Introduction
vLLM is an open-source LLM inference engine designed to maximize throughput, responsiveness, and GPU efficiency through advanced system-level optimizations. Built by researchers at UC Berkeley and adopted across industry-scale deployments, vLLM introduces a novel PagedAttention mechanism that significantly reduces memory fragmentation and unlocks continuous batching, allowing LLMs to serve multiple concurrent requests with low latency and high throughput. vLLM powers LLM inference infrastructure used across product features, internal tools, and agentic systems—enabling robust, real-time AI experiences.
Key benefits of using vLLM include:
Continuous Batching for High Throughput: Efficiently merges incoming requests on the fly without sacrificing latency—ideal for high-QPS workloads like chat, search, and streaming agents.
GPU Memory Efficiency via PagedAttention: Minimizes memory overhead and fragmentation, allowing larger batch sizes and more concurrent users per GPU.
Support for Popular Open-Source Models: Runs a wide range of Hugging Face and custom models, including LLaMA, Mistral, Falcon, GPT-NeoX, and ChatGLM—with support for quantized and FP16 formats.
OpenAI-Compatible API: Provides a drop-in REST/gRPC interface aligned with OpenAI’s v1/chat/completions spec, making it easy to plug into existing tooling and frontends.
Scalability and Deployment Flexibility: Optimized for both single-node and multi-GPU setups, with Kubernetes-native deployment patterns and compatibility with Triton, KServe, and Ray Serve.
vLLM is used to power latency-sensitive applications such as AI copilots, retrieval-augmented Q&A systems, document analysis agents, and multitenant LLM gateways. It integrates with LangChain, LangGraph, Ollama, and vector databases to deliver scalable, production-ready language intelligence across services and teams. By adopting vLLM, you can deliver high-performance, cost-efficient LLM inference at scale—empowering teams to build responsive, intelligent applications without compromising on speed or infrastructure efficiency.
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