How Nvidia leverages GPUs

As one might have expected, NVIDIA’s sales has seen remarkable growth, with its fiscal year 2024 revenue reaching $60.9 billion. That represents a 126% increase from the previous year, informs Nvidia in a press release.

Furthermore, market analysts are highly optimistic about NVIDIA’s future performance. With a current stock price of around $830.41, analysts have set a 12-month target price averaging $916.76, indicating potential for further growth of approximately 10.4%. This bullish outlook is supported by NVIDIA’s strong performance and the expectation of continued demand for its AI and data center technologies​ (Stock Analysis)​.

One of the reason why Nvidia is so effective on the market lies in its long-lived advantage in not only developing GPU, but also software platforms that enable to leverage them. 

The GPU (Graphics Processing Unit) excels in handling parallel computations, performing repetitive tasks simultaneously, making it ideal for graphics rendering and data-intensive scientific computing. On the other hand, the CPU is adept at managing a variety of tasks with more diverse requirements.

While GPUs were primarily built for graphics rendering, in recent years they had enjoyed a boom of popularity partly thanks to the field of AI. The massive parallelism of GPUs was a perfect match for accelerating the math-intensive matrix computations involved in deep learning, which are massively parallel operations.

However, there needs to be a reasonable interface with the GPU. This is where Nvidia came in with it; its CUDA extends the parallel computing tasks of GPUs beyond specific applications in graphics rendering to general-purpose computing, enabling GPUs to efficiently process large datasets across various domains.

Nvidia’s early mover advantage in 2010s with a mature GPU programming platform like CUDA perfectly positioned the company to dominate as deep learning started booming years later. It makes sense that CUDA eventually got a native support in most of the frameworks for AI learning, as it was the most robust and easiest to use thanks to the simple API interface. The way it does this is by facilitating a streamlined transfer of various types of data to the GPU, allowing for efficient computation and the generation of desired results. And as these data now can by run on a graphic card instead of normal CPU, it is possible to execute thousands of threads simultaneously.

By leveraging the computational power of NVIDIA GPUs, tasks like model inference can be significantly accelerated, without the need of outsourcing the computations to a cloud provider. Some users have even noted that this local GPU acceleration can yield quicker responses from large language models (LLMs) compared to using APIs of AIs that are ran by cloud providers. However, it is needed to mention that Nvidias CUDA technology and chips are used both by the proponents of edge-computing and the cloud providers, which may be the reason for Nvidia’s extraordinary success. 

Author: Oldřich Příklenk

Picture: https://chatgpt.com