Beyond Benchmarks: The Economics of AI Inference
This provides an economic basis for model deployment decisions and market-based pricing of AI inference resources, addressing a critical factor in the commercial viability of LLMs.
The paper tackles the problem of high inference costs for Large Language Models (LLMs) by introducing an economic framework, analyzing marginal cost and economies of scale using empirical data from WiNEval-3.0, and revealing principles like diminishing marginal cost and an optimal cost-effectiveness zone.
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.