analiza aiqbench
KV-Repurpose introduces a novel testbed for segment-wise KV-cache reuse, demonstrating significant efficiency gains in token-level cache hit rates over traditional prefix caching methods. This research directly addresses bottlenecks in large language model serving by enabling better utilization of memory when retrieved documents appear after varying system prompts or conversation histories. The findings are critical for optimizing inference latency and throughput in Retrieval-Augmented Generation (RAG) workloads without compromising output quality.
KV-Repurpose: A Testbed for Evaluating Non-Prefix KV-Cache Reuse
arXiv:2610.13060v1 Announce Type: new Abstract: KV-cache reuse in large language model serving is commonly limited to exact prompt prefixes, preventing reuse when the same retrieved document appears after different system prompts, conversation histories, or dynamic metadata. We present KV-Repurpose, an open-source, vLLM-based system for developing and evaluating segment-wise KV-cache reuse beyond exact prefixes. The system combines cache-aware segment scheduling, prompt construction, capacity management, workload generation, and runtime monitoring. Experiments on long-context benchmarks show that naive segment reuse largely preserves output quality for single-document question answering and summarization, while exposing clear limitations in cross-document reasoning. Under a Zipfian RAG-style workload in which 20% of requests share a fixed prefix, KV-Repurpose achieves a substantially higher token-level cache hit rate than prefix caching at the same 30 GB cache capacity, 63.9% versus 3.3%, resulting in lower TTFT. Even with 1 TB of cache capacity, prefix caching reaches only a 17.9% hit rate in this workload. Source code is available under the Apache-2.0 license at https://github.com/taiga333/eval-kv-repurpose.
Abstract:KV-cache reuse in large language model serving is commonly limited to exact prompt prefixes, preventing reuse when the same retrieved document appears after different system prompts, conversation histories, or dynamic metadata. We present KV-Repurpose, an open-source, vLLM-based system for developing and evaluating segment-wise KV-cache reuse beyond exact prefixes. The system combines cache-aware segment scheduling, prompt construction, capacity management, workload generation, and runtime monitoring. Experiments on long-context benchmarks show that naive segment reuse largely preserves output quality for single-document question answering and summarization, while exposing clear limitations in cross-document reasoning. Under a Zipfian RAG-style workload in which 20% of requests share a fixed prefix, KV-Repurpose achieves a substantially higher token-level cache hit rate than prefix caching at the same 30 GB cache capacity, 63.9% versus 3.3%, resulting in lower TTFT. Even with 1 TB of cache capacity, prefix caching reaches only a 17.9% hit rate in this workload. Source code is available under the Apache-2.0 license at this https URL.
Computation and Language (cs.CL)
arXiv:2610.13060 [cs.CL]
(or arXiv:2610.13060v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.13060 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Text integral sub licența deschisă a publicației · 254 cuvinte
Subiecte conexe
Composable CXL Memory as a Kubernetes-Native Shared Memory for LLM Serving
arXiv · 11 sept. 2026

Get started with OpenAI GPT-5.6 Sol, Terra, and Luna on Amazon Bedrock
AWS · 24 iul. 2026

Informe de riesgos de la IA de frontera (febrero–marzo de 2026)
METR · 19 mai 2026

Frontier AI Risk Report (February–March 2026)
METR · 19 mai 2026