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원문 (영어)
Count the Work Done. Not Just the Cores. August 20, 2026 Published Data Center Hide Show Image --> Server room data center - 3d rendering For agentic AI, infrastructure should be measured by the work it reliably completes—per dollar, per watt and per rack.
--> Share In this article: By: Karin Eibschitz Segal The debate about CPUs for Agentic AI has become too narrow. Much of the industry conversation centers on single-thread performance, core density and rack throughput. All three matter. But none alone answers the question customers ultimately have: How much useful agent work can my infrastructure reliably complete within the latency, cost and power limits my business requires?
Core count tells customers how much compute capacity goes into a system. Agentic AI requires a measure of how effectively the whole system converts that capacity into completed work. Concurrency changes the answer A single agent loop makes performance look straightforward: finish each step faster and the workflow moves faster.
Production systems run many agents at once, competing for CPU, memory, I/O and accelerator resources. Under that load, the bottleneck changes. In a recent public analysis using 739 anonymized Claude Code conversations—coding-agent workflows involving multi-turn reasoning, code generation and tool use—CPU-side processing and wait time rose from less than 1% of total latency for a single request to as much as 15% at 32 concurrent requests.
Most of that increase came from scheduling and queueing— not computation. A faster core can execute work faster. It cannot make a queue disappear. Waiting also reaches the most expensive part of the system. For code-execution agents, the same research estimates that GPUs perform useful work during only 50% to 60% of wall-clock time.
원문: Intel Newsroom — "Count the Work Done. Not Just the Cores." (2026-08-20) 공식 원문: https://newsroom.intel.com/opinion/count-the-work-not-just-the-cores
