Is gemma-4-12B-it-qat-q4_0-unquantized or Wizard-Vicuna-30B-Uncensored smaller?

Both publish quantizations; compare the table for the exact sizes.

Which uses less memory at long context?

gemma-4-12B-it-qat-q4_0-unquantized — its KV cache at 32K context is 19.7x smaller, because of how its attention layers cache.

Model comparison

gemma-4-12B-it-qat-q4_0-unquantized vs Wizard-Vicuna-30B-Uncensored

These two publish different quantization sets; the table below has the exact sizes. At long context the gap widens: gemma-4-12B-it-qat-q4_0-unquantized's KV cache at 32K is 19.7× smaller, which usually matters more than the difference in weights.

From the file· summed bytes, KV per layer

Side by side

	gemma-4-12B-it-qat-q4_0-unquantized	Wizard-Vicuna-30B-Uncensored
Parameters	12.0B	32.5B
Architecture	gemma4	llama
Layers	48	60
Native context	262,144	2,048
Mixture of experts	no	no
Quantizations published	2	33
Smallest quantization	6.50 GiB	6.63 GiB
Q4_K_M	—	18.27 GiB
Licence	apache-2.0	other

KV cache by context

the term that decides long-context viability

Context	gemma-4-12B-it-qat-q4_0-unquantized	Wizard-Vicuna-30B-Uncensored	Ratio
4,096	0.72 GiB	6.09 GiB	8.48×
8,192	0.97 GiB	12.19 GiB	12.58×
16,384	1.47 GiB	24.38 GiB	16.60×
32,768	2.47 GiB	48.75 GiB	19.75×
65,536	4.47 GiB	97.50 GiB	21.82×
131,072	8.47 GiB	195.00 GiB	23.03×