding your budget. That's where vLLM comes in - but even this powerful inference engine can be made faster and smarter.

In this post, we'll explore five cutting-edge optimization techniques that can dramatically improve your vLLM performance:

1. Prefix Caching - Stop recomputing what you've already computed
2. FP8 KV-Cache - Pack more memory efficiency into your cache
3. CPU Offloading - Make your CPU and GPU work together
4. Disaggregated P/D - Split processing and serving for better scaling
5. Zero Reload Sleep Mode - Keep your models warm without wasting resources

Each technique addresses a different bottleneck, and together they can significantly improve your inference pipeline performance. Let's explore how these optimizations work.

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.