RunPod

Getting Started

1. Create an account at runpod.io and add credits via credit card or cryptocurrency.
2. Launch a GPU pod by selecting your desired GPU type (A100, H100, RTX 4090, etc.) and a pre-built template.
3. Connect to your pod via SSH, Jupyter Notebook, or VS Code for interactive development and training.
4. Deploy a serverless endpoint for production inference by uploading your model and configuring autoscaling.

Key Features

• Competitive GPU pricing offers A100s, H100s, and consumer GPUs at prices significantly below major cloud providers.
• Serverless GPU endpoints deploy inference APIs with automatic scaling, pay-per-second billing, and zero cold starts.
• Pre-built templates provide one-click deployment of popular frameworks like PyTorch, TensorFlow, and Stable Diffusion.
• Spot and on-demand instances choose between cheaper interruptible spots or guaranteed on-demand GPU access.
• Network storage persistent volumes that survive pod restarts for storing datasets, checkpoints, and model weights.
• Community cloud access to a distributed network of GPU providers for additional capacity and competitive pricing.