Abstract
The new model introduces a time-aware approach to TCO, transforming workload economics by dramatically reducing CPU utilization, shortening execution times, and cutting energy consumption. Purpose-built for data compression and decompression, the device under test also enables advanced workloads—such as Retrieval-Augmented Generation (RAG)—to run AI inference at scale with lower latency and higher throughput. By accelerating both document retrieval and the compression/decompression of underlying databases, this hardware-based accelerator delivers a faster and energy-efficient workhorse for datacentric computing. It transforms efficiency into a strategic advantage, unlocking performance and scalability for enterprise and hyperscale environment