Grid Inference Worker

Turn-key text inference worker for AI Power Grid. Run a local model, connect to the Grid, and start earning.

Download

Grab the latest binary for your platform from Releases:

Platform	File
Windows x64	grid-inference-worker-windows-x64.exe
macOS ARM64	grid-inference-worker-macos-arm64.zip
Linux x64	grid-inference-worker-linux-x64
Linux ARM64	grid-inference-worker-linux-arm64

Windows — Double-click the exe. A setup wizard opens in your browser at http://localhost:7861.

macOS — Unzip, then open Grid Inference Worker.app.

Linux — chmod +x grid-inference-worker-linux-x64 && ./grid-inference-worker-linux-x64

No Python or dependencies needed. Just install a backend (Ollama is easiest), run the worker, and follow the wizard.

You'll need a Grid API key — register here.

Once your worker is running, chat with your model at aipg.chat — select your model in the upper selector.

CLI Flags

Override config from the command line. The web dashboard is always available at http://localhost:7861 regardless of how you start the worker.

grid-inference-worker \
  --model llama3.2:3b \
  --backend-url http://127.0.0.1:11434 \
  --api-key YOUR_API_KEY \
  --worker-name my-worker

--model NAME            Model name (e.g. llama3.2:3b)
--backend-url URL       Backend URL (e.g. http://127.0.0.1:11434)
--api-key KEY           Grid API key
--worker-name NAME      Worker name on the grid
--port PORT             Web dashboard port (default: 7861)
--gui                   Show the desktop control window (default for binaries)
--no-gui                Skip the desktop control window
--install-service       Install as a system service (auto-start on boot)
--uninstall-service     Remove the system service
--service-status        Check if the service is installed

Environment Variables

Copy .env.example to .env and fill in your values, or configure through the web setup wizard.

Variable	Default	Description
GRID_API_KEY	(required)	Your Grid API key (register)
MODEL_NAME		Model to serve (e.g. llama3.2:3b)
BACKEND_TYPE	ollama	ollama or openai
OLLAMA_URL	http://127.0.0.1:11434	Ollama endpoint
OPENAI_URL	http://127.0.0.1:8000/v1	OpenAI-compatible endpoint (vLLM, SGLang, etc.)
OPENAI_API_KEY		API key for OpenAI-compatible backend
GRID_WORKER_NAME	Text-Inference-Worker	Worker name on the grid
GRID_MAX_LENGTH	4096	Max generation length
GRID_MAX_CONTEXT_LENGTH	4096	Max context window (auto-detected from backend)
GRID_NSFW	true	Accept NSFW jobs
WALLET_ADDRESS		Base chain wallet for rewards

Run from Source

Requires Python 3.9+.

pip install -e .
grid-inference-worker

On Windows you can also use:

.\scripts\run.ps1

Docker

cp .env.example .env
# Edit .env with your values
docker compose up -d

The dashboard is available at http://localhost:7861.

Install as a Service

Run the worker on boot without needing to stay logged in. Works on Windows (startup registry), Linux (systemd), and macOS (launchd).

# Configure the worker first (run it once to set up .env), then:
grid-inference-worker --install-service

# Check status
grid-inference-worker --service-status

# Remove
grid-inference-worker --uninstall-service

Supported Backends

Backend	Type	Setup
Ollama	ollama	Install Ollama, ollama pull llama3.2:3b, done
LM Studio	ollama	Load a model, enable server in Developer tab
vLLM	openai	--served-model-name + set OPENAI_URL
SGLang	openai	Point OPENAI_URL at SGLang's OpenAI endpoint
LMDeploy	openai	lmdeploy serve api_server + set OPENAI_URL
KoboldCpp	openai	Enable OpenAI-compatible endpoint

Ollama is the easiest way to get started. The setup wizard auto-detects it and lets you pick a model.

For any backend that exposes an OpenAI-compatible API (/v1/chat/completions), set BACKEND_TYPE=openai and point OPENAI_URL at it.

vLLM Documentation

For high-performance inference with vLLM, see our detailed guides:

• vLLM Setup Guide - Installation, configuration, and integration
• vLLM Optimization Guide - Performance tuning, benchmarking, and production best practices