Artificial intelligence seems like magic, but it's really about computation. An AI workload is simply the set of tasks or calculations a computer system must perform to run an AI application. Think of it as the 'work' the AI is doing, from recognizing your face to translating a sentence.

These workloads generally fall into two main categories: training and inference. Understanding the difference is key to understanding how AI systems function and why they need so much computing power.

Training is the learning phase. It’s like a student cramming for a final exam. During training, an AI model is fed enormous datasets to learn patterns and relationships. For an image recognition model, this might mean showing it millions of pictures of cats so it learns what a cat looks like.

This process is incredibly demanding. The model makes a guess, checks its answer against the correct label, and slightly adjusts its internal parameters to get closer to the right answer next time. It repeats this process millions of times, fine-tuning itself until it becomes accurate. Training is a massive, one-time (or occasional) effort that requires immense computational horsepower.

Inference is the 'doing' phase. After all that studying, the student is now taking the exam. Inference is when the trained model is put to use in the real world to make predictions on new, unseen data. When you ask a smart assistant a question or your phone identifies a face in a photo, that's inference.

Compared to training, a single inference task is much faster and less computationally expensive. However, a popular service might perform billions of inference tasks every day. So while each one is small, the cumulative workload can be enormous.

So what are these computations that make AI work? At the heart of modern AI, especially deep learning, are operations on multi-dimensional arrays of numbers called tensors.

If you're familiar with vectors (a list of numbers) and matrices (a grid of numbers), you already understand the basic idea. A vector is a 1D tensor, and a matrix is a 2D tensor. A deep learning model might use tensors with many more dimensions to represent complex data like color images (height, width, color channels) or video.

The fundamental operation in these models is matrix multiplication. The network learns by multiplying input data (represented as tensors) by weight tensors, over and over, across many layers. Each multiplication transforms the data, helping the network identify increasingly complex patterns.

Now, imagine the tensors $W$ and $X$ are massive, with thousands of rows and columns. A deep learning model performs billions or even trillions of these calculations to train. This is why AI workloads are so demanding and why specialized hardware is needed to perform these specific operations efficiently.

The world of AI is moving incredibly fast. Researchers are constantly developing larger and more sophisticated models. The computational power required to train these cutting-edge models has been doubling roughly every six months, a rate that far outpaces general computing improvements.

This explosive growth has profound implications for hardware. A standard CPU can handle matrix multiplication, but not at the scale or speed required by modern AI. The parallel nature of these calculations is much better suited for GPUs (Graphics Processing Units) and other specialized processors.

This ever-increasing demand puts pressure on hardware to become more powerful, efficient, and specialized. The performance of an AI application is no longer just about clever software; it's fundamentally tied to the capabilities of the underlying hardware that runs its workload. Understanding this relationship is crucial for anyone looking to build, deploy, or simply make sense of the AI revolution.