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Towards Uncertainty-Aware Low-Bit Quantized LLMs for On-Device Inference 

Sparrenberg, Lorenz; Schneider, Tobias; Deußer, Tobias; Berger, Armin; Sifa, Rafet (2026-03-06)
Quantizing large language models (LLMs) significantly reduces memory usage and computational requirements, enabling efficient on-device inference. However, aggressive quantization can degrade model performance and exacerbate ...
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Small and Fast LLMs on Commodity Hardware: Post-Training Quantization in llama.cpp 

Sparrenberg, Lorenz; Deußer, Tobias; Berger, Armin; Sifa, Rafet (2025-11-24)
Large Language Models (LLMs) have demonstrated remarkable capabilities but their significant computational and memory demands hinder widespread deployment, especially on resource-constrained devices. Quantization, the ...

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AuthorBerger, Armin (2)
Deußer, Tobias (2)
Sifa, Rafet (2)Sparrenberg, Lorenz (2)Schneider, Tobias (1)SubjectLLM (2)classification (1)GGUF (1)GPT (1)Inference Efficiency (1)K-quants (1)Large Language Models (1)large language models (1)llama.cpp (1)Model Compression (1)... View MoreClassification (DDC)004 Informatik (2)... View MoreResource Type
Konferenzveröffentlichung (2)
... View MoreDate Issued2025 (1)2026 (1)

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