How to Use GPU and TPU Acceleration in Colab 2026

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Introduction

How to Use GPU and TPU Acceleration in Colab 2026

Google Colab is a powerful platform for running Python code and machine learning experiments without the need for high-end local hardware. One of its most valuable features is GPU and TPU acceleration, which can drastically reduce training times for deep learning models. In this guide, we’ll show you exactly how to enable and use GPUs and TPUs in Colab, provide step-by-step instructions, best practices, and troubleshoot common issues, so you can fully leverage Colab’s cloud computing power.

Why Use GPU and TPU in Colab?

Using CPU-only processing can be slow for complex computations, especially for tasks like deep learning, image processing, and neural network training. GPUs (Graphics Processing Units) and TPUs (Tensor Processing Units) are specialized hardware designed to handle parallel computations efficiently.

Benefits of GPU/TPU in Colab:

How to Enable GPU or TPU in Google Colab

Follow these simple steps to enable hardware acceleration in your Colab notebook.

Step 1: Open Colab Notebook

• Go to Google Colab.
• Open a new or existing notebook.

Step 2: Change Runtime Type

1. Click Runtime → Change runtime type.
2. In the popup window, select your hardware accelerator:
   • GPU
   • TPU
   • Leave as None if no acceleration is needed.
3. Click Save.

Step 3: Verify Hardware

Run the following code to check your GPU or TPU:

For GPU:

import torch
if torch.cuda.is_available():
    print("GPU is available:", torch.cuda.get_device_name(0))
else:
    print("GPU not available")

For TPU:

import tensorflow as tf
try:
    tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
    print("TPU is available:", tpu.master())
except ValueError:
    print("TPU not available")

Using GPU Acceleration in Colab

Once GPU is enabled, you can run TensorFlow, PyTorch, or other frameworks on GPU without any additional setup.

Example with PyTorch:

import torch

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
x = torch.rand(3, 3).to(device)
y = torch.rand(3, 3).to(device)
z = x + y
print(z)

Tips for GPU usage:

• Use .to(device) to move tensors to GPU.

• Monitor GPU usage with:

!nvidia-smi

• Large batch sizes may cause out-of-memory errors; adjust accordingly.

Using TPU Acceleration in Colab

TPUs are ideal for large-scale deep learning tasks with TensorFlow.

Example with TensorFlow:

import tensorflow as tf

resolver = tf.distribute.cluster_resolver.TPUClusterResolver()
tf.config.experimental_connect_to_cluster(resolver)
tf.tpu.experimental.initialize_tpu_system(resolver)
strategy = tf.distribute.TPUStrategy(resolver)

with strategy.scope():
    model = tf.keras.Sequential([
        tf.keras.layers.Dense(128, activation='relu', input_shape=(784,)),
        tf.keras.layers.Dense(10, activation='softmax')
    ])
    model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

Best Practices:

• Use TPUs with TensorFlow only (PyTorch support is limited via XLA).

• TPUs require datasets to be batched efficiently.

• Prefer tf.data.Dataset pipelines for TPU training.

Common Mistakes and Troubleshooting

Alternatives to Colab GPU/TPU

If you need more consistent performance or longer runtimes:

• Kaggle Kernels: Free GPU support with some limitations.
• AWS EC2 GPU Instances: Paid, high-performance GPUs.
• Google Cloud AI Platform: Professional TPU/GPU instances.
• Local Machine: Install CUDA-enabled GPU for local training.

Examples of Speed Gains

Using hardware acceleration in Colab can significantly reduce time for both research and production tasks.

Conclusion

Leveraging GPU and TPU acceleration in Colab is a game-changer for machine learning practitioners. By following the steps above, you can speed up model training, experiment faster, and optimize workflows—all without investing in expensive hardware.

Start using GPUs and TPUs today to unlock Colab’s full potential!

CTA: Share this guide with fellow developers and bookmark it for your next Colab project!

FAQ

1. Can I use GPU and TPU at the same time in Colab?
No, Colab allows either GPU or TPU per notebook session, not both simultaneously.

2. How do I know if my notebook is using GPU?
Run !nvidia-smi for GPU or check torch.cuda.is_available() in PyTorch.

3. Are TPUs faster than GPUs?
TPUs are faster for large-scale TensorFlow models, but GPUs are more versatile for different frameworks.

4. Does using GPU/TPU cost money in Colab?
Basic Colab provides free access, but Colab Pro/Pro+ offers longer runtimes and priority GPU/TPU access.

5. Why is my GPU not detected in Colab?
Ensure you have enabled GPU in Runtime → Change runtime type, and restart the runtime.

6. Can I run PyTorch models on TPU?
Native TPU support is limited in PyTorch. TensorFlow is recommended for TPUs.

7. How do I prevent out-of-memory errors on GPU?
Reduce batch size, simplify models, and clear unused variables using torch.cuda.empty_cache().

8. How long can a Colab session last with GPU/TPU?
Free sessions last around 12 hours, while Colab Pro may extend to 24 hours.

9. Does enabling GPU/TPU slow down CPU tasks?
No, GPU/TPU acceleration runs in parallel with CPU operations.

10. Can I use GPU/TPU for data preprocessing?
Yes, but it’s mainly beneficial for tensor-heavy computations. For general data preprocessing, CPU is sufficient.

11. How do I switch between GPU and TPU in an existing notebook?
Go to Runtime → Change runtime type and select the desired hardware. Restart the runtime afterward.

12. Are there any limitations on GPU/TPU usage in Colab?
Yes, free users face usage limits, and some GPU/TPU types may not always be available.