vLLM

A high-throughput open-source LLM serving engine known for efficient continuous batching and KV-cache management in production inference.

How it works

vLLM introduces PagedAttention, a KV cache management system inspired by virtual memory paging. Instead of pre-allocating a contiguous block of KV cache per request (which wastes memory due to fragmentation), PagedAttention stores KV blocks in non-contiguous physical pages and maps them via a page table. This allows near-perfect KV cache utilisation across concurrent requests. Combined with continuous batching (adding new requests to the batch as old ones finish), vLLM achieves very high GPU utilisation and throughput.

Why it matters

vLLM is the de facto standard open-source LLM serving engine, used by research labs, startups, and enterprises alike. Its PagedAttention innovation reduced memory waste in KV cache allocation by ~60%, directly translating to higher batch sizes and throughput. Before vLLM, serving LLMs efficiently required proprietary infrastructure. vLLM made high-throughput serving accessible to the broader community, accelerating the deployment of open-source models in production. It is a model for how systems-level innovation can be as impactful as algorithmic advances.