[{"data":1,"prerenderedAt":12},["ShallowReactive",2],{"stack-tr-en-vllm":3},{"tagline":4,"description":5,"strengths":6,"useCase":11},"The open-source inference engine for serving LLMs at high throughput on your GPUs.","vLLM is an inference and serving engine for large language models: PagedAttention manages the KV cache in pages, like virtual memory, and continuous batching keeps the GPU saturated, multiplying throughput compared with a naive generation loop. It exposes an OpenAI-compatible API, loads Hugging Face models, and supports quantization (FP8, AWQ, GPTQ), tensor parallelism, prefix caching and multi-LoRA. It's our choice when an open-weights model has to be served in production, on your own infrastructure, at a controlled cost.",[7,8,9,10],"PagedAttention: paged KV cache, GPU memory used without fragmentation.","Continuous batching: maximum throughput under load, stable per-request latency.","OpenAI-compatible API: existing clients switch over without a rewrite.","Quantization, multi-GPU parallelism, prefix caching, speculative decoding and multi-LoRA.","Serving open-weights LLMs in production, sovereign on-premise inference, batch document processing.",1792675979515]