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A GPU, or Graphics Processing Unit, is a specialized electronic circuit designed to rapidly manipulate and alter memory to accelerate the creation of images and video. GPUs were originally designed to accelerate the rendering of 3D graphics in video games, but their use has expanded to other applications such as scientific simulations, artificial intelligence, and cryptocurrency mining.
GPUs are typically more specialized and powerful than the central processing unit (CPU) found in most computers. While CPUs are designed to handle a broad range of tasks, GPUs are optimized to handle large amounts of parallel processing, making them particularly effective at handling tasks that require a lot of data to be processed simultaneously.
GPUs are commonly used in high-performance computing environments, such as data centers, where large amounts of data need to be processed quickly and efficiently.
In recent years, GPUs have become increasingly important in the field of artificial intelligence and machine learning. The high parallel processing capabilities of GPUs are particularly well-suited to the type of calculations required in training and executing machine learning algorithms.
GPUs are in higher demand than ever with the explosion in the development of large language models (LLMs) like ChatGPT, plus popular text-to-image generators, such as DALL-E, Stable Diffusion, and Midjourney.
The gold rush to monetize next-generation AI products has created a supply snarl in GPUs. Companies are raising billions of dollars and the industry needs servers running on GPUs as quickly as possible.
NVIDIA (NVDA) and Advanced Micro Devices (AMD) are the world’s largest GPU players. In just the past 6 months, NVDA is up 108%, and AMD is up 38.7%.
And the company that manufactures NVDA's and AMD's chips, Taiwan Semiconductor Manufacturing (TSM), is up 37.4% in the same time frame.
But it’s not just NVDA, AMD, and TSM. According to technology expert Jeffery Brown, a new generation of “AI application-specific semiconductors” will get supercharged due to the current shortage in GPUs and computing systems.
“They are designed to be far more efficient and cost-effective in running AI compared to general-purpose GPUs,” Brown says. Private companies like Graphcore, Intel's (INTC) Habana Labs, Groq, and more come to mind. Their products are in high demand from early-stage tech companies because of the sheer scarcity of NVDA and AMD's graphic processing unit (GPU) systems.
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On the date of publication, Andy Mukolo did not have (either directly or indirectly) positions in any of the securities mentioned in this article. All information and data in this article is solely for informational purposes. For more information please view the Barchart Disclosure Policy here.
