Is smollm-360M-instruct-add-basics or LFM2.5-1.2B-Instruct smaller?

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

Which uses less memory at long context?

LFM2.5-1.2B-Instruct — its KV cache at 32K context is 3.3x smaller, because of how its attention layers cache.

Model comparison

smollm-360M-instruct-add-basics vs LFM2.5-1.2B-Instruct

These two publish different quantization sets; the table below has the exact sizes. At long context the gap widens: LFM2.5-1.2B-Instruct'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

	smollm-360M-instruct-add-basics	LFM2.5-1.2B-Instruct
Parameters	362M	1.2B
Architecture	llama	lfm2
Layers	32	16
Native context	2,048	128,000
Mixture of experts	no	no
Quantizations published	8	23
Smallest quantization	0.19 GiB	0.45 GiB
Q4_K_M	—	0.68 GiB
Licence	—	other

KV cache by context

the term that decides long-context viability

Context	smollm-360M-instruct-add-basics	LFM2.5-1.2B-Instruct	Ratio
4,096	0.16 GiB	0.05 GiB	3.33×
8,192	0.31 GiB	0.09 GiB	3.33×
16,384	0.63 GiB	0.19 GiB	3.33×
32,768	1.25 GiB	0.38 GiB	3.33×
65,536	2.50 GiB	0.75 GiB	3.33×
131,072	5.00 GiB	1.50 GiB	3.33×