Cut MoE Inference Costs by 60–80%

Modular MoE restructures how expert weights are stored, routed, and updated. Instead of keeping every expert resident across 5–8 GPUs, we extract a frozen shared core, compress the per-expert residuals by 8–16× (hierarchical shared-core extraction combined with S2LC—Shared Spectral Low-Rank Compression—spectral compression), and load only the active domain module on demand. The result: 1–2 GPUs per instance, sub-millisecond domain switching, and the ability to add or roll back capabilities without retraining.