Comparison of Top 6 Free Cloud GPU Services
Advancements in AI and machine learning have increased demand for GPUs used in high-performance computing. Building dedicated GPU infrastructure involves high upfront costs, while cloud-based services provide more affordable access. Free GPU platforms support researchers, developers, and organizations with limited budgets.
See the detailed information about the top 6 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.
1It’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
Lightning AI
The free tier of Lightning AI offers:
- 80 free GPU hours per month
- 4-hour restarts: Free Studios run 24/7 but require a restart every 4 hours
- No credit card required
- 50 GB persistent storage
- Access to high-end GPUs: L40s, A100, H100, H200
Limitations and considerations of using free GPU
When you’re using a free cloud GPU service, here are some things to keep in mind:
Beware of usage restrictions
- You’ll have to worry about how long you can keep a session going, because there may be time limits in place
- You’ll have a weekly or monthly quota on how much you can use
- The service may shut down your session automatically if you leave it idle for a bit.
Keep an eye on performance
- You’ll be sharing resources with other users, which can slow things down
- During peak hours you may be stuck waiting in a queue
- The availability of a GPU can be spotty, depending on demand.
Technical limitations
- Not all frameworks will work with your free GPU
- You might find yourself running out of storage space
- Network access may be restricted in some cases
Best practices for using free cloud GPUs
To make the most of free cloud GPU resources:
Manage your resources
Save work frequently
Monitor usage quotas
Keep sessions active when needed
Optimizing your code
Prepare code locally before GPU execution
Use efficient data loading techniques
Implement proper error handling
Selecting the right platform
Choose based on project requirements
Consider framework compatibility
Check community support availability
When to upgrade to paid services?
Consider upgrading when you need:
- Consistent access to a GPU and can’t rely on free cloud services
- A more powerful GPU to get the job done
- The collaboration features that a paid service will provide
- Longer processing times since free cloud GPU resources come with limited runtime and limited session time.
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
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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