vLLM: Efficient Large Language Model Inference

As large language models grow in size and context length, inference becomes constrained by KV-cache memory usage. vLLM addresses this issue by introducing PagedAttention, a memory management strategy inspired by operating system virtual memory.

Core Idea

PagedAttention divides KV-cache into fixed-size blocks, reducing fragmentation and enabling flexible batching.

System Design

The system integrates dynamic request scheduling, efficient memory reuse, and extensibility for different hardware backends, making it suitable for production-scale LLM deployment.

Bridges research-level LLMs with real-world deployment constraints

Production Impact

vLLM has been widely adopted in LLM serving stacks due to its balance of throughput, latency, and operational simplicity.

vLLM Inference Engine (Berkeley Technical Report)