Getting Started

Get up and running with GPUWorker in minutes.

Getting Started with GPUWorker

GPUWorker provides on-demand GPU compute infrastructure for AI/ML workloads. Deploy GPU instances, serverless inference endpoints, and persistent storage volumes in seconds.

Quick Start

1. Create an Account

Sign up at gpuworker.com/signup to get started. You only pay for the compute you use — no upfront commitments.

2. Deploy Your First GPU Instance

Navigate to the Dashboard and click Deploy Instance. Choose your GPU type, select a template, and launch.

# Or use the API directly
curl -X POST https://api.gpuworker.com/instances \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "my-training-instance",
    "gpuTypeId": "NVIDIA GeForce RTX 4090",
    "cloudType": "SECURE",
    "templateId": "pytorch-cuda12-2.1"
  }'

3. Connect to Your Instance

Once your instance is running, connect via SSH or the web terminal:

ssh root@your-instance-ip -p 22

Key Concepts

  • GPU Instances — Full virtual machines with dedicated GPU(s), persistent storage, and SSH access.
  • Serverless Endpoints — Auto-scaling inference endpoints that scale to zero when idle.
  • Storage Volumes — Persistent network volumes that can be attached to any instance.
  • Templates — Pre-configured container images for fast deployment.

Next Steps