vLLM — Technical Overview

vLLM is an open-source, self-hostable LLM inference and serving engine built for high-throughput, low-latency production workloads. Originally developed at UC Berkeley (founded 2023), it has accumulated 93,000+ GitHub stars, reflecting rapid adoption across the ML engineering community.

Core Architecture Highlights

PagedAttention — a novel KV-cache memory management technique that eliminates memory fragmentation and dramatically increases GPU utilization.
Continuous batching & chunked prefill — dynamically groups incoming requests to maximize hardware throughput without head-of-line blocking.
Distributed inference — supports tensor, pipeline, data, expert, and context parallelism for scaling across multi-GPU and multi-node clusters.
Speculative decoding — n-gram, suffix, and EAGLE strategies for latency reduction on autoregressive generation.
Multi-LoRA serving — serve multiple fine-tuned LoRA adapters concurrently from a single base model.

API & Compatibility

Drop-in OpenAI-compatible REST API server, plus Anthropic Messages API and gRPC support.
Supports 200+ model architectures available on Hugging Face.

Quantization Support

FP8, INT8, INT4, GPTQ, AWQ, GGUF, and more — enabling efficient deployment across a wide range of hardware budgets.

Hardware Support

NVIDIA GPUs, AMD GPUs, x86/ARM/PowerPC CPUs, Google TPUs, Intel Gaudi, and additional accelerators.

Integrations

LangChain, LlamaIndex, Hugging Face, Docker, Kubernetes, Ray Serve, NVIDIA Triton, NVIDIA Dynamo, BentoML, KServe, KubeRay, SkyPilot, Modal, RunPod, LiteLLM, Haystack, AutoGen, Dify, and many more.