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October 10, 2025Open Access

MIRAGE: KV Cache Optimization through Parameter Remapping for Multi-tenant LLM Serving

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Authors

RLRuihao LiSPShagnik PalVPVineeth Narayan Pullu

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Overview

Observational analysis shows MIRAGE reduces latency in multi-tenant LLM environments, suggesting better memory efficiency.

Key Points

  • MIRAGE reduces tail time-between-token latency by up to 82.5%, improving user experience significantly.
  • The approach leverages parameter remapping to reclaim memory for KV cache, addressing dynamic cache swapping issues.
  • Using modern hardware like the NVIDIA Grace Hopper Superchip, MIRAGE achieves higher throughput for concurrent model serving.
  • MIRAGE is particularly advantageous in multi-tenant scenarios, optimizing memory usage across inactive models.

Cite This Study

Li et al. (2025) studied this question.

synapsesocial.com/papers/68e861857ef2f04ca37e398chttps://doi.org/10.48550/arxiv.2507.11507
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Elastic Memory Remapping for Multi-tenant LLM Serving2026
  2. 2Model Tells You Where to Merge: Adaptive KV Cache Merging for LLMs on Long-Context Tasks2024 · 1 citations
  3. 3Scaling Long-Context LLMs via Unified KV Cache Optimization: A Comparative Study of Paged Attention and Quantization2026
  4. 4AdaptiveKV: Accelerating KV Cache Offloading with a Bandwidth-Adaptive Memory Allocation Mechanism2026
  5. 5Accelerating LLM Inference via Dynamic KV Cache Placement in Heterogeneous Memory System2025