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New method predicts llama.cpp throughput using GGUF metadata

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a method to predict the single-sequence throughput of llama.cpp, a popular framework for running large language models, using GGUF metadata. This approach employs roofline-shaped predictors with quantization-specific scale factors, fitted on reference models. The study tested 318 measurements across 53 configurations on two Apple M4 Max systems and an NVIDIA RTX 5080, achieving a mean absolute percentage error (MAPE) as low as 11.6% in some leave-one-host-out tests. The findings indicate that GGUF structure aids performance prediction across different systems, though fitted efficiencies are not universally applicable. AI

IMPACT Provides a method for optimizing the deployment and performance tuning of large language models on local hardware.

RANK_REASON Academic paper detailing a new methodology for predicting model throughput. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New method predicts llama.cpp throughput using GGUF metadata

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Academic paper detailing a new methodology for predicting model throughput. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Xinyu Qiu, Chuhong Xu, Bo Su, Ziyao Chen, Ruiyang Xu, Shimeng Dai ·

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

    arXiv:2609.14864v1 Announce Type: new 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 ho…

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