3 Reasons Competitors Can’t Stop NVIDIA’s Runaway Growth

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NVIDIA 's (NASDAQ: NVDA) saw its Data Center group grow sales by 427% last quarter. It also has the highest margins of any major technology company. So the question is, what stops competitors like AMD ( Nasdaq: AMD ) and Intel ( Nasdaq: INTC ) from catching up with NVIDIA? We explore three of the competitive advantages that make NVIDIA so hard to disrupt.

What Are the Competitive Advantages That Keep NVIDIA Ahead of the Competition?

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Here are some highlights from the discussion between 24/7 Wall Street Analysts Eric Bleeker and Austin Smtih.

• Last week we talked about NVIDIA's Annual Stockholder Meeting and how CEO Jensen Huang specifically addressed the advantages and strategies that will keep the company ahead of the competition .
• So, we wanted to dig into the details more here because the question of how NVIDIA maintains its competitive advantage is one of the biggest questions in all of investing right now.

We'll explore three key areas that investors need to understand when analyzing NVIDIA.

1. The History

Simply put, NVIDIA has been intensely focused on artificial intelligence longer than any company on Earth. They created CUDA (more on this below) in 2007 and supported the industry after an initial breakthrough in an ImageNet competition in 2012.

With the iPhone, Apple came into a market that had existed for a decade, which was smartphones. It’s hard to overstate how much NVIDIA had to create artificial intelligence as a market. NVIDIA worked individually with academics on the foundation of deep learning and getting GPUs in use across AI research at universities. Then the company spent years building software to program GPUs when Wall Street largely thought the efforts were a waste. These efforts created software libraries that made programming to GPUs in new industries easier. Importantly, since NVIDIA was so involved in the creation of modern artificial intelligence models, they've also long had a vision of how to build a full stack to support AI computing. That involves software, hardware like their computer chips, as well as networking.

2. CUDA We touched on NVIDIA creating CUDA libraries to expand AI into new industries already.

But it must be emphasized, NVIDIA has been working on CUDA since 2007. Programming GPUs is a hard market. AMD 's version of CUDA - ROCm- has struggled at times to add even basic features. It's worth noting that AMD is really putting in a lot more investment to their software right now, which is often cited as one of the strongest bull cases for the company.

But here’s the reality, a lot of people in the field around AI don’t necessarily specialize in programming. They understand programming at a high level and have some experience, but it's not their specialty. Here's an excellent overview if you'd like to understand how CUDA is used in practice across the artificial intelligence industry . So, the key idea here is that CUDA had a massive head start where its used in almost every academic setting in an outsized way. This is probably its strongest advantage beyond the strength of its software that competitors are racing to catch up with. The pipeline from research and academia to AI is so strong and most people in the field are incredibly comfortable with CUDA.

3. NCCL One of the best thinkers in the AI space claimed this was NVIDIA’s biggest competitive advantage this week . NCCL is an acronym for NVIDIA Collective Communications Library.In short, it’s software that makes communication between groups of GPUs much simpler and optimized. This is important because the next phase of GPU growth is clusters going from about 4,000 GPUs to discussions of clusters with a million GPUs used for training.

This competitive advantage may be a bit less defensible in the long run. For example, Broadcom spent significant time on their latest earnings call discussing networking growth and claimed to be engaged with 7 of the top 8 data center operators on the networking side.

But, this does illustrate the biggest area for value generation in the next few years is the push toward clusters with a million processors. As long as NVIDIA can keep its value proposition high to customers, it has the best platform from software, to its chips, to networking for this next phase of AI growth.

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