Token-Operations-Oriented Inference Optimization Techniques for Large Models
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 ser...
| Authors | Shiguo Lian, Kai Wang, Zhaoxiang Liu et al. |
|---|---|
| Primary category | cs.SE |
| Published | 18 June 2026 |
| Last updated at source | 24 July 2026 |
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