AI infrastructure teams struggle to optimize chip selection between compute and storage performance for Agent workloads

AI infrastructure decision-makers and chip investors are uncertain about which metrics (tokens/s vs. memory bandwidth vs. latency) actually matter for Agent-based systems, leading to suboptimal hardware investments and deployment bottlenecks. Current benchmarking approaches focus on throughput alone, missing the complex trade-offs required for agentic AI systems that demand both computational speed and memory efficiency.