[D] Evaluating the inference efficiency of Sparse+Linear Hybrid Architectures (MiniCPM-SALA)

We’ve seen a lot of talk about Hybrid models lately (like Jamba). I just noticed that OpenBMB and NVIDIA are running a performance sprint (SOAR 2026) specifically to benchmark MiniCPM-SALA (Sparse+Linear) on SGLang.

The challenge is to optimize sparse operator fusion and KV-cache efficiency for ultra-long context. Since the leaderboard just opened today, I was wondering: from a systems research perspective, do you think this hybrid approach will eventually surpass standard Transformers for inference throughput in production?

Has anyone here done a deep dive into SGLang’s graph compilation for sparse kernels?

Specs: https://soar.openbmb.cn/en/competition