Open-source LLM serving engine focused on high throughput and efficient GPU memory for many concurrent requests—known for PagedAttention and continuous batching.
vLLM targets production inference on GPUs: it batches requests dynamically and manages the KV cache in pages to reduce fragmentation. It is a common choice behind OpenAI-compatible APIs in Kubernetes when teams outgrow laptop-only local runtimes.
When it fits
High QPS, shared GPU pools, and standardized HTTP/gRPC frontends. You still own model governance, observability, and capacity planning.