About Sogni Fast Worker
Sogni Fast Worker is a Docker-based service for contributing supported NVIDIA GPU capacity to the Sogni Supernet. It can run on a dedicated Windows or Linux machine, on an otherwise-idle personal system, or on a GPU hosting platform that supports custom containers.
After installation, use dashboard.sogni.ai/fast-workers to check status, configure downtime alerts, inspect jobs and earnings, review health telemetry, and change settings supported by the worker. See Using the Workers Dashboard for a complete tour.
#Choose a worker type
There are three current Fast Worker types:
| Worker | Best fit | Configuration |
|---|---|---|
| Comfy Worker | Current image, video, music, editing, and Comfy-hosted workflows | Dashboard settings begin with 1.0.189; use the latest supported release for the current catalog and live-apply behavior; .env is also supported |
| Stable Diffusion Worker | Stable Diffusion community models and ControlNet workloads | .env |
| LLM Worker | Supported language and vision-language inference | Deployment environment |
The former Flux Worker is retired. Do not install or reactivate it; current Flux-family workflows are served by Comfy Worker.
Available models and their resource requirements change with the production catalog. Consult the live Comfy workflow catalog or Stable Diffusion model catalog before sizing a machine. A generic GPU name or VRAM total does not guarantee eligibility for every workflow.
You can change worker type by running the current installer again. A worker advertises only the workloads compatible with its type, GPU, cached files, and operator settings.
#Requirements
Before installing, provide:
- A Sogni account. Use one primary account and create a separate Fast Worker NFT token ID for each simultaneously running GPU worker.
- A supported NVIDIA GPU. Some current workloads begin at 16 GB of VRAM; others require 20 GB, 24 GB, 32 GB, or more. The worker filters the live catalog for the detected hardware.
- Docker Desktop using its Linux/WSL2 backend on Windows, or Docker Engine with NVIDIA Container Toolkit on Linux.
- Enough host RAM and fast persistent storage for the chosen workflows. Comfy model storage can reach several hundred gigabytes and changes as workflows are added or retired. Reserve additional space for Docker images, temporary downloads, logs, and the operating system.
- Stable Internet access, safe power delivery, and physical cooling sized for sustained GPU compute.
- The Sogni account API key and the unique Fast Worker NFT token ID. The interactive installer guides the initial authentication.
GPU workloads can run for long periods when network demand is high. Sogni's telemetry, render-time rests, and temperature guard are best-effort operational aids—not a safety certification or a substitute for proper power, airflow, cooling, maintenance, vendor limits, and independent monitoring. The hardware owner/operator is responsible for safe installation and operation. Read Protecting your hardware before starting a worker.
#How earnings work
A connected worker advertises eligible workloads, receives jobs from the Supernet, and is credited for successfully completed work under the applicable payment path.
- Regular Spark- and SOGNI-paid jobs credit worker earnings as jobs complete.
- Unlimited subscription-covered jobs are an optional second path accounted through the monthly Worker Subscription Revenue Share Pool. Review participation and results in Subscription Earnings.
Demand, job mix, worker speed, availability, model eligibility, network policy, payment values, and operating costs all vary. Historical dashboard or leaderboard results are not a promise of future workload, revenue, profit, or cost recovery.
#Start here
#Terms and support
Operating a worker is governed by the Sogni Terms of Service and Privacy Policy. The Terms include the controlling provisions for Worker operation, operator responsibilities, eligibility, rewards, warranties, and liability; this setup guide does not replace them.
For help, email [email protected] or join the Sogni Discord.