We often hear about Large Language Models (LLMs) like GPT-4 having billions of parameters and being trained on massive datasets. But have you ever wondered: Where is all that data actually stored? And more fundamentally, how does a computer even store knowledge in the first place?
Let’s take a journey from the world of electric charges to the vast neural networks powering today’s AI.
Data at the Most Basic Level: 0s and 1s
At its core, all digital data is just binary — a series of 0s and 1s. These bits are represented physically using electric charges or magnetic states:
- In RAM or CPU/GPU memory, bits are stored using transistors and capacitors that are either charged (1) or not charged (0).
- In SSDs, data is stored using floating-gate transistors that trap electrons to represent binary states.
- In hard drives, tiny magnetic domains are flipped to represent 0s and 1s.
So yes — all the text, images, and even this blog post are ultimately just patterns of electric charge.
What Are LLM Parameters?
LLMs like GPT-3 or GPT-4 are made up of parameters — these are the internal values (mostly weights and biases) that the model learns during training. These parameters are just numbers, typically stored as 16-bit or 32-bit floating-point values.
For example:
- GPT-3 has 175 billion parameters
- If each parameter is stored as a 2-byte float, that’s about 350 GB of raw data
These parameters encode the model’s “knowledge” — the patterns it has learned from vast amounts of text data.
Where Is This Data Stored?
When you use an LLM, its parameters are loaded into memory so it can process your input and generate responses. Here's where the data lives:
- During training or inference: Parameters are stored in GPU memory (VRAM) for fast access.
- When idle: They’re saved on SSDs or hard drives as model checkpoint files.
Each of these storage mediums uses semiconductor technology to store billions (or trillions) of bits — each one a tiny switch or charge-holding cell.
How Can So Much Be Stored?
Thanks to decades of progress in hardware engineering:
- Billions of transistors can now fit on a single chip
- Memory density has skyrocketed
- Parallel processing in GPUs allows LLMs to operate efficiently despite their size
This is why modern AI models can be so large — the hardware has evolved to keep up.
A Simple Analogy
Think of an LLM like a giant frozen brain:
- Its “knowledge” is frozen into the parameters (numbers)
- When you load it into memory (like thawing it), it can start “thinking” — processing your input and generating intelligent responses
Conclusion
While LLMs may seem magical, they’re grounded in the very real physics of electricity, silicon, and binary logic. The next time you chat with an AI, remember: behind the scenes, it’s all just a symphony of tiny charges dancing across billions of microscopic switches.
Microsoft