Training and inference for AI large models may sound sophisticated, but in essence, it's "fortune-telling"—except it's calculating data, not your love life.
In the AI field, GPUs (graphics processing units) are more important than CPUs (central processing units). Even more importantly, only NVIDIA GPUs work well, while Intel and AMD lag far behind.
GPU vs CPU: One is a Brawl, the Other is a Solo Champion
Imagine training an AI large model is like moving bricks.
A CPU is like an "all-rounder"—a single entity capable of handling many tasks: computation, logic, management, no matter how complex, it's proficient at everything. However, it has few cores, at most a few dozen. No matter how fast it moves bricks, it can only move a few, or at most a few dozen at a time. It works itself to death with low efficiency.
A GPU, on the other hand? It has a staggering number of cores, often thousands or tens of thousands. Although each core can only move one brick, there are so many of them! With thousands or tens of thousands of "little workers" pitching in 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 a huge pile of red bricks waiting to be moved. It's simple work that doesn't require brains, just hands.
The GPU's "massive core parallelism" capability comes in handy here, allowing it to process thousands or tens of thousands of small tasks simultaneously, making it dozens or even hundreds of times faster than a CPU.
The CPU? It's better suited for serial complex tasks, like playing a single-player game or writing a document. For AI's mountain of bricks, it can only move a few or a few dozen at a time; it would collapse from exhaustion before catching up to a GPU.
Why Does NVIDIA Dominate? AMD and Intel Are Crying in the Corner
Alright, now the question arises: NVIDIA isn't the only one with GPUs; AMD and Intel also have graphics cards. Why does the AI community flock to NVIDIA's products? The answer is simple and brutal—NVIDIA doesn't just sell hardware; it has "held hostage" 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 is like having a cheat code—simple and efficient. AMD has its own ROCm, and Intel has OneAPI, but these two are either not mature enough or feel like solving math problems compared to the ease of using CUDA.
Second, first-mover advantage + a market built with money. NVIDIA bet on AI early, pushing CUDA over a decade ago, and essentially trained AI researchers to become "NVIDIA believers." What about AMD and Intel? By the time they reacted, NVIDIA had already firmly occupied the AI territory. Trying to catch up now? Too late.
Third, the hardware is no slouch either. NVIDIA's GPUs (like the A100, H100) are optimized specifically for AI, with high memory bandwidth and explosive computing power. AMD and Intel's graphics cards might be great for gaming, but they always seem to fall short on AI tasks. To put it simply, NVIDIA is an "AI brick-moving excavator," while AMD and Intel are still using "household shovels"—the efficiency gap is huge.
The AI World: Lots of Money, Not Much Sense
So, GPUs beat CPUs because "many hands make light work," while NVIDIA's dominance is a combination of "hardware + software + foresight."
AMD and Intel aren't without opportunities, but they need to step up their game. Otherwise, they can only watch NVIDIA continue to count money until their hands cramp.
In the AI industry, burning money is routine. Choosing NVIDIA GPUs is like buying a "cheat code"—expensive, but it wins at the starting line. Isn't it funny? Before AI saves the world, it first saved NVIDIA's stock price!