Inference economics of language models

Ege Erdil

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arXiv · 2506.04645

Inference economics of language models

Ege Erdil

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Abstract

We develop a theoretical model that addresses the economic trade-off between cost per token versus serial token generation speed when deploying LLMs for inference at scale. Our model takes into account arithmetic, memory bandwidth, network bandwidth and latency constraints; and optimizes over different parallelism setups and batch sizes to find the ones that optimize serial inference speed at a given cost per token. We use the model to compute Pareto frontiers of serial speed versus cost per token for popular language models.

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Ege Erdil. 2025-06-05. Inference economics of language models. https://arxiv.org/abs/2506.04645

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