White Paper: KV Cache Offload to Improve AI Inferencing Cost and Performance

This paper explores a disaggregated key-value (KV) storage architecture designed to efficiently offload KV cache tensors for generative AI workloads. By synergizing Wiwynn's OCP ORv3-compliant servers with Pliops' hardware-accelerated data path, this framework delivers a highly scalable and cost-effective solution for AI inferencing. 

Our integrated approach ensures optimal resource allocation, significantly reducing the massive GPU memory demands inherent in multi-turn processing and long-context applications. This solution transforms inefficient, compute-heavy KV recomputations into a streamlined store-and-restore pipeline, enabling enterprises and CSPs to maintain low-latency, high-throughput inference while minimizing infrastructure CapEx and OpEx. 

See how our end-to-end KV cache offloading system overcomes the GPU memory wall to achieve 5-8x higher request throughput and 5-7x faster prefill latency compared to baseline systems. By maintaining strict SLA compliance—even with prompt prefix lengths scaling up to 9,000 tokens—we empower engineering teams to efficiently monetize AI inference models at scale. Download the whitepaper to explore our technical architecture and implement a highly scalable, cost-effective infrastructure for your demanding AI workloads.