How to Use Runpod

Runpod is an AI-focused cloud platform that provides on-demand GPU infrastructure for developers, researchers, startups, and businesses. You can use it to run AI models, train and fine-tune models, generate images and videos, and build AI applications without purchasing physical GPU hardware.

This guide explains how to use Runpod from creating an account to deploying a GPU workload and managing your resources.

Step 1: Create a Runpod Account

Visit Runpod and create an account.

After signing in, you’ll access the Runpod dashboard, where you can manage your Pods, GPUs, storage, Serverless endpoints, and other resources.

Step 2: Add Credits

Runpod uses usage-based pricing for its cloud infrastructure.

Add credits to your account before starting a workload when required. The amount you spend depends on the GPU, storage, and how long your resources remain active.

Check the current pricing before selecting a GPU because prices and availability can change.

Step 3: Choose the Right GPU

Select a GPU according to your AI workload.

Runpod offers a wide range of GPUs, including NVIDIA RTX GPUs and data-center GPUs such as the A100, H100, H200, B200, and B300.

Consider:

• VRAM requirements

• Model size

• Processing requirements

• Expected runtime

• Budget

• GPU availability

Don’t automatically select the most powerful GPU. Choose one that provides enough performance and memory for your specific workload.

Step 4: Create a Pod

Runpod Pods provide GPU or CPU environments where you can run your own applications and AI workloads.

Create a new Pod and select the GPU you want.

You can configure the environment, storage, networking, and other options before deploying it.

Step 5: Choose a Template

A template provides a preconfigured environment for a particular workload.

Depending on your requirements, you can choose environments for:

• AI development

• Machine learning

• Jupyter

• ComfyUI

• Model inference

• Other AI applications

Using a suitable template can save time compared with manually installing every dependency.

Step 6: Configure Storage

Choose the storage required for your workload.

AI models and datasets can be large, so make sure you have enough space for:

• Models

• Datasets

• Checkpoints

• Generated files

• Application files

Runpod provides persistent storage options that can keep data available independently of a specific compute instance.

Step 7: Deploy the Pod

Review the GPU, template, storage, and other settings.

Then deploy your Pod.

Once it is running, you can access your environment and start configuring your AI workload.

Step 8: Access Your Environment

Runpod allows you to work with your Pod using different methods, including:

• JupyterLab

• SSH

• VS Code

• Web-based applications

• Configured endpoints

JupyterLab is useful if you want to work with Python notebooks directly in your browser.

Step 9: Install Your AI Model

After connecting to your Pod, install or download the AI model you want to use.

The exact process depends on the model and framework.

A general workflow is:

Download Model → Install Dependencies → Configure Environment → Load Model → Run

Make sure the model is compatible with your selected GPU and available VRAM.

Step 10: Run Your Workload

You can now use your GPU for tasks such as:

• AI inference

• Model training

• Fine-tuning

• Image generation

• Video generation

• Speech processing

• AI development

Runpod gives you control over the environment, allowing you to use the frameworks and applications required for your project.

Step 11: Monitor GPU Usage

While your workload is running, monitor your resources.

Pay attention to:

• GPU utilization

• VRAM

• CPU usage

• Storage

• Runtime

• Application logs

If your GPU utilization is consistently low, you may be able to use a smaller or less expensive configuration.

Step 12: Save Your Work

Before stopping your Pod, make sure important files are stored in persistent storage or another reliable location.

Save:

• Model checkpoints

• Datasets

• Generated files

• Code

• Configuration

• Results

This helps prevent losing important work when compute resources are stopped or removed.

Step 13: Stop the Pod

When you’re finished, stop or terminate the compute resource according to your needs.

This is important because GPU resources are usage-based. Leaving a GPU running when it isn’t needed can increase your costs.

Step 14: Use Runpod Serverless

If you are building an AI application that receives requests from users, Runpod Serverless may be more suitable than keeping a Pod running continuously.

Serverless can automatically scale workers based on demand.

A typical workflow is:

User → Application → API → Runpod Serverless → GPU Worker → AI Model → Response

Runpod positions Serverless for scalable AI inference and production workloads.

Step 15: Create a Serverless Endpoint

Create a Serverless endpoint and configure the required model or worker.

Depending on your workload, you can configure:

• GPU type

• Worker limits

• Scaling

• Container image

• Environment variables

• Request settings

The exact configuration depends on the application you’re deploying.

Step 16: Connect Your Application

Once the endpoint is configured, your application can communicate with Runpod through its API.

For example:

Website → API Request → Runpod → AI Model → API Response

This allows you to integrate GPU-powered AI into websites, SaaS products, mobile applications, and internal tools.

Runpod provides API documentation for managing its infrastructure and services.

Example: Run an Image Generation Application

A basic image-generation workflow can look like:

User Prompt

↓

Your Website

↓

Runpod API

↓

GPU Worker

↓

Image Model

↓

Generated Image

You can use this approach to build your own AI image-generation application.

Example: Run an AI Model

For a model-powered application:

User Input → Your Application → Runpod → AI Model → Response

Runpod handles the GPU infrastructure while your application handles the user interface and business logic.

Example: Train a Model

For training:

Dataset → Runpod GPU → Training → Checkpoint → Evaluation → Final Model

For larger distributed training workloads, Runpod also provides Cluster infrastructure.

How to Reduce Runpod Costs

Choose the Correct GPU

Use a GPU that meets your requirements without unnecessarily paying for excess performance.

Stop Idle Resources

Don’t leave unused GPUs running.

Use Serverless When Appropriate

For workloads with unpredictable demand, Serverless can help scale resources based on requests.

Monitor Runtime

Keep track of how long your GPU is active.

Use Persistent Storage

Store important files separately from temporary compute resources where appropriate.

Common Runpod Mistakes

• Choosing an unsuitable GPU

• Not checking VRAM requirements

• Leaving Pods running unnecessarily

• Not using persistent storage

• Forgetting to save model checkpoints

• Skipping workload testing

• Ignoring GPU utilization

• Not monitoring costs

• Using a complex setup when a template would work

Final Verdict

Using Runpod starts with a simple process:

Create Account → Add Credits → Choose GPU → Deploy Pod → Configure Environment → Run AI Workload → Save Data → Stop Resources

Once you understand Pods, you can move to Serverless for scalable AI inference or Clusters for larger multi-GPU workloads.

Runpod is particularly useful when you need flexible GPU access for AI development, model training, inference, image generation, video generation, and other compute-intensive applications.