What is Google Colab and Why You Should Use It in 2025?

What is a Colab? It's Google's powerful cloud computing tool that gives us free access to GPUs, TPUs, and essential Python libraries without installing anything on our computers.

We've found that Google Colab works seamlessly through your browser, specifically built on Project Jupyter. Because it allows us to store our notebooks directly in Google Drive and comes with pre-installed Python modules, it is perfect for both inexperienced and seasoned users.

Official Link: https://colab.research.google.com

How to Use Google Colab in 2025

If you are new to Google Colab, here are easy steps to get started:

Step 1: Go to Google Colab

Open your browser and visit https://colab.research.google.com.You just need a Google account to use it.

Step 2: Create a New Notebook

Click on “New Notebook”.This opens a blank page where you can start writing Python code.

You will see cells — these are small boxes where you can type your code or text.

Create a New Notebook with Google Colab

Step 3: Write and Run Code

Click inside a cell and type your code. For example:

print("Hello, Google Colab 2025!")

Write and Run Code Google Colab

Then press Shift + Enter or click the Run button to execute it.

Step 4: Save Your Work

Your notebook is automatically saved in Google Drive.You can also rename it by clicking on the title at the top left corner.

Step 5: Use Free GPU or TPU (Optional)

If you are doing AI or machine learning work, you can use GPU or TPU for faster performance.

Go to the menu bar → click Runtime → choose Change runtime type → select GPU or TPU under hardware accelerator → click Save.

Now your code will run much faster.

How to use Use Free GPU or TPU in Google Colab

Step 6: Install and Import Libraries

You can use Python libraries easily.Example:

import numpy as np
import pandas as pd

If something is not installed, just run:

!pip install library_name

Step 7: Connect Google Drive

If you want to access files or datasets from your Drive, use this command:

from google.colab import drive
drive.mount('/content/drive')

Now you can open and save files directly to your Drive.

Step 8: Share Your Notebook

Click the Share button on the top-right corner.You can allow others to view, comment, or edit your notebook — just like Google Docs.

Step 9: Download Your Notebook

If you want to save your notebook locally, go to File → Download → Download .ipynb or Download .py.

Step 10: Reopen and Continue Anytime

All your notebooks are saved in Google Drive.You can reopen them anytime by going to Colab again and selecting File → Open Notebook → Google Drive.

What Can I Do in Google Colab?

1. Run Python CodeYou can start coding in Python without setting up anything. Just open a new notebook and type your code.

Example:

print("Hello from Google Colab")

2. Learn Data Science and Machine LearningGoogle Colab already has popular libraries like Pandas, NumPy, TensorFlow, and Scikit-learn. You can use it to clean data, visualize graphs, and train machine learning models.

3. Train AI Models Using GPU or TPUGoogle Colab gives you free access to GPUs and TPUs. These help train AI and deep learning models faster.

4. Collaborate with OthersYou can share your notebook with friends or team members just like you share a Google Doc. They can view, comment, or even edit your code.

5. Use Google Drive and GitHubYou can save your work in Google Drive or directly open projects from GitHub. This makes it easy to continue your work from anywhere.

6. Automate TasksYou can use Colab to automate daily work like reading data from Google Sheets, sending reports, or analyzing files.

Why Use Google Colab

1. It is completely free

2. No need to install Python

3. You get free cloud storage with Google Drive

4. You can use GPU and TPU for faster performance

5. You can share your projects easily

   
   

How to Get Started

1. Go to https://colab.research.google.com

2. Click on “New Notebook”

3. Start typing your Python code

4. Save it in Google Drive

   
   

That’s it. You are ready to use Google Colab.

Google Colab is a feature-rich and highly customizable platform designed for Python programming, data science, and machine learning. It is ideal for beginners and experts alike since it provides cloud-based execution, eliminating the need for local installs.

The tasks you can do in Google Colab are detailed below, along with their real-world applications.

Key Features & Use Cases of Google Colab

Why Google Colab Remains Relevant in 2025?

In the fast-changing world of technology, many tools come and go. But Google Colab continues to be one of the most useful and reliable platforms for students, developers, and data scientists. Even in 2025, Google Colab remains a top choice for coding, machine learning, and AI experiments.

Let’s understand why it is still so popular and relevant today.

1. It’s Free and Easy to Use

Google Colab is completely free. You don’t need to install Python, Jupyter Notebook, or any heavy software. You just need a Google account. Open Colab in your browser, and you can start coding instantly. This simple setup saves a lot of time, especially for beginners.

2. It Offers Free GPU and TPU Access

One of the biggest reasons people still use Google Colab in 2025 is because of its free access to GPUs and TPUs. These are powerful processors that help you train AI and deep learning models faster. For students or small developers who can’t afford expensive hardware, this feature is a big advantage.

3. It’s Great for Learning and Teaching

Many schools, colleges, and online courses still use Google Colab to teach Python, data science, and machine learning. It’s perfect for beginners because everything runs online, and teachers can easily share notebooks with students.

You can also find thousands of free tutorials and code examples made for Google Colab.

4. It’s Perfect for Collaboration

Just like Google Docs, you can share your Colab notebooks with others. Multiple people can work on the same code, leave comments, or view results. This makes teamwork and research projects much easier.

In 2025, remote learning and online collaboration are still very common — and Colab supports this perfectly.

5. It Supports AI and Data Science Workflows

Google Colab is fully loaded with popular Python libraries like TensorFlow, PyTorch, NumPy, and Pandas. This means you can run almost any machine learning, data visualization, or AI project without extra setup.

Even large datasets stored on Google Drive can be directly used in Colab with a few lines of code.

6. Regular Updates from Google

Google keeps improving Colab every year. In 2025, users enjoy faster runtime options, better integration with Google Drive, and more stable GPU performance. These updates make sure the tool stays useful and modern.

7. It Connects Well with the Cloud

Colab connects easily with Google Drive, GitHub, and other cloud platforms. This makes it simple to store, share, and access your projects from anywhere. As more people shift to cloud computing, this feature becomes even more valuable.

8. It’s Ideal for Experimentation

If you want to quickly test a new AI idea or small project, Google Colab is perfect. You can write code, test models, and visualize results — all in one notebook. It’s flexible and doesn’t require complex setup or local installations.

Google has been making Colab work better with its other tools, like Google Cloud and BigQuery. This means you can easily connect your Colab notebooks to those services. It's pretty handy for managing big projects and handling complicated tasks. No more switching between different platforms. You can import and work with large datasets right in Colab. It's simple and efficient.

Even though other platforms are improving, Google Colab still stands out. It’s a top choice for data scientists and AI developers. One of the biggest reasons is that you can run code on GPUs for free! That's huge for startups, researchers, and students who need powerful computing without the cost.

Plus, with the better integration of Google Cloud, BigQuery, and Vertex AI, Colab becomes even more useful for big projects. It gives you the tools you need to work on extensive datasets and apply complex algorithms without any hassle.

Google Colab: A Complete Guide to Its Features & Benefits

One of the best things about Google Colab is that it has native support for popular machine learning libraries like TensorFlow, PyTorch and Scikit-learn. Since it’s in the cloud, you don’t have to manually install packages or configure drivers to use hardware acceleration. And it has integrated storage with Google Drive so you can save and share your notebooks easily.

Google Colab truly shines when it comes to machine learning projects. The platform's pre-configured environment makes training basic models straightforward and efficient. For instance, you can start with:

1. Image Recognition: Train models to recognize handwritten digits using the MNIST dataset

2. Basic Deep Learning: Create simple neural networks using Keras API

3. Data Classification: Build models for categorizing various types of data

The platform offers flexibility in handling datasets from multiple sources. For Kaggle datasets, you'll need to:

• Generate an API token from your Kaggle account

• Upload the token to Colab

• Use the Kaggle API to download datasets directly

One significant advantage of using Colab for ML projects lies in its GPU/TPU access. These accelerated computing options substantially speed up model training compared to standard CPU processing. To optimize your workflow:

• Choose GPU/TPU runtime for smaller models

• Configure runtime settings based on project requirements

• Save trained models for future use

• Utilize TensorBoard for visualizing training progress

For data storage during projects, Colab provides integration with both Google Drive and Google Cloud Platform. This enables seamless access to large datasets and secure storage of trained models. Additionally, the platform supports real-time collaboration, making it ideal for team projects and educational settings.

In general, Google Colab is a great alternative to a local Jupyter Notebook with free GPU/TPU acceleration, effortless Google Drive integration, and live collaboration. Unlike Jupyter, which involves manual configuration and local computing resources, Colab eliminates these constraints, making it the perfect solution for machine learning tasks.

Google Colab vs Jupyter Notebook: A Feature Comparison

How do I get started with Google Colab?

Getting started with Google Colab is super easy. Just go to official link. You’ll need to log in using your Google account. Once you’re in, you can do a bunch of things!

You can create a new notebook right away. If you have files saved in Google Drive, you can upload them too. Plus, if you’ve got notebooks saved on GitHub, you can bring those into Colab as well.

Once everything is set up, you can start coding. It’s really simple! You can also train models and look at your data in a clear way. In just a few minutes, you’ll be up and running.

What Happens After 12 Hours of Google Colab?

Google Colab has a session timeout policy to efficiently manage resources and ensure fair access for everyone. Colab will automatically shut down a running notebook after 12 hours (or sooner if it is idle).

This means that unless precautions such as reloading data when restarting or saving progress to Google Drive are taken, all saved variables, runtime states, and imported datasets would be lost.

Google Colab offers three levels, with varying runtime limits and hardware availability:

User Type	Session Duration	Hardware Access	Memory Limit
Free Users	Up to 12 hours	Limited GPU/TPU	Standard Memory
Colab Pro	Up to 24 hours	Faster GPUs (Tesla T4)	Higher Memory
Colab Pro+	Even longer sessions	Powerful GPUs (A100, V100)	Maximum Memory

Google Colab Session Lifecycle

When Google Colab session expires, users need to restart their kernel and reload data in order to work further. The following flowchart illustrates this process:

Flowchart of the Google Colab Session Lifecycle

Is Google Colab Good for Coding?

Google Colab is one of the best platforms for coding, especially for Python-based projects. Here’s why:

Best for Python Development

• Pre-installed libraries such as NumPy, Pandas, TensorFlow, and Matplotlib included, no need for manual installation.

• Easy to code, run, and debug Python programs with a native Jupyter Notebook interface.

Great for Machine Learning & Data Science

• GPU/TPU acceleration drastically accelerates deep learning model training.

• Integrated Google Drive, GitHub, and BigQuery direct access makes data set management a breeze.

Suitable for Beginners & Professors

• Interactive coding environment, so ideal for Python learning & AI courses.

• No complicated local setup, just open a notebook and code immediately.

Google Colab is still a robust, free, and open platform for coding, machine learning, and data science. You can be a beginner looking to learn Python or an AI scientist training deep learning models. Colab provides a seamless experience with cloud resources. So, what are you waiting for? Give it a try!

Does Google Colab offer free GPUs?

Yes, Google Colab does offer free access to GPUs for all users. When starting a notebook, you can select “GPU” as your hardware accelerator under “Runtime > Change runtime type.” If available, Google will allocate a GPU—usually a Tesla T4 or P100—to your session automatically. Free sessions with GPUs have usage limits and may disconnect after a certain period (typically 12 hours), and the availability can change based on Colab’s server load.

How much is A100 GPU in Colab?

The powerful NVIDIA A100 GPU is not available on the free plan. To use A100, you need to upgrade to a paid plan such as Colab Pro or Colab Pro+:

• Colab Pro: Starts at around $9.99/month and gives you priority for better hardware, but A100 allocation is not always guaranteed.

• Colab Pro+: Costs about $49.99/month and offers higher priority for A100 access and longer runtimes.

• Pay-As-You-Go: You can also buy compute units to access A100 even if you’re not on one of the subscriptions. The A100 typically costs between $0.60–$1.20 per hour, depending on current demand and region.

  
  

How to free GPU RAM in Google Colab?

If your GPU memory is full and you want to free it up, here are some simple steps:

1. Delete unnecessary variables: Remove large variables by running del variable_name in a notebook code cell.

   
   

2. Force cleanup with Python's garbage collector:

python

import gc gc.collect()

1. This tries to reclaim memory that’s no longer needed.

2. Clear PyTorch CUDA cache (if using PyTorch):

python

import torch torch.cuda.empty_cache()

1. This helps clear any cached tensor memory specific to PyTorch.

2. Restart the runtime: From the menu bar, choose “Runtime > Restart runtime.” This will completely reset your notebook and clear all memory, including GPU RAM. This is often the quickest way to resolve stubborn GPU memory issues.

3. Reduce batch sizes and release unused tensors: While training models, keep batch sizes small and make sure you aren’t storing tensors or data longer than needed.