Until this week, the dominant narrative about Nvidia was: For the first few years of the AI boom, Nvidia was the only source of cutting-edge GPUs, allowing it to reap huge profits as the industry scaled out. In recent years, hyperscalers like Amazon and Google have started developing their own chips, and Nvidia is no longer the only game in town, leaving many investors wondering how durable its advantages really are.
It’s a compelling story, and mostly true. After increasing its market capitalization tenfold from early 2023 to mid-2025, Nvidia stock has taken a slower trajectory over the past year due to concerns about GPU competition.
A new narrative has taken shape since the company’s earnings on Wednesday, with investors beginning to realize that Nvidia’s advantages go far beyond GPUs. As AI computing grows to gigawatts, orchestration becomes an increasingly complex task. Not surprisingly, Nvidia has built much of the cutting-edge hardware needed to process GPUs, giving the company a significant advantage in systems surrounding GPUs, even as competition for GPUs themselves has increased.
Despite much talk about computing as a commodity, operating large data centers at peak efficiency remains incredibly difficult, and that challenge only grows as deployments become larger and faster.
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A look at the details of what Nvidia actually sells will tell you some of that. The company is currently rolling out its Vera Rubin architecture. It combines a Rubin GPU with a collection of other units, including Vera CPUs, Groq 3 LPX inference accelerators, and similar racks for storage and networking.
Over the past week, I’ve been talking to the folks at Nvidia about what these systems actually do, and the results have been surprising. Like the Rubin GPU itself, these are very specialized systems, but rather than being token hogs, they make sure that everything outside of the GPU runs as efficiently as possible. If the GPU is the engine, these are the rest of the car.
Vera CPUs are specifically focused on data orchestration issues. “Vera is important because there’s a limit to how much memory you can put on a single server or any type of computing platform,” Jason Hardy, Nvidia’s vice president of storage technology, told me.
As the computing power of data centers has expanded, so has their memory capacity. This is why companies like Micron got rich in the second wave of the infrastructure boom. However, sending that data to the GPU at the right time is not easy. As companies try to push the number of tokens per watt lower and lower, they are realizing how important the direction of that kind of traffic is.
“For these operations, we saw more than a 3x improvement due to Vera CPU acceleration,” Hardy said. “We can now get the most out of flash by getting all the performance without being a bottleneck.”
You can see other non-Nvidia versions with the same problem. When OpenAI developed the Jalapeño chip, the main focus was to avoid these challenges entirely by minimizing the amount of data that needed to be moved.
“We designed Jalapeno to minimize data movement and communication delays,” the company said in a blog post earlier this month. “Its large domain ensures that the entire workload stays within one connected system, minimizing data movement and ensuring that complete requests are fast and efficient from start to finish.”
This is a different approach that completely avoids data movement by running the workload within one integrated chip. However, the overall logic is the same, increasing efficiency through more processor cycles as well as smarter traffic control. This opens up a whole new layer of infrastructure for companies to compete on.
A new focus on data orchestration is not an automatic win for Nvidia. As with GPUs, the company will have to compete with rival chipmakers and hyperscalers. But the competition is moving to a new layer, where it’s less about building competing GPUs and more about making the whole system work efficiently.
And Nvidia appears to have a commanding lead, at least in the early stages.
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