GPU (Graphics Processing Unit)
"GPU (Graphics Processing Unit)" refers to a semiconductor processor originally developed for personal computers and game consoles to perform "ultra-parallel computation" (executing multiple processes simultaneously) for calculating the brightness and color of a large number of pixels to smoothly render high-definition 3D graphics and game visuals.
The physical structure of GPUs, which enables them to "perform ultra-parallel processing of simple multiplications and additions (matrix operations) across thousands of cores simultaneously," perfectly matched the mathematical computation models of artificial intelligence's "deep learning." This convergence has made GPUs the most critical and scarce high-tech infrastructure (computational resource) physically supporting the modern AI revolution.
- The Decisive Difference Between CPUs and GPUs: While a CPU is a "processor (few cores) where a single, brilliant mathematician sequentially solves complex problems," a GPU is a "processor (multi-core) where thousands of elementary school students simultaneously tackle simple arithmetic drills all at once."
- NVIDIA's Dominant Monopoly and the CUDA Ecosystem: NVIDIA's early provision of "CUDA," a programming environment enabling GPUs for general-purpose computing beyond graphics (GPGPU), created a monopolistic structure where AI researchers worldwide cannot develop without NVIDIA GPUs (such as A100, H100, B200) and CUDA.
- Power Consumption and National Security: AI training requires data centers with tens of thousands of GPUs operating in parallel. Their power consumption (a CO2 emissions issue) and export restrictions on cutting-edge GPUs (e.g., US-China trade friction) have become top agendas in international politics.
Why are GPUs Absolutely Essential for AI "Training," and CPUs are Not Sufficient?
AI neural network training involves repeatedly performing massive matrix calculations to gradually adjust tens of millions to trillions of "parameter weights" based on input data. If a CPU were to attempt this, with only a few to a few tens of computational cores, it would be forced to process sequentially, taking "hundreds of years," regardless of how fast its clock speed is. In contrast, a GPU, with its thousands of processing cores, can perform matrix operations in parallel and instantaneously, completing the same calculations in "a few days." This effectively makes practical deep learning research impossible with any semiconductor other than a GPU.
Specific Use Cases and Conversational Examples of "GPU"
CFO (Chief Financial Officer) A:"Regarding our in-house AI model training, cloud computing costs have doubled month-over-month. Wouldn't purchasing 300 million JPY worth of the latest NVIDIA GPU servers and migrating to on-premise infrastructure offer better long-term depreciation benefits?"
CTO (Chief Technology Officer) B:"Theoretically, yes, but the latest GPUs (such as H100) are in fierce global demand, and orders have lead times of over half a year. Furthermore, building an on-premise GPU cluster would incur tens of millions of JPY per month just for air conditioning electricity and data center maintenance. For now, I believe securing flexible GPU allocations (on-demand instances) from major cloud vendors and scaling that way is a lower-risk strategy."
Performance Structure Comparison: CPU (Central Processing Unit) vs. GPU (Graphics Processing Unit)
| Comparison Metric | CPU (Central Processing Unit) | GPU (Graphics Processing Unit) |
|---|---|---|
| Number of Cores | Few (a few to several tens of cores). | Many (thousands to tens of thousands of cores). |
| Strengths in Computation | Complex conditional branching, sequential processing, overall OS control. | Simple matrix operations, image rendering, parallel computing (deep learning). |
| Power Consumption | Relatively low (tens to approx. 100W). | Extremely high (high-performance AI GPUs consume 350W to over 1000W per unit). |
Frequently Asked Questions (FAQ)
Q: Is it impossible to develop AI using GPUs other than NVIDIA's (e.g., AMD or Intel)?A: Technically, it is possible, but software environment (ecosystem) compatibility is the challenge. Most AI frameworks worldwide (e.g., PyTorch, TensorFlow) are developed and optimized for NVIDIA's "CUDA." While competing environments like AMD's "ROCm" are rapidly evolving, NVIDIA GPUs continue to be chosen by over 90% of the market from a development efficiency standpoint, as existing libraries and code run without errors.
Fair Use of Semiconductor Resources and Procurement Etiquette
Amidst the AI bubble, hoarding thousands of the latest GPUs on a large scale and leaving them idle (unutilized) solely for budget acquisition or stock price measures (IR appeals), even when there's no actual in-house AI model development, is a serious industry etiquette violation. Such actions exacerbate the global semiconductor shortage and create dead stock of unnecessary power infrastructure. The pinnacle of etiquette in the advanced technology industry is to fairly contract and procure only the amount necessary for one's development roadmap, and to offer unused resources as cloud instances to other startups or academic research institutions, fostering a sharing mindset.
About "GPU (Graphics Processing Unit)"
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