{
  "id": 16324080,
  "title": "Before you expand your GPU pool, measure the outcome",
  "url": "https://urgent.news/2026/10/24/before-you-expand-your-gpu-pool-measure-the-outcome",
  "topic": "tech",
  "section": "Tech",
  "published": "2026-10-24T12:00:00.000Z",
  "source": {
    "name": "The New Stack",
    "slug": "the-new-stack",
    "url": "https://thenewstack.io/optimize-kubernetes-gpu-pools/"
  },
  "original_language": "en",
  "account": "When contemplating an increase in your GPU pool for AI workloads on Kubernetes, it's crucial to measure the results before proceeding. A full GPU allocation and a long queue of jobs may suggest expanding the pool, but there could be other factors at play. Before expanding, define what success means, when it must be completed, and the associated costs. An allocated GPU, a busy GPU, and a workload producing valuable results are distinct concepts. Understanding this distinction can help teams optimize existing capacity before investing in additional resources. Begin by examining the workloads and their use cases. Some jobs, like customer service chatbots, require constant inference capacity, while others, like model evaluation, may only need attention before an upcoming release. Classify workloads as \"inference\" or \"training,\" but remember that some batch inference can wait while training jobs often have strict deadlines. For each workload, document its deadline, expected runtime, resource requirements, and whether interruptions are acceptable. Define what constitutes a valuable output, such as a specific quality threshold and latency objective for inference services, or a completed request with valid JSON response. Be wary of using start-time flexibility as a substitute for capacity expansion. For instance, if four GPUs are available between noon and 5 p.m., and four evaluation jobs requiring two GPUs each are ready at noon, running two jobs at noon and two at 1 p.m. can complete all four jobs by 2 p.m. without needing extra GPUs. This staggered approach reduces peak demand and still meets deadlines without expanding the pool. Tools like Kueue can help with admission control by using quotas and policies to determine work admission times. However, you still need to define and enforce deadlines and workload requirements. Flexible queueing can aid in capacity planning and minimize the need for early job starts. Even with high GPU activity, this does not guarantee additional throughput. Nvidia's profiling documentation highlights that streaming multiprocessor activity may include warps waiting on memory. Serving metrics like running, waiting requests, cache occupancy, and preemption counts offer more insight. In vLLM, KV-cache usage measures the fraction of occupied cache blocks but does not directly indicate total serving capacity. To accurately measure sustainable capacity, conduct a representative load test while keeping the model, hardware, serving settings, and request mix constant. Increase the arrival rate and observe throughput, quality, and latency. The highest sustained rate that meets the workload's requirements, including error limits and latency percentiles, provides a useful capacity estimate. For interactive generation, consider metrics like time to first token and subsequent token delivery. Comparing production traffic with tested capacity and evaluating serving capacity against application-level objectives is crucial. Low cache occupancy may warrant further investigation, but claiming significantly higher throughput without testing is not advisable. Connect OpenCost data to verified outcomes to bridge the gap between cost and results. Use OpenCost's allocation API to gather workload cost data at defined intervals and join it with serving telemetry and outcome records using workload identities and timestamps. OpenCost also offers opt-in inference cost tracking, including cost per million tokens. When calculating cost per verified outcome, include all relevant costs, such as GPU, CPU, memory, and other infrastructure expenses, and document how shared and idle costs are assigned. Account for retries, failures, startup, and idle periods in the total cost, counting the final business result only once.",
  "summary": "For teams running AI workloads on Kubernetes, a fully allocated GPU pool and a growing batch-job queue might make expanding The post Before you expand your GPU pool, measure the outcome appeared first on The New Stack .",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}