# What is vLLM? Definition & Technical Mechanics

> **Source:** [SPIDITS AI AI Glossary](https://spidits.com/ai-glossary)  
> **Category:** Hardware & Infrastructure  
> **Canonical Citation:** [https://spidits.com/ai-glossary/vllm](https://spidits.com/ai-glossary/vllm)

## Definition
vLLM is a high-throughput, memory-efficient serving engine for LLMs that utilizes PagedAttention to manage KV cache memory. By dynamically allocating KV cache blocks like virtual memory in operating systems, it eliminates memory fragmentation and increases serving throughput.

## Architectural Mechanics & Deep Dive
vLLM is a high-speed inference engine designed to optimize the serving throughput of Large Language Models in production. The core innovation of vLLM is PagedAttention, which treats the Key-Value (KV) cache like virtual memory in operating systems. By partitioning KV cache entries into non-contiguous physical memory pages and allocating them dynamically, vLLM eliminates fragmentation and supports massive batch sizes.

## Industry Applications & Real-World Use Cases
Real-time LLM hosting, high-throughput API services, and enterprise inference scaling

## Frequently Asked Questions

### What is PagedAttention in vLLM?
An attention key-value storage manager that divides the KV cache into small pages, allocating them dynamically in non-contiguous physical memory blocks.

### How much faster is vLLM than standard Hugging Face serving?
It can achieve 10x to 30x higher serving throughput depending on batch size and context lengths.

## Related Terminology
- [kv-cache](https://spidits.com/ai-glossary/kv-cache)
- [gpu](https://spidits.com/ai-glossary/gpu)
- [inference](https://spidits.com/ai-glossary/inference)

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*From the SPIDITS AI Knowledge Base (300+ terms indexed). Explore full technical definitions: [https://spidits.com/ai-glossary](https://spidits.com/ai-glossary)*