Modern AI breakthroughs rely on advanced infrastructure that powers real-time services while supporting intelligent IoT and consumer devices at the edge. This infrastructure serves as the engine for continuous intelligence in applications ranging from healthcare research to customer service systems.
In the inference-driven AI landscape, every delay, bottleneck, or inefficient use of power directly impacts human outcomes and operating costs. Traditional approaches of optimizing performance, latency, memory bandwidth, storage throughput, and networking independently no longer work.
Inference workloads are continuous, geographically distributed, and highly sensitive to response times. This requires systems to be designed for scale, resilience, and efficiency from the outset rather than treating these concerns as afterthoughts.