Is SambaLingo-Japanese-Chat or Qwen3-8B smaller?

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

Which uses less memory at long context?

Qwen3-8B — its KV cache at 32K context is 3.6x smaller, because of how its attention layers cache.

Model comparison

SambaLingo-Japanese-Chat vs Qwen3-8B

These two publish different quantization sets; the table below has the exact sizes. At long context the gap widens: Qwen3-8B's KV cache at 32K is 3.6× smaller, which usually matters more than the difference in weights.

From the file· summed bytes, KV per layer

Side by side

	SambaLingo-Japanese-Chat	Qwen3-8B
Parameters	6.9B	8.2B
Architecture	llama	qwen3
Layers	32	36
Native context	4,096	40,960
Mixture of experts	no	no
Quantizations published	22	51
Smallest quantization	1.52 GiB	2.12 GiB
Q4_K_M	—	4.68 GiB
Licence	llama2	apache-2.0

KV cache by context

the term that decides long-context viability

Context	SambaLingo-Japanese-Chat	Qwen3-8B	Ratio
4,096	2.00 GiB	0.56 GiB	3.56×
8,192	4.00 GiB	1.13 GiB	3.56×
16,384	8.00 GiB	2.25 GiB	3.56×
32,768	16.00 GiB	4.50 GiB	3.56×
65,536	32.00 GiB	9.00 GiB	3.56×
131,072	64.00 GiB	18.00 GiB	3.56×