Nvidia, a leading producer of graphics processing units (GPUs), has reported a remarkable 265% year-on-year increase in revenue, driven by soaring global demand for artificial intelligence (AI) equipment.
In its latest fourth-quarter financial results, Nvidia disclosed revenue of $22.1 billion, marking a 22% increase from the previous quarter and a significant surge from the same period last year.
Jensen Huang, the founder and CEO of Nvidia, credited the strong sales performance to the rising global appetite for accelerated computing and generative AI. The company currently boasts a market capitalization of $1.67 trillion.
The news comes as Nvidia surpasses Tesla, led by Elon Musk, as the most-traded stock on Wall Street. Traders exchanged approximately $30 billion worth of Nvidia shares in the last 30 trading sessions, compared to an average of around $22 billion for Tesla during the same period, according to a Reuters report.
Elon Musk recently confirmed Tesla’s plans to invest over $500 million in procuring AI hardware from Nvidia in 2024 alone. He emphasized the significant financial commitment required to remain competitive in the AI landscape, stating that “the table stakes for being competitive in AI are at least several billion dollars per year at this point.”
Despite Tesla’s partnership with Nvidia, the electric vehicle company also intends to purchase AI-related hardware from Nvidia’s primary GPU-manufacturing rival, AMD.
Nvidia’s RTX series, introduced in September 2018, has become a favored platform for enthusiasts of generative AI, gamers, and creators. In Q3 2023, Nvidia reported revenue of $18.1 billion, supported by a robust market capitalization of $1.2 trillion.
Yann LeCun, the chief AI scientist for Facebook AI Research at Meta, acknowledged Nvidia’s dominant position in the AI hardware industry in December 2023. He described Nvidia as “supplying the weapons” in the ongoing AI competition.
However, LeCun also criticized the prevalent use of text-based models for training generative AI systems, stating that “text is a very poor source of information.” He highlighted the limitations of training systems on extensive amounts of textual data, noting that they often struggle to understand basic concepts like equivalence.