From the doc: > At the moment we aren't seeing a significant improvement in the performance of the KAN Convolutional Networks compared to the traditional Convo

From the doc: > At the moment we aren't seeing a significant improvement in the performance of the KAN Convolutional Networks compared to the traditional Convolutional Networks. So, inference speed probably not improved. As for model size in bytes, is there any reason to assume it's not directly related to parameter count? I didn't see any mentions of pruning/quantization/other optimizations, so I'll naively go with KKAN being about 400KB. (For inference purposes) Either way, probably still a bit too early to think about productionizing KANs - there's still a ton of unanswered questions. The biggest one for now probably the fact that they are sloo-ooo-ooow to train.(10x slower than MLPs) On the upside, you can probably get quite a bit of traction if you publicly look at KANs on low power accelerators, it seems a very open topic :)