Bengaluru: Training models is difficult and it’s hard for everyone in the industry, said Andrew Feldman, CEO of US-based artificial intelligence company Cerebras Systems, at the Mint Digital Innovation Summit 2024 on Friday.
“Why is training models difficult? Because large models don’t fit on GPUs (graphics processing units). This means developers must cut the model into many pieces and spread them over hundreds and thousands of GPUs. They must rewrite the model to work across clusters. This means they go from about 600 lines of code to more than 20,000 lines of code,” said Feldman.
“This is what we sought to avoid. We did that by building chips. By building chips and memory systems which were vastly larger than even the largest models. We put these chips in the systems that we design,” he added.
Feldman further added, "We use these systems to build clusters and over time these clusters became larger and larger to the point where today we are building the largest supercomputers in the world.”
Once you have solved the difficulty of models, you can use computers to do interesting AI work, Feldman noted.
As we work with various players in the industry, these three behaviours describe the dimensions of the majority of the players, said Feldman.
Explaining this further, he said, “Either you have model building expertise which is exceptional, you have rare interesting data in which to find insights, or you have access to a partner that provides competitive differentiation.”
Cerebras Systems is an American artificial intelligence company with offices in Sunnyvale and San Diego, Toronto, Tokyo and Bangalore, India. The company builds computer systems for complex artificial intelligence deep learning applications.
“If you look at OpenAI, they had insight into model building - they used open source data and partnered with Microsoft for computers. A company like Mayo has some of the world’s leading medical data that they have spent years curating. They partnered with Cerebras for models,” he added.
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