GGUF Quantization Explained: Q4, Q5, Q8, and FP16
Quantization is the technique that makes large AI models runnable on consumer hardware. By reducing the precision of model weights from 16-bit floating point to 4-bit integers, we can shrink VRAM requirements by 75% with surprisingly small quality loss.
Quantization Formats
| Format | Bits | Size vs FP16 | Quality | Best For |
|---|---|---|---|---|
| Q4_K_M | 4.5 | ~28% | ~85% | Most users — best efficiency |
| Q5_K_M | 5.5 | ~34% | ~90% | Better quality, moderate savings |
| Q6_K | 6.5 | ~41% | ~95% | High quality with good savings |
| Q8_0 | 8.0 | ~50% | ~98% | Near-lossless, if VRAM allows |
| FP16 | 16.0 | 100% | 100% | Reference quality, maximum VRAM |
How to Choose
Rule of thumb: Use the highest quality quantization that fits in your VRAM with ~10% headroom. If you have 12GB VRAM and a 7B model needs 5.5GB at Q4_K_M vs 9.5GB at Q8_0, go with Q8_0 — you have the room.
VRAM Estimation Formula
For any GGUF model: VRAM (GB) = (Parameters in Billions x Bits per Weight) / 8 + 0.5GB overhead
Example: Llama 3.1 8B at Q4_K_M = (8 x 4.5) / 8 + 0.5 = 5.0GB
This is a simplified estimate. Context length, KV cache, and batch size add more. For precise calculations, use our model database which includes verified VRAM requirements per quantization.