Since you're using GH200s for these optimizations you're restricted to single device workloads (since GH series are SOC architecture). Kimi K2 (and many other large MoE models) requires multiple devices. Does that mean you can't scale these optimizations to multi-device workloads?

See my other comments about static profiling of kernels. There are ways of improving the search that keep runtime at the heart of it.

See my comment on a deeper thread about this. Eventually we will implement static profiling for common kernels so the search doesn't actually have to manually run all of them; many will have a known runtime that we can tie to them.

Not today but we will implement memoization of kernels for each hardware backend, yes.

Met the CEO of Zed. Very humble and deeply technical. Glad to see they're doing well!