# For Node Runners ## **Introduction** As a **Node Runner**, you contribute GPU-powered computation to the **DIVS decentralized network**, running **Vision-Language Models (VLMs)** to verify claims in images submitted by Builders. Node Runners help build a **trustless, censorship-resistant truth layer for online images**. This guide walks you through installing the node client, configuring your compute resources, and starting your first verification tasks. *** ## **1️⃣ Prerequisites** ✅ **Hardware Requirements:** We support a range of models — from GPU-hungry giants to ones that can chill on your laptop. * **GPU (recommended):** NVIDIA with CUDA is ideal. More VRAM = happier models. * **CPU (possible):** 4+ cores, but It’ll work… eventually. Great time to grab a coffee. Or two. * **RAM:** At least 8GB. For bigger models, 16GB+ is safer. * **Architecture**: x86\_64 and ARM supported (some models prefer x86 + CUDA). * **Disk:** 120+ GB free space for keeping things comfy ✅ **Software Requirements** * **Docker (v20+)** for containerized setup. That’s it. No additional installs or builds — just pull the image and run. ✅ **Network Requirements** Our protocol uses peer-to-peer communication over UDP. * **Ports:** Open **UDP ports 12000–12009** on your router or firewall. * **Connectivity:** A stable public internet connection is best. NAT traversal is attempted, but port forwarding is recommended. * **Docker note:** Make sure Docker can expose the above ports correctly. ✅ **DIVS Wallet Configuration** * **Automatic:** Node keys are auto-generated on first run. * **Optional override:** You can supply your own key using environment variables when starting the container. *** ## **2️⃣ Run the DIVS node** ### Create a Volume So that the node will not pull models again and again ``` docker volume create wtns-vol ``` ### 🚀 Option 1: With NVIDIA GPU (Recommended) For the best performance and support for larger models, run your Watchtower using a CUDA-enabled NVIDIA GPU: ``` docker run \ -d \ --gpus all \ --network=host \ -v wtns-vol:/root \ -e WALLET_PUBLIC_KEY=0x_your_key_here \ -e MODEL_NAME=MODEL_NAME \ -e NETWORK=testnet \ --name mywatchtower \ witnesschain/infinity-watch-nvidia:2.0.0 ``` ### 🧪 Option 2: CPU-Only (Lightweight Model) No GPU? You can still join the network by running a smaller model on your CPU: ``` docker run \ -d \ -v wtns-vol:/root \ -e WALLET_PUBLIC_KEY=0x_your_key_here \ -e MODEL_NAME=MODEL_NAME \ -e NETWORK=testnet \ --name mywatchtower \ witnesschain/infinity-watch:2.0.0 ``` {% hint style="info" %} Note: if you want to use your own private key for your watchtower, add the environment variable PRIVATE\_KEY in your docker run command -e PRIVATE\_KEY="your\_custom\_private\_key" {% endhint %} ### **Models Supported** Following are the models we support as of now. Use the below models to pick one for the MODEL\_NAME variable. {% hint style="success" %} We keep adding models frequently. If you want to add your model to the list, write to us at {% endhint %}
🤗 HuggingFace family RAM Requirement
HuggingFaceTB/SmolVLM2-2.2B-Instruct6 GB
HuggingFaceTB/SmolVLM-500M-Instruct2 GB
HuggingFaceTB/SmolVLM-256M-Instruct1 GB
🔮 Qwen FamilyRAM Requirement
Qwen/Qwen2.5-VL-7B-Instruct16 GB
Qwen/Qwen2.5-VL-3B-Instruct8 GB
🧠 GLM FamilyRAM Requirement
zai-org/GLM-4.1V-9B-Thinking22 GB