arXiv:2610.15417v1 Announce Type: new
Abstract: Mixture-of-experts (MoE) models activate only a few experts per token, but when their expert weights are offloaded to host memory, host-to-device (H2D)...
By Hongliang Cao, Xiuyu Li, Siqi Wang, Ziming Wang, Jingmin Zhu, Hailong Yang
The paper investigates how speculative decoding—where a draft model proposes multiple future tokens that are then verified by the target model—affects energy consumption in large language model serving under latency constraints. It finds that energy savings depend heavily on the acceptance rate of drafted tokens, which varies by model architecture, drafting scheme, and the type of text (e.g., conversation vs. code, English vs. Romansh). The study shows that while speculation can reduce energy per token at small batch sizes, at larger batches the verification cost dominates, potentially turning speculation into an energy loss, especially when acceptance rates are low.
By Bole Ma, Ayesha Afzal, Jan Eitzinger, Harald Koestler, Gerhard Wellein
The paper investigates why speculative decoding (SD) performs better than expected on sparse mixture-of-experts (MoE) models. By evaluating 10 large MoE models, the authors find that recent fine‑grained models achieve higher speedups at low batch sizes, contrary to prior predictions of a concave speedup‑batch relationship. They attribute this to lower verification costs driven by temporal overlap of draft tokens and GPU execution dynamics, and develop a maximum‑entropy IPF framework that accurately predicts unique expert counts from routing overlap, closely matching real‑model behavior.
By Ekagra Ranjan, Komal K. Teru, Bharat Venkitesh, Acyr Locatelli
arXiv:2607. 17283v1 Announce Type: new Abstract: Single-stream autoregressive decoding of large language models is bound by memory bandwidth: each generated token requires one full forward pass through the target model, and successive passes cannot be parallelized.
By Param Chordiya
The paper introduces DuoRate, a method for allocating precision in Mixture-of-Experts (MoE) models that separates storage and decoding rates. It stores a uniform tier of weights once and then adapts read precision per workload using a Lagrangian rate‑distortion optimization, reducing the store‑read gap. Experiments on Qwen3-30B-A3B and GSM8K show that DuoRate achieves higher accuracy at lower bit‑per‑weight (bpw) rates compared to fixed‑format baselines.
By Mengqi Guo, Zhe Wei, Yuan Yuan, Jiunn Bin Lim, Boyi Pan, Michael Bi Mi
arXiv:2607. 24434v1 Announce Type: cross Abstract: 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.
By Dengke Han