Is Yi-6B-Chat or Qwen3-8B smaller?

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

Which uses less memory at long context?

Yi-6B-Chat — its KV cache at 32K context is 2.3x smaller, because of how its attention layers cache.

Model comparison

Yi-6B-Chat vs Qwen3-8B

These two publish different quantization sets; the table below has the exact sizes. At long context the gap widens: Yi-6B-Chat's KV cache at 32K is 2.3× smaller, which usually matters more than the difference in weights.

From the file· summed bytes, KV per layer

Side by side

	Yi-6B-Chat	Qwen3-8B
Parameters	6.1B	8.2B
Architecture	llama	qwen3
Layers	32	36
Native context	4,096	40,960
Mixture of experts	no	no
Quantizations published	24	51
Smallest quantization	1.33 GiB	2.12 GiB
Q4_K_M	—	4.68 GiB
Licence	apache-2.0	apache-2.0

KV cache by context

the term that decides long-context viability

Context	Yi-6B-Chat	Qwen3-8B	Ratio
4,096	0.25 GiB	0.56 GiB	2.25×
8,192	0.50 GiB	1.13 GiB	2.25×
16,384	1.00 GiB	2.25 GiB	2.25×
32,768	2.00 GiB	4.50 GiB	2.25×
65,536	4.00 GiB	9.00 GiB	2.25×
131,072	8.00 GiB	18.00 GiB	2.25×