Is openbuddy-deepseek-67b-v15.3-4k or Qwen3-Coder-30B-A3B-Instruct 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 4.0x smaller, because of how its attention layers cache.

Model comparison

openbuddy-deepseek-67b-v15.3-4k vs Qwen3-Coder-30B-A3B-Instruct

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 4.0× smaller, which usually matters more than the difference in weights.

From the file· summed bytes, KV per layer

Side by side

	openbuddy-deepseek-67b-v15.3-4k	Qwen3-Coder-30B-A3B-Instruct
Parameters	67.4B	30.5B
Architecture	llama	qwen3moe
Layers	95	48
Native context	4,096	262,144
Mixture of experts	no	yes, 128 experts
Quantizations published	20	46
Smallest quantization	13.65 GiB	7.46 GiB
Q4_K_M	—	17.28 GiB
Licence	other	apache-2.0

KV cache by context

the term that decides long-context viability

Context	openbuddy-deepseek-67b-v15.3-4k	Qwen3-Coder-30B-A3B-Instruct	Ratio
4,096	1.48 GiB	0.38 GiB	3.96×
8,192	2.97 GiB	0.75 GiB	3.96×
16,384	5.94 GiB	1.50 GiB	3.96×
32,768	11.88 GiB	3.00 GiB	3.96×
65,536	23.75 GiB	6.00 GiB	3.96×
131,072	47.50 GiB	12.00 GiB	3.96×