Invest in the next Nvidia

Invest in the next Nvidia

For years, AI models have been powered by one type of hardware: graphics processing units, or GPUs. Originally designed for rendering video game graphics, GPUs turned out to be highly effective at handling the complex mathematical calculations required for artificial intelligence. Unlike traditional computer processors (CPUs), which execute tasks sequentially, GPUs process thousands of small tasks in parallel, making them ideal for training machine learning models.

As AI advanced, demand for computing power surged. Nvidia, the dominant supplier of GPUs, became the backbone of AI computing. Tech companies responded by building massive GPU clusters—racks of thousands of Nvidia chips linked together to handle the increasing complexity of AI training. This approach has worked, but it comes with significant trade-offs. Managing thousands of GPUs is expensive, energy-intensive, and requires specialized software to coordinate workloads across the entire system. More critically, as AI models scale up, GPUs are struggling to keep pace. The inefficiencies of distributed computing are becoming a bottleneck.

What if AI hardware didn’t need to be built this way at all?

Instead of piecing together thousands of smaller chips, what if a single, massive processor could handle an entire AI workload? Eliminating the need for complex distributed systems, reducing energy waste, and dramatically simplifying AI model development?

That’s exactly the bet one company is making. Rather than relying on the decades-old GPU model, it has developed an entirely different kind of processor—one designed from the ground up to accelerate AI. Its technology has already been adopted by national research labs, Fortune 500 companies, and leading AI developers. And as demand for AI compute continues to skyrocket, this company’s approach could fundamentally reshape the industry.

Its name?

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