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

arXiv:2609.14864 · cs.AI, cs.LG, cs.PF · Submitted 2026-09-14 · Read on arXiv

cs.AI, cs.LG, cs.PF

Submitted: 2026-09-14

Updated: 2026-09-14

Comments: 27 pages: 5-page manuscript with 4 figures, followed by a 22-page supplementary results appendix. Submitted to ICASSP 2027

Code: https://github.com/ggml-org/llama.cpp

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Mac Studio M4 Max — 199× in this paper
• Decode — 83× in this paper · explained in 2 episodes
• Quantization — 16× in this paper · explained in 12 episodes
• Calibration — 15× in this paper · explained in 7 episodes
• Accuracy — 4× in this paper · explained in 1 episode
• Benchmarking — 4× in this paper · explained in 3 episodes
• Provenance — 3× in this paper · explained in 3 episodes
• Benchmark — 2× in this paper · explained in 2 episodes
• Trace — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Core — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Hybrid Architectures — 1× in this paper · explained in 3 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Mixture of Experts — 1× in this paper · explained in 2 episodes
• Mixture-of-Experts (MoE) — 1× in this paper · explained in 7 episodes
• Mixture-of-Experts (MoE) Models — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes

Sources

• RooflineBench: A Benchmarking Framework for On-Device LLMs via Roofline Analysis
• LIMINAL: Exploring The Frontiers of LLM Decode Performance

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