LiquidAI's LFM2.5-8B-A1B - only 1B active params, but is it actually useful?

LiquidAI released LFM2.5-8B-A1B, a hybrid model with 8B total parameters but only 1B active per token (A1B). It is designed for on-device deployment and GGUF quants are available. The pitch is essentially that it can run on almost any hardware given the active parameter count.

The question is whether 1B active parameters is enough for anything beyond basic tasks, even with 8B of total capacity to draw from. Hybrid architectures can be surprisingly capable, but there is a floor somewhere.

For anyone who has tried this model: what tasks does it handle well, and where does the 1B active limit become a real bottleneck? Is it competitive with any dense 3B-4B models, or does the MoE overhead eat into the advantage?