AI Research
Optimal Model Activation Policies for Inference Networks of Large Language Models
Advancing cost-efficiency in multi-expert inference architectures
Recent advances in large language models (LLMs) have rendered them necessary for Natural Language Processing (NLP) tasks, and their high inference cost motivates the study of cost-performance trade-offs. In practice, several expert LLMs are used in synergy for inference, either i...
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ArXiv CS.CL
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ArXiv CS.CL
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Optimizing activation policies allows for smarter resource allocation, significantly reducing the compute overhead required to run complex multi-expert models. This shift represents a critical step toward making high-performance AI deployment both economically sustainable and technically efficient.
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