LRMoE is a memory-aware training system for Sparse Mixture-of-Experts models that recalibrates backward demand and balances retained workload. It plans retention quotas and expert placement during forward execution, then uses a load recalibration metric in backpropagation to select token‑expert pairs within these quotas. The system also employs capacity‑aware routing and physical compaction to reduce communication, computation, and buffer demands, achieving 21.2‑44.8% faster training on large models compared to SYMI.
arXiv:2510. 19366v2 Announce Type: replace-cross Abstract: Mixture-of-Experts (MoE) scales model capacity through sparse activation, and is becoming an important architecture for large language models (LLMs).
By Xinfeng Xia, Xiaofeng Hou, Jiacheng Liu, Wenfeng Wang, Mingxuan Zhang, Peng Tang, Chao Li, Minyi Guo
Dynamic Expert Quantization (DynaExq) is a runtime-aware mixed-precision serving system designed for single‑GPU Mixture‑of‑Experts (MoE) inference under a hard high‑bandwidth memory (HBM) envelope. It treats the problem as an online, budget‑constrained precision allocation task, keeping the most frequently used experts at higher precision while relegating the rest to low‑precision fallbacks. By estimating expert hotness from router traces and asynchronously promoting or demoting experts, DynaExq maintains a fully materialized expert set during the forward pass, improving accuracy and throughput compared to static post‑training quantization and offloading/prefetch baselines.
whyItMatters":"DynaExq enables efficient deployment of large MoE models on memory‑limited GPUs by dynamically allocating precision based on runtime expert usage, thereby reducing memory footprint and latency while boosting accuracy and throughput."
By Kexin Chu, Dawei Xiang, Zixu Shen, Yiwei Yang, Zecheng Liu, Wei Zhang
arXiv:2609.38090v1 Announce Type: new
Abstract: Mixture-of-Experts (MoE) models are a compelling architecture for scaling model capacity, making them especially attractive for deployment on resource-...
By Sanjali Yadav, Bahar Asgari
The paper introduces a cache‑aware post‑training framework for Mixture‑of‑Experts (MoE) models that jointly adapts the MoE backbone and lightweight auxiliary cache routers while keeping the native Top‑K expert‑selection rule. Two modes are proposed: Temporal Router, which predicts same‑layer reuse and retains experts for future tokens, and Spatio‑Temporal Router, which adds a Spatio Router that refines the temporal cache using the causal predecessor’s hidden state. Experiments on Qwen3 and GPT‑OSS across GSM8K, MATH, and CommonsenseQA show that Temporal Router improves cache hit rates and reduces expert‑weight traffic, while Spatio‑Temporal Router achieves the best load‑adjusted efficiency, outperforming strong prefetching baselines.
By Zhenhe Wu, Yaping Jin, Qinghua Xing, Hang Zhou, Wei He, Xianjie Wu, Xianfu Cheng, Jian Yang, Hanting Chen
arXiv:2608. 07964v1 Announce Type: cross Abstract: Load Balancing has emerged as a critical problem in expert-parallel distributed inference of Mixture-of-Experts (MoE) models.
By Yize Wu, Ke Gao, Ling Li, Yanjun Wu
arXiv:2606. 04101v1 Announce Type: cross Abstract: Large-scale expert parallelism (EP) is becoming pivotal for training and serving frontier MoE models, but it also amplifies device-level expert load imbalance into compute stragglers, token all-to-all bottlenecks, and activation-memory spikes.
By Xinming Wei, Chao Jin, Tuo Dai, Yinmin Zhong, Shan Yu, Chengxu Yang, Bingyang Wu, Zili Zhang, Jing Mai, Qianchao Zhu, Zhouyang Li, Yuliang Liu, Guojie Luo
arXiv:2609.36222v1 Announce Type: new
Abstract: Large language models are increasingly expensive to serve. In large-scale serving systems, autoregressive decoding is often bottlenecked by transferrin...
By Ali Abbasi, Justin Shi, Soheil Kolouri
The paper introduces RelayMoE, a ring‑based execution model for Mixture‑of‑Experts (MoE) training that reduces memory usage by avoiding full top‑k expanded dispatch buffers. RelayMoE dynamically chooses between expert and token routing based on communication volume and overlaps data transfer with computation, enabling efficient backward recomputation. Experiments on 30B–57B MoE models show up to a 2× speedup over Megatron‑LM and a 2.02× throughput improvement while extending trainable sequence lengths by up to 2.85×.
By Arnab Kanti Tarafder, Jaume Guasch-Mart\'i, Gokcen Kestor, Jie Ren
WiSP (Working‑Set Paging) is a routing‑aware expert pager that allows Mixture‑of‑Experts models to run on GPUs that cannot hold the entire expert pool by paging experts in and out of VRAM while preserving byte‑identical outputs. On a 24 GiB RTX 3090, WiSP doubles decode throughput compared to static offload when the model does not fit, and its companion policy MV‑WSA allocates VRAM between resident experts and KV cache based on marginal latency benefit, reducing end‑to‑end time by up to 1.19× without altering model outputs.
By Jiamu Zhang, Liang Wu, Mayank Darbari, Liangjie Hong
The paper introduces 3D-MoE, a hardware–software co‑designed system that optimizes Mixture‑of‑Experts (MoE) decoding on 3D‑stacked near‑memory processing (NMP) by exploiting expert complementarity. It groups experts offline, maps them locally to balance load, and uses a lightweight Collaborative Memory‑Network to stream hot expert weights, achieving an average 4.63× speedup over expert‑parallelism and 1.58×–1.86× throughput gains over existing NMP baselines with only a 2.11% area increase.
By Yudong Pan, Lian Liu, Shixin Zhao, Yintao He, Kaiyan Chang, Tianhua Han, Chenyang Ai, Haonan Zhu, Mengdi Wang, Yinhe Han, Ying Wang
arXiv:2607. 08782v1 Announce Type: cross Abstract: Expert parallelism has become the prevailing paradigm to serve Mixture-of-Experts (MoE) models.
By Qianli Liu, Kaibin Guo, Zicong Hong, Peng Li, Fahao Chen, Haodong Wang, Jian Lin, Song Guo