DefiledAI Tools
QUANT QUALITY ESTIMATOR
Visualize the real quality/size tradeoff for every quantization level. Based on perplexity benchmarks across model sizes. Larger models tolerate aggressive quantization far better than smaller ones — this tool shows you exactly where the cliff is.
Model Size
Quick presets:
Available VRAM (GB)
Quants that fit:
Q8_0Q6_KQ5_K_MQ5_K_SQ4_K_MQ4_K_SQ4_0Q3_K_LQ3_K_MQ3_K_SQ2_KIQ2_MIQ1_M
Best quality fit: Q8_0
Selected: Q4_K_M
+0.48%
Excellent quality loss vs F16
Est. VRAM (7B)4.6 GB
Bits per weight4.8
Size reduction vs F1670%
Quality Loss vs File Size — 7B Models
← Bar = perplexity increase vs F16GB = estimated VRAM for 7B modelRed GB = doesn't fit in 8GB
Q4_K_M Quality Loss — All Model Sizes
| Size | Quality Loss | Rating | Est. VRAM |
|---|---|---|---|
| 3B | +0.8% | Good | 2.0 GB |
| 7B | +0.48% | Excellent | 4.6 GB |
| 13B | +0.35% | Excellent | 8.5 GB |
| 34B | +0.25% | Excellent | 22.3 GB |
| 70B+ | +0.18% | Excellent | 46.0 GB |
Key insight: A Q3_K_M 70B model has 0.7% quality loss — less than a Q4_K_M 7B model at 0.48%. When VRAM is the constraint, a heavily quantized large model often beats a lightly quantized small one.