Documentation Index
Fetch the complete documentation index at: https://docs.runpod.io/llms.txt
Use this file to discover all available pages before exploring further.
The web interface for managing your compute resources, account, teams, and billing.
A pay-as-you-go compute solution designed for dynamic autoscaling in production AI/ML apps.
A framework for building distributed GPU applications using local Python scripts. Write functions with the @Endpoint decorator, and Flash automatically executes them on Runpod’s infrastructure.
A dedicated GPU or CPU instance for containerized AI/ML workloads, such as training models, running inference, or other compute-intensive tasks.
An AI model API hosted by Runpod that you can access directly without deploying your own infrastructure.
A managed compute cluster with high-speed networking for multi-node distributed workloads like training large AI models.
Persistent storage that exists independently of your other compute resources and can be attached to multiple Pods or Serverless endpoints to share data between machines.
A storage interface compatible with Amazon S3 for uploading, downloading, and managing files in your network volumes.
A repository for discovering, deploying, and sharing preconfigured AI projects optimized for Runpod.
Container
A Docker-based environment that packages your code, dependencies, and runtime into a portable unit that runs consistently across machines.
Data center
Physical facilities where Runpod’s GPU and CPU hardware is located. Your choice of data center can affect latency, available GPU types, and pricing.
Machine
The physical server hardware within a data center that hosts your workloads. Each machine contains CPUs, GPUs, memory, and storage.
Training
The foundational phase of AI development, where a model analyzes a massive dataset to learn patterns and relationships.
Fine-tuning
The process of adapting a pre-trained model to a specific task using a smaller, specialized dataset.
Inference
The execution phase where a trained model makes predictions on new data. When you prompt a model and it responds, that’s inference.
Serving
The process of deploying and managing a model for inference. When you deploy a model to a Serverless endpoint, that’s serving.
