Training and inference of large AI models may sound sophisticated, but in simple terms, it's like "fortune-telling"—except it's data being analyzed, not your love life.
In the AI field, GPUs are more important than CPUs, and what's even more critical is that only NVIDIA GPUs work well, while Intel and AMD lag far behind.
GPU vs CPU: One Fights in a Gang, the Other Is a Solo Champion
Imagine training a large AI model is like moving bricks.
The CPU is like an "all-rounder"—it can handle many tasks: computation, logic, management, no matter how complex, it excels at everything. But it has few cores, typically up to a few dozen at most.
No matter how fast it moves bricks, it can only handle a few, or at most a few dozen, at a time. It works hard but isn't very efficient.
The GPU, on the other hand, has a staggering number of cores—often thousands or even tens of thousands. While each core can only handle one brick at a time, it makes up for it with sheer numbers! With thousands of "helpers" working together, the bricks are moved in no time.
The core task of AI training and inference is "matrix operations"—simply put, it's a massive queue of numbers doing addition, subtraction, multiplication, and division, like countless red bricks waiting to be moved. It's simple work that doesn't require much brainpower, just manpower.
The GPU's "massive parallel processing" capability is perfectly suited for this, handling thousands or tens of thousands of small tasks simultaneously, making it dozens or even hundreds of times faster than the CPU.
The CPU? It's better suited for sequential, complex tasks, like playing a single-player game or writing a document. When it comes to AI's mountain of bricks, it can only handle a few or a few dozen at a time, and no matter how hard it tries, it can't keep up with the GPU.
Why NVIDIA Dominates: AMD and Intel Are Left Crying
Now, here's the question: NVIDIA isn't the only company making GPUs. AMD and Intel also produce graphics cards, so why does the AI world overwhelmingly prefer NVIDIA? The answer is simple and brutal—NVIDIA doesn't just sell hardware; it has "hijacked" the entire ecosystem.
First, its software ecosystem is unbeatable. NVIDIA has a killer feature called CUDA (a programming platform), tailor-made for its GPUs. When AI engineers write code to train models, using CUDA feels like cheating—it's simple and efficient.
AMD has its ROCm, and Intel has OneAPI, but these are either not mature enough or feel like solving math problems. They just don't compare to the ease of using CUDA.
Second, first-mover advantage and market dominance built with money. NVIDIA bet on AI early, promoting CUDA over a decade ago and turning AI researchers into "NVIDIA believers." AMD and Intel? By the time they realized what was happening, NVIDIA had already secured its dominance in the AI space. Now, trying to catch up? Too late.
Third, the hardware is no joke. NVIDIA's GPUs (like the A100 and H100) are optimized specifically for AI, with high memory bandwidth and explosive computational power. AMD and Intel's graphics cards might be great for gaming, but 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: Rich and "Foolish"
So, GPUs beat CPUs because "many hands make light work," and NVIDIA's dominance is the result of a combination of "hardware + software + foresight."
AMD and Intel aren't completely out of the game, but they need to step up their efforts. Otherwise, they'll just have to watch NVIDIA count its money until its hands cramp.
In the AI industry, burning money is the norm. Choosing NVIDIA's GPUs is like buying a "cheat code"—expensive, but it gives you a head start. Funny, isn't it? Before AI saves the world, it's already saved NVIDIA's stock price!