AI is like a “student” who needs to read through an entire library to learn how to write essays, and computing power (often called “compute”) is its “brainpower” and “study tools.” Because the library is so vast and the topics are so complex, the student must have super-powerful calculation abilities to learn quickly and answer accurately.
01
AI Needs to “Read” Millions of Books
To train an AI, it must read through millions or even billions of digital books and documents.
- Every time it reads a sentence, it performs massive calculations to analyze grammar, logic, and context.
- Without strong computing power, it would take decades for the AI to finish its “reading.”
02
The AI “Brain” is Enormous
A large model can have hundreds of billions of parameters. Think of these as hundreds of billions of tiny drawers that need to be constantly adjusted during learning.
- Adjusting these drawers requires ultra-fast computers—it’s like needing countless workers to labor simultaneously.
03
Answering Questions Requires Multi-Step Thinking
When you ask an AI a question, it doesn’t just recite a memorized answer; it thinks and generates the response in real-time.
- It is like a teacher solving a problem during an exam—every step requires “brainwork.”
- The longer and more complex the question, the more computing power is needed.
04
GPU: The AI “Super Calculator”
- A standard computer (CPU) is like one person solving a math problem stroke by stroke.
- A GPU is like having thousands of people using abacuses at once, increasing efficiency by thousands of times.
05
Massive Energy Consumption
Strong computing power equals high power consumption. Training a large model can sometimes consume millions of kilowatt-hours of electricity—more than the daily consumption of a small city.
06
A Real-Life Example
- Without Compute: One person using a pen and paper to solve 100 million math problems; it might take a lifetime.
- With Strong Compute: Tens of thousands of workers using calculators simultaneously; they can finish in a few days.
AI is trained using this “stadium-sized” scale of computational power.
07
Risks and Real-World Challenges
- High Energy Demand: AI’s high electricity usage puts pressure on energy grids.
- Expensive Equipment: A single high-end graphics card can cost tens of thousands of dollars; building a large cluster costs hundreds of millions.
- The “Compute Gap”: Those with more computing power will have more advanced AI, potentially widening the gap between countries and corporations.
08
Conclusion
AI requires massive computing power because it must:
- Process vast amounts of data (Training)
- Manage a massive memory bank (Parameters)
- Solve problems rapidly (Inference)
Without enough computing power, AI cannot learn or answer accurately. With sufficient power, AI becomes smarter and truly helpful to humanity.
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