Yeah, generally being able to focus on specific architectures lets you optimize better for those. However models of the same family (for example Qwen 3.5/3.6/ some 3.8 models) share the same architecture, so you only need to optimize once and new models can use the same kernels. There's also shared algorithms and kernels that can be optimized once and used across different families, so it's a bit nuanced.

We plan to support any model architecture that we believe is somewhere along or close to the pareto frontier. There's some model families that are outdated or more niche that we don't necessarily want to put our focus into.