When most people think about Artificial Intelligence today, they imagine typing into ChatGPT, Claude, or Gemini and instantly receiving a helpful response. It feels simple and effortless — as though the computer understands us.
But behind every conversation with these tools lies something extraordinary: a Large Language Model (LLM) — a vast digital brain so large and powerful that it cannot run on an ordinary computer.
This chapter explains, in clear and non-technical language, how these large models are accessed by everyday users through chat interfaces and how businesses and software systems connect to them behind the scenes. It also explores why they require specialised hardware and how global cloud companies like Amazon (AWS), Microsoft, and Google make them accessible to millions of people.
The most common way people interact with a Large Language Model is through a chat interface. This is what powers tools such as:
ChatGPT from OpenAI
Claude from Anthropic
Gemini from Google
These platforms provide a web page or mobile app where you can simply type your question and get an instant response. It’s conversational, intuitive, and feels natural — more like talking to a knowledgeable assistant than using software.
When you ask ChatGPT, “Write a short explanation of cash flow for a business owner,” you’re not running the model on your laptop. Instead, your question travels over the internet to a large computer system — a data centre where the model lives. The system processes your question, generates a response, and sends it back to you within seconds.
That seamless experience hides the incredible scale of what’s happening behind the scenes.
LLMs are called “large” for a reason. They contain billions of internal connections, known as parameters. Each parameter is a small piece of knowledge or pattern that helps the model understand language and context.
For example, OpenAI’s GPT-4 and Anthropic’s Claude 3 are estimated to have tens or even hundreds of billions of parameters — imagine a digital library with billions of pages constantly cross-referencing one another.
To store and process all that information, these models need:
Massive memory and storage — far more than even the most powerful desktop computer could provide.
High-speed processors called GPUs — short for graphics processing units.
GPUs are special computer chips originally designed for video games and graphics. Unlike regular processors, which handle one task at a time, GPUs can handle thousands of small tasks simultaneously.
That makes them perfect for the kind of mathematical calculations that LLMs require when generating sentences, analysing text, or reasoning about complex topics.
So when you ask a question, hundreds or thousands of GPUs may briefly work together to calculate your answer. This happens inside specialised facilities called data centres, where powerful computers run 24 hours a day, cooled by advanced systems to prevent overheating.
These data centres are what make generative AI possible at global scale.
It’s natural to wonder why ChatGPT or Claude doesn’t just “live” on your laptop or phone. The simple reason is size and power.
A single Large Language Model can be hundreds of gigabytes or even terabytes in size — that’s equivalent to thousands of full-length movies stored on a single system. No personal computer could hold that much data, let alone perform the trillions of calculations required to generate responses quickly.
Instead, the model runs on powerful computers owned by major technology companies. Your device only needs to send and receive text. This setup is similar to how you might stream a movie: the film isn’t stored on your phone; it plays from a remote server. With LLMs, your text conversation is “streamed” from an AI supercomputer in the cloud.
While individuals use chat interfaces like ChatGPT or Claude’s website, companies can also connect their own applications to these same models — quietly, behind the scenes.
For example, an accounting software company might let its users click a button that says “Summarise my financial performance.” When clicked, the software sends the relevant information to a Large Language Model, receives the generated summary, and displays it inside the app — all without the user needing to visit ChatGPT or Claude directly.
In this way, businesses can embed AI intelligence inside their own systems — turning everyday tools into smarter assistants that read, summarise, or explain information automatically.
These connections make AI part of daily workflows rather than a separate tool, allowing accountants, analysts, and managers to use AI insights right inside their existing software.
All of this is made possible by cloud computing — massive networks of computers owned and operated by companies like:
Amazon Web Services (AWS)
Microsoft (Azure)
Google Cloud
These providers maintain enormous data centres across the world, filled with high-performance machines equipped with GPUs and advanced networking equipment.
Instead of every business needing to buy their own supercomputers, cloud providers let them “rent” computing power as needed. This approach is far more efficient and makes cutting-edge AI available to anyone — from global banks to small startups.
When you use ChatGPT, Claude, or Gemini, your requests are processed on this cloud infrastructure. The scale and reliability of these networks ensure that millions of users can interact with AI simultaneously, all over the world, without delay.
Not all AI models are the same size. Think of them as different vehicles built for different purposes:
Large models (like GPT-4, Claude 3, or Gemini 1.5) are like cargo ships — huge, capable, and versatile, but expensive to operate. They can handle complex reasoning and large documents.
Specialised models are trained for specific tasks, such as medical or financial topics. They’re like skilled technicians — smaller but deeply knowledgeable.
Compact or local models are lightweight versions designed to run on laptops or even mobile phones. They handle simple tasks quickly without needing a cloud connection.
This diversity allows AI to be everywhere: from global cloud systems supporting enterprises to personal assistants that run privately on your own device.
The intelligence of an AI model is linked closely to its scale — the amount of data it learns from and the computational resources available to it.
Training a frontier model (such as GPT-4 or Claude 3) requires thousands of GPUs running for weeks or months, consuming enormous amounts of electricity and engineering expertise. These are not desktop projects; they are global infrastructure efforts.
This is why only a few organisations — OpenAI, Anthropic, Google, and Meta — currently build models at this level. Their models are then made accessible through partnerships with major cloud platforms so that others can use them safely and reliably.
In essence, the cloud is the “engine room” of modern AI — it provides the space, power, and security needed to operate these immense digital brains.
It may seem unbelievable that a short message typed on your phone travels halfway across the world, is processed by supercomputers running billions of calculations, and returns an answer in seconds. Yet that is exactly how today’s AI systems function.
The journey looks like this:
You ask a question using ChatGPT, Claude, or Gemini.
Your question travels through the internet to powerful computers in a data centre.
The LLM processes your text, predicts the most accurate and useful response, and sends it back.
You see the result in your chat window almost instantly.
This seamless process hides extraordinary complexity — a global collaboration between human creativity, massive computing power, and cloud infrastructure.
Large Language Models represent one of the greatest technological achievements of our time — digital systems capable of understanding and generating human language. But they are also enormous, both in data size and in computing requirements.
Rather than living on our personal computers, these models reside in vast cloud environments powered by GPUs and managed by global companies like Amazon, Microsoft, and Google.
Through chat interfaces such as ChatGPT, Claude, and Gemini, we access this intelligence effortlessly — asking questions, writing reports, or analysing data as though speaking to a knowledgeable colleague.
For most professionals, the magic of AI lies in its simplicity: you type, it responds. But behind that simplicity is a global network of powerful machines and collaborations that make generative AI accessible to everyone, everywhere — quietly transforming the way the world thinks, works, and communicates.