{"data":{"slug":"vllm","etype":"software","name":"vLLM","cat":"Inference","subtitle":"The open-source serving engine that made GPU inference efficient.","summary":"vLLM's PagedAttention treats the KV cache like virtual memory, ending the fragmentation that wasted most inference GPU memory and multiplying throughput. Born at UC Berkeley in 2023, it became the default open serving engine across clouds and enterprises, with hardware vendors (NVIDIA, AMD, AWS, Google) all contributing backends.","attrs":null,"kf":{"maker":"UC Berkeley + community","since":"2023","license":"Apache 2.0"},"aliases":["PagedAttention"],"ticker":null,"exchange":null,"hq":null,"founded":null,"website":null,"status":"Active","region":null,"asof":"2026-01-15","sources":[{"url":"https://github.com/vllm-project/vllm","label":"vLLM"}]},"meta":{"found":true,"count":1,"served_from":"site cache (no live upstream calls)","as_of":"2026-10-14T09:46:26+00:00","disclaimer":"Research and information only — not investment advice. Structured records carry basis labels (vendor spec / reported / estimate); market quotes are delayed/indicative.","attribution":"Records compiled from vendor documentation, official filings and reputable public reporting; market quotes via financial data providers. See /data-sources."}}