arXiv AI By Hongliang Cao, Xiuyu Li, Siqi Wang, Ziming Wang, Jingmin Zhu, Hailong Yang

SpecFit: Not All Draft Tokens Cost the Same in Offloaded MoE Inference

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DraftExpert: Expansion-Aware Self-Speculative Decoding for End-Device MoE Inference

Large Mixture-of-Experts (MoE) language models are attractive for end-device deployment because only a small subset of experts is active per token, but their routed expert weights often exceed accelerator memory. We target latency-critical single-user settings where routed experts are staged on demand from CPU memory to a GPU or from Flash to a mobile NPU.