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LLM working set does not saturate, experiment finds

By PulseAugur Editorial · [1 sources] ·

An experiment investigating the memory usage of language models revealed that the working set of data required by these models does not saturate as previously assumed. Contrary to expectations, the amount of resident memory needed continues to increase even after processing a significant number of tokens. This finding challenges the idea that a small, fixed AI

IMPACT Challenges assumptions about LLM memory efficiency, suggesting higher resident memory requirements than previously thought.

RANK_REASON Research paper detailing experimental findings on LLM memory usage. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

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

LLM working set does not saturate, experiment finds

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Research paper detailing experimental findings on LLM memory usage. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Seth Wheeler ·

    The Working Set That Never Saturated

    <p>Every experiment in this series had charged each byte of a count table as resident memory. That is a real assumption and nobody had examined it, which I noticed only after reading someone else's project: a third-party runtime that runs 35B and 80B mixture-of-experts models on …

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