Is SmolVLM-256M-Instruct or Qwen2.5-0.5B-Instruct smaller?

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

Which uses less memory at long context?

Qwen2.5-0.5B-Instruct — its KV cache at 32K context is 1.9x smaller, because of how its attention layers cache.

Model comparison

SmolVLM-256M-Instruct vs Qwen2.5-0.5B-Instruct

These two publish different quantization sets; the table below has the exact sizes. At long context the gap widens: Qwen2.5-0.5B-Instruct's KV cache at 32K is 1.9× smaller, which usually matters more than the difference in weights.

From the file· summed bytes, KV per layer

Side by side

	SmolVLM-256M-Instruct	Qwen2.5-0.5B-Instruct
Parameters	256M	494M
Architecture	llama	qwen2
Layers	30	24
Native context	8,192	32,768
Mixture of experts	no	no
Quantizations published	2	32
Smallest quantization	0.16 GiB	0.31 GiB
Q4_K_M	—	0.37 GiB
Licence	apache-2.0	apache-2.0

KV cache by context

the term that decides long-context viability

Context	SmolVLM-256M-Instruct	Qwen2.5-0.5B-Instruct	Ratio
4,096	0.09 GiB	0.05 GiB	1.88×
8,192	0.18 GiB	0.09 GiB	1.88×
16,384	0.35 GiB	0.19 GiB	1.88×
32,768	0.70 GiB	0.38 GiB	1.88×
65,536	1.41 GiB	0.75 GiB	1.88×
131,072	2.81 GiB	1.50 GiB	1.88×