Artificial intelligence is evolving rapidly, and AI hardware advancements are revolutionizing training and inference for large language models.
This transition reshapes how businesses and researchers approach compute-intensive tasks, such as training and inference for large language models. As the demand for AI capabilities grows, chip design and system architecture innovations are driving unprecedented performance gains, efficiency and scalability, according to Andrew Feldman (pictured), co-founder and chief executive officer of Cerebras Systems Inc.
“Obviously, big chips aren’t right for everything — they’re wrong for your cell phone, and they’re probably wrong for your car,” Feldman said. “But in AI, we’re not interested in the behavior of one chip. We’re interested in the behavior of tens of thousands of chips. And tens of thousands of little tiny chips create a tremendous complexity of trying to tie them together again.”
Feldman spoke with theCUBE Research’s Dave Vellante and John Furrier at SC24, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. …