Is markdownizer or gemma-4-E4B-it smaller?

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

Which uses less memory at long context?

gemma-4-E4B-it — its KV cache at 32K context is 7.9x smaller, because of how its attention layers cache.

Model comparison

markdownizer vs gemma-4-E4B-it

These two publish different quantization sets; the table below has the exact sizes. At long context the gap widens: gemma-4-E4B-it's KV cache at 32K is 7.9× smaller, which usually matters more than the difference in weights.

From the file· summed bytes, KV per layer

Side by side

	markdownizer	gemma-4-E4B-it
Parameters	8.0B	8.0B
Architecture	llama	gemma4
Layers	32	42
Native context	131,072	131,072
Mixture of experts	no	no
Quantizations published	24	36
Smallest quantization	1.88 GiB	3.30 GiB
Q4_K_M	—	4.64 GiB
Licence	—	apache-2.0

KV cache by context

the term that decides long-context viability

Context	markdownizer	gemma-4-E4B-it	Ratio
4,096	0.50 GiB	0.12 GiB	4.06×
8,192	1.00 GiB	0.18 GiB	5.63×
16,384	2.00 GiB	0.29 GiB	6.97×
32,768	4.00 GiB	0.51 GiB	7.91×
65,536	8.00 GiB	0.94 GiB	8.48×
131,072	16.00 GiB	1.82 GiB	8.80×