Training and inference of large AI models might sound sophisticated, but in essence, it's like "fortune-telling"—only it's data being processed, not your love life.
In the AI field, GPUs (graphics processing units) are more critical than CPUs (central processing units), and even more importantly, only NVIDIA GPUs really deliver, while Intel and AMD lag far behind.
GPU vs CPU: Teamwork vs. Solo Champion
Imagine training an AI model is like moving bricks.
The CPU is a "jack-of-all-trades," capable of handling many tasks: computation, logic, management—no matter how complex, it excels at everything. However, it has a limited number of cores, typically only a few dozen at most.
Even if it moves bricks quickly, it can only handle a few or at most a few dozen at a time, making it inefficient despite its hard work.
The GPU, on the other hand, has an astonishing number of cores—often thousands or even tens of thousands. While each core can only move one brick at a time, the sheer number of workers makes up for it! With thousands of "helpers" working together, the bricks are moved in no time.
The core task in AI training and inference is "matrix operations"—simply put, it's a massive queue of numbers performing addition, subtraction, multiplication, and division, much like countless red bricks waiting to be moved. It's simple, repetitive work that doesn't require much brainpower.
The GPU's "massive parallel processing" capability is perfectly suited for this, handling thousands or even tens of thousands of small tasks simultaneously, making it dozens or even hundreds of times faster than the CPU.
The CPU, however, is better suited for sequential, complex tasks like playing a single-player game or writing a document. When faced with the sheer volume of "bricks" in AI, it can only move a few or a few dozen at a time, struggling to keep up with the GPU.
Why NVIDIA Dominates: AMD and Intel Left in the Dust
Now, here's the question: NVIDIA isn't the only one making GPUs—AMD and Intel also produce graphics cards. So why does the AI community overwhelmingly prefer NVIDIA? The answer is simple and blunt—NVIDIA doesn't just sell hardware; it has "hijacked" the entire ecosystem.
First, its software ecosystem is unbeatable. NVIDIA has a game-changer called CUDA (a programming platform), tailor-made for its GPUs. When AI engineers write code to train models, using CUDA feels like having a cheat code—simple and highly efficient.
AMD has its ROCm, and Intel has OneAPI, but these alternatives are either not mature enough or as cumbersome as solving complex math problems. They simply can't match the ease of CUDA.
Second, first-mover advantage and market investment. NVIDIA bet on AI early, promoting CUDA over a decade ago and effectively training AI researchers to become "NVIDIA believers." AMD and Intel? By the time they caught on, NVIDIA had already secured its dominance in the AI space. Trying to catch up now? Too late.
Third, the hardware is no slouch either. NVIDIA's GPUs (like the A100 and H100) are optimized specifically for AI, with high memory bandwidth and explosive computational power. While AMD and Intel's graphics cards are great for gaming, they fall short in AI tasks. To put it simply, NVIDIA is like an "AI brick-moving excavator," while AMD and Intel are still using "household shovels"—the efficiency gap is huge.
The AI World: Big Money, Simple Choices
So, GPUs beat CPUs because of "strength in numbers," while NVIDIA's dominance comes from a combination of "hardware + software + foresight."
AMD and Intel aren't completely out of the race, but they need to step up their game. Otherwise, they'll just have to watch NVIDIA continue to count its profits until its hands cramp.
In the AI industry, burning money is the norm. Choosing NVIDIA GPUs is like buying a "cheat code"—expensive, but it gives you a head start. Isn't it ironic? Before AI saves the world, it's already saved NVIDIA's stock price!