Unlock stock picks and a broker-level newsfeed that powers Wall Street. Upgrade Now
Nasdaq Correction: Time to Buy the Dip on Nvidia?

In This Article:

The Nasdaq Composite (NASDAQINDEX: ^IXIC) has moved into correction territory (down at least 10% from all-time highs). A significant contributor to that drop has been Nvidia (NASDAQ: NVDA) stock, which is down about 20% year to date, as of this writing. The chipmaker reported outstanding results recently, but investor concerns about tariffs and the U.S. suddenly looking like it could be headed toward a recession have rattled the market in the near term.

For investors interested in Nvidia who have a long-term mindset, this price correction presents a great opportunity to scoop up Nvidia shares on the cheap. Let's look at three reasons why the stock is a must-buy for the long term on this dip.

1. Nvidia is the artificial intelligence infrastructure leader

With an approximate 90% market share for graphics processing units (GPUs), Nvidia is the dominant leader among the chip designers that are powering the artificial intelligence (AI) infrastructure buildout. While originally designed to speed up graphics rendering in video games, Nvidia's GPUs' fast processing times have made them ideal for helping train large language models (LLMs) and running inference for AI.

In addition, the company's CUDA (Compute Unified Device Architecture) platform has helped create a wide moat for the company. Nvidia became the first company to allow GPUs to be programmed for tasks outside their original purpose back in 2006 through CUDA. As such, developers learned to program GPUs using Nvidia's software platform.

Rival Advanced Micro Devices (NASDAQ: AMD) didn't introduce its ROCm (Radeon Open Compute) software platform until about 10 years later in 2016. Meanwhile, through CUDA-X, which was built on top of CUDA, Nvidia now has a full software stack comprised of libraries, microservices, and tools designed to accelerate applications in the areas of AI and high-performance computing.

CUDA and CUDA-X continue to be the primary reason for Nvidia's dominance, particularly in AI model training. In a recent study, semiconductor research outfit Semianalysis found AMD's latest GPUs unusable for AI training out of the box due to software bugs, while praising Nvidia's chips. As such, Nvidia remains the company best positioned to continue to benefit from AI infrastructure growth.

AI chip.
Image source: Getty Images

2. AI data center infrastructure continues to grow

Despite Chinese AI company DeepSeek's claims of building an effective AI model cheaply (how effective it actually is is in dispute), the best-known way to advance AI models currently is through brute compute-power force. This means building out systems with more and more AI chip clusters.