> ## Documentation Index > Fetch the complete documentation index at: https://docs.levelrail.com/llms.txt > Use this file to discover all available pages before exploring further. # Self-host vLLM with Docker > A fast, high-throughput inference server that exposes an OpenAI-compatible API for open-weight language models. A fast, high-throughput inference server that exposes an OpenAI-compatible API for open-weight language models. This page describes the Levelrail template for vLLM (AI). It deploys the services below as one Docker Compose app on your own server. Project site: [https://docs.vllm.ai](https://docs.vllm.ai). Recommended memory: about 8192 MiB. This template reserves an NVIDIA GPU, so it needs a node with a GPU and the NVIDIA container runtime. ## Services, ports and volumes | Service | Image | Container ports | Volumes | | - | - | - | - | | `vllm` | `vllm/vllm-openai:v0.30.0` | 8000 | `vllm_hf_cache -> /root/.cache/huggingface` | ## Environment variables | Service | Variable | Value | | - | - | - | | `vllm` | `HF_HOME` | Preset in the template | Passwords and keys marked as generated are created for you when the app is deployed and stored as secrets. Values are not shown here. ## Deploy vLLM with Levelrail In the dashboard, open **Apps**, choose **New app**, then **Browse templates**, and select vLLM. Review the Compose body and deploy. With the CLI: ```bash theme={"dark"} levelrail-cli templates deploy vllm --name my-vllm ``` See the [service template catalog](/templates-and-registry) for how templates work, and [Getting started](/getting-started) if you have not installed Levelrail yet. ## More AI templates * [AnythingLLM](/self-host/anythingllm) * [ComfyUI](/self-host/comfyui) * [Flowise](/self-host/flowise) * [Flowise (Postgres + Qdrant)](/self-host/flowise-postgres) * [Jupyter GPU Notebook](/self-host/jupyter-gpu) All templates are listed in the [self-host gallery](/self-host). This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.