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IS Q4GOOD ENOUGH?
Every quant trades size for quality. See exactly how much smaller — and how much worse — each GGUF level gets, and which one is the best that still fits your card.
Math runs in your browserQuality from llama.cpp perplexity data
Sweet spot for NVIDIA RTX 4090
F16
Indistinguishable from full precision.
Total footprint
18.3 GB
Weights
16.1 GB
Quality cost
~0%
vs F16
1.0× smaller
Quant
Size
Quality (lower = better)
Fit
F16
16.1 GB
+0%
lossless
fits
Q8_0
8.53 GB
+0.05%
lossless
fits
Q6_K
6.58 GB
+0.1%
excellent
fits
Q5_K_M
5.71 GB
+0.3%
excellent
fits
Q5_K_S
5.56 GB
+0.6%
excellent
fits
Q4_K_M
4.87 GB
+1%
good
fits
Q4_K_S
4.60 GB
+2%
good
fits
Q4_0
4.57 GB
+4.5%
acceptable
fits
Q3_K_M
3.92 GB
+4.5%
acceptable
fits
Q3_K_S
3.51 GB
+10%
degraded
fits
Q2_K
3.36 GB
+16%
poor
fits
Quality cost is approximate mean perplexity increase vs F16, from llama.cpp's published numbers. Smaller models degrade more at the same quant — treat these as a relative ranking, not a guarantee. Total footprint includes KV cache at the chosen context plus ~10% overhead.
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