Token-Operations-Oriented Inference Optimization Techniques for Large Models

Shiguo Lian; Kai Wang; Zhaoxiang Liu; Wen Liu; Minjie Hua; Yutong Liu; Jiangze Yan; Xin Wang; Cong Wang; Yilin Zhang; Yi Shen; Jieyun Huang; Fang Zhao; Huanlin Gao; Ping Chen; Xinyu Yang; Kaikai Zhao; Yantao Li; Yao Zhao; Xinggang Wang; Huishuai Zhang; Dongyan Zhao; Junping Du; Tao Chen; Xiang Gao; Qinghuai Ma

arXiv · 2606.20295

Token-Operations-Oriented Inference Optimization Techniques for Large Models

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Abstract

Large model inference optimization serves as a key foundation for supporting the scalable, low-cost, and highly stable operation of large model services. Centered on token-oriented inference optimization technology, this paper proposes for the first time a four-layer technical architecture consisting of Multi-model Fusion, Model Optimization, Compute-Model Fusion, and Compute-Network-Model Fusion. It systematically reviews the key technologies and current industry status across these four levels and analyzes the application value of related technologies in real-world business scenarios. This paper provides a practical technical path for reducing token production costs, improving token service efficiency, ensuring the stability of token supply, and driving the transition of large model services from being merely callable to being operable.

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Shiguo Lian, Kai Wang, Zhaoxiang Liu, Wen Liu, Minjie Hua, Yutong Liu, Jiangze Yan, Xin Wang, Cong Wang, Yilin Zhang, Yi Shen, Jieyun Huang, Fang Zhao, Huanlin Gao, Ping Chen, Xinyu Yang, Kaikai Zhao, Yantao Li, Yao Zhao, Xinggang Wang, Huishuai Zhang, Dongyan Zhao, Junping Du, Tao Chen, Xiang Gao, Qinghuai Ma. 2026-07-24. Token-Operations-Oriented Inference Optimization Techniques for Large Models. https://arxiv.org/abs/2606.20295

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