Beyond Benchmarks: The Economics of AI Inference

📝 Original Info

• Title: Beyond Benchmarks: The Economics of AI Inference
• ArXiv ID: 2510.26136
• Date: 2025-10-30
• Authors: 정보가 제공되지 않았습니다. (논문에 명시된 저자 정보를 입력해 주세요.)

📝 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.

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