Persistent Memory Logic Loop (PMLL) Architecture: Memory Footprint Reduction in Large Language Models

Large-scale Transformer-based language models (LLMs) such as GPT-3 and GPT-4 require substantial memory resources, with GPT-3's 175 billion parameters demanding approximately 356 GB at 16-bit precision. This memory burden stems from dense weight matri

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Published
2025-08-25
DOI
https://doi.org/10.36227/techrxiv.175613226.63459477/v1
Primary Topic
Topic Modeling
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article
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article

Persistent Memory Logic Loop (PMLL) Architecture: Memory Footprint Reduction in Large Language Models

J. Edwards
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Topic Modeling
article

Persistent Memory Logic Loop (PMLL) Architecture: Memory Footprint Reduction in Large Language Models

J. Edwards
article en

Abstract

Large-scale Transformer-based language models (LLMs) such as GPT-3 and GPT-4 require substantial memory resources, with GPT-3's 175 billion parameters demanding approximately 356 GB at 16-bit precision. This memory burden stems from dense weight matri

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Topic Modeling
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Persistent Memory Logic Loop (PMLL) Architecture: Memory Footprint Reduction in Large Language Models — J. Edwards · (2025) | TGRS Research Map | TGRS