If you’ve ever heard someone say “that model has 8 billion parameters” and nodded like you absolutely knew what that meant… welcome. You’re among friends.
Parameters are one of the most frequently-mentioned, least-explained concepts in modern AI. They’re also the reason models like ChatGPT can feel like a genius… while secretly doing something that sounds far less magical:
Predicting the next chunk of text. Really, really well.
🧩 So What Is a Parameter?
A parameter (also called a weight) is a number inside a model that controls its behaviour.
If you want a mental picture, don’t imagine a robot brain.
Imagine a sound mixer.
Each slider changes how much one input matters compared to another.
Inputs → [ MIXER SLIDERS ] → Output
(parameters)
In normal machine learning, you might have 20–200 sliders.
In modern language models, you have billions or trillions.
Yes. Trillions.
No. That’s not a typo.
🏠 The Simplest Example: Predicting Rent
Let’s start with a deliberately boring example: predicting rent.
Old-school programming approach
A developer writes rules like:
- rent = (square metres × 5) + (floor number × 20)
rent = sqmtrs * 5 + floor * 20
This works… until it doesn’t (when rent prices inevitably go up).
Machine learning approach
Machine learning says:
“Let’s not hard-code the multipliers. Let’s learn them from data.”
So we create a model like this:
rent = (A × square metres) + (B × floor number)
Here, A and B are parameters.
During training, the model learns the best values for A and B by looking at lots of examples.
🏋️ Training vs 🔮 Inference (Two Phases You’ll Hear Everywhere)
Machine learning has two main phases:
1) Training
You show the model lots of examples and adjust the parameters so it gets better.
Data → Model → Wrong? adjust sliders → repeat
2) Inference
Once training is done, you freeze the parameters and use the model to make predictions.
New input → Model (frozen sliders) → Output
That’s the whole machine learning loop.
And those “sliders”? Parameters.
🎛️ The Sound Mixer Analogy (You’re Welcome)
Think of parameters like a sound engineer adjusting a band mix.
- Training = rehearsal
- Inference = live performance
During rehearsal, the engineer tweaks the sliders.
During the show, hands off.
TRAINING: tweak tweak tweak
INFERENCE: don't touch the board
This analogy scales surprisingly well.
Because modern AI is basically…
A ridiculous number of mixers stacked on top of each other.
🧠 Neural Networks: Mixers of Mixers of Mixers
In a neural network, you don’t have one mixer. You have layers of them.
Each layer:
- mixes inputs
- produces an output
- passes it to the next layer
Inputs → [Mixer] → [Mixer] → [Mixer] → Output
(layer) (layer) (layer)
Now multiply that by:
- thousands of mixers
- each with many sliders
- stacked into many layers
That’s why parameter counts explode.
Why stacking matters (a simple intuition)
If you only had mixers that just adjusted volumes, stacking wouldn’t help much. You could compress it into one mixer.
But neural networks add a crucial trick:
Nonlinearity (also called an activation function)
Translation:
Each layer slightly transforms the signal so the next layer can learn something new.
You don’t need to memorize the math. Just remember:
- without nonlinearity → the network is basically a fancy linear equation
- with nonlinearity → the network can learn complex patterns
🧱 So What Does a Parameter Do in a Language Model?
In an LLM, parameters control how the model maps:
- an input sequence of tokens → into
- the most likely next token
A parameter is not:
- a fact (“Paris is the capital of France”)
- a database entry
- a sentence stored somewhere
It’s more like:
- a tiny dial that nudges the model toward certain patterns
🔤 Tokens: The Model’s “Chunks of Text”
LLMs don’t usually work one letter at a time or one word at a time.
They work in tokens: small chunks of text.
Example (roughly):
"unbelievable!" → ["un", "believ", "able", "!"]
LLMs are trained to do this:
Given tokens so far → predict the next token
🧠 “Pre-trained” Means: Fed the Internet (and Then Some)
During training, the model is shown lots of text.
For example, it might see a sentence like:
“The capital of France is Paris.”
Training turns this into a prediction task:
- Input: “The capital of France is”
- Target output: “Paris”
If the model predicts something else, the training process nudges trillions of parameters ever so slightly so that next time, “Paris” becomes more likely.
That’s it. That’s the trick.
🧙 Why This Feels Like a Conjuring Trick
Here’s the part that melts people’s brains:
Even though the model is “just” predicting tokens, it can:
- solve hard science questions
- write code
- explain complex topics
- reason step-by-step (sometimes)
This is often called emergent intelligence:
When a system becomes capable of new behaviours simply because it got big enough and trained long enough.
It’s not that the model “contains” a PhD.
It’s that the parameters encode patterns so richly that PhD-level reasoning can emerge as a side effect.
🪄 The Typewriter Effect: Why It Prints One Token at a Time
ChatGPT doesn’t generate a whole paragraph in one go.
It does this loop:
- predict the next token
- append it to the input
- predict the next one
- repeat
Input → predict token → append → predict next → append → ...
That’s why you see the “typing” animation.
It’s not theatrical. It’s literal.
🧠 “Memory” Is Mostly an Illusion (A Useful One)
ChatGPT feels like it remembers what you said earlier.
But the core model doesn’t have memory like humans do.
Instead, the app sends the model:
the entire conversation so far (within its context window)
So when you refer back to something, the model is just reading it again in the input.
Every message:
[conversation so far] + [new user message] → model → reply
That creates a convincing illusion of memory.
📈 Parameter Counts: The Numbers Got Silly, Fast
Here’s a simplified timeline (historical counts are commonly cited; modern labs often don’t disclose):
| Model | Approx. Parameters | Why It Mattered |
|---|---|---|
| GPT-1 | 117M | “Okay, transformers work.” |
| GPT-2 | 1.5B | “Text generation is getting serious.” |
| GPT-3 | 175B | “Wait… what is happening?” |
| GPT-4 | (not confirmed publicly; widely speculated huge) | “Reasoning jumps again.” |
| Modern frontier models | undisclosed | Likely massive, but more efficient per parameter |
One important nuance:
We’ve gotten better at squeezing more capability into fewer parameters.
The smallest model I have used is called Gemma which only has ~270M parameters
- Yet it can outperform much older models with far more parameters
So “more parameters” helps… but training quality and architecture matter a lot too.
🧠 Bigger Models vs Smarter Use: Two Kinds of “Scaling”
Modern AI progress comes from two different levers:
1) Training-time scaling (bigger model)
- more parameters
- more training data
- more training compute
- typically more capability
2) Inference-time scaling (smarter use)
You keep the model the same size, but make it perform better by:
- asking it to reason step-by-step
- giving it more helpful context
- using tools like RAG (Retrieval-Augmented Generation)
- “budget forcing” tricks like inserting “wait” to extend reasoning
Here’s the cheat sheet:
| Scaling Type | When it happens | What you change | Example |
|---|---|---|---|
| Training-time | before you use the model | parameters, data, compute | bigger model sizes (mini → full) |
| Inference-time | while using the model | prompt, reasoning, context | step-by-step reasoning, RAG |
And in the last year or two, inference-time scaling has become a major deal.
Because it’s often cheaper than training a bigger model.
💰 Why Model “Sizes” Exist (Nano / Mini / Opus / etc.)
Frontier labs often ship multiple variants:
- smaller models → faster and cheaper (and fewer carbon emissions and less electricity and water wasted)
- larger models → better at hard tasks but more expensive, emit tons of carbon and use exorbitant amounts of electricity and water
Think of it as:
Small model: quick assistant
Big model: deep thinker (with a bigger bill)
Even when labs don’t publish parameter counts, the pricing and performance usually give away the pattern.
🧾 A Quick “What Parameters Are Not” List
Parameters are not:
- a database of facts
- explicit rules
- stored Wikipedia pages
- a memory of your conversation
Parameters are:
- numbers that shape how the model transforms inputs into outputs
- learned during training
- frozen during inference
- the reason the model behaves consistently
🏁 Final Takeaway: Predictive Text on Steroids (Yes, Really)
If you want the bluntest summary:
A large language model is predictive text…
with a Transformer architecture…
trained on enormous text…
with trillions of parameters acting like tiny sliders.
And somehow, from that, intelligence emerges.
It’s both straightforward and deeply weird.
If you walk away with just one intuition, let it be this:
Parameters are the model’s learned “settings.”
The more settings, the more patterns it can encode.
And the better the training, the more useful those settings become.
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