Is Gemma-3-R1984-4B or llama-3-youko-8b smaller?

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

Which uses less memory at long context?

Gemma-3-R1984-4B — its KV cache at 32K context is 5.0x smaller, because of how its attention layers cache.

Model comparison

Gemma-3-R1984-4B vs llama-3-youko-8b

These two publish different quantization sets; the table below has the exact sizes. At long context the gap widens: Gemma-3-R1984-4B's KV cache at 32K is 5.0× smaller, which usually matters more than the difference in weights.

From the file· summed bytes, KV per layer

Side by side

	Gemma-3-R1984-4B	llama-3-youko-8b
Parameters	4.3B	8.0B
Architecture	gemma3	llama
Layers	34	32
Native context	131,072	8,192
Mixture of experts	no	no
Quantizations published	3	2
Smallest quantization	2.20 GiB	5.34 GiB
Q4_K_M	2.32 GiB	—
Licence	—	llama3

KV cache by context

the term that decides long-context viability

Context	Gemma-3-R1984-4B	llama-3-youko-8b	Ratio
4,096	0.25 GiB	0.50 GiB	2.02×
8,192	0.33 GiB	1.00 GiB	3.07×
16,384	0.48 GiB	2.00 GiB	4.15×
32,768	0.79 GiB	4.00 GiB	5.03×
65,536	1.42 GiB	8.00 GiB	5.63×
131,072	2.67 GiB	16.00 GiB	5.99×