> ## 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.

# Concepts

> Key concepts and terminology for understanding Runpod's platform and products.

## [Runpod console](https://console.runpod.io)

The web interface for managing your compute resources, account, teams, and billing.

## [Serverless](/serverless/overview)

A pay-as-you-go compute solution designed for dynamic autoscaling in production AI/ML apps.

## [Flash](/flash/overview)

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.

## [Pod](/pods/overview)

A dedicated GPU or CPU instance for containerized AI/ML workloads, such as training models, running inference, or other compute-intensive tasks.

## [Public Endpoint](/public-endpoints/overview)

An AI model API hosted by Runpod that you can access directly without deploying your own infrastructure.

## [Instant Cluster](/instant-clusters)

A managed compute cluster with high-speed networking for multi-node distributed workloads like training large AI models.

## [Network volume](/storage/network-volumes)

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.

## [S3-compatible API](/storage/s3-api)

A storage interface compatible with Amazon S3 for uploading, downloading, and managing files in your network volumes.

## [Runpod Hub](/hub/overview)

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.