Earlier this year, the company also released to researchers the largest publicly available AI language model in the world - the Microsoft Turing model for natural language generation. The goal, says the company, is to make its large AI models, training optimization tools, and supercomputing resources available through Azure AI services and GitHub so developers, data scientists, and business customers can easily leverage the power of AI at Scale.
“By now most people intuitively understand how personal computers are a platform," says Scott. "You buy one and it's not like everything the computer is ever going to do is built into the device when you pull it out of the box. That's exactly what we mean when we say AI is becoming a platform."
"This is about taking a very broad set of data and training a model that learns to do a general set of things and making that model available for millions of developers to go figure out how to do interesting and creative things with,” he says.
Training such massive AI models requires advanced supercomputing infrastructure, or clusters of state-of-the-art hardware connected by high-bandwidth networks. It also needs tools to train the models across these interconnected computers.
The supercomputer developed for OpenAI is a single system with more than 285,000 CPU cores, 10,000 GPUs, and 400 gigabits per second of network connectivity for each GPU server. Compared with other machines listed on the TOP500 supercomputers in the world, says the company, it ranks in the top five, and, hosted in Azure, it benefits from all the capabilities of a robust modern cloud infrastructure, including rapid deployment, sustainable datacenters, and access to Azure services.
OpenAI CEO Sam Altman says, "As we've learned more and more about what we need and the different limits