Scaling Laws for Efficient Inference in Transformer Models

摘要

We investigate the relationship between model size and inference efficiency in transformer-based language models.

引言

Recent work on scaling laws has primarily focused on training compute optimality (Kaplan et al., 2020). However, less attention has been paid to inference efficiency across different model scales.

方法

We conduct systematic experiments across model scales from 125M to 13B parameters, measuring latency, throughput, and memory consumption under various batch sizes.

实验

Our experiments span three GPU architectures and four deployment scenarios.

结果

We discover non-linear scaling patterns that suggest optimal deployment configurations for latency-constrained applications.

讨论

The implications extend to edge deployment and real-time systems.

结论

We provide actionable guidelines for practitioners.