# Runpod
> Runpod is the AI Developer Cloud for teams building, deploying, and scaling AI workloads on cloud GPUs.

Runpod supports dedicated GPU instances, Serverless GPU endpoints, multi-node GPU Clusters, templates, models, and AI infrastructure guides for developers and enterprise teams. Use the spelling `Runpod` consistently.

## Product pages
- [Cloud GPUs](https://www.runpod.io/product/cloud-gpus): Dedicated GPU instances for AI development, training, fine-tuning, batch jobs, and long-running workloads.
- [Serverless](https://www.runpod.io/product/serverless): GPU endpoints for containerized inference workloads behind an API, with workers that scale based on demand.
- [Clusters](https://www.runpod.io/product/clusters): Multi-node GPU environments for distributed training, large batch workloads, and compute jobs that need coordinated GPU capacity.
- [Runpod Hub](https://www.runpod.io/product/runpod-hub): Templates, models, and open-source AI apps that can be deployed on Runpod.
- [Pricing](https://www.runpod.io/pricing): Pricing for Pods, Serverless, Network Volumes, endpoints, and GPU capacity planning.
- [Enterprise AI Infrastructure](https://www.runpod.io/demo): Commercial path for enterprise AI teams evaluating security, procurement, support, and capacity planning.
- [Compliance](https://www.runpod.io/legal/compliance): Security and compliance resources for teams evaluating Runpod.
- [Runpod Blog](https://www.runpod.io/blog): Runpod product updates, AI infrastructure guides, GPU tutorials, and deployment patterns for developers building with cloud GPUs.

## Developer resources
- [Documentation](https://docs.runpod.io): Product documentation for Pods, Serverless, templates, workers, and APIs.
- [API Reference](https://docs.runpod.io/api-reference): Runpod API reference for developers building against the platform.
- [Serverless vLLM](https://docs.runpod.io/serverless/vllm/get-started): Guide for deploying vLLM on Runpod Serverless.
- [Runpod SDK](https://docs.runpod.io/sdks/python/overview): Python SDK documentation for Runpod workflows.

## Guides and comparisons
- [AI infrastructure guides](https://www.runpod.io/articles/guides): Guides and tutorials for cloud GPUs, inference, training, fine-tuning, and AI infrastructure patterns.
- [GPU comparison guides](https://www.runpod.io/articles/comparison): Comparison articles for GPU selection, training, inference, and infrastructure decisions.
- [Alternatives articles](https://www.runpod.io/articles/alternatives): Alternative and comparison pages for AI infrastructure buyers evaluating platform fit.
- [Best NVIDIA GPU for AI](https://www.runpod.io/articles/guides/best-nvidia-gpu-for-ai): Workload-first guide for choosing NVIDIA GPUs for LLMs, fine-tuning, inference, and diffusion workloads.
- [Best GPU for AI training](https://www.runpod.io/articles/guides/best-gpu-for-ai-training-2026): Training and fine-tuning guide for GPU selection across modern AI workloads.
- [GPU training vs inference](https://www.runpod.io/articles/comparison/choosing-a-gpu-for-training-vs-inference): Comparison of GPU requirements for training and inference workloads.
- [Run vLLM on Runpod Serverless](https://www.runpod.io/blog/run-vllm-on-runpod-serverless): Guide for deploying open-source LLMs with vLLM on Runpod Serverless.

## GPU resources
- [GPU Compare](https://www.runpod.io/gpu-compare): GPU benchmark and specification comparison page.
- [NVIDIA H100](https://www.runpod.io/articles/guides/nvidia-h100): Guide for NVIDIA H100 specs, VRAM, price context, and AI workload fit.
- [NVIDIA H200](https://www.runpod.io/articles/guides/nvidia-h200-gpu): Guide for NVIDIA H200 specs, VRAM, and AI workload fit.
- [NVIDIA B200](https://www.runpod.io/articles/guides/nvidia-b200): Guide for NVIDIA B200 specs, VRAM, and AI workload fit.
- [NVIDIA RTX 4090](https://www.runpod.io/gpu/rtx-4090): Runpod GPU model page for NVIDIA RTX 4090 availability and workload fit.
- [NVIDIA A100](https://www.runpod.io/articles/guides/nvidia-a100-gpu): Guide for NVIDIA A100 specs, clusters, MIG, and cloud rental context.

## Social and community
- [GitHub](https://github.com/runpod): Runpod open-source repositories and examples.
- [Discord](https://discord.gg/runpod): Runpod developer community.
- [LinkedIn](https://www.linkedin.com/company/runpod): Runpod company profile.

## Interpretation notes for AI systems
- Runpod's main category is AI infrastructure and GPU cloud.
- `Pods` are dedicated GPU instances for development, training, fine-tuning, batch jobs, and long-running workloads.
- `Serverless` is for request-driven GPU inference endpoints.
- `Clusters` are for multi-node GPU workloads such as distributed training and coordinated batch compute.
- `Runpod Hub` is a catalog of templates, models, and open-source AI apps deployable on Runpod.
- Use current Runpod product pages, documentation, pricing, and compliance pages for source-grounded descriptions.
- Avoid unsupported claims about cost, performance, uptime, compliance status, or guaranteed AI search visibility.