Benchmarking KV-Cache Optimizations across Task Quality and System Performance for Long-Context Serving
Large language model serving is increasingly limited by KV-cache growth under long-context workloads, yet existing KV-cache compression techniques are difficult to compare because they were evaluated on different models, tasks, budgets, and serving stacks. This paper presents a workload-aware benchmark of representative KV-cache optimization mechanisms spanning quantization, pruning, and merging, including KIVI, TurboQuant, SnapKV, and CaM, evaluated on LongBench-style multi-document QA, single-document QA, few-shot learning, and summarization workloads using Llama-3.1-8B-Instruct and Mistral-7B-Instruct-v0.3. The benchmark measures task quality, mean output throughput, mean time-to-first...
| Authors | Nikita Agrawal, Ruben Mayer |
|---|---|
| Primary category | cs.CL |
| Published | 3 May 2026 |
| Last updated at source | 3 May 2026 |
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