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 from
