en.Wedoany.com Reported - China Unicom Digital Intelligence, in collaboration with Tsinghua University, Peking University, Zhejiang Lab, and multiple other universities, research institutions, and industry partners, released the industry's first white paper on "Large Model Inference Optimization Technology for Token Operations."

The white paper proposes, for the first time, a four-layer inference technology architecture: "Multi-Model Fusion—Model Optimization—Model-Compute Fusion—Compute-Network-Model Fusion." It systematically reviews key technologies and industry status at each layer, and analyzes the application value of these technologies in real business scenarios. These technologies aim to reduce token production costs, improve token service efficiency, ensure token supply stability, and drive large model services from "callable" to "operable."

At the multi-model fusion layer, it involves quantifying model capability boundaries, intelligent routing, model cascading, and model ensemble, addressing how to match different requests with appropriate models.

At the model optimization layer, it involves low-complexity attention mechanisms, mixture-of-experts architecture optimization, speculative decoding, model quantization, model distillation, chain-of-thought optimization, memory management, key-value cache compression, and diffusion model generation path optimization, aiming to reduce the cost per token generation.

At the model-compute fusion layer, it involves operator fusion, memory access optimization, basic operator acceleration, engine parameter tuning, and model architecture adaptation, to improve execution efficiency between models and hardware.

At the compute-network-model fusion layer, it involves multi-machine inference parallelism, dynamic batching, cache reuse, sticky session routing, load balancing, rate limiting, and degradation, to ensure stable supply of large-scale token services.