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vLLM v0.5.5 Documentation
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  • OpenAI Compatible Server
  • Deploying with Docker
  • Distributed Inference and Serving
  • Production Metrics
  • Environment Variables
  • Usage Stats Collection
  • Integrations
  • Loading Models with CoreWeave’s Tensorizer
  • Frequently Asked Questions
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  • vLLM v0.7.2 Documentation
  • vLLM v0.7.1 Documentation
  • vLLM v0.7.0 Documentation
  • vLLM v0.6.6 Documentation
  • vLLM v0.6.5 Documentation
  • vLLM v0.6.4 Documentation
  • vLLM v0.6.3 Documentation
  • vLLM v0.6.2 Documentation
  • vLLM v0.6.1 Documentation
  • vLLM v0.6.0 Documentation
  • vLLM v0.5.5 Documentation
  • vLLM v0.5.4 Documentation
  • vLLM v0.5.3 Documentation
  • vLLM v0.5.2 Documentation
  • vLLM v0.5.1 Documentation
  • vLLM v0.5.0 Documentation
  • vLLM v0.4.3 Documentation
  • vLLM v0.4.2 Documentation
  • vLLM v0.4.1 Documentation
  • Getting Started
    • Installation
    • Installation with ROCm
    • Installation with OpenVINO
    • Installation with CPU
    • Installation with Neuron
    • Installation with TPU
    • Installation with XPU
    • Quickstart
    • Debugging Tips
    • Examples
      • API Client
      • Aqlm Example
      • Cpu Offload
      • Gguf Inference
      • Gradio OpenAI Chatbot Webserver
      • Gradio Webserver
      • LLM Engine Example
      • Lora With Quantization Inference
      • MultiLoRA Inference
      • Offline Inference
      • Offline Inference Arctic
      • Offline Inference Audio Language
      • Offline Inference Chat
      • Offline Inference Distributed
      • Offline Inference Embedding
      • Offline Inference Encoder Decoder
      • Offline Inference Mlpspeculator
      • Offline Inference Neuron
      • Offline Inference Tpu
      • Offline Inference Vision Language
      • Offline Inference With Prefix
      • OpenAI Audio API Client
      • OpenAI Chat Completion Client
      • OpenAI Completion Client
      • OpenAI Embedding Client
      • OpenAI Vision API Client
      • Save Sharded State
      • Tensorize vLLM Model
  • Serving
    • OpenAI Compatible Server
    • Deploying with Docker
    • Distributed Inference and Serving
    • Production Metrics
    • Environment Variables
    • Usage Stats Collection
    • Integrations
      • Deploying and scaling up with SkyPilot
      • Deploying with KServe
      • Deploying with NVIDIA Triton
      • Deploying with BentoML
      • Deploying with Cerebrium
      • Deploying with LWS
      • Deploying with dstack
      • Serving with Langchain
      • Serving with llama_index
    • Loading Models with CoreWeave’s Tensorizer
    • Frequently Asked Questions
  • Models
    • Supported Models
    • Adding a New Model
    • Enabling Multimodal Inputs
    • Engine Arguments
    • Using LoRA adapters
    • Using VLMs
    • Speculative decoding in vLLM
    • Performance and Tuning
  • Quantization
    • Supported Hardware for Quantization Kernels
    • AutoAWQ
    • BitsAndBytes
    • INT8 W8A8
    • FP8 W8A8
    • FP8 E5M2 KV Cache
    • FP8 E4M3 KV Cache
  • Automatic Prefix Caching
    • Introduction
    • Implementation
  • Performance benchmarks
    • Benchmark suites of vLLM
  • Developer Documentation
    • Sampling Parameters
    • Offline Inference
      • LLM Class
      • LLM Inputs
    • vLLM Engine
      • LLMEngine
      • AsyncLLMEngine
    • vLLM Paged Attention
    • Input Processing
      • Input Processing Pipeline
    • Multi-Modality
      • Adding a Multimodal Plugin
    • Dockerfile
    • Profiling vLLM
  • Community
    • vLLM Meetups
    • Sponsors
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