---
title: "Discussion on \"vLLM Deep Dive: How PagedAttention, Continuous Batching and the V1 Engine Serve LLMs in Production\" | Hashnode"
description: "Discussion on \"vLLM Deep Dive: How PagedAttention, Continuous Batching and the V1 Engine Serve LLMs in Production\". Every layer above it routes, scales and governs requests. vLLM is the layer that turns GPU memory into tokens."
url: "https://hashnode.com/posts/vllm-deep-dive-how-pagedattention-continuous-batching-and-the-v1-engine-serve-llms-in-production/6af1a0c4a32bf9f449f76bd3"
---

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Discussion

# [vLLM Deep Dive: How PagedAttention, Continuous Batching and the V1 Engine Serve LLMs in Production](https://blog.shubhamtatvamasi.com/vllm-deep-dive-how-pagedattention-continuous-batching-and-the-v1-engine-serve-llms-in-production?utm_source=hashnode&utm_medium=feed)

Every layer above it routes, scales and governs requests. vLLM is the layer that turns GPU memory into tokens.

20h ago · 52 min read · blog.shubhamtatvamasi.com  
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-   [#vllm](https://hashnode.com/tag/vllm)
-   [#llm](https://hashnode.com/tag/llm)
-   [#kubernetes](https://hashnode.com/tag/kubernetes)
-   [#mlops](https://hashnode.com/tag/mlops)
-   [#gpu](https://hashnode.com/tag/gpu)

## Written by

[Shubham Tatvamasi](https://hashnode.com/@ShubhamTatvamasi)

@ShubhamTatvamasi

  
[View profile](https://hashnode.com/@ShubhamTatvamasi)

AI infrastructure engineer. Inference, GPUs and GitOps on Kubernetes.

## Responses 1

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

[43m 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.