Token-Operations-Oriented Inference Optimization Techniques for Large Models

The paper introduces a novel four-layer technical architecture for optimizing inference in large models, focusing on token-oriented techniques. It includes Multi-model Fusion, Model Optimization, Compute-Model Fusion, and Compute-Network-Model Fusion, providing a comprehensive review of related technologies and their application in real-world scenarios. This approach aims to reduce token production costs and enhance service efficiency, which is crucial for practitioners aiming to improve the operational stability and scalability of large model services.