arXiv Machine Learning
Oct 21

Specter: Fast MoE Inference via Quantization-Enabled Multi-Step Speculative Offloading

Specter is an inference system for mixture-of-experts (MoE) models that uses quantization to enable speculative decoding and expert offloading. By running a lightweight quantized draft model on the device, Specter predicts future expert usage and pre‑organizes expert movement, reducing PCIe I/O bottlenecks. The approach yields an average 1.50× speedup (up to 1.88×) in time per output token compared to state‑of‑the‑art systems, making single‑GPU MoE inference more practical on commodity hardware.

By Wenfeng Wang, Xiaofeng Hou, Xinfeng Xia, Xinkai WAng, Mingxuan Zhang, Peng Tang, Jiacheng Liu, Chao Li