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llama.cpp introduces moe-cache for improved expert prioritization

By PulseAugur Editorial · [2 sources] ·

The llama.cpp project has released version b12170, introducing a new "moe-cache" system. This hybrid cache, inspired by LRFU and MQ, aims to prioritize frequently used experts within different classes, ranked by decaying usage history. Initial benchmarks show performance improvements, with a warm cache yielding a 6-7% decode speed increase and 8% fewer uploads compared to the previous SLRU cache. AI

IMPACT This new caching mechanism could improve inference speed and efficiency for local LLM deployments.

RANK_REASON This is a software release for an open-source project focused on LLM inference optimization, detailing a new caching mechanism.

Read on r/LocalLLaMA →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

llama.cpp introduces moe-cache for improved expert prioritization

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9 / 100
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Research
This is a software release for an open-source project focused on LLM inference optimization, detailing a new caching mechanism.
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2 independent sources
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infra, product
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Same-day
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COVERAGE [2]

  1. llama.cpp — Releases TIER_1 English(EN) · sanmai ·

    b12170: moe-cache : LRFU/MQ-like hybrid cache, ClassLRU (#31233)

    <ul> <li> <p>moe-cache : LRFU/MQ-inspired hybrid cache</p> </li> <li> <p>make the clock advance by N tokens: use the real token count (ne[1] is always 1, ids have n_expert_used entries per token)</p> </li> <li> <p>sel is a RESHAPE node, the orig input is in src[0], so sel-&gt;src…

  2. r/LocalLLaMA TIER_1 English(EN) · /u/pmttyji ·

    moe-cache : LRFU/MQ-like hybrid cache, ClassLRU by sanmai · Pull Request #31233 · ggml-org/llama.cpp

    <table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1xpfvfz/moecache_lrfumqlike_hybrid_cache_classlru_by/"> <img alt="moe-cache : LRFU/MQ-like hybrid cache, ClassLRU by sanmai · Pull Request #31233 · ggml-org/llama.cpp" src="https://external-preview.redd.it/7Q7…

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