Is SmolVLM-500M-Instruct or Qwen3-1.7B smaller?

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

Which uses less memory at long context?

SmolVLM-500M-Instruct — its KV cache at 32K context is 2.8x smaller, because of how its attention layers cache.

Model comparison

SmolVLM-500M-Instruct vs Qwen3-1.7B

These two publish different quantization sets; the table below has the exact sizes. At long context the gap widens: SmolVLM-500M-Instruct's KV cache at 32K is 2.8× smaller, which usually matters more than the difference in weights.

From the file· summed bytes, KV per layer

Side by side

	SmolVLM-500M-Instruct	Qwen3-1.7B
Parameters	507M	2.0B
Architecture	llama	qwen3
Layers	32	28
Native context	8,192	40,960
Mixture of experts	no	no
Quantizations published	2	48
Smallest quantization	0.41 GiB	0.50 GiB
Q4_K_M	—	1.03 GiB
Licence	apache-2.0	—

KV cache by context

the term that decides long-context viability

Context	SmolVLM-500M-Instruct	Qwen3-1.7B	Ratio
4,096	0.16 GiB	0.44 GiB	2.80×
8,192	0.31 GiB	0.88 GiB	2.80×
16,384	0.63 GiB	1.75 GiB	2.80×
32,768	1.25 GiB	3.50 GiB	2.80×
65,536	2.50 GiB	7.00 GiB	2.80×
131,072	5.00 GiB	14.00 GiB	2.80×