Is Qwen3-Coder-30B-A3B-Instruct or llama-3-youko-8b smaller?

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

Which uses less memory at long context?

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

Model comparison

Qwen3-Coder-30B-A3B-Instruct vs llama-3-youko-8b

These two publish different quantization sets; the table below has the exact sizes. At long context the gap widens: Qwen3-Coder-30B-A3B-Instruct'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

	Qwen3-Coder-30B-A3B-Instruct	llama-3-youko-8b
Parameters	30.5B	8.0B
Architecture	qwen3moe	llama
Layers	48	32
Native context	262,144	8,192
Mixture of experts	yes, 128 experts	no
Quantizations published	46	2
Smallest quantization	7.46 GiB	5.34 GiB
Q4_K_M	17.28 GiB	—
Licence	apache-2.0	llama3

KV cache by context

the term that decides long-context viability

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