NVIDIA‘s (NASDAQ:NVDA) stock price climbed 121.9% last year, according to data provided by S&P Global Market Intelligence. The graphics specialist released new graphics processing units (GPUs) across its two largest segments — data center and gaming — that drove high demand.
In September, NVIDIA made waves in the chip industry by announcing the $40 billion acquisition of ARM Holdings, controlled by SoftBank Group. The deal still must pass regulatory scrutiny but would significantly enhance NVIDIA’s ability to deliver cutting-edge artificial intelligence (AI) solutions using ARM’s chip designs.
Top cloud companies are deploying NVIDIA’s new A100 GPUs, which are based on the latest Ampere chip architecture. This fueled a record year for NVIDIA’s data center business, with segment revenue surging by 80% year over year in the fiscal first quarter, 167% in the second, and 162% in the third.
On top of strong data-center results, interest in gaming exploded in 2020, delivering record sales for NVIDIA’s RTX GPU business. The segment hit a quarterly record of $2.27 billion in revenue in the fiscal third quarter, up 37% year over year.
One catalyst to watch in the short term is the impact that the recent surge in cryptocurrency prices could have on growth for the gaming segment. The RTX 30 series graphics cards have been sold out, partly due to high demand from cryptocurrency miners. This means the gaming segment is likely looking at terrific results by the time NVIDIA reports fiscal fourth-quarter earnings results.
As for the data center segment, NVIDIA is still in the early ramp-up stage for its A100 GPU. It is seeing tech giants and researchers buy more of these new chips for conversational AI and recommender systems.
Looking at the big picture, the ARM deal could significantly extend NVIDIA’s leadership in the semiconductor industry. But regardless of when the deal gets approved, NVIDIA’s current leadership in graphics processing puts the company in a great position to deliver returns to investors, as organizations increasingly rely on the advanced computing power of GPUs to handle AI workloads.