by venturebeat.com — Gary Grossman Edelman – Generative AI is gaining wider adoption, particularly in business. Most recently, for instance, Walmart announced that it is rolling-out a gen AI app to 50,000 non-store employees. As reported by Axios, the app combines data from Walmart with third-party large language models (LLM) and can help employees with a range of tasks, from speeding up the drafting process, to serving as a creative partner, to summarizing large documents and more. Deployments such as this are helping to drive demand for graphical processing units (GPUs) needed to train powerful deep learning models. GPUs are specialized computing processors that execute programming instructions in parallel instead of sequentially — as do traditional central processing units (CPUs). According to the Wall Street Journal, training these models “can cost companies billions of dollars, thanks to the large volumes of data they need to ingest and analyze.” This includes all deep learning and foundational LLMs from GPT-4 to LaMDA — which power the ChatGPT and Bard chatbot applications, respectively.
Riding the generative AI wave
The gen AI trend is providing powerful momentum for Nvidia, the dominant supplier of these GPUs: The company announced eye-popping earnings for their most recent quarter. At least for Nvidia, it is a time of exuberance, as it seems nearly everyone is trying to get ahold of their GPUs. Erin Griffiths wrote in the New York Times that start-ups and investors are taking extraordinary measures to obtain these chips: “More than money, engineering talent, hype or even profits, tech companies this year are desperate for GPUs.” In his Stratechery newsletter this week, Ben Thompson refers to this as “Nvidia on the Mountaintop.” Adding to the momentum, Google and Nvidia announced a partnership whereby Google’s cloud customers will have greater access to technology powered by Nvidia’s GPUs. All of this points to the current scarcity of these chips in the face of surging demand. Does this current demand mark the peak moment for gen AI, or might it instead point to the beginning of the next wave of its development?
How generative tech is shaping the future of computing