LLM Quantization Types Explained 2026: Q4, Q5, Q8 and What Each GGUF Level Costs You
Model download pages list a dozen GGUF files - Q2_K, Q4_K_M, Q5_K_S, Q8_0 - and the size differences are obvious but the quality differences are not. What does each quantization level actually cost in quality?
💡 What You Will Learn
Model download pages list a dozen GGUF files - Q2_K, Q4_K_M, Q5_K_S, Q8_0 - and the size differences are obvious but the quality differences are not. What does each quantization level actually cost in
📜 Table of Contents
What Quantization Does
Quantization stores model weights in fewer bits: 16-bit floats become 8, 4, or even 2 bits. A 70B model at 16-bit needs ~140GB; at 4-bit it fits in ~40GB. The catch is precision loss - and the art is choosing where the loss stops mattering.
The GGUF Level Names Decoded
GGUF files from llama.cpp use a naming scheme: the letter (Q = quantized, K = k-quant improved layout) plus bit depth, plus a suffix.
| File | Bits/weight | Size vs 16-bit | Typical quality |
|---|---|---|---|
| Q2_K | 2-3 | ~18% | poor - visible degradation |
| Q3_K_M | 3-4 | ~24% | usable only for tiny models |
| Q4_K_M | 4-5 | ~33% | the 2026 sweet spot |
| Q5_K_M | 5-6 | ~40% | near-lossless for most tasks |
| Q6_K | 6 | ~47% | barely distinguishable from FP16 |
| Q8_0 | 8 | ~62% | essentially lossless |
| FP16 | 16 | 100% | reference |
Suffix meanings: _M = middle (balanced), _S = small (more aggressive), _L = large (closer to original).
What the Quality Loss Actually Looks Like
Measured on standard benchmarks, Q4_K_M typically loses 1-3% versus FP16 - often within noise. Q2_K can lose 10%+ and shows visible degradation in reasoning tasks. But the real-world pattern: for chat and creative writing, Q4-Q5 is indistinguishable; for math, code, and long reasoning chains, drop the quantization level or keep Q5+.
The Memory Equation
Quantization mainly buys memory headroom, not speed. On CPU, smaller files mean less RAM bandwidth per token - which can speed things up. On GPU, a Q4 model can fit layers in VRAM that a Q8 cannot, which is the real win (see the CPU guide for speed numbers).
Practical Selection Rules (2026)
- Default to Q4_K_M - the community consensus sweet spot.
- Go Q5_K_M or Q6_K when the task is math/code-heavy or the model is small enough that size does not matter.
- Only use Q2/Q3 for models that would not fit otherwise - accept the quality hit.
- Download the smallest file that passes YOUR eval, not the smallest that boots. Run 20 of your real prompts through Q4 vs Q5 and compare.
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