Instella‑MoE is a fully open Mixture‑of‑Experts language model with 16 billion total parameters and 2.8 billion active parameters per token, trained from scratch on AMD Instinct GPUs. It incorporates a sparsely activated MoE design with Gated Multi‑head Latent Attention and FarSkip‑Collective connectivity, and follows a multi‑stage pipeline that includes pre‑training, long‑context extension, supervised fine‑tuning, direct preference optimization, and reinforcement learning with Multi‑Teacher On‑Policy Distillation. The model achieves an average score of 76.7 on pre‑training benchmarks and 73.2 on instruction‑following, reasoning, math, coding, and chat benchmarks, outperforming comparable fully open and open‑weight models, and its full training pipeline, weights, and code are released for reproducibility.
By Jiang Liu, Sudhanshu Ranjan, Prakamya Mishra, Yonatan Dukler, Gowtham Ramesh, Jialian Wu, Ximeng Sun, Wen Xie, Chaojun Hou, Vikram Appia, Zhenyu Gu, Zicheng Liu, Emad Barsoum
The paper introduces Edge0, a streaming mixture‑of‑experts (MoE) inference engine that enables a 35‑billion‑parameter MoE model to run on consumer hardware by predicting routing decisions one token ahead. Edge0 uses a per‑layer prerouter to prefetch the necessary experts from SSD, and an unmerged recovery LoRA trained on the student path to recover quality lost to 4‑bit quantization and routing replacement. On a single 24‑GB machine, Edge0 serves the 35B MoE at 20 tokens per second while keeping peak active memory below 3 GiB, achieving performance close to its fp16 teacher across five public benchmarks.
By Yu Lin, Yiming Wang, Runyuan Cai, Hanze Liu, Xiaodong Zeng
arXiv:2602. 05711v2 Announce Type: replace-cross Abstract: Mixture-of-Experts (MoE) architectures are evolving towards finer granularity to improve parameter efficiency.
By Jingze Shi, Zhangyang Peng, Yizhang Zhu, Yifan Wu, Guang Liu, Yuyu Luo
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
arXiv:2609.39350v1 Announce Type: cross
Abstract: As model sizes continue to scale, distributed training has become inevitable. Automatic parallelization techniques can derive efficient training para...
By Mengyuan Fan, Peizhuang Cong, Zixiao Huang, Si Xu, Tong Qiao, Yanghao Li, Jing Yang, Tong Yang, Quanlu Zhang, Yu Wang
arXiv:2609.14237v1 Announce Type: cross
Abstract: LLM serving systems increasingly disaggregate inference into finer-grained stages, with recent approaches separating attention from FFN or MoE execut...
By Zikun Li, Yixuan Mei, Shiqi Pan, Zixuan Chen, Xiaowen Zhang, Mengdi Wu, Shuhuai Lin, Yutong Yang, Zhihao Zhang, Xupeng Miao, Rashmi Vinayak, Zhihao Jia
As model sizes continue to scale, distributed training has become inevitable. Automatic parallelization techniques can derive efficient training parallelism strategies at low cost while achieving supe...
arXiv:2606. 27866v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) language models scale model ability with sparsely activated experts, making this architecture a standard recipe for modern large models.
By Fan Mo, Yuxuan Han, Geng Zhang, Wangbo Zhao, Yang You
arXiv:2608. 14385v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) models have been widely adopted in real-time interactive applications such as coding assistants, real-time audio-video interaction systems.
By Zewen Jin, Shen Fu, Zeping Duan, Shannon Wang, Weihao Wu, Chengjie Tang, Congkun Ai, Ping Gong, Zijian Dai, Youhui Bai, Cheng Li
arXiv:2411. 00918v5 Announce Type: replace-cross Abstract: Mixture of experts (MoE) architectures have become a cornerstone for scaling up and are a key component in most large language models such as GPT-OSS, DeepSeek-V3, Llama-4, and Gemini-2.
By Nam V. Nguyen, Thong T. Doan, Luong Tran, Van Nguyen, Quang Pham
arXiv:2606. 16825v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) architectures efficiently scale Large Language Models (LLMs) by activating only a small fraction of their experts per token, yet the full parameter count - dominated by the expert parameters - must be held in training and inference memory.
By Martin Jaggi
arXiv:2608. 15299v1 Announce Type: cross Abstract: Sparsely-activated Mixture-of-Experts (MoE) Transformers universally fix the same number of routed experts across all layers, a convention that ignores the well-documented heterogeneity in layer-wise redundancy.
By Lie Li, Wen Li, Junxiao Shen, Gusheng Hu