---
title: "Nasdaq Correction: Time to Buy the Dip on Nvidia?"
type: "News"
locale: "zh-CN"
url: "https://longbridge.com/zh-CN/news/231260127.md"
description: "The Nasdaq Composite has entered correction territory, largely due to Nvidia's stock decline. Despite Nvidia's strong recent performance, concerns over tariffs and a potential recession have affected investor sentiment. However, for long-term investors, this dip presents a buying opportunity. Nvidia leads in AI infrastructure with a 90% GPU market share and continues to benefit from growing AI data center investments. Additionally, its stock is currently attractively priced, making it a compelling option for investors looking to capitalize on future AI growth."
datetime: "2025-03-10T18:17:51.000Z"
locales:
  - [zh-CN](https://longbridge.com/zh-CN/news/231260127.md)
  - [en](https://longbridge.com/en/news/231260127.md)
  - [zh-HK](https://longbridge.com/zh-HK/news/231260127.md)
---

> 支持的语言: [English](https://longbridge.com/en/news/231260127.md) | [繁體中文](https://longbridge.com/zh-HK/news/231260127.md)


# Nasdaq Correction: Time to Buy the Dip on Nvidia?

The **Nasdaq Composite** (^IXIC -4.23%) has moved into correction territory (down at least 10% from all-time highs). A significant contributor to that drop has been **Nvidia** (NVDA -5.33%) 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** (AMD -2.96%) 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.

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.

Recent AI model iterations have needed exponentially more GPU chips to be trained on than their predecessors. For example, **Meta Platform**'s Llama 4 LLM needed 10x as many GPUs to be trained on than Llama 3. Elon Musk-backed xAI, meanwhile, originally used 5x the GPUs (100,000) to build out its Grok 3 model before bumping it up to 200,000 GPUs.

Meanwhile, cloud computing companies, along with other tech companies, are pouring money into building AI data centers. The big three cloud computing companies plan to spend a combined $255 billion building out AI data centers this year to help keep up with demand.

Cloud computing is an infrastructure-as-a-service platform, and customers have been using these services to help customize and build their own AI models and applications. Meanwhile, Meta plans to spend up to $65 billion in capital expenditures (capex) this year largely aimed at expanding its AI infrastructure, while a consortium led by OpenAI and **Softbank** have pledged to spend $500 billion over the next few years building out AI data centers in the U.S. through Project Stargate.

This all points to a lot of continued future spending on AI infrastructure in the years ahead. As such, Nvidia remains well-positioned to continue to grow.

## 3\. Nvidia stock is inexpensive

The third big reason to own Nvidia is that its stock remains attractively priced. The stock currently trades at a forward price-to-earnings ratio (P/E) of 24 times 2025 analyst estimates and a price/earnings-to-growth ratio (PEG) of below 0.5. PEG ratios under 1 typically indicate a stock is undervalued, and growth stocks will often trade at PEGs well above 1.

Data by YCharts.

Nvidia isn't a software-as-a-service (SaaS) company with a recurring and predictable revenue stream, so it isn't going to command the same type of valuation multiple as these types of companies -- nor should it. However, its current valuation is inexpensive, given that we still appear to be in the early days of AI and that AI infrastructure spending will continue to rise. As such, the recent pullback looks like a solid buying opportunity in the stock long term.

### 相关股票

- [NVIDIA (NVDA.US)](https://longbridge.com/zh-CN/quote/NVDA.US.md)

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