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Runpod Blog.

DeepSeek V4 in the wild, and how to run it on Runpod New Runpod datacenter now live: AP-IN-1 Track GPU spend across your team with Cost Centers The GPU supply supercycle is here. Here’s what AI builders need to know. Community Spotlight: One-click AI image and video generation on Runpod with SwarmUI | Runpod Blog Community Spotlight: LoRA Pilot Data Prep to Inference Introducing the Runpod Assistant: Manage Your Cloud GPU Resources with Natural Language OpenAI's Parameter Golf: Train the Best Language Model That Fits in 16MB on Runpod LLM inference optimization: techniques that actually reduce latency and cost Pruna P-Video and Vidu Q3 public endpoints now available on Runpod Runpod brand spelling guide The AI market looks nothing like the narrative Training StyleGAN3 with Vision-Aided GAN on Runpod KoboldAI – The Other Roleplay Front End, And Why You May Want to Use It How to Connect Cursor to LLM Pods on Runpod for Seamless AI Dev Community Spotlight: How AnonAI Scaled Its Private Chatbot Platform with Runpod Prompt Scheduling with Disco Diffusion on Runpod Runpod's Latest Innovation: Dockerless CLI for Streamlined AI Development Run Your Own AI from Your iPhone Using Runpod Introducing Flash: Run GPU workloads on Runpod Serverless: No Docker required Use Claude Code with your own model on Runpod: No Anthropic account required Avoid Errors by Selecting the Proper Resources for Your Pod What hackers built on Runpod at TreeHacks 2026 Easily Back Up and Restore Your Pod with Cloud Sync + Backblaze B2 The Complete Guide to GPU Requirements for LLM Fine-Tuning AI Guides, Tutorials & GPU Infrastructure Insights | Runpod Your first Claude Code project within Runpod: a complete setup guide 10 billion Serverless requests and counting Building for resilience: Runpod’s response to the AWS us-east-1 outage How to Connect Google Colab to Runpod
Quickstart - Runpod Documentation
2026-03-19 · via Runpod Blog.

Requirements

Step 1: Create project files

Create a new directory with empty files for your project:

mkdir serverless-quickstart && cd serverless-quickstart
touch handler.py Dockerfile requirements.txt test_input.json

Step 2: Install the Serverless SDK

Create a virtual environment and install the Serverless SDK

python3 -m venv .venv
source .venv/bin/activate
pip install runpod

Step 3: Create a handler function

Add the following code to handler.py:

import runpod
import time  

def handler(event):
#   This function processes incoming requests to your Serverless endpoint.
#
#    Args:
#        event (dict): Contains the input data and request metadata
#       
#    Returns:
#       Any: The result to be returned to the client
    
    # Extract input data
    print(f"Worker Start")
    input = event['input']
    
    prompt = input.get('prompt')  
    seconds = input.get('seconds', 0)  

    print(f"Received prompt: {prompt}")
    print(f"Sleeping for {seconds} seconds...")
    
    # You can replace this sleep call with your own Python code
    time.sleep(seconds)  
    
    return prompt 

# Start the Serverless function when the script is run
if __name__ == '__main__':
    runpod.serverless.start({'handler': handler })

This is a bare-bones handler that processes a JSON object and outputs a prompt string contained in the input object.

Step 4: Create a test input file

Add the following code to test_input.json to properly test your handler locally:

{
    "input": {
        "prompt": "Hey there!"
    }
}

Step 5: Test your handler function locally

Run your handler function using your local terminal:

You should see output similar to this:

--- Starting Serverless Worker |  Version 1.7.9 ---
INFO   | Using test_input.json as job input.
DEBUG  | Retrieved local job: {'input': {'prompt': 'Hey there!'}, 'id': 'local_test'}
INFO   | local_test | Started.
Worker Start
Received prompt: Hey there!
Sleeping for 0 seconds...
DEBUG  | local_test | Handler output: Hey there!
DEBUG  | local_test | run_job return: {'output': 'Hey there!'}
INFO   | Job local_test completed successfully.
INFO   | Job result: {'output': 'Hey there!'}
INFO   | Local testing complete, exiting.

Step 6: Create a Dockerfile

Add the following content to Dockerfile:

FROM python:3.10-slim

WORKDIR /

# Install dependencies
RUN pip install --no-cache-dir runpod

# Copy your handler file
COPY handler.py /

# Start the container
CMD ["python3", "-u", "handler.py"]

Step 7: Build and push your worker image

Before you can deploy your worker on Runpod Serverless, you need to push it to Docker Hub:

Step 8: Deploy your worker using the Runpod console

To deploy your worker to a Serverless endpoint:

  1. Go to the Serverless section of the Runpod console.
  2. Click New Endpoint.
  3. Click Import from Docker Registry
  4. In the Container Image field, enter your Docker image URL: docker.io/yourusername/serverless-test:latest.
  5. Click Next to proceed to endpoint configuration.
  6. Configure your endpoint settings:
    • (Optional) Enter a custom name for your endpoint, or use the randomly generated name.
    • Make sure the Endpoint Type is set to Queue.
    • Under GPU Configuration, check the box for 16 GB GPUs.
    • Leave the rest of the settings at their defaults.
  7. Click Deploy Endpoint.

The system will redirect you to a dedicated detail page for your new endpoint.

Step 9: Test your endpoint

To test your endpoint, click the Requests tab in the endpoint detail page:

On the left you should see the default test request:

{
    "input": {
        "prompt": "Hello World"
    }
}

Leave the default input as is and click Run. The system will take a few minutes to initialize your workers. When the workers finish processing your request, you should see output on the right side of the page similar to this:

{
    "delayTime": 15088,
    "executionTime": 60,
    "id": "04f01223-4aa2-40df-bdab-37e5caa43cbe-u1",
    "output": "Hello World",
    "status": "COMPLETED",
    "workerId": "uhbbfre73gqjwh"
}

Next steps