How is ChatGPT So Powerful? A Look at the Hardware Behind OpenAI’s Latest AI Language Model

By now, you’ve probably heard of ChatGPT, the latest AI language model from OpenAI that has taken the internet by storm. With 6 billion parameters and the ability to generate coherent and convincing text on a wide range of topics, ChatGPT has been hailed as a breakthrough in natural language processing and AI in general.

But have you ever wondered how ChatGPT is able to do what it does? After all, processing language at this scale is no small feat, and it must require a lot of computing power to achieve. In this post, we’ll take a look at the hardware behind ChatGPT and try to understand what it takes to train and run such a powerful AI model.

First of all, it’s worth noting that ChatGPT is not a standalone AI system. Rather, it is part of a larger family of AI language models called GPT (short for “Generative Pre-trained Transformer”). The first GPT model was introduced by OpenAI in 2018, with 117 million parameters, and subsequent versions have increased in size and complexity, culminating in ChatGPT with its 6 billion parameters.

So, how is ChatGPT trained? According to OpenAI, the model is fine-tuned from a larger pre-trained model using a technique called unsupervised learning. Essentially, the model is fed a massive amount of text from the internet and other sources, and it learns to predict the next word or sentence based on the context. Over time, the model gets better and better at this task, and it becomes able to generate text on its own that is coherent and relevant to the input prompt.

Source: Nvidia

However, training a model like ChatGPT requires a massive amount of computing power. OpenAI has not disclosed the exact hardware used to train ChatGPT, but we can make some educated guesses based on previous information about their AI infrastructure.

For example, the previous GPT-3 model, which has “only” 175 billion parameters, was trained on a supercomputer with 285,000 CPU cores and 10,000 Nvidia V100 GPUs provided by Microsoft. This hardware setup allowed OpenAI to train the model in a relatively short amount of time, but it also came with a significant cost, both in terms of money and energy consumption.

Given that ChatGPT has fewer parameters than GPT-3 but still requires a lot of computing power to train, it’s likely that OpenAI used a similar or even more powerful hardware setup for this model. One possibility is that they used Nvidia A100 GPUs, which are the latest and most powerful GPUs from Nvidia as of early 2022. These GPUs offer significant speedups over the previous generation, and they are designed specifically for AI workloads like training and inference.

Another possibility is that OpenAI used AMD EPYC CPUs in conjunction with the Nvidia GPUs. AMD EPYC CPUs are also designed for server workloads and offer high performance and efficiency for AI tasks. Combining these CPUs with Nvidia GPUs can lead to even faster training times and better overall performance for AI models.

Of course, these are just educated guesses, and we won’t know for sure what hardware OpenAI used for ChatGPT unless they release more information about their AI infrastructure. However, one thing is clear: training and running powerful AI models like ChatGPT requires a lot of computing power, and it’s only going to get more demanding as AI models continue to increase in size and complexity.

Next, let’s take a closer look at the hardware behind ChatGPT and try to make a more informed guess about how we have come to some of these hardware predictions.

The Hardware Behind ChatGPT

The first step in understanding the hardware used to train ChatGPT is to look at the timeline. OpenAI confirmed that ChatGPT was fine-tuned from a model in the GPT-3.5 series, which finished training in early 2022. This provides us with a time frame to work with, and we can assume that the model was trained on hardware that was available at that time.

Next, we need to identify the type of hardware used. We know that ChatGPT was trained on Microsoft Azure infrastructure, and in June 2021, Microsoft announced the availability of Nvidia A100 GPU clusters to its Azure customers. Therefore, it’s reasonable to assume that ChatGPT was trained on Nvidia A100 GPUs.

But how many A100 GPUs were used? To answer that question, we need to look at the specifications of the A100. The A100 is based on the GA100 chip, which packs 54.2 billion transistors into a 826 millimeter squared large die produced by TSMC in a 7 nanometer node. Tensor performance gets another huge bump to over 300 teraflops, 2.5 times the performance of a single V100 GPU!

However, it’s unlikely that Microsoft replaced all 10,000 Volta GPUs in their supercomputer with 10,000 Ampere GPUs. ChatGPT is a more streamlined machine learning model, and it wouldn’t be a cost-effective decision to use Ampere, which is so much faster.

In October of 2021, before the training of ChatGPT started, Nvidia and Microsoft announced a new AI supercomputer they used to train a new and extremely large neural network called Megatron-Turning NLG. This neural network has 530 billion parameters and was trained on 560 Nvidia DGX A100 servers, each containing 8 Ampere A100 GPUs. Therefore, it’s safe to assume that ChatGPT was trained on a similar system to Megatron-Turing NLG, using multiple clusters of Nvidia DGX or HGX A100 servers.

Using this information, we can make an educated guess that ChatGPT was trained on 1,120 AMD EPYC 7742 server CPUs with over 70,000 CPU cores and 4,480 Nvidia A100 GPUs. This would provide close to 1.4 exaflops of FP16 tensor core performance. While we can’t confirm these specifications without official confirmation, it’s a reasonable deduction based on the available information.

Now, let’s take a look at the hardware used for ChatGPT inference. According to statements from Microsoft and OpenAI, ChatGPT inference is running on Microsoft Azure servers, and a single Nvidia DGX or HGX A100 instance is likely enough to run inference for ChatGPT. However, at the current scale, it would require well over 3,500 Nvidia A100 servers with close to 30,000 A100 GPUs to provide inference for ChatGPT.

This massive amount of hardware is required to keep the service running and costs between $500,000 to $1 million dollars per day. While the current level of demand for ChatGPT makes it worth it for Microsoft and OpenAI, it’s unlikely that such a system can stick to a free-to-use model in the long run unless better and more efficient hardware reduces the cost of running inference at scale.

With the future of AI looking bright, there’s a lot of new hardware on the way, and the entire hardware industry is starting to shift its focus on architectures specifically designed to accelerate AI workloads. In only a few years’ time, training a model like ChatGPT will be part of your average machine learning course in college, and running inference will be done on a dedicated AI engine inside of your smartphone.

As AI progress is hardware-bound, and the hardware is just getting started, we can expect fierce competition between companies like Nvidia and AMD. The upcoming CDNA3 based MI300 GPUs from AMD will provide strong competition for NVIDIA, especially when it comes to AI workloads.

References
Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., … Amodei, D. (2020). Language Models are Few-Shot Learners. arXiv preprint arXiv:2005.14165.

This project is a chatbot application that utilizes the OpenAI API to generate responses to user input. The application is built using Node.js and Express for the server-side logic, and JavaScript, HTML, and CSS for the client-side user interface. The project also makes use of the Vite development server and a Vanilla JavaScript framework for a lightweight and easy-to-use development environment.

Technical Details

Server-side

The server-side of the application is built using Node.js and Express. The Express framework is used to handle incoming HTTP requests and send responses. The server also utilizes the OpenAI API to generate responses to user input. This is done by making a POST request to the OpenAI API with the user’s input as the request body. The server then receives the response from the OpenAI API and sends it back to the client.

To authenticate with the OpenAI API, the application uses an API key stored in a .env file. This file is not included in the git repository for security reasons. The dotenv package is used to load the environment variables from the .env file into the application.

Client-side

The client-side of the application is built using JavaScript, HTML, and CSS. The JavaScript is used to handle the user’s input, send it to the server, and display the response from the server. The HTML and CSS are used to create the user interface.

The client-side JavaScript code uses the fetch API to send the user’s input to the server and receive the response. It also uses the setInterval function to create a loading animation while waiting for the response from the server.

Development Environment

The project uses the Vite development server for a fast and easy development experience. Vite is a lightweight development server that automatically reloads the application when files are saved. This eliminates the need for manual compilation and makes it easy to see changes in real-time.

The project also uses a Vanilla JavaScript framework for a lightweight and easy-to-use development environment. This framework provides a minimal set of functionality and doesn’t include any additional libraries or frameworks.

Advanced Technical Analysis

The architecture of the application is built using a client-server model, where the client side is responsible for handling the user interface and user interactions, and the server side is responsible for handling the logic and communication with the OpenAI API. The client side is built using HTML, CSS, and JavaScript, and utilizes the fetch API to send and receive data from the server. The server side is built using Node.js and Express, and utilizes the openai npm package to communicate with the OpenAI API.

The technologies used in this project include:

HTML, CSS, and JavaScript for the client side
Node.js and Express for the server side
openai npm package for communication with the OpenAI API
AWS EC2 and S3 for deployment

On the client side, the application utilizes JavaScript to handle user interactions and dynamically update the DOM with the response from the server. The client-side script uses the Fetch API to send a POST request to the server with the user’s input, and then uses the response to update the chat window on the page.

On the server side, the application uses the openai npm package to communicate with the OpenAI API and generate a response to the user’s input. The server-side script receives the user’s input from the client, and then uses the openai package’s createCompletion method to generate a response. This method takes in several options, such as the model to use, the prompt, and various other parameters that can be adjusted to customize the output.

In terms of implementation details, the project uses environment variables to keep the OpenAI API key secure, and is designed to be deployed on AWS using EC2 and S3. The client and server folders contain the necessary files to run the application, and the public folder contains the assets needed for the client-side.

Overall, the Intelligent Conversation application is a chatbot that utilizes the OpenAI API to generate responses to user input. It was developed using a client-server architecture, with the client side built using HTML, CSS, and JavaScript, and the server side built using Node.js and Express. The application utilizes the openai npm package to communicate with the OpenAI API and can be easily deployed on AWS using EC2 and S3.

Deployment

The application can be deployed to a hosting service such as AWS. To deploy the application to AWS, you will need an S3 bucket to host the static files and an EC2 instance to run the Node.js server.

You will also need to create an IAM role with the necessary permissions to access the S3 bucket and EC2 instance. Once the role is created, you can use it to launch the EC2 instance.

Once the EC2 instance is launched, you can use Git to clone the repository from GitHub and run npm install to install the necessary dependencies. Once the dependencies are installed, you can start the server by running node server.js.

Conclusion

This project demonstrates the use of the OpenAI API to create an intelligent chatbot application. The application is built using Node.js and Express for the server-side logic, and JavaScript, HTML, and CSS for the client-side user interface.

How OpenAI’s ChatGPT Achieves Its Power: A Look at the Hardware Behind the AI Language Model