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

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

Read the original on arXiv Machine Learning →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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