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  1. Home
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  3. /ModelEngine-Group/unified-cache-management
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repoGitHubTrust 82 · PrimaryPublished 15h agoLive · 15h ago

ModelEngine-Group/unified-cache-management

Persist and reuse KV Cache to speedup your LLM.

Lineage graph

Paper → model → repo connections mined from source citations (Tier-1 exact match).

Covers

newsI mapped which local LLMs actually fit each RAM tier, 8 to 128GB (open dataset)newsI compiled LLM inference pricing across 7 providers — the caching numbers are surprising(spreadsheet included) [R]newsBiggest, baddest model to fill 144GB VRAM + 120GB RAM to the brim, regardless of speednewsBest tps can I get with Qwen3.5 122B on 32GB VRAM + 64GB RAM?

Related to

tutorialEvaluate a model properly

Related across the graph

newsI compiled LLM inference pricing across 7 providers — the caching numbers are surprising(spreadsheet included) [R]newsI mapped which local LLMs actually fit each RAM tier, 8 to 128GB (open dataset)newsBest tps can I get with Qwen3.5 122B on 32GB VRAM + 64GB RAM?tutorialEvaluate a model properlynewsBiggest, baddest model to fill 144GB VRAM + 120GB RAM to the brim, regardless of speed
Knowledge path·NI compiled LLM inference pricing across 7 providers — the caching numbers are surprising(spreadsheet included) [R]→NI mapped which local LLMs actually fit each RAM tier, 8 to 128GB (open dataset)→NBest tps can I get with Qwen3.5 122B on 32GB VRAM + 64GB RAM?→RModelEngine-Group/unified-cache-management

Topics

ascendcudadeepseekdramgpuhbmkvcachellmnfsnpu

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Graph trust82Primary
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