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LLM in a flash: Efficient Large Language Model Inference with Limited Memory paper by Independent / Community
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Paper

LLM in a flash: Efficient Large Language Model Inference with Limited Memory

by Independent / Community 2312.11514
Free2AITools Nexus Index
59.1
S: Semantic 50

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A: Authority 89
P: Popularity 66
R: Recency 100
Q: Quality 65
Tech Context
Vital Performance —

Large language models (LLMs) are central to modern natural language processing, delivering exceptional performance in various tasks. However, their substantial computational and memory requirements present challenges, especially for devices with limited DRAM capacity. This paper tackles the challenge of efficiently running LLMs that exceed the available DRAM capacity by storing the model parameters in flash memory, but bringing them on demand to DRAM. Our method involves constructing an infer...

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Semantic Scholar 196 Citations
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Registry ID 2312.11514
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Academic & Research Attribution

BibTeX 
@misc{arxiv_2312_11514,
  author = {Unknown},
  title = {LLM in a flash: Efficient Large Language Model Inference with Limited Memory Paper},
  year = {2026},
  howpublished = {\url{https://arxiv.org/abs/2312.11514}},
  note = {Accessed via Free2AITools.}
}
APA Style
Unknown. (2026). LLM in a flash: Efficient Large Language Model Inference with Limited Memory [Paper]. Free2AITools. https://arxiv.org/abs/2312.11514

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βš–οΈ Free2AITools Nexus Index V2.0

Methodology How FNI works
Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 89
Popularity (P) 66
Recency (R) 100
Quality (Q) 65

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FNI V2.0 for LLM in a flash: Efficient Large Language Model Inference with Limited Memory: Authority (A:89), Popularity (P:66), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

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πŸ“ Executive Summary

"Large language models (LLMs) are central to modern natural language processing, delivering exceptional performance in various tasks. However, their substantial computational and memory requirements present challenges, especially for devices with limited DRAM capacity. This paper tackles the challenge of efficiently running LLMs that exceed the available DRAM capacity by storing the model parameters in flash memory, but bringing them on demand to DRAM. Our method involves constructing an infer..."

❝ Cite Node 

@article{Alizadeh-Vahid2026LLM,
  title={LLM in a flash: Efficient Large Language Model Inference with Limited Memory},
  author={Keivan Alizadeh-Vahid and Iman Mirzadeh and Dmitry Belenko and Karen Khatamifard and Minsik Cho and C. C. D. Mundo and Mohammad Rastegari and Mehrdad Farajtabar},
  journal={arXiv preprint arXiv:2312.11514},
  year={2026}
}

πŸ‘₯ Collaborating Minds

Keivan Alizadeh-Vahid Iman Mirzadeh Dmitry Belenko Karen Khatamifard Minsik Cho C. C. D. Mundo Mohammad Rastegari Mehrdad Farajtabar

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πŸ“Š Research Signals

πŸ“ˆ196CitationsSemantic Scholar
πŸ›οΈ89AuthorityFNI pillar
⏱️100RecencyFNI pillar
βœ…65QualityFNI pillar
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πŸ†” Identity & Source

id
2312.11514
slug
2312.11514
source
semantic_scholar
author
Unknown
license
ArXiv
tags
paper, research, academic

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null
params billions
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