You already know:

• Quantization shrinks the model
• GGUF makes it runnable
• 4-bit / 8-bit affect size

But when you download a model, you see scary names:

model.Q4_K_M.gguf
model.Q5_K_S.gguf
model.Q8_0.gguf

These are not random names.

They tell you exactly:

How smart the model will feel
How fast it will run
How much RAM it will use

This is the final layer of understanding local AI.

First: What These Names Represent

A quantized model does two things:

1. Compress numbers
2. Decide how to reconstruct them during inference

Different methods = different behavior

So quantization types are basically:

Different compression algorithms for the AI brain

Breaking the Name Format

Take:

Q4_K_M

Split it:

Another:

Q5_K_S

Another:

Q8_0

Step 1 — Bit Level (Quality Layer)

This decides the raw intelligence retention.

So:

More bits = closer to original model brain

Step 2 — Algorithm Family (K vs non-K)

Older quantization (like Q4_0, Q5_0)
→ compresses blindly

K-quantization (Q4_K, Q5_K, etc)
→ compresses intelligently using block statistics

Meaning:

Instead of compressing each number alone,
it compresses groups of neurons together.

So it preserves patterns.

Result:

This is why modern GGUF models almost always use K-quant.

Step 3 — Variant Letters (S, M, L)

These adjust how aggressive compression is.

So:

Q4_K_S → fastest usable
Q4_K_M → best balance
Q4_K_L → slow but smartest 4bit

The Famous Ones Explained

Q4_K_M (Most Recommended)

Best general local AI format.

• Good reasoning
• Good speed
• Fits in small RAM

This is why almost all Ollama models default to it.

👉 Daily usage sweet spot

Q5_K_S

Higher intelligence but still lightweight.

• Better coding
• Better logic
• Slightly slower

Good for developers.

Q8_0

Almost original model.

• Very accurate
• Heavy RAM
• Slow CPU

Used when quality matters more than speed.

Real Feel Difference

Same model — different quantization:

Model didn’t change.
Only brain precision changed.

Why This Matters More Than Model Size

A 7B Q8 can feel smarter than a 13B Q3.

Because brain clarity > brain size.

So practical performance =

Model Size × Quantization Quality

Not size alone.

Quick Selection Guide

Low RAM laptop (8GB):
→ Q4_K_M

Coding / technical work:
→ Q5_K_M or Q5_K_S

Powerful PC:
→ Q6_K or Q8_0

Fastest chat:
→ Q4_K_S

Final Understanding

Quantization level controls precision of thoughts.

Model size controls capacity of thoughts.

So:

Size = how much the AI can know
Quantization = how clearly it can think

Both together decide real intelligence.

Explore related articles:

• Quantization in AI Models (4-bit, 8-bit, GGUF) — A Clear Detailed Guide
• 7B, 13B, and 70B in AI Models — What “Parameters” Really Mean
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• Build Your Own Free Offline AI Chatbot Using Ollama + Open WebUI (Complete Guide)
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