Beyond Benchmarks: The Economics of AI Inference
By: Boqin Zhuang , Jiacheng Qiao , Mingqian Liu and more
Potential Business Impact:
Makes AI cheaper to use and better.
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.
Similar Papers
The Price of Progress: Algorithmic Efficiency and the Falling Cost of AI Inference
Machine Learning (CS)
AI gets smarter and cheaper to use.
Cost-of-Pass: An Economic Framework for Evaluating Language Models
Artificial Intelligence
Makes AI cheaper and better for different jobs.
Sometimes Painful but Certainly Promising: Feasibility and Trade-offs of Language Model Inference at the Edge
Machine Learning (CS)
Makes smart computer programs run on phones.