GPU tagged posts

Large language models (LLMs) are increasingly automating tasks like translation, text classification and customer service. But tapping into an LLM’s power typically requires users to send their requests to a centralized server—a process that’s expensive, energy-intensive and often slow.

Now, researchers have introduced a technique for compressing an LLM’s reams of data, which could increase privacy, save energy and lower costs. Their findings are published on the arXiv preprint server.

The new algorithm, developed by engineers at Princeton and Stanford Engineering, works by trimming redundancies and reducing the precision of an LLM’s layers of information...

It all started at a Denny’s in San Jose in 1993. Three engineers—Jensen Huang, Chris Malachowsky and Curtis Priem—gathered at the diner in what is now the heart of Silicon Valley to discuss building a computer chip that would make graphics for video games faster and more realistic. That conversation, and the ones that followed, led to the founding of Nvidia, the tech company that soared through the ranks of the stock market to briefly top Microsoft as the most valuable company in the S&P 500 this week.

The company is now worth over $3.2 trillion, with its dominance as a chipmaker cementing Nvidia’s place as the poster child of the artificial intelligence boom—a moment that Huang, Nvidia’s CEO, has dubbed “the next industrial revolution.”

On...

Rice University computer scientists have demonstrated artificial intelligence (AI) software that runs on commodity processors and trains deep neural networks 15 times faster than platforms based on graphics processors.

“The cost of training is the actual bottleneck in AI,” said Anshumali Shrivastava, an assistant professor of computer science at Rice’s Brown School of Engineering. “Companies are spending millions of dollars a week just to train and fine-tune their AI workloads.”

Shrivastava and collaborators from Rice and Intel will present research that addresses that bottleneck April 8 at the machine learning systems conference MLSys.

Deep neural networ...