Is dots.ocr or Qwen3-1.7B smaller?

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

Which uses less memory at long context?

dots.ocr — its KV cache at 32K context is 4.0x smaller, because of how its attention layers cache.

Model comparison

dots.ocr vs Qwen3-1.7B

These two publish different quantization sets; the table below has the exact sizes. At long context the gap widens: dots.ocr's KV cache at 32K is 4.0× smaller, which usually matters more than the difference in weights.

From the file· summed bytes, KV per layer

Side by side

	dots.ocr	Qwen3-1.7B
Parameters	3.0B	2.0B
Architecture	qwen2	qwen3
Layers	28	28
Native context	131,072	40,960
Mixture of experts	no	no
Quantizations published	28	48
Smallest quantization	0.48 GiB	0.50 GiB
Q4_K_M	—	1.03 GiB
Licence	mit	—

KV cache by context

the term that decides long-context viability

Context	dots.ocr	Qwen3-1.7B	Ratio
4,096	0.11 GiB	0.44 GiB	4.00×
8,192	0.22 GiB	0.88 GiB	4.00×
16,384	0.44 GiB	1.75 GiB	4.00×
32,768	0.88 GiB	3.50 GiB	4.00×
65,536	1.75 GiB	7.00 GiB	4.00×
131,072	3.50 GiB	14.00 GiB	4.00×