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GGUF-Metadata Prediction of Single-Sequence llama.cpp Throughput Across Three Systems

2026-09-14 00:31:14
Xinyu Qiu, Chuhong Xu, Bo Su, Ziyao Chen, Ruiyang Xu, Shimeng Dai
arXiv_AI
     
Abstract

We predict single-sequence model throughput from GGUF metadata using roofline-shaped predictors with quantization-specific scale factors fitted on reference models. The scored cohort comprises 318 phase-depth measurements from 53 host-file configurations on two Apple M4 Max systems and an NVIDIA RTX 5080. On host-specific held-out sets of four, five, and two configurations, an active-parameter decode model obtains 13.1%, 14.4%, and 36.1% mean absolute percentage error (MAPE), versus 49.4%, 55.3%, and 51.9% when charging total parameters. Leave-one-host-out coefficients fitted on the other two systems yield 11.6%, 16.8%, and 36.0% test MAPE. A low-bit model ladder changes ordering across runtime stacks. The P2 prefill baseline gives 18.7%, 22.2%, and 108.2% test MAPE. GGUF structure helps on all three systems, but fitted efficiencies are not universal.

Abstract (translated)

URL

https://arxiv.org/abs/2609.14864

PDF

https://arxiv.org/pdf/2609.14864.pdf

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