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vLLM

★ 96.3k
Apache License 2.0Open Source — No Paywall2 Containers — 2 containers — light hosting footprintPackage · SourceNo external DB57 documented maintainers

About

vLLM is an open-source library for serving and running large language models efficiently. It is aimed at developers and teams that need high-throughput inference for a wide range of model families,...

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Features

  • LLM inference and serving
  • PagedAttention memory management
  • continuous batching
  • chunked prefill
  • prefix caching
  • streaming outputs
  • structured output generation
  • tool calling
  • OpenAI-compatible API server
  • gRPC support

Details

Last Updated
Nov 5, 2026
Created
Feb 9, 2023
Install Methods
package-managersource
Requirements
uv or pip
API
RESTGRPC
Privacy & Independence
No Cloud Required
Deployment

Deployment: Docker Compose ✓2 Containers — light hosting footprintRuns as 2 containers

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Run vLLM with Docker Compose

Compose file from the vLLM project repository.

2 services · 2 unclassified (prometheus, grafana)

no resource limits declared

needs persistent storage (1 volume)

examples/observability/prometheus_grafana/docker-compose.yaml · @ 8d9b52f · scanned Nov 3, 2026

# docker-compose.yaml
version: "3"

services:
  prometheus:
    image: prom/prometheus:latest
    extra_hosts:
      - "host.docker.internal:host-gateway"     # allow a direct connection from container to the local machine
    ports:
      - "9090:9090"   # the default port used by Prometheus
    volumes:
      - ${PWD}/prometheus.yaml:/etc/prometheus/prometheus.yml # mount Prometheus config file

  grafana:
    image: grafana/grafana:latest
    depends_on:
      - prometheus
    ports:
      - "3000:3000" # the default port used by Grafana

This file may be out of date. Check the vLLM documentation for current setup steps.

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Metadata extracted from README on Aug 7, 2026

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