Inside vLLM: Anatomy of a High-Throughput LLM Inference System (2025)
This article provides a technical breakdown of the vLLM inference system, explaining how it achieves high throughput for large language models. It covers core components like the KV-cache manager and the engine's architecture.
Why it matters
Understanding the internals of vLLM is critical for engineers looking to optimize the deployment and serving of large-scale AI models.
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.
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 ).
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