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Abstract

Executive Summary
As enterprises scale generative AI and agentic AI deployments, a critical infrastructure challenge is emerging:the rapid growth of inference state is outpacing GPU memory capacity, creating a bottleneck that directly impacts service quality and cost.Long-context workloads, including multi-turn assistants, retrieval-augmented generation (RAG) applications, and autonomous agent pipelines, generate large volumes of key-value (KV) cache data that must be retained across requests.When GPU memory is exhausted, inference platforms are forced to discard this cached context and recompute it fromAbstract
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