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arXiv cs.CL
arXiv cs.CL
7/8/2026
Benchmarking KV-Cache Optimizations across Task Quality and System Performance for Long-Context Serving

Benchmarking KV-Cache Optimizations across Task Quality and System Performance for Long-Context Serving

Short summary

This paper benchmarks four KV-cache optimization mechanisms—KIVI, TurboQuant, SnapKV, and CaM—across quantization, pruning, and merging strategies using Llama-3.1-8B and Mistral-7B on QA, few-shot, and summarization workloads. Results show compression ratio alone poorly predicts end-to-end performance: KIVI4 offers stable quality, SnapKV excels at long-context throughput, and CaM shows high workload sensitivity. The findings recommend workload-aware selection of KV-cache mechanisms rather than one-size-fits-all compression for long-context serving deployments.

  • •Benchmarks KIVI, TurboQuant, SnapKV, and CaM across multiple LLMs and task types
  • •Compression ratio alone is a poor predictor of real serving performance
  • •Workload-aware KV-cache selection outperforms one-size-fits-all approaches

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