Benchmarking KV-Cache Optimizations across Task Quality and System Performance for Long-Context Serving
arXiv:2607.05399v1 Announce Type: cross Abstract: 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
The increasing complexity of large language models and their deployment in real-world applications necessitates more efficient serving mechanisms, especially for long-context workloads.
Optimizing KV-cache performance directly impacts the cost, latency, and capabilities of AI serving infrastructure, accelerating AI adoption and economic integration.
The ability to benchmark and compare different KV-cache optimization techniques will lead to more efficient and scalable deployment of long-context AI models, impacting service providers and end-users.
- · Cloud AI providers
- · Large Language Model developers
- · AI infrastructure companies
- · Inefficient AI serving architectures
- · High-cost data centers
Improved cost-effectiveness and performance of long-context large language models in production.
Faster innovation cycles for AI applications requiring significant contextual understanding and memory.
Enhanced accessibility and commercial viability of advanced AI capabilities across various industries due to reduced operational costs.
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Read at arXiv cs.AI