Study Quantifies CPU-Only Scaling Laws for Edge Language and Vision Models

Global: Study Quantifies CPU-Only Scaling Laws for Edge Language and Vision Models

Researchers have released a systematic benchmark that evaluates how large language models (LLMs) and vision‑language models (VLMs) perform on central‑processing‑unit (CPU)‑only hardware typical of edge devices. The study, posted on arXiv in December 2025, measures computational load, memory usage, and energy consumption while varying input text length and image resolution, aiming to clarify performance trade‑offs for local inference.

Benchmark Methodology

The authors employed continuous sampling of processor and memory metrics combined with area‑under‑curve (AUC) integration to capture resource utilization over time. This unified approach allowed direct comparison across workloads and hardware platforms, ensuring that reported scaling relationships reflect sustained inference behavior rather than isolated snapshots.

CPU Platforms Tested

Two representative CPU tiers were selected: a MacBook Pro M2, reflecting mainstream laptop‑class deployment, and a Raspberry Pi 5, illustrating constrained, low‑power embedded environments. Both devices rely exclusively on their central processors, without dedicated graphics accelerators, to execute the models.

Scaling Law for Language Models

Analysis of LLM inference revealed an approximately linear relationship between computational cost and token length. As the number of input tokens increases, processor cycles and memory demands rise proportionally, confirming a predictable scaling pattern that can inform workload sizing on edge CPUs.

Scaling Behavior of Vision‑Language Models

Vision‑language models displayed a distinct “resolution knee.” Above an internal resolution clamp, compute remains relatively constant regardless of image size, while below the clamp, computational demand drops sharply. This behavior suggests that preprocessing images to an optimal resolution can significantly reduce CPU load without sacrificing model performance.

Impact of Quantum‑Inspired Compression

The study introduced a quantum‑inspired compression technique that reduced processor and memory usage by up to 71.9% and lowered energy consumption by up to 62%, while preserving or even improving semantic accuracy. These gains demonstrate that model compression can serve as a low‑cost lever for sustainable edge inference.

Implications for Edge AI

By quantifying these scaling laws, the research provides developers with concrete metrics to balance accuracy, latency, and power constraints on CPU‑only devices. The findings support strategic decisions such as selecting appropriate token lengths, optimizing image resolution, and applying compression to achieve efficient on‑device AI.

Future Directions

The authors note that further work is needed to validate the observed patterns across additional hardware configurations and to explore the interaction of compression techniques with emerging model architectures. Extending the benchmark to include real‑world application scenarios could refine guidance for industry practitioners.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.