Quickstart

This guide walks you through installing GPU CLI, authenticating, and running your first workload on a cloud GPU.

1. Install GPU CLI

curl -fsSL https://gpu-cli.sh/install.sh | sh

This installs the gpu command to ~/.gpu-cli/bin and adds it to your PATH.

2. Connect Your GPU Provider

GPU CLI uses your provider API key to provision GPUs. The default hosted path today is RunPod, and you pay your provider directly. No gpu login is required first.

1. Get your API key from RunPod Settings
2. Add it to GPU CLI:

gpu auth runpod

Enter your API key when prompted. An SSH key for pod access is generated automatically. Use gpu auth vast, gpu auth thunder, or gpu auth ionet for the other providers.

Or skip the prompt entirely by exporting the provider API key, which GPU CLI picks up automatically:

export RUNPOD_API_KEY=rpa_xxxxxxxxxxxx

3. Optional: GPU CLI Account

gpu login

gpu login is optional. It adds a free GPU CLI account (portal identity and owned-node/fleet features). It is not required to rent GPUs or run jobs, so you can skip it and come back later. The CLI is free with no usage caps; you only pay your GPU provider for compute at their rate, with no markup.

4. Check Available GPUs

See what GPUs are available and their prices:

gpu inventory

Filter to show only available GPUs:

gpu inventory --available

5. Run Your First Command

Test that everything works:

gpu run python -c "import torch; print(f'CUDA: {torch.cuda.is_available()}, Device: {torch.cuda.get_device_name(0) if torch.cuda.is_available() else \"N/A\"}')"

GPU CLI will:

1. Provision a GPU pod (auto-selects best available)
2. Run your command
3. Stream output back to your terminal
4. Auto-stop the pod after 5 minutes of idle

6. Initialize a Project

For a real project, initialize GPU CLI in your project directory:

cd my-ml-project
gpu init

This creates a gpu.jsonc configuration file. You can customize:

• GPU type
• Output patterns to sync back
• Port forwarding
• And more

See Configuration for all options.

7. Run a Training Script

gpu run python train.py

Your code syncs to the pod, runs on the GPU, and outputs sync back automatically.

8. Try the LLM Workflow

GPU CLI also ships a routed LLM workflow for Ollama and vLLM:

gpu llm run

Use this for a pod-based chat UI and local API surface that auto-resumes on request. See LLM Inference for the full guide.

9. Check Status

See what's running:

gpu status

Or open the interactive dashboard:

gpu dashboard

Next Steps

• LLM Inference: Run Ollama or vLLM with wake-on-request routing
• Configuration Reference: Customize your gpu.jsonc
• Commands Reference: All CLI commands
• Organizations & Service Accounts: Organizations, service accounts, and CI/CD tokens
• Hosted Proxy Quickstart: Create a shared OpenAI-compatible endpoint for your org
• Troubleshooting: Common issues and solutions