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.

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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.

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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

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"

Models Supported

Following are the models we support as of now. Use the below models to pick one for the MODEL_NAME variable.

We keep adding models frequently. If you want to add your model to the list, write to us at [email protected]

🤗 HuggingFace family	RAM Requirement
HuggingFaceTB/SmolVLM2-2.2B-Instruct	6 GB
HuggingFaceTB/SmolVLM-500M-Instruct	2 GB
HuggingFaceTB/SmolVLM-256M-Instruct	1 GB

🔮 Qwen Family	RAM Requirement
Qwen/Qwen2.5-VL-7B-Instruct	16 GB
Qwen/Qwen2.5-VL-3B-Instruct	8 GB

🧠 GLM Family	RAM Requirement
zai-org/GLM-4.1V-9B-Thinking	22 GB