Self-host vLLM
AI Chat InterfacesHigh-throughput LLM inference and serving engine with PagedAttention and continuous batching — an OpenAI-compatible API server built for GPU-backed, multi-user production workloads.
Key facts
Reach for vLLM when a GPU server needs to serve many people at once — continuous batching and PagedAttention keep one card ahead of a whole team's chat traffic, behind the same OpenAI-compatible API your clients already speak. If it's just you, or your VPS has no GPU, Ollama does the job with a tenth of the setup.
What you need
- Any VPS with at least 16384 MB of RAM
- A domain you control — most self-hosted setups need HTTPS in front of them
- About an afternoon — budget time for troubleshooting
Install
Run these commands on your server:
# vLLM — official image needs an NVIDIA GPU (CPU builds exist but are a separate image)
docker run -d --runtime nvidia --gpus all -v ~/.cache/huggingface:/root/.cache/huggingface \
-p 8000:8000 --ipc=host vllm/vllm-openai:latest --model Qwen/Qwen3-0.6BWhat you take on
vLLM is production-grade serving infrastructure, and it asks you to operate like it: