How to Serve a Vision AI Model Locally with vLLM and Reka Edge

Running an AI model as a one-shot script is useful, but it forces you to restart the model every time you need a result. Setting it up as a service lets any application send requests to it continuously, without reloading the model. This guide shows how to serve Reka Edge using vLLM and an open-source plugin, then connect a web app to it for image description and object detection. The vLLM plugin is available at github.com/reka-ai/vllm-reka . The demo Media Library app is at github.com/fboucher/media-library . Prerequisites You need a machine with a GPU and either Linux, macOS, or Windows (with WSL). I use UV , a fast Python package and project manager, or pip + venv if you prefer. Clone the vLLM Reka Plugin Reka models require a dedicated plugin to run under vLLM, not all models need this extra step, but Reka's architecture requires it. Clone the plugin repository and enter the directory: git clone https://github.com/reka-ai/vllm-reka cd vllm-reka The repository contains the plugin cod