Developing in the cloud just got a whole lot smarter. In this guide, we'll walk through setting up Claude Code within a RunPod environment, creating GPU-accelerated projects, and pushing your work to GitHub—all from within a secure cloud pod with network volume storage.

By the end of this tutorial, you'll know how to:

• Set up and install Claude Code within a pod
• Work with Claude Code to scope and develop projects
• Create projects that utilize GPU instances
• Push your projects to GitHub for version control
• Use AI coding assistance responsibly and effectively

What is Claude Code?

Claude Code is Anthropic's official command-line AI coding assistant that brings the power of Claude directly into your terminal. Think of it as an AI pair programmer that can actually write, edit, and execute code on your behalf.

Prior to Claude Code, many AI-assisted coding workflows involved tedious copy-and-pasting. You'd manually search for functions to replace, then manually paste or modify changes—a messy and error-prone process.

What makes Claude Code particularly powerful is its tool-based approach. It can:

• Edit files directly
• Execute bash commands
• Perform web searches
• Chain actions together to accomplish complex tasks
• Create checkpoints before making changes (so you can revert if needed)

The Git safety net

While Claude Code has built-in safety features, your session-based checkpoints don't persist. That's why we're coupling it with Git in this tutorial. Git becomes your permanent safety net—your time machine for code. Commit often, and you'll always have a way back.

Using AI to code: finding the right balance

Let's address the elephant in the room: using AI to write code is controversial. The internet loves to polarize people on this topic, so here's my take on using it safely and responsibly.

The "Vibe Coding" trap

On one extreme, you have what critics call "vibe coding"—developers who spam prompts, accept whatever the AI generates, and end up with a codebase they fundamentally don't understand. That's someone who ships code to production without being able to explain what any of it does, can't debug when things break, and has essentially outsourced their thinking to an algorithm. That's reckless and unsustainable.

The "Real Developers Only" fallacy

On the other extreme, you have people who refuse to touch AI tools at all, insisting that if you didn't type every character yourself, you're not a real developer. Even if you did, you'd still find critics complaining you didn't do it in a lower-level language. Completely disregarding AI means leaving productivity gains on the table and ignoring the reality that development has always involved leveraging tools—from IDEs to Stack Overflow to library documentation.

The sweet spot

The answer lies in the middle. Here's my framework:

Use AI to accelerate what you already understand, not to replace understanding.

Now that we've covered the philosophy, let's get practical.

Setting up your Runpod environment

Step 1: Deploy your pod

1. Navigate to the Pods section in RunPod
2. Select whatever GPU spec you need - feel free to test with a lower end spec, since all of the actual code inference is done by Claude in this case
3. Change your template to the most recent PyTorch version
4. Increase storage space if needed
5. Set any necessary overrides (open HTTP ports for an API process, for example)
6. Click Deploy

Wait for your pod to spin up. For easier workflow, consider splitting your interface into two windows—one with a file browser and terminal on the left, and another terminal on the right where we'll install Claude Code.

Step 2: Install Claude Code

Run the installation command (link available in the video description):

# curl -fsSL https://claude.ai/install.sh | bash

Step 3: Authenticate Claude Code

Start Claude Code by typing:

claude

You'll be prompted to:

1. Set your preferred text display method
2. Choose your account type (Pro, Max, or API access)

For Pro account users:

• Select the login option
• It will attempt to open a browser (which will fail in the pod environment)
• Copy the authorization URL and paste it into your local browser
• Click Authorize
• Copy the provided code and paste it back into Claude Code

Login successful! You're now ready to start coding.

Step 4: Set directory permissions

Claude Code will ask if you trust the files in this folder. Since you're working within a pod (not on your local machine), there's no concern about anything we're doing here breaking out into your local PC.

Important security note: If running Claude Code on a local machine, be very careful about the permissions you grant, as it can read, write, and execute files in any directory you give it access to. Ensure that you follow the principle of least privilege and only run it from within a subdirectory you can easily restore if you need to; there's no reason to run it from, say, your root folder and giving it access to your entire machine. Even when running it from within a pod,

In the pod environment, approve the permissions and you're ready to go.

Testing Claude Code: Hello world

Let's verify everything works with a simple test:

make me a hello world in Python

Claude Code will think for a moment, then create the file. Allow the edits, refresh your file browser, and you'll see hello.py. Run it in your terminal:‍

python3 hello.py

If all goes well (and it should) it will create a small Python script that will print a "Hello World" statement.

Setting up GitHub integration

Create a new repository

1. Go to GitHub and click the + sign
2. Select New repository
3. Name it (e.g., "my-runpod-project")
4. Click Create repository

Generate a Personal Access Token

1. Go to Settings → Developer settings
2. Select Personal access tokens
3. Choose between fine-grained or classic tokens (we'll use classic for this example)
4. Click Generate new token
5. Give it a name
6. Grant repo access
7. Copy the token (you won't see it again!)

Configure Git in your Pod

First, ask Claude Code to create a README:

create a readme.md for this script for GitHub

‍‍Then configure Git with your credentials:‍

git config --global user.name "Your Name"git config --global user.email "your.email@example.com"git initgit add .git commit -m "Initial commit"git remote add origin https://github.com/yourusername/my-runpod-project.gitgit branch -M maingit push -u origin main

‍When prompted for credentials:

• Username: Your GitHub username
• Password: Paste the personal access token you created

Check your GitHub repository—your code has been pushed successfully from your pod!

Working with Claude Code: A practical example

Now, we'll expand on our "Hello World" and have it actually do something with the GPU.

Using Plan Mode

Ask Claude Code (shift-tab to switch to Plan Mode):

I want to do a hello world using CUDA instead of just a print. What are my options?

Plan mode is excellent for asking questions without making immediate changes. Claude Code will:

• Analyze your request
• Present multiple options
• Explain what each approach would involve

This lets Claude Code think through your codebase and examine possibilities rather than jumping straight to modifications.

Reviewing the plan

After Claude Code finishes planning, it will outline:

1. Print GPU information using CUDA
2. Create an array
3. Copy data to the device
4. Run a kernel
5. Copy results back
6. Create a requirements.txt for dependency management
7. Show expected output

If the plan looks good, approve it and let Claude Code implement the changes.

Running the CUDA code

Execute the updated script:

python3 hello.py

Claude Code may warn you about potential messages (like CUDA initialization warnings). The output should show:

• "Hello from the GPU"
• Array data processed on the GPU

Examining the code

Open the generated code to see what Claude Code created:

• Sets the current GPU device
• Prints the GPU name
• Displays compute capability
• Implements CUDA array operations

Pro tip: You can ask Claude Code to explain any part of the code directly:

What does compute capability mean?


You don't have to tab back to documentation or search engines—Claude Code will explain concepts in context.

Committing your changes

Once you're satisfied with the working code:

git add .
git commit -m "Updated version - now uses GPU"
git push

Re-enter your credentials, and your GPU-accelerated code is now safely stored in your GitHub repository.

Account types and costs

Here's what you need to know about accessing Claude Code as a single user:

API credits (Minimum $5)

• Cheapest initial entry point for testing
• Least cost-efficient on a per-token basis
• Good for determining if Claude Code works for you technically
• Not recommended for ongoing use

Pro plan ($20/month)

• Recommended for most users
• Solid 2-4 hours of usage per day
• Great for personal projects and experimentation
• Best balance of cost and capability

Max plans ($100 and $200/month)

• $200 plan offers ~20x the rate limit of Pro
• Most efficient per-token cost (if you use it enough)
• Suitable for all-day development work
• Best for professional or intensive use

Self-hosted option

• Use Claude Code with an Anthropic-compatible API
• Run models like Qwen 3 via Ollama
• Potentially even cheaper
• Can run directly on Runpod
• Note: Output quality varies widely between models
• Technical functionality remains the same

We'll go into how to perform self-hosting in our next article, but the TL;DR is that you would use something like Ollama or anything else that can handle the Anthropic-compatible API to host a model and then configure Claude Code to send requests to it.

Understanding Claude's models

Claude offers three models with different capabilities and costs:

1. Haiku - Fastest and most cost-effective
2. Sonnet - Balanced performance and capability
3. Opus - Most capable, highest cost

Higher-end models consume your usage cap faster. Here's my recommended approach:

Start small and work your way up. Begin with Haiku for simple tasks (like the Hello World example in this tutorial). If you hit a roadblock after your third consecutive attempt, that's a good sign to move up to the next model tier.

You'll develop intuition for which model suits which tasks over time.

What's next?

This tutorial covered the fundamentals to get you up and running. We've barely scratched the surface of what's possible with Claude Code on Runpod. This is just the tip of the iceberg. Stay tuned for more in-depth tutorials on leveraging AI-assisted development in your cloud environment.

Key takeaways

• Claude Code transforms terminal-based development with intelligent assistance
• Git integration provides essential version control and safety
• Responsible AI usage means accelerating understanding, not replacing it
• Runpod provides the perfect cloud environment for GPU-accelerated development
• Start with simpler models and scale up as needed

Ready to revolutionize your development workflow? Set up your Runpod environment today and experience the power of AI-assisted coding in the cloud.

Have questions or want to share your Claude Code experiences? Feel free to reach out to our community on Discord.

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