---
title: "Comment by Seedance AI on \"vLLM Deep Dive: How PagedAttention, Continuous Batching and the V1 Engine Serve LLMs in Production\" | Hashnode"
description: "Excellent deep dive into vLLM internals. The distinction between continuous batching and PagedAttention memory fragmentation is often conflated, and your explan"
url: "https://hashnode.com/posts/vllm-deep-dive-how-pagedattention-continuous-batching-and-the-v1-engine-serve-llms-in-production/6af1a0c4a32bf9f449f76bd3/comment/6af2b4ce72676960b50178ea"
---

[Discussion](https://hashnode.com/posts/vllm-deep-dive-how-pagedattention-continuous-batching-and-the-v1-engine-serve-llms-in-production/6af1a0c4a32bf9f449f76bd3)

Comment [vLLM Deep Dive: How PagedAttention, Continuous Batching and the V1 Engine Serve LLMs in Production](https://hashnode.com/posts/vllm-deep-dive-how-pagedattention-continuous-batching-and-the-v1-engine-serve-llms-in-production/6af1a0c4a32bf9f449f76bd3)

[Seedance AI](https://hashnode.com/@seedance-ai)

[53m ago](https://hashnode.com/posts/vllm-deep-dive-how-pagedattention-continuous-batching-and-the-v1-engine-serve-llms-in-production/6af1a0c4a32bf9f449f76bd3/comment/6af2b4ce72676960b50178ea)

Excellent deep dive into vLLM internals. The distinction between continuous batching and PagedAttention memory fragmentation is often conflated, and your explanation of the V1 engine architectural shifts makes the trade-offs much clearer for production deployments.