diff --git a/_posts/2024-07-12-howto-export-colab-for-vscode.md b/_posts/2024-07-12-howto-export-colab-for-vscode.md
index b39dab5c70db..ba4822256669 100644
--- a/_posts/2024-07-12-howto-export-colab-for-vscode.md
+++ b/_posts/2024-07-12-howto-export-colab-for-vscode.md
@@ -77,9 +77,14 @@ To run your notebook, you need to activate a Python runtime:
 
 Now you're ready to run your notebook:
 
-1. You can run individual cells by clicking the play button next to each cell or by using the keyboard shortcut Shift+Enter.
+1. You can run individual cells by clicking the play button next to each cell or using the keyboard shortcut Shift+Enter.
 2. To run all cells, you can use the "Run All" button at the top of the notebook or in the command palette (Ctrl+Shift+P, then search for "Run All").
 
+## Advanced: Need More Computational Power?
+
+While running notebooks locally in VS Code gives you more control over your environment, you might find yourself needing more computational power, especially for intensive machine learning tasks. If you're looking for alternatives to Colab's free GPUs, consider exploring GPU rental services like [RunPod](https://runpod.io?ref=7su8gs12) and [Vast.ai](https://cloud.vast.ai/?ref_id=145250). These platforms offer affordable GPU rentals on an hourly basis, allowing you to scale your resources based on your project requirements.
+
+
 ## Conclusion
 
 By following these steps, you can successfully run your Google Colab notebooks in Visual Studio Code. This allows you to leverage the power of VS Code's extensive feature set while working with your familiar Jupyter notebooks. Remember that when running notebooks locally, you'll be using your own machine's resources instead of Colab's. This approach gives you more control over your development environment and allows you to work offline, making it an excellent option for many data science and machine learning projects.
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