Apple is ramping up research and development of its own AI chip to reduce its reliance on third-party developers, potentially finally completely ending its decades-long unhappy relationship with Nvidia.

Nvidia has become one of the world's most valuable companies thanks to strong demand for its artificial intelligence (AI) server chips from

Nvidia is soaring now, but could its dominance fade in the next few years? Two tech titans could outshine Nvidia by 2029.

Apple has shared details on a collaboration with NVIDIA to greatly improve the performance of large language models (LLMs) by implementing a new

Apple's latest machine learning research could make creating models for Apple Intelligence faster, by coming up with a technique to almost triple the rate of generating tokens when using Nvidia GPUs.

Apple and Microsoft are closer than Nvidia to reaching a market cap of $4 trillion. Nvidia's new Blackwell GPU platform could provide the spark to catapult it past those two tech giants. It's the time of year for making predictions about the coming new year.

Apple and NVIDIA shared details of a collaboration to improve the performance of LLMs with a new text generation technique for AI.

This chart features all eight American technology stocks with valuations of $1 trillion or more, and their respective returns in 2024 so far. Buying an exchange-traded fund (ETF) with a high level of exposure to those trillion-dollar market leaders might be a simpler option for investors compared to buying them individually.

Nvidia (NASDAQ: NVDA) has been one of the hottest stocks on Wall Street over the past two years and performed well enough that it's vying for the title of the world's largest company against Apple. Nvidia performed so well in years past that a rise to $5 trillion doesn't seem all that far away,

Yuchen Jin, a computer scientist and Chief Technology Officer of the AI startup Hyperbolic Labs, has had his green card application denied by the United States Citizenship and Immigration Services (USCIS).

Complex data center workloads like training machine learning models and running artificial intelligence (AI) applications would take a very long time if powered only by central processing units (CPUs).