Q5_K_M vs Q6_K: VRAM, Quality and Speed
Q6_K uses about 16% more memory than Q5_K_M (6.59 vs 5.69 bits per weight). On Qwen 3 32B with an 8K context that is 28.0 GB versus 24.6 GB. Q6_K costs about 16% more memory than Q5_K_M for a quality gain few people can detect.
VRAM on popular models
Total memory with an 8K context and FP16 KV cache. The last column is the smallest standard VRAM size that holds each version.
| Model | Q5_K_M | Q6_K | Difference | Smallest tier |
|---|---|---|---|---|
| Llama 3.1 8B | 7.1 GB | 8.0 GB | +0.8 GB | 8 GB / 10 GB |
| Qwen 3.5 9B | 7.3 GB | 8.2 GB | +0.9 GB | 8 GB / 10 GB |
| Qwen 3 14B | 11.9 GB | 13.5 GB | +1.6 GB | 16 GB / 16 GB |
| Mistral Small 3.1 24B | 18.0 GB | 20.5 GB | +2.5 GB | 20 GB / 24 GB |
| Qwen 3.6 27B | 19.5 GB | 22.3 GB | +2.8 GB | 24 GB / 24 GB |
| Gemma 4 31B | 22.8 GB | 26.0 GB | +3.2 GB | 32 GB / 32 GB |
| Qwen 3 32B | 24.6 GB | 28.0 GB | +3.4 GB | 32 GB / 32 GB |
| Qwen 3.6 35B-A3B | 24.4 GB | 28.1 GB | +3.7 GB | 32 GB / 32 GB |
| Llama 3.3 70B | 50.1 GB | 57.5 GB | +7.4 GB | 64 GB / 64 GB |
| Qwen 3.5 122B-A10B | 82.2 GB | 94.9 GB | +12.8 GB | 96 GB / 128 GB |
Largest model each VRAM size can run
The biggest model released since 2025 that fits fully at 8K context.
Context you can fit
Longer contexts need room for the KV cache. The longest context that fits fully at each format, with an FP16 cache:
| Model | GPU | Q5_K_M | Q6_K |
|---|---|---|---|
| Qwen 3 14B | 16 GB | 27K | 18K |
| Qwen 3.6 27B | 24 GB | 56K | 11K |
| Qwen 3 32B | 24 GB | Does not fit | Does not fit |
| Gemma 4 31B | 32 GB | 100K | 59K |
| Llama 3.3 70B | 48 GB | Does not fit | Does not fit |
Download size
File sizes in decimal GB, as Hugging Face shows them. Use the GGUF size calculator for any other model.
| Model | Q5_K_M | Q6_K |
|---|---|---|
| Llama 3.1 8B | 5.7 GB | 6.6 GB |
| Qwen 3 14B | 10.5 GB | 12.2 GB |
| Qwen 3.6 27B | 19.1 GB | 22.2 GB |
| Qwen 3 32B | 23.3 GB | 27.0 GB |
| Llama 3.3 70B | 50.2 GB | 58.2 GB |
Quality
Both are close to lossless for most uses. llama.cpp's reference measurement puts Q5_K_M at about 0.014 and Q6_K at about 0.004 perplexity above FP16. Q6_K is the usual choice when you want near-Q8 quality in less memory.
Speed
Text generation reads every weight once per token, so smaller formats generate faster on the same GPU. Estimated on an RTX 4090 24GB:
| Model | Q5_K_M | Q6_K |
|---|---|---|
| Llama 3.1 8B | ~101 tok/s | ~89 tok/s |
| Qwen 3 32B | ~12 tok/s (partial CPU offload) | ~6.0 tok/s (partial CPU offload) |
Verdict
Q6_K costs about 16% more memory than Q5_K_M for a quality gain few people can detect. Choose Q6_K for small models (under 8B), for coding or for multilingual use when it fits; otherwise Q5_K_M gives nearly the same result.
Frequently asked questions
Is Q6_K worth it over Q5_K_M?
Only if you have the memory to spare. The quality difference is small, and the memory could instead go to a longer context.
How much more VRAM does Q6_K need than Q5_K_M?
About 16% more for the weights (6.59 vs 5.69 bits per weight). For Qwen 3 32B with an 8K context that is 28.0 GB versus 24.6 GB.