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Runpod console

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

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Serverless

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

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Runpod SDK

The runpod Python package, the single SDK for building on Runpod. Define GPU functions with decorators such as @app.queue, @app.task, and @app.api on a runpod.App, deploy them as apps, call existing Serverless endpoints, build Serverless workers, and manage Pods and other resources. It includes the rp command-line interface.

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Flash

The decorator-based framework that is now part of the Runpod SDK. The standalone runpod-flash package gets no fixes or updates after October 29, 2026. For new projects, use the Runpod SDK; to move existing code, see Migrating from Flash.

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Pod

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

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Public Endpoint

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

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Instant Cluster

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

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Network volume

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. Available in standard and high-performance tiers.

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S3-compatible API

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

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Runpod Hub

A repository for discovering, deploying, and sharing preconfigured AI projects optimized for Runpod.

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Container

A Docker-based environment that packages your code, dependencies, and runtime into a portable unit that runs consistently across machines.

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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.

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Machine

The physical server hardware within a data center that hosts your workloads. Each machine contains CPUs, GPUs, memory, and storage.

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Training

The foundational phase of AI development, where a model analyzes a massive dataset to learn patterns and relationships.

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Fine-tuning

The process of adapting a pre-trained model to a specific task using a smaller, specialized dataset.

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Inference

The execution phase where a trained model makes predictions on new data. When you prompt a model and it responds, that’s inference.

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Serving

The process of deploying and managing a model for inference. When you deploy a model to a Serverless endpoint, that’s serving.
Last modified on October 26, 2026