Is SmolVLM-500M-Instruct or gemma-3-1b-it smaller?

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

Which uses less memory at long context?

gemma-3-1b-it — its KV cache at 32K context is 8.5x smaller, because of how its attention layers cache.

Model comparison

SmolVLM-500M-Instruct vs gemma-3-1b-it

These two publish different quantization sets; the table below has the exact sizes. At long context the gap widens: gemma-3-1b-it's KV cache at 32K is 8.5× smaller, which usually matters more than the difference in weights.

From the file· summed bytes, KV per layer

Side by side

	SmolVLM-500M-Instruct	gemma-3-1b-it
Parameters	507M	1000M
Architecture	llama	gemma3
Layers	32	26
Native context	8,192	32,768
Mixture of experts	no	no
Quantizations published	2	28
Smallest quantization	0.41 GiB	0.52 GiB
Q4_K_M	—	0.75 GiB
Licence	apache-2.0	gemma

KV cache by context

the term that decides long-context viability

Context	SmolVLM-500M-Instruct	gemma-3-1b-it	Ratio
4,096	0.16 GiB	0.04 GiB	4.21×
8,192	0.31 GiB	0.05 GiB	5.93×
16,384	0.63 GiB	0.08 GiB	7.44×
32,768	1.25 GiB	0.15 GiB	8.53×
65,536	2.50 GiB	0.27 GiB	9.21×
131,072	5.00 GiB	0.52 GiB	9.59×