Is IQuest-Coder-V1-40B-Instruct 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 3.3x smaller, because of how its attention layers cache.

Model comparison

IQuest-Coder-V1-40B-Instruct 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 3.3× smaller, which usually matters more than the difference in weights.

From the file· summed bytes, KV per layer

Side by side

	IQuest-Coder-V1-40B-Instruct	Qwen3-30B-A3B-Thinking-2507
Parameters	39.8B	30.5B
Architecture	llama	qwen3moe
Layers	80	48
Native context	131,072	262,144
Mixture of experts	no	yes, 128 experts
Quantizations published	23	51
Smallest quantization	7.91 GiB	7.05 GiB
Q4_K_M	—	17.28 GiB
Licence	other	apache-2.0

KV cache by context

the term that decides long-context viability

Context	IQuest-Coder-V1-40B-Instruct	Qwen3-30B-A3B-Thinking-2507	Ratio
4,096	1.25 GiB	0.38 GiB	3.33×
8,192	2.50 GiB	0.75 GiB	3.33×
16,384	5.00 GiB	1.50 GiB	3.33×
32,768	10.00 GiB	3.00 GiB	3.33×
65,536	20.00 GiB	6.00 GiB	3.33×
131,072	40.00 GiB	12.00 GiB	3.33×