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title: 'Benchmarking: cost, throughput, latency, and energy'
seo_title: 'Benchmarking: cost, throughput, latency, and energy — video'
description: An inference benchmark is comparable only when workload, measurement boundary, and hardware are fixed, and latency distribution, useful throughput, cost, and energy are reported.
keywords: LLM inference benchmark, TTFT, TPOT, ITL, throughput, goodput, cost per task, energy per inference, MLPerf, AIPerf, vLLM
date: '2026-09-12T00:00:00+00:00'
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    <h1>Benchmarking: cost, throughput, latency, and energy</h1><p>An inference benchmark is comparable only when workload, measurement boundary, and hardware are fixed, and latency distribution, useful throughput, cost, and energy are reported.</p>
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    <div class="s5-video-watch__section-head"><span class="s5-eyebrow">Video summary</span><h2 id="video-summary-title">The ideas to retain</h2></div>
    <div class="s5-video-watch__snippet-grid"><article><span>01</span><h2>A benchmark is a protocol, not a scalar</h2><p>An interpretable result needs a contract that fixes at least:</p></article>
<article><span>02</span><h2>The workload should resemble the problem you are trying to solve</h2><p>An average of 512 input tokens and 128 output tokens is not a distribution.</p></article>
<article><span>03</span><h2>Request rate and concurrency describe different load models</h2><p>A benchmark configured only with concurrency=64 commonly behaves like a closed loop: when one request completes, another fills the slot. A benchmark driven by a fixed or stochastic arrival…</p></article></div>
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<li><a href="?t=12" data-s5-video-seek="12"><time>0:12</time><span>A common boundary makes metrics comparable</span></a></li>
<li><a href="?t=24" data-s5-video-seek="24"><time>0:24</time><span>Acceptance requires SLO, cost, and constraints together</span></a></li></ol></section>
  
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  <section class="s5-video-watch__related" aria-labelledby="related-videos-title"><div class="s5-video-watch__section-head"><span class="s5-eyebrow">Next step</span><h2 id="related-videos-title">Related videos</h2></div><div class="s5-video-watch__related-grid"><article><a href="https://5sigmas.com/en/videos/series/evaluating-ai-systems-production/06-observability-failure-taxonomies-production-eval-repair-feedback-loops/"><img src="https://5sigmas.com/en/series/evaluating-ai-systems-production/06-observability-failure-taxonomies-production-eval-repair-feedback-loops.jpg" alt="" loading="lazy" width="1280" height="720"><span>Other topics · 0:36</span><strong>Observability, failure taxonomies, and feedback loops</strong></a></article>
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