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

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.

BE Semiconductor (OTC:BESIY) is likely to see several positive catalysts over the next few years, including benefiting from the iPhone 18 Pro and a change in Nvidia's (NASDAQ:NVDA) Quantum InfiniBand switch,

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.

This growth stock is expected to experience a surge in revenue, profit, and cash flow in the upcoming years. Where to invest $1,000 right now? Our analyst team just revealed what they believe are the 10 best stocks to buy right now.

Could Nvidia stock fall by about 50% to levels of around $65 in the near term from the roughly $130 level it is at currently? We believe this is a real possibility. Nvidia has seen its business boom,

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).