Generative AI - a subset of AI

Today’s generative AI tools like ChatGPT have created excitement throughout the industry over new possibilities, but the compute required for its models have put a spotlight on performance, cost, and energy efficiency as top concerns for enterprises today.

As generative AI models get bigger, power efficiency becomes a critical factor in driving productivity with a wide range of complex AI workload functions from data pre-processing to training and inference. Developers need a build-once-and-deploy-everywhere approach with flexible, open, energy efficient and more sustainable solutions that allow all forms of AI, including generative AI, to reach their full potential.

It is unlikely that generative AI will completely obsolete present computing devices. While generative AI has made significant advances in recent years, it still relies on computing devices to operate.

Generative AI is a subset of artificial intelligence that involves training models to generate new content or data, such as images, music, or text. These models require significant computing resources to operate, and are often trained on specialized hardware, such as graphics processing units (GPUs) or tensor processing units (TPUs).

While generative AI has the potential to revolutionize certain industries, such as creative fields like art and music, it is not likely to replace traditional computing devices entirely. Computing devices are still needed to run the software and applications that power much of our daily lives, from email and social media to productivity tools and video conferencing software.

Furthermore, the computing power required to run generative AI models is still beyond the reach of many consumers and small businesses. While larger organizations may be able to afford the specialized hardware and expertise needed to run these models, smaller entities may not have the resources to do so.

While technological innovation certainly plays a significant role in shaping our world, it is not accurate to say that it is all about technology innovation and war.

Intel has taken steps to ensure it is the obvious choice for enabling generative AI with Intel’s optimization of popular open-source frameworks, libraries, and tools to extract the best hardware performance while removing complexity. At the same time, AMD has started investing in developing advanced technologies that can enable the development of generative AI. AMD Radeon Instinct MI100 GPU is designed specifically for HPC and AI workloads, offering up to 7.4 teraflops of double-precision performance.

Whereas, Intel's AI hardware accelerators and inclusion of built-in accelerators to 4th Gen Intel Xeon Scalable processors provide performance per watt gains to address the performance, price and sustainability needs for generative AI.

it is absolutely true that customers often have to pay for all new technology innovations.

Dr. Deepak Kumar Sahu, President & CEO, VARINDIA