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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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Flash

A framework for building distributed GPU applications using local Python scripts. Write functions with the @remote decorator, and Flash automatically executes them on Runpod’s infrastructure.

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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.

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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.