What is an LLM Really?

Think of an LLM as a massive mathematical function that has learned patterns from text. It’s essentially a huge neural network with billions of “weights” (parameters) that have been trained to predict the next word in a sequence.

When you type “The weather today is…” the model uses all its learned patterns to calculate: “What word is most likely to come next?” It might predict “sunny” or “rainy” based on context.

Local vs. Cloud: What’s the Difference?

Cloud (like ChatGPT):

The model runs on someone else’s powerful servers
You send your text over the internet
Fast responses but requires internet + costs money
No privacy – your data goes to their servers

Local (your machine):

The entire model lives on YOUR computer
No internet needed once downloaded
Completely private – nothing leaves your machine
Free to use once you have it, but requires powerful hardware

Why VRAM Matters So Much

Here’s the key insight: The entire model must fit in memory to run efficiently.

When you load a 7B parameter model:

Each parameter is a number (like 3.14159…)
7 billion numbers need to be stored in RAM
Your GPU’s VRAM is much faster than regular RAM for this kind of math
If the model doesn’t fit, performance becomes unusably slow

Think of it like trying to work on a huge spreadsheet – you need enough RAM to load the whole thing, or it becomes painfully slow.

What Actually Happens During Different Tasks

Inference/Chat:

Model is loaded into VRAM (one-time cost)
For each word you type, it does billions of calculations
Outputs one word at a time based on probabilities
Like having a conversation, but the “person” is doing math

Fine-tuning:

Takes an existing trained model
Shows it new examples to learn from
Adjusts the billions of parameters slightly
Much more memory-intensive than just chatting
Like teaching someone who already knows English to also speak in a specific style

The Size vs. Quality Trade-off

Bigger models (13B, 30B, 70B parameters):

Better at reasoning, more knowledgeable
Need more VRAM and run slower
Like having a more experienced expert

Smaller models (7B, 3B):

Faster, use less memory
Still quite capable for many tasks
Like having a smart junior assistant

Quantization: The Compression Trick

Remember how each parameter is a number? Originally they might be stored as:

FP16: Very precise (like 3.14159265…)
4-bit: Less precise (like 3.1…)

Quantization reduces precision to save memory. It’s like compressing a photo – you lose some quality but the file gets much smaller. For LLMs, this often works surprisingly well because the models are somewhat robust to this kind of precision loss.

Why This Matters for You

With your 16GB setup, you’re getting:

Freedom: Run models without internet or ongoing costs
Privacy: Your conversations stay on your machine
Experimentation: Try different models, modify them, see how they work
Learning: Understand how these systems actually function

You’re essentially building your own private AI assistant that you completely control!

Does this help clarify what’s actually happening under the hood?

What is an LLM Really?