Intel is taking steps to ensure it is the obvious choice for enabling generative AI with Intel’s optimisation of popular open-source frameworks, libraries and tools to extract the best hardware performance while removing complexity. Intel's AI hardware accelerators and inclusion of built-in accelerators to4th Gen Intel Xeon Scalable processors provide performance and performance per watt gains to address the performance, price and sustainability needs for generative 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 artificial intelligence 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.
Today, Hugging Face, an open-source library for machine learning, published results that show inference runs faster on Intel’s AI hardware accelerators than any GPU currently available on the market, with Habana Gaudi2 running inference 20 percent faster on a 176 billion parameter model than Nvidia’s A100.
AI has come a long way, but there is still more to be discovered. Intel’s commitment to true open access of AI and sustainability will enable broader access to the benefits of the technology, including generative artificial intelligence, through an open ecosystem.
Generative AI with its ability to mimic human-generated content presents an exciting opportunity to transform many aspects of how we work and live. However, this quickly evolving technology exposes the complexities of the compute required to successfully leverage AI in the datacentre.