🎯 Key points for quick navigation: 00:56 *💻 Google Colab has vague GPU usage limits and may result in runtime errors or disconnects* 02:16 *📦 Paperspace Gradient offers persistent storage but lacks the ability to create private notebooks* 03:26 *🌟 Kaggle provides 30 hours of GPU usage per week with quality hardware and easy use* 04:18 *🚀 AWS SageMaker Studio Lab offers high-quality GPU hours, persistent storage, and ease of use* 06:17 *💡 Lightning AI provides 22 free GPU hours per month, quality hardware, and a user-friendly VS Code interface* 09:12 *💡 Tier list breakdown: Kaggle, AWS SageMaker Studio Lab, and Lightning AI are in S tier; Google Colab, Saturn Cloud, and GitHub Code Spaces in B tier; Azure ML Notebooks, Google Vertex ML Notebooks, and Hugging Face Spaces in C tier; and Deep Note in D tier.* Made with HARPA AI
Knowing the exact stats of each GPU would be great. I don't think any of them allow you to run/train a 70b model without heavily quantizing the model. It would be great to know if any offering allows you to do it
thanks for the video. many i never heard of. i personally had no luck with amazon ai sagemaker studio. don't remember the exact name. tried it once and didn't get gpu.
@@sweetdream73 Lightning AI or other alternatives such as Colab, they are all machines like your computer. You don't need a tutorial how to run your project on Lightning AI or Colab or Kaggle. If you use jupyter notebook, just open it on Lightning AI, If you run your project in terminal, then open the terminal on Lightning AI.