Unlike traditional GPUs that you install in your computer, cloud GPUs are graphics processing units hosted on remote servers that you can access over the internet. This means you can harness powerful computing capabilities without investing in expensive hardware. Free GPU access can help data scientists to have more computational power, it is especially important in the cases where developers need to use deep learning models and artificial intelligence. Google cloud and google drive integrations makes Google Colab a good candidate to select, but other free GPU providers offers different strengths, so developers can carefully consider their options. The landscape of AI models and neural networks has transformed dramatically with the advent of free platforms that provide free access to GPU memory and GPU machine resources. These platforms enable researchers and developers to conduct training processes and fine tuning with minimal effort, offering both public and private notebooks for collaborative work. While some services require credit card details or provide free credits, others maintain a genuine free tier that can be accessed through a simple sign up process, though availability might be limited due to high demand. When working with these platforms, developers can easily set permissions for the projects they create and choose between CPU and GPU resources based on their computational needs. While other platforms might offer more extensive features, these free services provide all the essential tools needed to develop and experiment with neural networks, making advanced development of ai models accessible to a broader audience.

Comparison of Top 5 Free Cloud GPU Services

Cem Dilmegani

updated on Apr 29, 2025

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AI and machine learning advancements have surged demand for GPUs, essential for high-performance computing. Dedicated GPU infrastructure requires high capital, but cloud-based services now offer cost-effective access. Free GPU platforms are increasingly vital for researchers, developers, and budget-conscious organizations. See the detailed information about the top 5 free cloud GPU providers below:

Cloud GPU services

Google Colab

Google Colaboratory is a notebook-based instance that allows users to write and execute Python code in a web-based interactive environment.¹

It’s designed for data science and machine learning tasks, and users can access it by signing in to their Google account.
Google Colab provides Nvidia K80s or a Tesla T4 GPU with up to 16 GB of memory with 12-hour session limits.
No credit card required.
It supports background execution, allowing users to run their code in the background while working on other tasks.

Kaggle

Kaggle is a popular platform for data science and machine learning enthusiasts, offering 50k publicly available data sets.
Developers can join to the data science competitions.
It provides a notebook service with at least 30 hours/week of GPU usage, allowing developers to access NVIDIA Tesla P100.
In the cases where hardware accelerators are needed, a TPU v3-8 can be added to Notebook for free.²

Codesphere

Codesphere is an end-to-end DevOps platform that combines IDE and infrastructure, offering:³

Free shared GPU
20 GB of storage
Workspaces enter standby mode after approximately 60 minutes of inactivity.

Paperspace Gradient

Paperspace offers:

Limited GPU hours for small projects
Multiple framework support
Credit card required for verification
5 GB storage
Notebooks created under the Free Plan are public, so it is not suitable for sensitive information.⁴

Amazon SageMaker Studio Lab

Amazon’s free alternative to SageMaker offers:

15GB persistent storage
No AWS account or credit card required
Full compatibility with popular ML frameworks
Jupyter Lab interface
Built-in Git integration
Terminal access
Pre-installed common data science libraries⁵

Limitations and considerations of using free GPU

When using free cloud GPU services, keep in mind:

Usage restrictions
- Time limits per session
- Weekly or monthly quotas
- Automatic disconnection after idle periods
Performance considerations
- Shared resources might affect speed
- Queue times during peak hours
- Variable GPU availability
Technical limitations
- Not all frameworks supported
- Limited storage space
- Restricted network access

Best practices

To make the most of free cloud GPU resources:

Resource management
- Save work frequently
- Monitor usage quotas
- Keep sessions active when needed
Code optimization
- Prepare code locally before GPU execution
- Use efficient data loading techniques
- Implement proper error handling
Platform selection
- Choose based on project requirements
- Consider framework compatibility
- Check community support availability

When to upgrade to paid services?

Consider upgrading when:

Projects require consistent GPU access
Longer processing times are needed since free cloud GPU resources come with limited runtime and limited session time.
More powerful GPU specifications are required
Team collaboration features are necessary

See our article on Cloud GPU providers to find a suitable paid service for your needs.

Choosing the right free cloud GPU provider

Consider your task requirements and suitable GPU
Evaluate the platform limitations and private notebooks
Choose a provider that offers background execution and supports your deep learning tasks

FAQ

Reference Links

Google Colab

Notebooks Documentation | Kaggle

Pricing - Codesphere

Alternative to Colab Pro: Comparing Google's Jupyter Notebooks to Gradient Notebooks (Updated!)

Paperspace by DigitalOcean Blog

SageMaker Studio Lab

Principal Analyst

Cem Dilmegani

Principal Analyst

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Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 55% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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