The tech world is awash with generative AI, which for a company like Nvidia, is a good thing. The company that a decade ago put its money down on AI to be its growth engine has in the intervening years been pulling together the bits and pieces of hardware and software – and partnerships – to be ready when that future arrived.
The recent GTC 2023 show was all about what Nvidia is doing in AI, from new offerings to strategies and roadmaps, and the message that Nvidia has many of the pieces in place to help enterprises adopt AI into their businesses continues to be spread far and wide. That includes earlier this week, when Manuvir Das, Nvidia’s vice president of enterprise computing, delivered the keynote at the MIT Future Compute event in Cambridge, Massachusetts.
Das came to the event with a few messages, including that enterprises absolutely will need to hop on the generative AI train, whether through broadly used technologies from the likes of startup OpenAI, a partner of Nvidia’s whose GPT-4, ChatGPT, Dall-E, and other products are rapidly being integrated throughout Microsoft’s expansive portfolio and embraced by other vendors, or through efforts within the enterprises themselves, using their own data to train the large-language models (LLMs).
Another message was around the evolving compute landscape and AI. More of the world’s work is being done via computing, which means more energy is being consumed. Somewhere around 1 percent to 2 percent of the world’s energy consumption done by datacenters and use cases like compute-hungry generative AI will only increase that, according to Das. The rate of growth is unsustainable and computing needs become even more efficient. Unsurprisingly, Nvidia sees a key step is to have as many workloads as possible – not only those based on AI and machine learning – running on accelerated systems based on GPUs, which he argued can handle the compute demands while keeping power consumption low, essentially taking over the position of driving computing innovation that the now-faltering Moore’s Law once occupied.
“The message I was conveying to this audience, because the topic was the future of computing, is that ‘Yes, the new use cases like generative AI have been built on accelerated computing, so those are good,” Das tells The Next Platform. “But in order to find space for these new workloads and to prevent the world from having to go through this massive expansion of compute, we need to take all the stuff we are already doing in datacenters, domain by domain, and basically move that all to this accelerated computing, because that’s how you get 10 times the output from
