How do you use Google Colab’s free GPU for AI?
If you ask someone why they are not working with the latest LLM or why they are not able to build out an AI agent system, the first thing that comes out of their mouth is
I don’t have a GPU
But, my friends, you might all know that Google Colab provides you with a free GPU, then why aren’t you using that? In this particular blog post, I will explain in detail its key features, what it can do and how it compares with other GPUs
Its free for all, hence everyone owns a GPU, whether you know it or not
Data Science in Your Pocket – No Rocket Science
What Is Google Colab?
Think of Google Colab as your personal AI lab in the cloud — a Jupyter Notebook hosted by Google that runs Python code right in your browser. The kicker? It gives you free access to a GPU so you can train real models without buying a high-end gaming PC or renting expensive cloud instances.
No credit cards required, just a google account
How to enable Google Colab GPU?
https://medium.com/media/6aa543bfba75f0ea60e40e2aaf208f28/href
⚙️ Meet the Engine: NVIDIA T4 GPU on Colab
Here’s where the power lies. The NVIDIA T4 GPU is what Colab usually assigns in the free tier. While it’s not the fastest GPU on the block, it’s got some serious capabilities:
Most of the LLMs that are released on Hugging Face can be run on Google Colab without paying a single penny. So this is that capable.
This GPU isn’t going to train GPT-4, but it will let you fine-tune BERT, train CNNs, experiment with GANs, and deploy cool demos AI applications.
Why does this matter?
Tensor Cores are specifically designed to accelerate deep learning (matrix multiplications, yay!). If your framework uses mixed precision training (like FP16), you’ll benefit big time.
T4 is optimized more for inference than training — which is perfect for trying out LLMs, Whisper, or image models.
Low power, small footprint — that’s why cloud providers love them. Cheap to run, great for multi-tenant workloads like Colab.
Session Limits & Gotchas
Bad things first
Okay, let’s be clear: Colab’s free GPU comes with limits. Here’s what you need to know:
Session Time: ~12 hours max (sometimes less)
Idle Timeout: ~90 mins of inactivity = booted
RAM: ~12 GB (can sometimes upgrade to 25 GB)
Disk Space: ~100 GB (temporary)
GPU Availability: Not guaranteed. Sometimes you get a T4. Sometimes a slower K80. Roll of the dice.
But its free, so you can’t complain
What Can You Actually Do with a T4?
Here’s what Colab’s T4 is great for:
✅ Fine-tuning HuggingFace models (smaller LLMs)
✅ Image classification with CNNs
✅ Training small to mid-sized Transformers
✅ Running inference on Whisper, BERT, etc.
✅ Building GANs for art or face generation
Just don’t try to train/infer a 65-billion-parameter LLM from scratch. T4 will cry.
How does T4 compare with the latest GPUs like RTX 4090 or Nvidia H100?
Unfortunately, there is just no comparison. These are expensive systems and are not available for free. But T4 is free, so it puts it on the front of the race for accessibility to all the users.
1. Turing vs Ampere vs Hopper (4090 vs T4 vs H100)
T4 (Turing architecture) sits one generation behind the RTX 30-series (Ampere) and two behind H100 (Hopper). It’s like comparing:
- T4 = Toyota Prius (efficient, affordable, gets the job done)
- 4090 = Lamborghini (super fast, flashy, but $$$)
- H100 = NASA Rocket Engine (built for massive scale, not casual driving)
So T4 isn’t slow — it’s just not built to be the fastest. It’s optimized for smart, multi-user environments.
2. Training AI models
Let’s break it down with some baseline comparisons. Suppose you’re training a BERT model:
That’s 25x speed difference between T4 and H100. But H100s cost more than your car. Colab’s free T4? Zero dollars.
3. Cost-to-Performance Breakdown
For hobbyists, students, and solo developers, Colab + T4 = the sweet spot.
Power Tips to Maximise T4 on Colab
Here’s how to squeeze every ounce out of that T4:
Use FP16 (Mixed Precision) Training
Mount Google Drive to save models and logs
Checkpoint regularly in case your session times out
Use datasets that fit in RAM or stream batches efficiently
Clear GPU cache to avoid memory leaks:
import gc, torch
gc.collect()
torch.cuda.empty_cache()
Final Thoughts: Why You Should Care
Whether you’re:
Building your first neural net
Fine-tuning a HuggingFace model
Creating an AI resume helper
Exploring generative AI
Google Colab’s free GPU gives you everything you need to start, and nothing you need to pay. It’s the perfect stepping stone from “AI curious” to “AI capable.”
Understanding Google Colab Free GPU in detail was originally published in Data Science in Your Pocket on Medium, where people are continuing the conversation by highlighting and responding to this story.
