In this comprehensive guide, we will look at the key differences between CPU and GPU.

The Central Processing Unit (CPU) often referred to as the brain of the computer is a general purpose processor built from billions of transistors. It is designed to manage the entire system, coordinating the flow of data between the hardware and the software.

It is a fundamental to modern computing, as it acts as a backbone that executes commands and processes needed for your computer and Operating System.

The CPU plays a main role in determining, how fast a process should run on your computer such as browsing the web, creating spreadsheets, etc…

The architecture of the CPU is latency oriented, means that it is designed to complete individual tasks with minimal delay.

To achieve this, it uses a small number of highly independent processing units called cores. Basic level CPUs consist of 2 cores which are useful for web browsing and emails. Modern CPUs contains 6 to 8+ cores which are useful for gaming.

It uses serial or sequential processing mode, which means that it executes processes step by step.

Also to achieve this, CPU uses a execution workflow or pipeline called

Fetch : Gets the instructions from the memory

Decode : Breaks down the binary code (0s and 1s) into simple control signals so that the processor to understand easily.

Execute : Executes the process using the Arithmetic and Logic Unit (ALU) that whatever the instructions requires.

GPU (Graphics Processing Unit) generally made for rendering 3D graphics, video games. The GPU has many smaller and more specialized cores.

The cores inside GPU deliver a huge performance by working together (parallel) and divide the processing tasks across multiple cores simultaneously.

The GPU excels at highly parallel tasks like rendering visuals during gameplay, manipulating video data during content creation.

But overtime it has evolved to become indispensable for AI research and development.

The architecture of GPU is built to maximize the total number of tasks completed simultaneously rather than the speed of a single task.

Basic level GPU cores counts from 500 to 2000 cores . Some of the modern GPUs used for AI and Data center GPUs like (H100) may exceed 16896 cores.

It uses Parallel execution, breaking large and complex problems into multiple subtasks that are processed at the same time.

Some of the modern GPUs include, Tensor Cores, which are specially designed to accelerate matrix multiply accumulate (MMA) operations used in deep learning.

Now, lets dive into our core concept:

Machine Learning, especially deep learning, involves large amounts of mathematical computations during model training.

These computations are largely based on linear algebra operations such as:

An example for matrix multiplication is given below as a picture.

Since these operations can be performed on many data points simultaneously, they are highly parallelizable.

For example:

SIMT: GPU uses a Single Instruction Multiple Threads ( a thread is the smallest unit of execution inside a program) execution model, where a single instruction is dispatched to a group of 32 or more threads simultaneously

In simple terms: Instead of telling each thread what to do separately,
the GPU gives one instruction to a group of threads ( 32 threads or more ), and they all execute it at the same time.

Now you might ask.

“ I would rather buy a Gaming GPU, Why should I go for specific GPU for ML training ?”

While it is possible to run small ML models on gaming laptop, But there are specialized enterprise level GPUs that are required for professional and large scale AI because of some of the technical reasons:

VRAM: Modern Large Language Models (LLM), have massive memory footprints. For example: a 70 billion parameter model requires 140 GB of memory just to load.

While gaming GPUs come with 8 to 24 GigaBytes of VRAM, Flagship Enterprise GPUs offer up to 192 GB HBM3e Memory. which allows large models to fit and run on a single chip.

Memory Bandwidth: Machine Learning is often limited by how fast data can be fed. Here Enterprise GPUs use High Bandwidth Memory (HBM), can reach speeds of 3.35 TB/s (H100) to 4.8 TB/s (H200), compared to 1 TB/s on gaming GPUs, because of the lower bandwidth of the GDDR memory used in gaming cards.

Interconnect Speed (NVLink): Professional ML often requires clustering (that means, when we train very large machine learning models one GPU will not be enough so we need to use multiple GPUs)

Technologies like NVLink allow enterprise GPUs to communicate with each other much faster than the standard PCIe bus used in gaming systems.

NVLink is a high speed connection technology by NVIDIA that lets GPUs communicate with each other much faster than the traditional methods.

Peripheral Component Interconnect Express (PCIe) is a high speed computer expansion bus standard used to connect hardware devices such as graphics cards, SSDs, and network adapters to a motherboard.

AI data centers expenditure is to reach about 400 to 450 billion dollars globally, with half of that means around 250 to 300 billion dollars should be spent on chips alone.This expenditure is to rise about 1 trillion by 2028.

This is where most of the money is flowing. Companies are getting GPUs for the following below:

NVIDIA alone generated over $62B quarterly data center revenue recently.

Machine learning isn’t just about smarter models. It is about faster computation.

From matrix multiplication to parallel threads and high speed GPU communication, the real power of AI comes from doing more work at the same time.

Behind every breakthrough model is hardware designed for parallelism, turning massive mathematical workloads into practical solutions.

As models grow larger and more complex, the ability to scale computation efficiently becomes just as important as the algorithms. In the end, progress in AI is not just driven by ideas but by how fast we can execute them.