vllm

vLLM Skill

High-throughput and memory-efficient LLM inference engine.

Ecosystem Graph Preview

graph LR
  vllm["vllm"]:::core
  classDef core fill:#f9f,stroke:#333,stroke-width:4px;
  ollama -- "alternative to" --> vllm
  vllm -- "alternative to" --> ollama
  vllm -- "integrates with" --> langchain

Recommended Next Skills

• ollama (Score: 0.92) Why: Direct relationship, Both are AI, Shared ecosystem (ai), Can deploy to docker, Similar network profile
• langchain (Score: 0.82) Why: Direct relationship, Both are AI, Shared ecosystem (ai), Similar network profile
• llamaindex (Score: 0.33) Why: Both are AI, Shared ecosystem (ai), Similar network profile

Quick Start

vLLM is designed for high-concurrency production deployments. It uses PagedAttention to efficiently manage attention key-value memory, increasing throughput by up to 24x compared to HuggingFace Transformers.

pip install vllm
python -m vllm.entrypoints.openai.api_server --model meta-llama/Llama-2-7b-chat-hf

Production Patterns

PagedAttention

LLMs generate output token-by-token, causing massive fragmentation in GPU memory. vLLM solves this by treating VRAM like an OS virtual memory page table, drastically increasing the batch size of concurrent user requests.

Architecture & Scaling

OpenAI API Compatibility

vLLM runs an API server that perfectly matches the OpenAI specification. You can instantly replace your expensive OpenAI endpoint with a self-hosted vLLM endpoint without changing any client code.

Error Recovery

If vLLM fails to start with Out of Memory errors, reduce the gpu_memory_utilization flag (default is 0.90) to reserve more VRAM for the PyTorch runtime overhead.

Security Notes

vLLM does not include production authentication mechanisms. You must place it behind a reverse proxy (like NGINX or Traefik) and handle API Key validation at the proxy layer.