Is Open_Gpt4_8x7B_v0.2 or Qwen3-30B-A3B-Thinking-2507 smaller?

Qwen3-30B-A3B-Thinking-2507 is smaller at Q4_K_M: 17.28 GiB against 26.43 GiB.

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

Open_Gpt4_8x7B_v0.2 vs Qwen3-30B-A3B-Thinking-2507

At Q4_K_M, Qwen3-30B-A3B-Thinking-2507 is the smaller download — 18,556,685,824 bytes against 28,379,301,312. 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

	Open_Gpt4_8x7B_v0.2	Qwen3-30B-A3B-Thinking-2507
Parameters	46.7B	30.5B
Architecture	llama	qwen3moe
Layers	32	48
Native context	32,768	262,144
Mixture of experts	yes, 8 experts	yes, 128 experts
Quantizations published	8	51
Smallest quantization	15.99 GiB	7.05 GiB
Q4_K_M	26.43 GiB	17.28 GiB
Licence	apache-2.0	apache-2.0

KV cache by context

the term that decides long-context viability

Context	Open_Gpt4_8x7B_v0.2	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×