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KV Cache Placement Strategies Explored for LLM Memory Efficiency

By PulseAugur Editorial · [1 sources] ·

A new research paper explores optimal placement strategies for KV caches across different memory tiers (GPU HBM, CPU DRAM, SSD) to manage scarce GPU memory. The study, conducted using a discrete event simulator, found that tiering memory can support significantly more concurrent sessions and reduce costs, though the placement policy itself had a minimal impact on throughput. Different policies like recency, reuse frequency, and EWMA were evaluated, with reuse frequency performing best for agent and document question answering workloads, while recency was better for chat. AI

IMPACT Optimizing KV cache placement can significantly increase LLM session capacity and reduce operational costs.

RANK_REASON Research paper on optimizing LLM inference infrastructure. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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

KV Cache Placement Strategies Explored for LLM Memory Efficiency

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Research paper on optimizing LLM inference infrastructure. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Srikanta Datta Tumkur, Jay Iyer, Mehar Simhadri, Sai Pavan Kumar, Sai Kapil Kumar, Ramesh Nampelly ·

    Where Should the KV Cache Live? Placement Policies Across GPU, CPU, and SSD for Long-Lived Sessions

    arXiv:2609.16215v1 Announce Type: new Abstract: GPU high bandwidth memory is scarce and expensive, and KV caches consume much of it as chats, agent loops, and document question answering accumulate state. Systems such as Mooncake, LMCache, FlexGen, InfiniGen, and AttentionStore e…

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