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SEOUL · DAILY · ISSUE No.341 WE TRIED IT · THE PICK · BEHIND IT 2026.11.14 · SAT
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인텔, 에이전트 AI 성능 평가 기준 전환 주장

JD 젤리비 편집국 · 2026.08.20 SHARE · COPY LINK

무슨 발표인가

  • 코어 수 아닌 '완료한 작업'으로 성능 측정
  • 비용·전력·랙당 신뢰성 있는 작업량 중심
  • 동시 실행 환경에서 병목이 변함

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

#Intel Newsroom
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인텔, 에이전트 AI 성능 평가 기준 전환 주장

젤리비 편집국·2026.08.20·5 MIN
IN THIS PIECE
무슨 발표인가 원문 (영어)

무슨 발표인가

  • 코어 수 아닌 '완료한 작업'으로 성능 측정
  • 비용·전력·랙당 신뢰성 있는 작업량 중심
  • 동시 실행 환경에서 병목이 변함

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

#Intel Newsroom
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