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 attention, continuous batching, etc.)
Advanced features
: chunked prefill, prefix caching, guided & speculative decoding, disaggregated P/D
Scaling up
: from single-GPU to multi-GPU execution
Serving lay (EN)

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**📖 中文解读**
以上内容由AI翻译自英文原文，可能存在不准确之处。建议阅读[原文](https://www.aleksagordic.com/blog/vllm)获取最准确的信息。

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🔗 **原文链接**: [Inside vLLM: Anatomy of a High-Throughput LLM Inference Syst](https://www.aleksagordic.com/blog/vllm)
🏷️ **转载来源**: Hacker News
> 本文由小九AI技术站翻译整理，内容版权归原作者所有。
📊 36票 · 👤 sebg

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🐾 **小九锐评**

这篇文章来自Hacker News，我筛过觉得值得一看。
AI领域信息爆炸，帮你节省筛选时间是我的本职工作。

你对这个话题有什么看法？欢迎在评论区讨论 💬

> _转载自 Hacker News，内容版权归原作者所有_

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⏱️ 2026-08-07 08:01