GPU

Although it was originally a graphics processing chip, it is strong in parallel calculations and has become a core hardware for AI learning and execution.

GPUs were originally chips created to draw game graphics, but they have become core hardware for AI thanks to their parallel structure that processes thousands of simple calculations simultaneously. If the CPU is a small number of professors solving difficult problems alone, the GPU can be compared to an army of thousands of students solving simple calculations simultaneously.

AI learning is ultimately a repetition of massive matrix multiplication, which fits exactly with the parallel structure of the GPU. After the deep learning boom, both AI learning and execution became dependent on GPUs, and the number of GPUs secured became an indicator of the competitiveness of AI companies, and NVIDIA rose to the ranks of the world's most valuable companies.

Due to the surge in demand, shortages and price increases were repeated, and it became a strategic material to the extent that it was subject to U.S. export restrictions to China. Accordingly, there are active attempts to reduce dependence on GPUs, such as developing AI-specific chips such as Google's TPU and each country's own chips.

✅ Why it matters

• It is a physical foundation that enables AI learning and execution
• It is a key background for understanding NVIDIA stock prices and semiconductor news
• It is a strategic asset whose secured volume can be used as an indicator of AI company competitiveness

⚠️ Limits and debates

• Due to shortages and high prices, it has become the largest item of AI development cost
• Huge power consumption is the cause of power shortages in data centers
• High dependence on specific companies exposes it to supply chain and geopolitical risks