aiqbench takeMachine Learning

This paper introduces a novel four-layer architecture for token-oriented inference optimization aimed at reducing costs and improving efficiency in large model services. By systematically reviewing technologies across multi-model fusion to compute-network integration, it offers a practical path toward making LLM operations more stable and scalable rather than just callable. The work addresses critical infrastructure challenges essential for the sustainable deployment of generative AI systems.

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Token-Operations-Oriented Inference Optimization Techniques for Large Models

arXivJuly 27, 20262 min read

arXiv:2606.20295v2 Announce Type: replace-cross 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.

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.

Software Engineering (cs.SE); Computation and Language (cs.CL)

arXiv:2606.20295 [cs.SE]

(or arXiv:2606.20295v2 [cs.SE] for this version)

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