Why AI model training using GPU instead of CPU

Mar 16, 2023#AI#ML

AI model training involves performing complex mathematical computations on large datasets. These computations are typically carried out using matrix operations and require a significant amount of processing power.

While it is possible to perform these computations on a CPU, training an AI model using a GPU is generally much faster and more efficient. Here are a few reasons why:

Parallel processing

Parallel processing is the execution of multiple computations or instructions simultaneously by using multiple processing units, such as CPU cores or GPU cores.

AI models use matrix calculations because they are a fundamental part of many machine learning algorithms. In order to perform these calculations efficiently, machine learning algorithms rely heavily on linear algebra operations, such as matrix multiplication, addition, and subtraction.

GPUs are designed to perform many operations in parallel, which makes them well-suited for matrix operations that are common in AI model training. This means that a single GPU can perform many calculations simultaneously, greatly speeding up the training process.

Specialized hardware

GPUs are designed specifically for performing complex mathematical calculations, while CPUs are designed for more general-purpose computing tasks. GPUs have many more processing cores than CPUs, allowing them to perform calculations much faster.

Memory bandwidth

Training an AI model involves moving large amounts of data between the CPU and the GPU. GPUs have a much higher memory bandwidth than CPUs, which means they can move data between the CPU and GPU much faster. This is critical for model training, which often involves working with massive datasets.

Memory bandwidth refers to the amount of data that can be transferred between a CPU or GPU and its memory in a given amount of time. It is typically measured in gigabytes per second (GB/s) and is a critical factor in determining the overall performance of a computing system.

A typical CPU might have a memory bandwidth of around 50-100 GB/s, while a high-end GPU might have a memory bandwidth of 500 GB/s or more.

The high memory bandwidth of GPUs is achieved through a combination of several factors, including: Wide memory buses, high memory clock speeds, and specialized memory architectures.

GPUs are designed with specialized memory architectures that are optimized for specific types of calculations. For example, some GPUs use HBM (High Bandwidth Memory) which stacks memory chips on top of each other to increase memory density and reduce power consumption.


While GPUs can be more expensive than CPUs, they are often more cost-effective for training AI models. Because GPUs are faster and more efficient at training models, they can reduce the amount of time and resources required to complete a training task.