Inside vLLM: Anatomy of a High-Throughput LLM Inference System (2025)

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

aleksagordic.com

August 6, 2026

36 min read

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Summary

vLLM is a high-throughput LLM inference system that utilizes components such as paged attention, continuous batching, prefix caching, and specdec. It supports multi-GPU and multi-node dynamic serving to enhance performance at scale.

Key Takeaways

vLLM is a high-throughput LLM inference system that utilizes components like paged attention, continuous batching, and prefix caching to enhance performance.
The LLM engine serves as the core building block of vLLM, enabling high-throughput inference in offline settings before transitioning to online, multi-GPU, multi-node configurations.
Key features of vLLM include chunked prefill, guided and speculative decoding, and a distributed serving layer for concurrent web access.
The system is designed for scalability, evolving from single-GPU execution to more complex multi-GPU and multi-node setups.

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