{"slug": "benchmarking-kv-cache-optimizations-across-task-quality-and-system-performance", "title": "Benchmarking KV-Cache Optimizations across Task Quality and System Performance for Long-Context Serving", "summary": "A new benchmark evaluates KV-cache optimization techniques—quantization, pruning, and merging—for long-context LLM serving, finding that compression ratio alone poorly predicts end-to-end performance. KIVI4 offers stable quality, SnapKV boosts throughput, and CaM improves QA but varies by workload, urging workload-aware selection.", "body_md": "arXiv:2607.05399v1 Announce Type: new\nAbstract: 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-token, and realized compression ratio across context-length buckets. The results show that the compression ratio alone is a poor predictor of end-to-end performance. KIVI4 provides the most stable quality across models, SnapKV delivers the strongest long-context throughput, and CaM yields large gains on selected QA workloads but exhibits substantial workload sensitivity in both quality and realized compression ratio. These findings motivate workload-aware selection of KV-cache mechanisms rather than one-size-fits-all compression and provide deployment guidance for long-context serving systems.", "url": "https://wpnews.pro/news/benchmarking-kv-cache-optimizations-across-task-quality-and-system-performance", "canonical_source": "https://arxiv.org/abs/2607.05399", "published_at": "2026-07-08 04:00:00+00:00", "updated_at": "2026-07-08 04:02:50.140436+00:00", "lang": "en", "topics": ["large-language-models", "ai-infrastructure", "ai-research"], "entities": ["KIVI", "TurboQuant", "SnapKV", "CaM", "Llama-3.1-8B-Instruct", "Mistral-7B-Instruct-v0.3", "LongBench"], "alternates": {"html": "https://wpnews.pro/news/benchmarking-kv-cache-optimizations-across-task-quality-and-system-performance", "markdown": "https://wpnews.pro/news/benchmarking-kv-cache-optimizations-across-task-quality-and-system-performance.md", "text": "https://wpnews.pro/news/benchmarking-kv-cache-optimizations-across-task-quality-and-system-performance.txt", "jsonld": "https://wpnews.pro/news/benchmarking-kv-cache-optimizations-across-task-quality-and-system-performance.jsonld"}}