Nvidia’s ballooning GPU business and big bets on divisions like autonomous driving continue to look better and better, with the company’s shares jumping more than 10% after it reported its first-quarter earnings.
In the first quarter this year, the company said it brought in $507 million in net income — up from $208 million in the first quarter a year ago. That doubled income comes as its revenue jumped 48% between the first quarter last year and this year. Much of Nvidia’s rapid ascent is thanks to the increasing need for GPUs that can handle deep learning problems like autonomous driving and speech recognition.
That’s giving Nvidia a new renewed growth story that Wall Street apparently loves. After being synonymous for graphics cards and gaming, Nvidia has emerged as a go-to provider for hardware for any company — especially startups tackling new problems — that needs to sift through an enormous pile of data and build a model that it can then efficiently tap on the spot.
Beyond AI, however, Nvidia’s other businesses appear to be doing pretty well. In particular, revenue for its Tegra processor more than doubled to $332 million — no doubt in part thanks to demand for the Nintendo Switch.
All this positive performance doesn’t guarantee Nvidia total immunity from competition in the GPU space, though for now it’s the golden child. Google, for example, is building its own chips for machine learning algorithms. There’s increased focus in the whole area, which makes sense that Nvidia is now going after areas where its products can specialize like autonomous driving.
Here’s what the stock looks like for the past year, just for some perspective:
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Nvidia beat out what Wall Street was looking for, bringing in $0.79 in earnings per share on revenue of $1.94. Analysts expected the company to report earnings of 67 cents per share with $1.91 billion in revenue. The company said its “datacenter GPU computing business nearly tripled from last year,” which is going to be critical for the company going forward. It fell into a leading position in hardware necessary for deep learning products that most companies — at least for now — in the space will need.
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