Beyond Benchmarks: The Economics of AI Inference

TL;DR

This work addresses the economic bottleneck of LLM inference by proposing an economics-of-inference framework that treats inference as an intelligent production process with a production function $Intelligence = f(\text{Cost}, \text{Model})$. Using WiNEval-3.0 data, it derives the LLM Inference Production Frontier, identifying diminishing marginal cost, diminishing returns to scale, and an optimal cost-effectiveness zone. The authors define a cost-quality Pareto frontier and provide practical methods to estimate hourly GPU cost and inference cost, culminating in a data-driven deployment tool that balances performance, quality, and cost under concurrency constraints. The framework is designed to be portable across hardware and cloud platforms and offers a foundation for market-based pricing and optimization of AI inference resources, shifting industry focus from parameter chasing to deployment efficiency.

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.

Figures (1)

• Figure 1: Model Quality vs. Inference Cost - 3D Pareto Frontier