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139 | 139 | "keywords": ["kubernetes","emulation","kind","kwok","kemu"], |
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141 | 141 | "mainEntityOfPage": "true", |
142 | | - "wordCount": "2577" |
| 142 | + "wordCount": "2543" |
143 | 143 | } |
144 | 144 | </script> |
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390 | 390 | <div class="flex flex-row flex-wrap items-center"> |
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393 | | - <time datetime="2025-11-04 00:00:00 +0000 UTC">4 November 2025</time><span class="px-2 text-primary-500">·</span><span title="Reading time">13 mins</span> |
| 393 | + <time datetime="2025-11-04 00:00:00 +0000 UTC">4 November 2025</time><span class="px-2 text-primary-500">·</span><span title="Reading time">12 mins</span> |
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448 | 448 | <section class="prose mt-0 flex max-w-full flex-col dark:prose-invert lg:flex-row"> |
449 | 449 |
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450 | 450 | <div class="min-h-0 min-w-0 max-w-prose grow"> |
451 | | - <p>Optimizing scheduling efficiency for AI workloads requires extensive experimentation and observation. |
452 | | -Extended GPU procurement lead times, often spanning months, mean existing infrastructure must be |
453 | | -maximized for utilization to avoid capacity bottlenecks. For high-end GPUs, supply constraints |
454 | | -eliminate cloud autoscaling advantages, making both cloud and on-premises environments equally |
455 | | -constrained in their ability to rapidly expand capacity on demand.</p> |
456 | | -<p>Maximizing infrastructure efficiency through optimized scheduling and high-density bin packing is |
457 | | -critical for achieving high workload throughput and resource utilization. Introducing scheduler |
458 | | -modifications at production scale is risky: configuration errors can render workloads unschedulable, |
459 | | -causing multi-day delays for dozens or hundreds of ML engineers, idling capacity, wasted on-demand |
460 | | -resources, and delayed delivery. It is imperative that scheduler modifications with a big blast |
461 | | -radius are tested and verified in a safe environment before shipping to production.</p> |
| 451 | + <p>Optimizing scheduling efficiency for AI workloads requires extensive experimentation and observation |
| 452 | +due to constrained supply and high costs of datacenter GPUs. Maximizing infrastructure efficiency through |
| 453 | +optimized scheduling and high-density bin packing is critical for achieving high workload throughput, |
| 454 | +resource utilization, and cost efficiency. Introducing scheduler modifications at production scale is risky: |
| 455 | +configuration errors can render workloads unschedulable, causing multi-day delays for dozens or hundreds |
| 456 | +of ML engineers, idling capacity, wasted on-demand resources, and delayed delivery. It is imperative that |
| 457 | +scheduler modifications with a big blast radius are tested and verified in a safe environment before |
| 458 | +shipping to production.</p> |
462 | 459 | <p>This problem is not new, and virtual/emulated clusters for scheduling experimentation and workload |
463 | 460 | right-sizing have been around for a long time. Emulated clusters provide a limited real cluster experience |
464 | 461 | for the fraction of the price and compute resources required to run them. To understand what an |
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