Produced by: Tarun Mishra Designed by: Muskan Arora
The University of Colorado Boulder reveals that as the world embraces the latest AI technologies, graphics processing units (GPUs) have become a sought-after commodity, with leading manufacturer NVIDIA witnessing a soaring market valuation exceeding US$2 trillion.
According to a research published in The Conversation, GPUs are not limited to high-end AI products; they are also integrated into phones, laptops, and gaming consoles. This widespread adoption underscores their importance in various technological realms.
Originally designed for generating and displaying complex 3D scenes, GPUs have evolved to handle tasks like video stream decompression. Compared to central processing units (CPUs), GPUs excel in parallel processing, making them indispensable for computationally intensive tasks.
While CPUs consist of a few powerful cores, GPUs boast thousands of smaller cores designed for simultaneous parallel processing. This unique architecture enables GPUs to perform a vast number of simple operations concurrently, setting them apart from traditional CPUs.
GPUs play a pivotal role in AI advancements, particularly in tasks like deep neural networks that rely on matrix multiplication. Their ability to efficiently handle parallel processing makes them ideal for accelerating machine learning algorithms.
GPU capabilities continue to evolve, driven by advancements in chip manufacturing. These enhancements, such as increased core counts and operating speeds, contribute to the growing efficiency and performance of GPUs in AI-related computations.
While traditional GPUs remain crucial for AI tasks, specialized accelerators like data center GPUs and tensor processing units are emerging to address specific machine learning requirements. However, the rapid evolution of AI algorithms poses challenges for the development and adoption of expensive specialized hardware.