Training and inferencing with AI large models sound sophisticated, but essentially it's all about "fortune-telling" – but calculating data, not your romantic fate.
In the AI field, GPUs (graphics processing units) are more important than CPUs (central processing units), and even more importantly, only NVIDIA GPUs are really 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 as similar to moving bricks.
A CPU is like an "all-rounder" who can handle many tasks: computation, logic, and management, no matter how complex. However, it has a limited number of cores, typically only a few dozen. No matter how fast it moves bricks, it can only carry a few or at most a few dozen at a time, making it inefficient despite the hard work.
On the other hand, a GPU has a staggering number of cores, easily reaching thousands or even tens of thousands. Although each core can only move one brick, the sheer number of cores compensates for it! With thousands or tens of thousands of "minions" working together, bricks are moved quickly and efficiently.
The core task of AI training and inferencing is "matrix operations" – simply put, a large number of numbers are lined up to perform addition, subtraction, multiplication, and division, like a massive pile of red bricks waiting to be moved, a simple task that doesn't require much intelligence.
The GPU's "massive core parallel processing" 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.
A CPU is more suitable for serial complex tasks, such as playing a single-player game or writing a document. With AI, there are too many bricks, and it can only move a few or a few dozen at a time. It will be exhausted and unable to catch up with the GPU.
Why Does NVIDIA Dominate? AMD and Intel are Crying in the Corner
Okay, now the question arises: NVIDIA isn't the only one making GPUs. AMD and Intel also have graphics cards. Why is the AI community using NVIDIA's products so eagerly? The answer is simple and straightforward – NVIDIA not only sells hardware, but it has also "kidnapped" the entire ecosystem.
First, unrivaled software ecosystem. NVIDIA has a trump card called CUDA (a programming platform), specifically tailored for its GPUs. AI engineers writing code to train models can get a boost by using CUDA, making it simple and efficient. AMD has its own ROCm, and Intel also has OneAPI, but these are either not mature enough or feel like solving math problems when used, lacking the smooth experience of CUDA.
Second, first-mover advantage + market built with money. NVIDIA bet on AI early on and promoted CUDA more than a decade ago, turning AI researchers into "NVIDIA believers." What about AMD and Intel? By the time they reacted, NVIDIA had already firmly occupied the AI territory. Want to catch up now? Too late.
Third, hardware is also top-notch. NVIDIA's GPUs (such as A100, H100) are specifically optimized for AI, with high memory bandwidth and explosive computing power. Although AMD and Intel graphics cards are great for gaming, they always fall short in AI tasks. To put it simply, NVIDIA is an "AI brick-moving excavator," while AMD and Intel are still "household shovels," with a huge difference in efficiency.
The Rich and Foolish AI Community
Therefore, GPUs outperform CPUs because of "strength in numbers," and NVIDIA's dominance is a combination of "hardware + software + foresight."
AMD and Intel are not without opportunities, but they need to step up their game, or they can only watch NVIDIA continue counting 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," expensive but winning at the starting line. Isn't it ridiculous? Before AI saves the world, it first saved NVIDIA's stock price!