Inside vLLM: Anatomy of a High-Throughput LLM Inference System - Aleksa Gordić

In this post, I'll gradually introduce all of the core system components and advanced features that make up a modern high-throughput LLM inference system. In particular I'll be doing a breakdown of how vLLM [1] works. This post is the first in a series. It starts broad and then layers in detail (following an inverse-pyramid approach) so you can form an accurate high-level mental model of the complete system without drowning in minutiae. Later posts will dive into specific subsystems. This post is structured into five parts: The LLM engine is the fundamental building block of vLLM. On its own, it already enables high-throughput inference - but only in an offline setting. You can't serve it to customers over the web yet. We'll use the following offline inference snippet as our running example (adapted from basic.py). This configuration is: From here, we'll gradually build up to an online, async, multi-GPU, multi-node inference system - but still serving a standard transformer. In this ex

In this post, I'll gradually introduce all of the core system components and advanced features that make up a modern high-throughput LLM inference system. In particular I'll be doing a breakdown of how vLLM [1] works. This post is the first in a series. It starts broad and then layers in detail (following an inverse-pyramid approach) so you can form an accurate high-level mental model of the complete system without drowning in minutiae. Later posts will dive into specific subsystems. This post is structured into five parts: LLM engine & engine core : fundamentals of vLLM (scheduling, paged att

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