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Prerequisites

  • A web browser
  • An SSH client (ssh on macOS/Linux, Windows Terminal, or PuTTY)
  • A credit or debit card
1

Create an account

Go to platform.deploygpu.ai and click Sign Up.Enter your email and choose a password. You’ll receive a verification email — click the link to activate your account.A default SSH key is automatically generated for your account.
2

Add credits

Navigate to Billing in the sidebar.Click one of the quick-add buttons (10,10, 25, 50,50, 100, 250,250, 500) or enter a custom amount. Complete payment via Stripe.Your credit balance updates immediately after payment.
3

Choose a GPU

Go to Compute Marketplace in the sidebar.
  1. Select a GPU type from the cards (e.g., RTX 4090, A100).
  2. Optionally filter by region, number of GPUs, or interconnect type.
  3. The offers table shows real-time pricing sorted by hourly cost.
  4. Click Deploy on the offer you want.
4

Configure and deploy

On the deploy page:
  1. Template — Pick a pre-configured environment (PyTorch + Jupyter is a good default) or use a bare CUDA image.
  2. Disk size — Adjust if needed (default depends on the template).
  3. SSH key — Your managed key is pre-selected. Select a different key if preferred.
  4. Review the cost breakdown showing your effective hourly rate and estimated runtime.
  5. Click Deploy.
Provisioning typically takes 1–3 minutes.
5

Connect via SSH

Once your deployment shows Running in the Deployments page, you’ll see connection details including the host, port, and user.
Replace <port>, <user>, and <host> with the values shown in your deployment details.If your template includes Jupyter, a Jupyter URL will also be displayed.

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Next steps

Templates

Explore all available pre-configured environments.

Volumes

Create persistent storage that survives across deployments.

API Keys

Generate API keys for programmatic access.

Billing

Set up auto top-up so you never run out of credits.