NVIDIA (NASDAQ: NVDA) recently reported powerful results for its fourth-quarter and fiscal year 2018. The graphics processing unit (GPU) specialist's quarterly revenue jumped 34% and adjusted earnings per share (EPS) soared 52%.
There was a wealth of information covered on the Q4 analyst conference call. Our focus here is on how artificial intelligence -- or more specifically, a burgeoning form of AI called deep learning -- is powering the company's growth and how it's expected to do so in the future. (Deep learning aims to mimic in machines how humans think or make inferences from data.)
For reference, here's how NVIDIA's market platforms performed in the quarter:
1. AI training is a huge growth driver for the data center platform
From CFO Colette Kress' remarks:
NVIDIA's GPUs have emerged as the gold standard for deep learning training. (Training, the first of the two-step deep learning process, involves teaching an artificial "neural network" how to make inferences from data like humans do.) Customers adopting the company's Volta-based Tesla V100 GPUs include Amazon Web Services (AWS), Alphabet's Google, IBM, Microsoft Azure, and Oracle in the United States, and Alibaba, Baidu, and Tencent in China.
While AI training is a huge growth driver for the data center platform, it's not the only one, as I've seen erroneously reported. High-performance computing (HPC) and virtualized computing are also helping to power growth. (On the Q4 earnings call, Kress said the company has recently begun seeing a convergence of AI and HPC; however, these are still largely two different growth drivers.)
2. Experiencing "growing traction" in the data center AI inference market
From Kress' remarks:
Inferencing, the second step in deep learning, involves machines applying their training to new data. While NVIDIA's GPUs are the platform of choice for deep learning training, it's only been very recently that they've begun to make inroads into inferencing. As recently as the Q2 earnings call, CEO Jensen Huang said that the company did "0% of our business in inferencing." CPUs dominate data center inferencing.
Huang said on the Q4 call that NVIDIA began shipping its Tesla P4, which is its data center inference processor, in the quarter. Huang believes the business opportunity is great: "My sense is that the inference market is probably about as large in the data centers as training, and the wonderful thing is everything that you train on our processor will inference wonderfully on our processors as well."
3. AI is an emerging growth driver for the professional visualization platform
From Kress' remarks:
I think it will pleasantly surprise many investors that AI is a burgeoning growth driver for the professional visualization platform. (Quadro is NVIDIA's workstation processor, which is targeted at design professionals.) Huang gave examples on the call as to AI applications: "[Y]ou could fill in damaged parts of a photograph, or [on] parts of the image that hasn't been rendered yet, you [can] use AI to fill in the dots. ... It's called generative design."
4. Opportunities in "inferencing at the edge" include self-driving vehicles, smart cities, drones, and other robots
In addition to the data center AI inference market, NVIDIA also has products targeting "inferencing at the edge," or "AI at the edge." From Huang's remarks:
Inferencing at the edge is at the cusp of taking off, and NVIDIA has laid the groundwork to be a big winner in such applications as driverless vehicles, smart cities, drones, and factory robots.
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