> ## Documentation Index
> Fetch the complete documentation index at: https://docs.warpscale.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Install the Warpscale vLLM plugin

> Install the warpscale-vllm package into your vLLM environment.

The vLLM plugin reports per-step composition and request statistics from your vLLM server to the probe. It is how Warpscale observes inference.

## Before you start

* **vLLM 0.23.x** and Python 3.10 or newer.

## Install the plugin

Install it into the **same environment as vLLM**.

```bash theme={null}
pip install warpscale-vllm
```

<Note>
  The plugin declares no dependencies on purpose. vLLM pins an exact, CUDA-matched `torch`, so declaring vLLM here would let pip swap it and break your GPU environment. The plugin is pure Python and rides whatever vLLM is already installed — which is also why the 0.23.x requirement is on you to satisfy rather than enforced by pip.
</Note>

See [Set up vLLM](/inference/setup) to turn it on.

## Upgrade

```bash theme={null}
pip install --upgrade warpscale-vllm
```

Check the supported vLLM version before upgrading either one. The plugin versions independently of the probe.

## Uninstall

```bash theme={null}
pip uninstall warpscale-vllm
```

Remove the `--distributed-executor-backend` and `--middleware` flags from your `vllm serve` command, and drop the vLLM and OTLP settings from the probe.