At the heart of every computer is a Central Processing Unit (CPU). A CPU is like a master chef in a kitchen. It's incredibly smart and versatile, capable of performing any complex task you give it, but it generally works on them one or two at a time (sequentially). This is great for most everyday computing tasks, like loading a web page or running a word processor. However, training and running a large AI model is a completely different kind of problem. It involves performing millions or billions of very simple, identical mathematical calculations (specifically, matrix multiplications) all at the same time. For this job, the single master chef is not the right tool. You need an army of thousands of kitchen assistants who can all chop vegetables simultaneously. This is the power of parallelism, and this is where the GPU comes in.
Why Your CPU Isn't Enough: The Power of Parallelism
At the heart of every computer is a Central Processing Unit (CPU). A CPU is like a master chef in a kitchen. It's incredibly smart and versatile, capable of performing any complex task you give it, but it generally works on them one or two at a time (sequentially). This is great for most everyday computing tasks, like loading a web page or running a word processor. However, training and running a large AI model is a completely different kind of problem. It involves performing millions or billions of very simple, identical mathematical calculations (specifically, matrix multiplications) all at the same time. For this job, the single master chef is not the right tool. You need an army of thousands of kitchen assistants who can all chop vegetables simultaneously. This is the power of parallelism, and this is where the GPU comes in.
The GPU: An Accidental AI Powerhouse
The Graphics Processing Unit (GPU) was originally designed for a very specific task: rendering 3D graphics for video games. This task also requires massive parallel processing—calculating the color and position of millions of pixels on the screen at once. To do this, GPUs are designed with thousands of smaller, simpler cores that can all work in parallel. In the early 2010s, AI researchers had a breakthrough realization: the mathematical operations required for deep learning were remarkably similar to those used in graphics rendering. They discovered that they could use GPUs, originally intended for gaming, to train their neural networks hundreds of times faster than with traditional CPUs. This discovery is arguably the single most important hardware development that enabled the modern AI revolution.
VRAM: The GPU's Memory
Beyond the number of cores, the most critical component of a GPU for AI is its Video RAM (VRAM). VRAM is a type of super-fast memory located directly on the GPU card. The entire AI model (which can be many gigabytes in size) needs to be loaded into the VRAM for the GPU to work on it. If the model is too large for the VRAM, it simply won't run. This is why the amount of VRAM (e.g., 8GB, 12GB, 24GB) is the most important specification to look at when choosing a GPU for running AI models locally. It's the limiting factor that determines how large and complex a model you can use.
The Next Step: Specialized AI Hardware
While GPUs are incredibly effective, they are still general-purpose parallel processors. As AI has become a massive industry, tech giants have started designing hardware specifically and exclusively for AI computations. These are known as Application-Specific Integrated Circuits (ASICs).
Google's TPU: The Tensor Processing Unit
The most famous of these is Google's Tensor Processing Unit (TPU). TPUs are designed from the ground up to do one thing and one thing only: perform the massive matrix calculations (tensor computations) that are the foundation of deep learning. Because they are so specialized, they can often be more powerful and more energy-efficient than GPUs for large-scale AI training and inference. The massive models developed by companies like Google, such as LaMDA and PaLM, are trained on enormous 'pods' of thousands of TPUs working together. When you use many cloud-based AI services, your request is likely being handled by a TPU in a Google data center.
