Quantization Deep Dive: GGUF Q4 vs Q5 vs Q6 vs Q8 Quality Loss

Every bit you drop from LLM weights saves memory but costs quality. This deep dive quantifies exactly how much quality you lose at each GGUF quantization level — with real benchmark data.

Understanding GGUF Quantization Formats

GGUF (llama.cpp's format) supports multiple quantization schemes. The main families are:

• Q2_K to Q3_K: 2–3 bits/weight. Aggressive compression, significant quality loss
• Q4_K_M / Q4_K_S: 4 bits/weight with k-quant grouping. The sweet spot for most use cases
• Q5_K_M / Q5_K_S: 5 bits. Near Q8 quality at 30% smaller size than Q8
• Q6_K: 6 bits. Essentially indistinguishable from FP16 in most tasks
• Q8_0: 8 bits. Reference quality, used to verify quantization impact

Quality vs Size Tradeoffs (Llama 3 8B)

Format	Size	MMLU	Perplexity vs FP16	vs FP16
FP16 (baseline)	16 GB	66.2	0%	—
Q8_0	8.7 GB	66.1	+0.1%	Imperceptible
Q6_K	6.6 GB	66.0	+0.3%	Imperceptible
Q5_K_M	5.7 GB	65.8	+0.8%	Minimal
Q4_K_M	4.8 GB	65.3	+1.8%	Noticeable on edge cases
Q3_K_M	3.9 GB	63.8	+4.2%	Noticeable
Q2_K	2.9 GB	59.1	+12%	Significant degradation

The K-Quant Advantage

Q4_K_M and Q5_K_M use k-quants — a mixed-precision approach that applies higher bit depth to the most sensitive weight groups. This recovers 30–50% of the quality lost by uniform Q4/Q5, at no size penalty. Always prefer _K_M variants over _0 variants when available.

For the hardware to run any of these formats efficiently, see the GPU recommendations at LLMHardware.io.

Recommendation by Use Case

• Creative writing / chat: Q4_K_M — quality loss imperceptible for most users
• Coding assistance: Q5_K_M or Q6_K — subtle errors appear at Q4 for complex code
• Math / reasoning: Q6_K or Q8_0 — quantization degrades chain-of-thought quality
• Maximum speed, low-stakes: Q3_K_M — acceptable for summarization and classification