Training and inferencing with AI large models sounds sophisticated, but it's essentially "fortune-telling" – just with data, not your love life.
In the field of AI, GPUs (graphics processing units) are more important than CPUs (central processing units). More importantly, only NVIDIA GPUs are effective, while Intel and AMD lag far behind.
GPU vs CPU: A Gang Fight vs. a One-on-One Duel
Imagine training an AI large model is like moving bricks.
A CPU is like an "all-rounder" who can do many things: calculations, logic, and management, no matter how complex. But it has fewer 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, which is inefficient.
What about GPUs? They have a frightening number of cores, easily 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 brothers working together, the bricks are moved very quickly.
The core task of AI training and inference is "matrix operations" – simply put, a bunch of numbers lined up to do addition, subtraction, multiplication, and division, just like a massive pile of red bricks waiting to be moved. It's simple work that doesn't require much brainpower.
The GPU's "massive core parallelism" capability comes in handy, allowing it to handle thousands or tens of thousands of small tasks simultaneously, making it dozens or even hundreds of times faster than a CPU.
What about the CPU? It's better suited for serial complex tasks, such as playing a single-player game or writing a document. There are too many bricks for AI, and it can only move a few or a few dozen at a time, so it can't keep up with the GPU even if it's exhausted.
Why Does NVIDIA Dominate? AMD and Intel are Crying in the Corner
Okay, now the question is: NVIDIA isn't the only one that makes GPUs, AMD and Intel also have graphics cards, so why does the AI community only use NVIDIA's products? The answer is simple – NVIDIA doesn't just sell hardware, it also "kidnapped" the entire ecosystem.
First, the software ecosystem is invincible. NVIDIA has a killer feature called CUDA (a programming platform) specifically designed for its GPUs. AI engineers who write code to train models can use CUDA like they're cheating, making it simple and efficient. AMD has its own ROCm, and Intel also has OneAPI, but they are either not mature enough or using them is like solving math problems. How can they be as easy to use as CUDA?
Second, first-mover advantage + money to build the market. NVIDIA bet on AI early on, launching CUDA more than ten years ago, and forcibly cultivated AI researchers into "NVIDIA believers". What about AMD and Intel? By the time they reacted, NVIDIA had already seized the AI territory. Want to catch up now? Too late.
Third, the hardware is also top-notch. NVIDIA's GPUs (such as A100, H100) are optimized for AI, with high memory bandwidth and explosive computing power. Although AMD and Intel's graphics cards are good for gaming, they always fall short on AI tasks. For example, NVIDIA is a "special excavator for AI brick moving", while AMD and Intel are still "household shovels", with a far different efficiency.
The Rich and Foolish AI Community
Therefore, the GPU completely wins over the CPU because of "more people, more power", and NVIDIA's dominance is a combination of "hardware + software + foresight".
AMD and Intel are not without opportunities, but they need to work harder, otherwise they can only watch NVIDIA continue to count money until their hands cramp.
In the AI industry, burning money is a daily routine. Choosing NVIDIA's GPU is like buying a "cheat code". It's expensive, but it wins at the starting line. Isn't it funny? Before AI saves the world, it saves NVIDIA's stock price first!