Is SmolVLM-256M-Instruct or Llama-3.2-1B-Instruct smaller?

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

Which uses less memory at long context?

SmolVLM-256M-Instruct — its KV cache at 32K context is 1.4x smaller, because of how its attention layers cache.

Model comparison

SmolVLM-256M-Instruct vs Llama-3.2-1B-Instruct

These two publish different quantization sets; the table below has the exact sizes. At long context the gap widens: SmolVLM-256M-Instruct's KV cache at 32K is 1.4× smaller, which usually matters more than the difference in weights.

From the file· summed bytes, KV per layer

Side by side

	SmolVLM-256M-Instruct	Llama-3.2-1B-Instruct
Parameters	256M	1.2B
Architecture	llama	llama
Layers	30	16
Native context	8,192	131,072
Mixture of experts	no	no
Quantizations published	2	39
Smallest quantization	0.16 GiB	0.39 GiB
Q4_K_M	—	0.75 GiB
Licence	apache-2.0	—

KV cache by context

the term that decides long-context viability

Context	SmolVLM-256M-Instruct	Llama-3.2-1B-Instruct	Ratio
4,096	0.09 GiB	0.13 GiB	1.42×
8,192	0.18 GiB	0.25 GiB	1.42×
16,384	0.35 GiB	0.50 GiB	1.42×
32,768	0.70 GiB	1.00 GiB	1.42×
65,536	1.41 GiB	2.00 GiB	1.42×
131,072	2.81 GiB	4.00 GiB	1.42×