The AI Chip Behind Nvidia’s Supersonic Stock Rally

When we think of groundbreaking tech, it’s often consumer products like smartphones or gaming consoles that steal the spotlight. However, this year, the tech world is buzzing about an obscure computer component that most people will never even see: the Nvidia H100 processor. This powerful chip has ushered in a new era of artificial intelligence (AI) tools, propelling Nvidia Corp. to become the world’s most valuable company, surpassing even Microsoft Corp.

What Is Nvidia’s H100 Chip?

The H100, affectionately named after computer science pioneer Grace Hopper, is a beefier version of a graphics processing unit (GPU) that typically resides in PCs, enhancing visual experiences for video gamers. But its true magic lies in its ability to turn clusters of Nvidia chips into single units capable of processing vast volumes of data at lightning speeds. This makes it an ideal fit for training the neural networks that drive generative AI. Nvidia, founded in 1993, made early investments in parallel computing, anticipating that this technology would eventually extend beyond gaming applications.

Why Is the H100 So Special?

Generative AI platforms learn by ingesting massive amounts of existing data. The more they see, the better they become at tasks like translating text, summarizing reports, and synthesizing images. The H100 outshines its predecessor, the A100, by being four times faster at training large language models (LLMs) and 30 times faster at responding to user prompts. Since its release in 2023, Nvidia has introduced even faster versions, including the H200 and the Blackwell B100 and B200. For companies racing to train LLMs for new tasks, this performance edge is critical. In fact, some of Nvidia’s chips are so vital for AI development that the US government has restricted their sale to China.

How Did Nvidia Become an AI Leader?

Based in Santa Clara, California, Nvidia dominates the graphics chip market. Its powerful GPUs, with thousands of processing cores, handle complex 3D renderings. In the early 2000s, Nvidia engineers repurposed these graphics accelerators for other applications, dividing tasks into smaller chunks and processing them simultaneously. AI researchers found that this approach finally made their work practical.

Competitors and Staying Ahead

Nvidia currently controls about 92% of the data center GPU market. While cloud giants like Amazon Web Services, Google Cloud, and Microsoft Azure are developing their own chips, Nvidia’s rivals—such as AMD and Intel—have struggled to make significant inroads in the AI accelerator market. Nvidia’s secret sauce isn’t just hardware performance; it’s also the CUDA language, allowing programmers to tailor graphics chips for AI workloads.

What’s Next for Nvidia?

The highly anticipated Blackwell release is expected to bring substantial revenue this year. Meanwhile, demand for H series hardware continues to surge. Nvidia’s CEO, Jensen Huang, champions the technology, urging governments and enterprises to embrace AI early. Once customers choose Nvidia for their generative AI projects, upgrading becomes a no-brainer.