QuantumLeap claims 2.3x MoE speedup

This is a credible-looking infra project with real engineering substance, but the interesting part is still the benchmark claim, not a finished platform. The numbers are strong for a 6GB consumer GPU, yet the 24GB+ projections need independent replication before anyone treats them as generalizable.

• –It targets a real bottleneck in MoE serving: expert movement, cache locality, and decode-time transfer overhead
• –Building on llama.cpp lowers friction, but it also means the win has to beat a pretty crowded optimization stack
• –The repo’s own framing suggests the gains come from a specific hardware/model mix, so portability is the key question
• –The right next test is not more synthetic runs, but cross-GPU validation on common 24GB cards with multiple MoE models
• –If reproducible, this is useful infrastructure for local inference; if not, it stays in the “promising benchmark” bucket