2 papers
cs.CL2026
Less Experts, Faster Decoding: Cost-Aware Speculative Decoding for Mixture-of-Experts
Jincheng Xie, Runheng Liu, Heyan Huang +4
Sparse Mixture-of-Experts (MoE) models have become an important approach for scaling Large Language Models (LLMs), but their inference efficiency depends strongly on expert activat…
cs.DC2026
SPECTRE: Hybrid Ordinary-Parallel Speculative Serving for Resource-Efficient LLM Inference
Jincheng Xie, Yawen Ling, Qi Xiao +4
LLM serving platforms are increasingly deployed as multi-model cloud systems, where user demand is often long-tailed: a few popular large models receive most requests, while many s…