Introduction to AI Workloads

Before selecting a single piece of hardware or provisioning a cloud instance, understanding the nature of the work it will perform is essential. The work in AI is divided into two primary, and fundamentally different, types of workloads: training and inference. While both involve neural networks and data, their computational patterns, resource demands, and performance goals differ. Grasping this distinction is the first step in designing infrastructure that is both performant and cost-effective.

The Training Phase: Forging the Model

Training is the process of teaching a machine learning model. Much like a student studying a textbook, the model learns by processing a large dataset and adjusting its internal parameters to minimize prediction errors. This is an iterative, computationally demanding, and often lengthy process.

The core of most deep learning training is a series of matrix operations. For a neural network, this involves a forward pass, where input data is fed through the network to generate a prediction, and a backward pass (backpropagation), where the model calculates the error in its prediction and uses that error to update its parameters, or weights. This cycle is repeated, often for millions or billions of examples over many iterations, called epochs.

The computational characteristics of training are:

• High Parallelism: A single training step involves thousands or millions of independent calculations, most notably matrix multiplications ($C = A \cdot B$). This workload is a perfect fit for the architecture of a Graphics Processing Unit (GPU), which contains thousands of cores designed to execute the same instruction on different data simultaneously.
• Large Data and Model Size: Training requires holding the model's parameters, the gradients calculated during backpropagation, and batches of training data in memory. For large models like modern language models or high-resolution computer vision models, this can require tens or even hundreds of gigabytes of high-speed memory (VRAM on a GPU).
• Iterative and Long-Running: A single training run can last for hours, days, or even weeks. The goal is to achieve the highest possible model accuracy, so the total time-to-train is a significant metric. The infrastructure must be stable and built for endurance.
• High Throughput: During training, the goal is to process as much data as possible in the shortest amount of time. This means optimizing the entire pipeline, from fast data storage and loading to efficient computation on the processor, to maximize data throughput.

The Inference Phase: Putting the Model to Work

Inference is the process of using a fully trained model to make a prediction on new, unseen data. Once a model is trained, its parameters are frozen. It is no longer learning. Instead, it's applying what it has learned. This is equivalent to the student, having finished studying, now taking an exam.

An inference workload consists of a single forward pass through the network. An input, like an image or a line of text, is provided to the model, which then performs a calculation and outputs a result, such as an object classification or a language translation.

The computational characteristics of inference are:

• Low Latency: For many applications, especially user-facing ones, the prediction must be returned almost instantly. A user waiting for a product recommendation or a self-driving car identifying a pedestrian cannot tolerate long delays. The time it takes to get a single prediction (latency) is often the most important performance metric.
• High Throughput (Concurrent Requests): A deployed model might need to serve predictions to thousands of users simultaneously. The system must be able to handle a high volume of concurrent, independent requests, a metric known as throughput.
• Computational Efficiency: Unlike training, inference does not involve backpropagation. It is a single, computationally lighter pass. While GPUs are still very effective, a powerful CPU or a specialized, smaller accelerator can often be a more cost-effective choice, especially if latency requirements are not in the single-digit millisecond range.
• Deployment Flexibility: Inference can happen anywhere: in a massive data center, on a local server, or even on a small edge device like a smartphone or sensor. This requires models to be optimized for different hardware and power constraints.

A Tale of Two Workloads

The diagram below illustrates the distinct flows and priorities of training and inference. Training is a cyclical, heavy-duty process focused on refinement, while inference is a linear, lightweight process focused on speed and efficiency.

The two distinct AI workloads. Training is an iterative loop focused on producing a high-quality model. Inference is a direct path from new data to a prediction using that trained model.

These differences directly dictate your infrastructure choices. A system built for rapid training experimentation will prioritize powerful multi-GPU servers with high-speed interconnects. Conversely, an infrastructure designed for cost-effective inference at scale might use a fleet of smaller CPU instances or specialized inference chips. Understanding which workload you are optimizing for is the foundation upon which all other infrastructure decisions are built.