> ## Documentation Index
> Fetch the complete documentation index at: https://runpod-b18f5ded-docs-runpod-allow-ip.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
> ## Agent Instructions
> If this page is missing information, contains outdated instructions, or doesn't fully answer the user's question, use the feedback tool to report it. In your feedback, be specific about what's missing, what appears out of date, or what needs to be corrected or updated, so the docs team can act on it directly.
# Welcome to Runpod
> Explore our guides and examples to deploy your AI/ML application on Runpod. Review setup and usage guidance in the Runpod documentation.
export const InferenceTooltip = () => {
return inference;
};
export const FineTuningTooltip = () => {
return fine-tuning;
};
export const TrainingTooltip = () => {
return training;
};
Runpod is a cloud computing platform built for AI, machine learning, and general compute needs. Whether you're or AI models, or deploying cloud-based applications for , Runpod provides scalable, high-performance GPU and CPU resources to power your workloads.
## Access GPUs instantly
Create an account, deploy your first GPU Pod, and use it to execute code.
Create API keys to manage your access to Runpod resources.
Learn about the key concepts and terminology for the Runpod platform.
Run Python functions on remote GPUs directly from your local terminal.
Pay-per-second computing with automatic scaling for production AI/ML apps.
Dedicated GPU or CPU instances for containerized AI/ML workloads.
## Use our model endpoints
Runpod offers [Public Endpoints](/public-endpoints/overview) for instant API access to pre-deployed AI models for image, video, audio, and text generation. No deployment or infrastructure required—just [create an API key](/get-started/api-keys) and make a request:
```python Python theme={null}
import requests
response = requests.post(
"https://api.runpod.ai/v2/black-forest-labs-flux-1-schnell/runsync",
headers={
"Authorization": "Bearer YOUR_API_KEY", # Replace YOUR_API_KEY with your actual API key
"Content-Type": "application/json"
},
json={
"input": {
"prompt": "A beautiful sunset over mountains", # Customize your prompt
"width": 1024,
"height": 1024
}
}
)
result = response.json()
print(result["output"]["image_url"])
```
```bash cURL theme={null}
# Replace YOUR_API_KEY with your actual API key
curl -X POST "https://api.runpod.ai/v2/black-forest-labs-flux-1-schnell/runsync" \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"input": {
"prompt": "A beautiful sunset over mountains",
"width": 1024,
"height": 1024
}
}'
```
For a list of available models, see the [model reference](/public-endpoints/reference).
## Guides and examples
Deploy a dedicated GPU with ComfyUI pre-installed and start generating images.
Build a ComfyUI worker and deploy it as a Serverless endpoint.
Use a hybrid local/remote script to generate images with SDXL.
Create a multi-model pipeline for video generation.
Create a REST API with automatic load balancing using Flash.
Deploy a large language model in minutes using vLLM on Serverless.
## High-performance clusters
Create a multi-node [Instant Cluster](/instant-clusters) for fully managed distributed GPU computing with high-speed networking between nodes.
Learn how Instant Clusters work and when to use them.
Set up managed Slurm for HPC workloads.
Run distributed PyTorch training across multiple nodes.
## Support
Submit a support request using our contact page.
Check the status of Runpod services and infrastructure.
Join the Runpod community on Discord.