Intel Reveals Details of New AI Chip to Fight Nvidia Dominance

The Intel logo is displayed on computer screens at SIGGRAPH 2017 in Los Angeles, California, US July 31, 2017. (Reuters)
The Intel logo is displayed on computer screens at SIGGRAPH 2017 in Los Angeles, California, US July 31, 2017. (Reuters)
TT

Intel Reveals Details of New AI Chip to Fight Nvidia Dominance

The Intel logo is displayed on computer screens at SIGGRAPH 2017 in Los Angeles, California, US July 31, 2017. (Reuters)
The Intel logo is displayed on computer screens at SIGGRAPH 2017 in Los Angeles, California, US July 31, 2017. (Reuters)

Intel detailed a new version of its artificial intelligence chip at its Vision event on Tuesday that takes aim at Nvidia's dominance in semiconductors that power AI.

Tech companies are hunting for an alternative source of the scarce chips that are needed for AI. Intel said that its new Gaudi 3 chip was capable of training a specific large language models 50% more quickly than Nvidia's prior generation H100 processor. It is also capable of computing generative AI responses, a process called inference, more quickly than the H100 chips for some of the models Intel tested.

"Our customers, first and foremost, are asking for choice in the industry," said Intel vice president, strategy and product management Jeni Barovian. "They are coming to us and they are expecting that Intel, as a computing leader, will follow the wave of (generative AI) and deliver solutions that meet their needs. And they are looking for an open approach."

Intel and Advanced Micro Devices have struggled to produce a compelling bundle of chips and the software necessary to build AI applications that can become a viable alternative to Nvidia. Nvidia controlled roughly 83% of the data center chip market in 2023, with a majority of the remaining 17% share held by Google's custom tensor processing units (TPUs) that it does not sell directly.

Intel used Taiwan Semiconductor Manufacturing Co's 5nm process to build the chips. Gaudi 3 includes two main processor chips fused together, and is more than twice as fast as its predecessor. The chip is designed to be strung together with thousands of others and when done so can generate an enormous amount of computer power.

The Gaudi 3 chip will be available to server builders such as Supermicro and Hewlett Packard Enterprise in the second quarter of this year.

The next generation of Gaudi chips will be code named Falcon Shores.



SDAIA, KAUST Launch MiniGPT-Med Model to Help Doctors Diagnose Medical Radiology through AI

SDAIA, KAUST Launch MiniGPT-Med Model to Help Doctors Diagnose Medical Radiology through AI
TT

SDAIA, KAUST Launch MiniGPT-Med Model to Help Doctors Diagnose Medical Radiology through AI

SDAIA, KAUST Launch MiniGPT-Med Model to Help Doctors Diagnose Medical Radiology through AI

The Center of Excellence for Data Science and Artificial Intelligence at the Saudi Data and Artificial Intelligence Authority (SDAIA) and King Abdullah University of Science and Technology (KAUST) have introduced the MiniGPT-Med model.

The large multi-modal language model is designed to help doctors quickly and accurately diagnose medical radiology using artificial intelligence techniques.

Dr. Ahmed Alsinan, the Artificial Intelligence Advisor at the National Center for Artificial Intelligence and head of the scientific team at SDAIA, explained that the MiniGPT-Med model is capable of performing various tasks such as generating medical reports, answering medical visual questions, describing diseases, locating diseases, identifying diseases, and documenting medical descriptions based on entered medical images.

The model was trained on different medical images, including X-rays, CT scans, and MRIs.

The MiniGPT-Med model, derived from large-scale language models, is specifically tailored for medical applications and demonstrates significant versatility across different imaging methods, including X-rays, CT scans, and MRI. This enhances its utility in medical diagnosis.

Dr. Alsinan highlighted that the MiniGPT-Med model was developed collaboratively by artificial intelligence specialists from SDAIA and KAUST.

The model exhibits advanced performance in generating medical reports, achieving 19% higher efficiency than previous models. It serves as a general interface for radiology diagnosis, enhancing diagnostic efficiency across various medical imaging applications.