arXiv:2606.03092v1 Announce Type: new Abstract: Inference-time scaling has emerged as a critical avenue for enhancing Large Language Models' performance, yet real-world deployment is constrained by strict computational budgets. In this work, we formulate inference budget allocation as a global constrained optimization problem governed by economic principles. By modeling per-query reasoning utility with a shifted-surge function, we derive an optimal allocation policy based on a global shadow price that equilibrates marginal utility under resource scarcity. Based on this theory, we propose Const
The proliferation of advanced LLMs and their increasing deployment in real-world applications highlights the urgent need to optimize their operational costs and resource allocation.
Optimal budget allocation for LLMs directly impacts their economic viability and scalability, influencing the pace of AI adoption and market dynamics.
The focus is shifting from pure performance to economically rational, resource-constrained inference, driving innovation in cost-effective AI solutions.
- · AI developers focused on efficiency
- · Cloud providers with optimized inference services
- · Enterprises deploying LLMs at scale
- · Inefficient LLM architectures
- · Developers ignoring inference costs
- · Companies with unrestricted compute budgets
Further research and development will concentrate on inference-time scaling and cost optimization techniques for large language models.
The economic principles derived could influence the design of future AI hardware, prioritizing efficiency over raw computational power.
This could lead to a 'democratization' of advanced AI by lowering operational barriers for smaller entities through more efficient resource use.
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Read at arXiv cs.AI