To say Nvidia Corporation (NASDAQ:NVDA) did well last quarter would be a considerable understatement. Nvidia shocked the world Tuesday evening when it unveiled its Q1 results. Earnings of 85 cents per share handily topped estimates of only 67 cents per share of NVDA stock, while revenue of $1.94 billion topped analysts’ outlooks of $1.91 billion. Moreover, per-share income was up 85% year-over-year on 48% growth in sales.
The stock responded accordingly. All told, Nvidia stock advanced nearly 18% the next day, with pleasantly surprised investors racing to get into a name they’d only been lukewarm on since the beginning of the year.
The big-time growth and earnings beat does lead to one overarching questions though … how did this tech company with respectable but seemingly limited prospects manage to do so well?
The close-second follow-up question: Can it keep doing it?
An Unappreciated Technology Comes Into Its Own
Those who know a little about Nvidia will know its core business has been graphics processing hardware — the piece of a computer that optimizes what’s displayed on the screen. Most casual tablet and computer users don’t give that aspect of the underlying technology a second thought. Hardcore video gamers do though, since most games require smooth, fast image rendering to make the game playable and glitch-free.
Thus was born the graphical processing unit, or GPU … a term/acronym Nvidia coined in 2000.
While GPUs have been getting better over the course of the past couple of decades, the potential use of their underlying technology was never fully appreciated until very recently. As it turns out though, the ‘brain’ of the GPU is very fast, making ideal use of an idea called floating point operations.
Floating point operations are simply higher-level math functions. A GPU processor handles decimal-based calculations much faster than a central processing unit, or CPU can. CPUs, though they’ve come a long way as well, were developed from the ground up to primarily process whole integers. That is to say, CPUs can handle a calculation like “2×3” much better than it can handle a calculation like “2.47×3.16.” GPUs, however, can handle “2.47×3.16” like a champ. [That’s the very Q&D explanation, unsatisfactory to most computer scientists, but more than adequate for the average investor.]
At the same time, the concept of parallel processing has caught on, and become something of a must-have for heavier-duty computing.
At the onset of the computer industry, while a processor could perform functions very fast, it could only do one computation at a time. About a decade ago, the concept of computer cores was launched, effectively turning one CPU into two, four or more processors and vastly improving their speed. This is parallel computing, and it has opened a lot of doors.