LightningAI: STOP PAYING for Google's Colab with this NEW & FREE Alternative (Works with VSCode)

In this video, the host introduces Lightning AI, a new and free alternative to Google Colab that provides a web-based VSCode interface. The platform offers one free Studio with 24/7 access, four cores, 16 GB RAM, and 22 GPU hours per month. The host expresses dissatisfaction with Google Colab's interface and lack of reliability, highlighting Lightning AI's seamless GPU integration and persistent storage. After signing up, users can create a studio and switch between CPU and GPU instances, as demonstrated when running LLaMa 3, which significantly speeds up with GPU usage. The host concludes by encouraging viewers to try Lightning AI and shares their intention to use it for future projects.

Takeaways

• 🎉 **Milestone Celebration**: The channel reached 1K subscribers in just a month, which is a significant achievement.
• 🚀 **Google Colab Alternative**: The speaker prefers local execution but uses Google Colab for high-end models due to its free GPU access.
• 🖥️ **Interface Dislike**: The speaker is not fond of Google Colab's interface, comparing it to a 1990s experience.
• 🚨 **Reliability Issues**: Google Colab has issues with GPU allocation, lack of persistent storage, and the risk of getting timed out after inactivity.
• 🆓 **Lightning AI Introduction**: Lightning AI is a new, free alternative offering a web-based VS Code interface with a free Studio that operates 24/7.
• ⏰ **Usage Limitations**: On the free tier, Lightning AI provides 22 GPU hours per month.
• 🔄 **Seamless GPU Access**: Users can easily add or detach a GPU to their instance as needed, making it a flexible solution.
• 📈 **Performance Boost**: Switching to a GPU instance significantly increases performance, as demonstrated by the increased token output per second.
• 📝 **VS Code Familiarity**: The platform provides a VS Code interface, which is familiar to many developers.
• 🔧 **Customization Options**: Users have the option to customize their environment, including switching between VS Code and Jupyter interfaces.
• 📊 **Usage Metrics**: The interface displays live CPU and other usage metrics, providing transparency on resource consumption.
• ⌛ **Access Wait Time**: There's a waiting list for new users, but access is typically granted within 2-3 days.

Q & A

What is the name of the new free alternative to Google Colab mentioned in the video?

The new free alternative to Google Colab mentioned in the video is Lightning AI.

What are some of the limitations of Google Colab that the speaker dislikes?

The speaker dislikes the outdated interface of Google Colab, the lack of persistent storage, the unreliability due to potential time-outs, and the need to re-setup the environment after each session.

What does Lightning AI provide to its users?

Lightning AI provides a web-based VS Code interface, one free Studio that can run 24/7, and 22 GPU hours on the free tier.

How does the speaker describe the process of transforming a VS Code instance into a GPU powerhouse in Lightning AI?

The speaker describes the process as seamless. Users can transform their VS Code instance into a GPU powerhouse by adding a GPU to the instance through the interface options.

What is the monthly limit for GPU usage on the free tier of Lightning AI?

On the free tier, users can use the GPU for a total of 22 hours in a month.

How long did it take for the speaker to gain access to Lightning AI after signing up?

The speaker got access to Lightning AI in about 2 days after signing up.

What is the first step to start using Lightning AI?

The first step to start using Lightning AI is to go to the Lightning AI site and sign up, which places you on a waiting list.

What is the difference between the CPU and GPU instances in terms of performance when running an LLM or diffusion model?

The CPU instance provides an output of about three tokens per second, which is relatively slow. In contrast, the GPU instance gives an instantaneous response with about 43 tokens per second.

How does the speaker propose to switch the machine type from default to a GPU option in Lightning AI?

The speaker proposes to switch the machine type by clicking on the first option on the right sidebar and then choosing the GPU option.

What is the benefit of using the GPU option in Lightning AI when working with large models?

The benefit of using the GPU option is the significant increase in processing speed and performance, allowing for faster execution of large models.

What is the speaker's final verdict on using Google Colab after discovering Lightning AI?

The speaker decides not to use Google Colab anymore and will be using Lightning AI for future work.

How does the speaker suggest users can provide feedback or express their interest in using Lightning AI?

The speaker suggests that users can provide feedback or express their interest by leaving comments and by liking and subscribing to the channel.

Outlines

00:00

🎉 Celebrating 1K Subscribers and Introduction to Lightning AI

The speaker begins by expressing gratitude for reaching 1,000 subscribers in just a month. They discuss their preference for local development but acknowledge the need to use Google Colab for high-end models due to its free GPU access. The speaker criticizes Google Colab's interface and lack of reliability, such as no persistent storage and the possibility of getting timed out. They introduce Lightning AI as a solution that provides a web-based VS Code interface with a free Studio that can run 24/7 and offers 22 free GPU hours. The Studio can be transformed into a GPU powerhouse when needed and comes with persistent storage. The speaker guides viewers on how to sign up, access, and use Lightning AI, demonstrating the process of running LLaMa 3 on both CPU and GPU instances.

05:02

🚀 Comparing LLaMa 3 Performance on CPU vs. GPU Instances

After installing LLaMa 3 through the terminal, the speaker tests its performance on a default CPU machine, achieving a rate of three tokens per second. They then switch the instance to a GPU instance by selecting the GPU option in the interface. With the T4 GPU selected, the speaker notes a significant improvement in performance, reaching about 43 tokens per second. The speaker expresses satisfaction with the new setup and declares an intention to use Lightning AI for future projects, inviting viewers to share their thoughts in the comments and to subscribe for more content.

Keywords

Google Colab

Google Colab is a cloud-based platform that provides free access to computing resources, including GPUs, which are essential for running high-end machine learning models. In the video, it is mentioned that everyone in the AI scene uses Google Colab due to its free GPU access, but the presenter prefers to work locally and dislikes the Google Colab interface.

High-end LLMs (Large Language Models)

High-end LLMs refer to sophisticated artificial intelligence models designed to process and understand large volumes of natural language data. They are used for various applications, including language translation, text generation, and more. The video discusses the need to run these models for creating content, which is why the presenter seeks an alternative to Google Colab.

Diffusion Models

Diffusion models are a type of deep learning model used in generative tasks, such as generating images or sounds from a given input. They are computationally intensive and require significant processing power, which is why access to GPUs is crucial. The video script mentions using diffusion models as part of the content creation process.

Local Environment

A local environment refers to software and tools installed and run directly on a user's computer, as opposed to cloud-based services. The presenter in the video prefers to work in a local environment but needs an alternative to Google Colab for running resource-intensive tasks.

Persistent Storage

Persistent storage is a type of data storage that retains data even after the system is powered off or closed. In the context of the video, the lack of persistent storage in Google Colab is a drawback as it means that any data processed is lost once the session ends, requiring the user to start from scratch.

Lightning AI

Lightning AI is presented as a new and free alternative to Google Colab. It offers a web-based Visual Studio Code (VSCode) interface with access to a free studio that can run 24/7 and includes 22 GPU hours per month. It is highlighted as a solution that provides terminal access, customization, and persistent storage.

VSCode (Visual Studio Code)

VSCode is a popular source-code editor developed by Microsoft. It is known for its rich ecosystem of extensions and support for debugging, Git control, syntax highlighting, intelligent code completion, and code refactoring. In the video, Lightning AI provides a web-based VSCode interface for coding and running models.

GPU (Graphics Processing Unit)

A GPU is a specialized electronic circuit designed to rapidly manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display device. In the context of the video, the GPU is crucial for running high-end LLMs and diffusion models, as it provides the necessary computational power.

Instance

In the context of cloud computing, an instance refers to a virtual machine that provides the environment to run applications or software. The video discusses transforming a VSCode instance into a GPU powerhouse by adding a GPU to it for running intensive tasks.

Token

In the context of language models, a token is a unit of text, such as a word or a punctuation mark, that a model processes. The video script mentions the speed of processing tokens per second as a measure of the performance of the LLM when run on a CPU versus a GPU.

Free Tier

The free tier refers to the level of service that is provided at no cost. In the video, Lightning AI offers a free tier that includes one free studio and 22 GPU hours per month, which is a significant benefit for users who cannot afford or do not wish to pay for additional computing resources.

Highlights

AI Code King reached 1K subscribers in just one month.

Google Colab is widely used for running high-end models due to free GPU access.

The presenter prefers local processing but uses Colab for large models.

Colab's interface is outdated and not user-friendly.

Colab often does not allocate a GPU and lacks persistent storage.

Users may experience timeouts on Colab after 5 minutes of inactivity.

Lightning AI is a new web-based VS Code interface alternative to Colab.

Lightning AI offers one free Studio with 24/7 operation and 22 GPU hours.

The free tier of Lightning AI includes a four-core, 16 GB RAM instance.

Lightning AI instances can be seamlessly switched to GPU mode.

The free tier limits GPU usage to 22 hours per month.

Lightning AI provides persistent storage, retaining data even after closing the browser.

The platform offers a waiting list for new users, with access granted within 2-3 days.

Users can create a studio in Lightning AI and start with a VS Code interface.

Lightning AI allows changing the machine type from CPU to GPU with a few clicks.

The platform provides options to switch the interface from VS Code to Jupyter-like.

A demonstration of running LLaMa-3 on Lightning AI showed a significant speed increase with GPU usage.

The presenter plans to use Lightning AI for future projects instead of Colab.

The video concludes with a call to action for viewers to share their thoughts in the comments.