Token-Operations-Oriented Inference Optimization Techniques for Large Models

2026-06-18

AI Summary

This work addresses the high inference costs, low service efficiency, and insufficient stability of large language models by proposing the first token-centric four-layer inference optimization framework. The architecture integrates multi-model fusion, model compression and quantization, compute-model co-optimization, and joint scheduling across computation, networking, and modeling. By systematically combining these key techniques, the framework substantially reduces the cost per generated token while significantly enhancing service efficiency and supply stability. It provides a holistic, efficient, stable, and cost-effective solution that enables large models to transition from being merely callable to truly operable at scale, thereby supporting their widespread deployment in real-world applications.

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.

Problem

• large model inference
• token operations
• inference optimization
• service stability
• cost reduction

Innovation

• token-oriented inference
• four-layer architecture
• model optimization
• compute-model fusion
• large model services