ChatGPT has become one of the most talked-about AI language models in recent times. But have you ever wondered what kind of hardware powers this impressive technology? In this blog, we will take a closer look at the hardware that makes ChatGPT possible.

To begin with, it's important to understand that there are two different phases of developing a machine learning model like ChatGPT, and each phase has its own unique hardware requirements. The first phase involves training the neural network, where it is fed with vast amounts of data that is processed by billions of parameters. This stage requires massive amounts of compute power, as the AI has to process large volumes of data repeatedly.

Once the training phase is completed, the second phase begins, which is the inference phase. During this phase, the fully trained neural network applies its learned behavior to new data. The inference phase is less resource-intensive when it comes to raw compute power, but high throughput and low latency are crucial because the AI is responding to many simultaneous requests.

The hardware required for the training phase is immense, but once training is complete, the hardware requirements for inference are much lower. However, deploying the AI to many users at the same time can significantly increase those requirements.

So what kind of hardware was used to train the neural network of ChatGPT? Microsoft and OpenAI have not released exact details about the hardware configuration, but we do know that ChatGPT was trained on Microsoft Azure infrastructure and that many AI models are trained on Nvidia GPUs.

In May 2020, Microsoft announced a new supercomputer built exclusively for OpenAI to train GPT-3, the predecessor to ChatGPT. The supercomputer used more than 285,000 CPU cores and over 10,000 GPUs, which places it within the top 5 of the TOP500 supercomputer list

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A scientific paper published by OpenAI in July 2020 reveals that all models were trained on Nvidia V100 GPUs on part of a high-bandwidth cluster provided by Microsoft. Nvidia's CUDA deep neural network library was utilized, meaning the training was primarily done on the GPUs, with the CPUs playing a supporting role.

The Nvidia V100 GPUs used in the training phase of ChatGPT are incredibly fast and powerful, which is why OpenAI and Microsoft selected this specific hardware. They are capable of processing vast amounts of data with incredible speed, making them ideal for training complex AI models like ChatGPT.

The hardware that powers ChatGPT is incredibly impressive and powerful, featuring some of the most advanced technology available today. By using specialized hardware like Nvidia V100 GPUs, ChatGPT's developers were able to create an AI language model that is revolutionizing the way we interact with computers and machines. As technology continues to evolve, we can only imagine what kind of hardware will be used to power the next generation of AI models.