Is llama-3-youko-8b or Qwen3-30B-A3B-Thinking-2507 smaller?

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

Which uses less memory at long context?

Qwen3-30B-A3B-Thinking-2507 — its KV cache at 32K context is 1.3x smaller, because of how its attention layers cache.

Model comparison

llama-3-youko-8b vs Qwen3-30B-A3B-Thinking-2507

These two publish different quantization sets; the table below has the exact sizes. At long context the gap widens: Qwen3-30B-A3B-Thinking-2507's KV cache at 32K is 1.3× smaller, which usually matters more than the difference in weights.

From the file· summed bytes, KV per layer

Side by side

	llama-3-youko-8b	Qwen3-30B-A3B-Thinking-2507
Parameters	8.0B	30.5B
Architecture	llama	qwen3moe
Layers	32	48
Native context	8,192	262,144
Mixture of experts	no	yes, 128 experts
Quantizations published	2	51
Smallest quantization	5.34 GiB	7.05 GiB
Q4_K_M	—	17.28 GiB
Licence	llama3	apache-2.0

KV cache by context

the term that decides long-context viability

Context	llama-3-youko-8b	Qwen3-30B-A3B-Thinking-2507	Ratio
4,096	0.50 GiB	0.38 GiB	1.33×
8,192	1.00 GiB	0.75 GiB	1.33×
16,384	2.00 GiB	1.50 GiB	1.33×
32,768	4.00 GiB	3.00 GiB	1.33×
65,536	8.00 GiB	6.00 GiB	1.33×
131,072	16.00 GiB	12.00 GiB	1.33×