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Where Will Nvidia Stock Be in 10 Years?

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Nvidia (NASDAQ: NVDA) has been a huge winner on the stock market in the past decade, turning an investment of just $100 into nearly $25,000 as of this writing on account of the outstanding revenue and earnings growth that it has delivered during this period.

The graphics card specialist has benefited from multiple growth trends during this period, such as the growth of the video gaming market, the advent of connected cars and autonomous driving technology, high-performance computing, and artificial intelligence (AI). However, Nvidia's remarkable surge in the past 10 years has made it the second-most valuable company in the world with a market cap of $3.4 trillion.

Investors, therefore, may be wondering if Nvidia has the ability to deliver substantial gains over the next decade as well, considering where it is now. Let's take a closer look at Nvidia's potential catalysts for the next decade and try to find out if it is worth buying the stock now in anticipation of more upside in the long run.

Nvidia still has a lot of room for growth over the next decade

Expecting Nvidia to replicate the gains it has clocked in the past decade over the next 10 years as well looks outlandish. A 250x jump in the stock price in the next decade would take its market cap to a whopping $850 trillion, and that isn't logical, as the global economy is expected to hit an estimated $155 trillion in 2035.

However, Nvidia can still deliver respectable gains in the coming decade thanks to the sizable end markets the company is likely to benefit from. For instance, the shift toward accelerated computing in data centers in order to speed up tasks and reduce power consumption and operating costs is expected to open a $1 trillion revenue opportunity for Nvidia, according to CEO Jensen Huang.

That level of investment may look like a lot initially, but it does seem possible considering that $2.6 trillion was spent on constructing data centers between 2017 and 2024. Those data centers are now in need of upgrades to tackle intensive workloads such as AI. As a result, data center workloads are likely to be shifted from central processing units (CPUs) to graphics processing units (GPUs) to extend the life of their server infrastructure.

That's because the parallel computing power of GPUs will allow them to tackle intensive workloads while offloading less intensive tasks to CPUs. This is likely to reduce the stress on the system as GPUs are designed to accomplish bigger calculations in a shorter time span, leading to reduced energy consumption and higher efficiency. Moreover, the reduced stress on CPUs means that they will experience lower wear and tear, thereby extending the life and computational power of data centers at the same time.