{"ID":2847871,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.26136","arxiv_id":"2510.26136","title":"Beyond Benchmarks: The Economics of AI Inference","abstract":"The inference cost of Large Language Models (LLMs) has become a critical factor in determining their commercial viability and widespread adoption. This paper introduces a quantitative ``economics of inference'' framework, treating the LLM inference process as a compute-driven intelligent production activity. We analyze its marginal cost, economies of scale, and quality of output under various performance configurations. Based on empirical data from WiNEval-3.0, we construct the first ``LLM Inference Production Frontier,'' revealing three principles: diminishing marginal cost, diminishing returns to scale, and an optimal cost-effectiveness zone. This paper not only provides an economic basis for model deployment decisions but also lays an empirical foundation for the future market-based pricing and optimization of AI inference resources.","short_abstract":"The inference cost of Large Language Models (LLMs) has become a critical factor in determining their commercial viability and widespread adoption. This paper introduces a quantitative ``economics of inference'' framework, treating the LLM inference process as a compute-driven intelligent production activity. We analyze...","url_abs":"https://arxiv.org/abs/2510.26136","url_pdf":"https://arxiv.org/pdf/2510.26136v1","authors":"[\"Boqin Zhuang\",\"Jiacheng Qiao\",\"Mingqian Liu\",\"Mingxing Yu\",\"Ping Hong\",\"Rui Li\",\"Xiaoxia Song\",\"Xiangjun Xu\",\"Xu Chen\",\"Yaoyao Ma\",\"Yujie Gao\"]","published":"2025-10-30T04:49:27Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}