aiqbench analysis

Reusing cached attention state from flash storage instead of recomputing it could reduce wasted GPU cycles during LLM inference, particularly for long-context and multi-turn workloads. The benefit depends on storage bandwidth, cache hit rates, and serving architecture, so teams should benchmark against their own latency and cost targets. For AI infrastructure readers, the key question is whether storage-tier caching meaningfully lowers inference cost per token.