vLLM

What is vLLM?

vLLM is an open-source LLM serving engine that uses PagedAttention to efficiently manage GPU memory for KV caches, enabling high-throughput inference with continuous batching. It eliminates memory waste from fixed-size KV cache allocation by treating attention memory like virtual memory pages in an operating system.

Traditional LLM serving pre-allocates contiguous GPU memory for each request's maximum possible sequence length. This wastes 60-80% of KV cache memory on padding for shorter sequences. vLLM's PagedAttention breaks KV caches into fixed-size blocks that are allocated on demand, similar to how operating systems use paging for virtual memory. This allows near-zero memory waste and enables serving 2-4x more concurrent requests on the same hardware.

Combined with continuous batching — which dynamically adds and removes requests from a running batch rather than waiting for all requests to complete — vLLM achieves throughput improvements of 2-24x compared to naive serving approaches. The engine supports tensor parallelism across multiple GPUs, speculative decoding, quantized models, and prefix caching out of the box.

Why does vLLM matter?

vLLM has become the de facto standard for self-hosted LLM inference, powering production deployments at organizations that need control over their model serving infrastructure. Its memory efficiency directly translates to lower cost per token, making large model deployment economically viable on fewer GPUs.

How is vLLM used in practice?

Teams deploy vLLM as an OpenAI-compatible API server that can serve any Hugging Face model with a single command. A common production pattern uses vLLM behind a load balancer with auto-scaling based on queue depth, serving models like Llama 3 or Mistral to internal applications without per-token API costs.