{"version":1,"type":"story","url":"https://digestai.news/story/gpu-utilisation-shows-compute-activity-not-whether-capacity-is-actuall","json":"https://digestai.news/story/gpu-utilisation-shows-compute-activity-not-whether-capacity-is-actuall.json","markdown":"https://digestai.news/story/gpu-utilisation-shows-compute-activity-not-whether-capacity-is-actuall.md","slug":"gpu-utilisation-shows-compute-activity-not-whether-capacity-is-actuall","headline":"GPU utilisation shows compute activity, not whether capacity is actually available","summary":"GPU utilisation metrics often mislead operators into thinking GPUs are idle when they are fully allocated. A 5% average utilisation reported in Cast AI’s 2026 Kubernetes Optimisation Report shows that most GPUs are over‑provisioned, yet 95% of capacity is not free.\n\nKubernetes assigns whole GPUs to pods by default, so a single workload can block all others even if it uses only a fraction of the device. Memory, placement rules, and application bottlenecks also cause queues. Cast AI supports time‑slicing, MIG, and MPS sharing methods, each with trade‑offs in memory isolation, hardware compatibility, and observability. Diagnosing the root cause—allocation state, memory occupancy, placement constraints, or application bottlenecks—requires a specific sequence of checks.\n\nINT4‑quantised 7‑B models use ~4 GB, and INT8 cuts requirements roughly in half. These figures illustrate why memory‑based sharing can be more effective than time‑slicing when VRAM is the limiting factor.","keyPoints":["5% average GPU utilisation in Cast AI 2026 report shows widespread over‑provisioning","Kubernetes allocates whole GPUs by default, blocking other workloads even when idle","MIG, time‑slicing, and MPS trade memory isolation, hardware needs, and observability"],"whyItMatters":"Misreading GPU utilisation can lead to wasted capacity, higher costs, and slower AI workloads, underscoring the need for accurate monitoring and sharing strategies.","category":{"slug":"enterprise","name":"Enterprise & Industry","url":"https://digestai.news/category/enterprise"},"entities":{"companies":["Cast AI","NVIDIA"],"models":[],"people":[]},"firstPublishedAt":"2026-09-25T08:03:06Z","updatedAt":"2026-09-25T08:03:06Z","sourceCount":1,"hasPrimarySource":false,"sources":[{"outlet":"AI News","title":"The GPU Shortage Inside Your Own Infrastructure: Why AI Workloads Queue While Capacity Sits Idle","url":"https://artificialintelligence-news.com/news/the-gpu-shortage-inside-your-own-infrastructure-why-ai-workloads-queue-while-capacity-sits-idle","publishedAt":"2026-09-25T08:03:06Z","type":"press","primary":false,"lead":true}],"sourceNotes":null,"discussions":[],"thread":null,"cite":{"text":"Digest AI, \"GPU utilisation shows compute activity, not whether capacity is actually available\", 25 September 2026, https://digestai.news/story/gpu-utilisation-shows-compute-activity-not-whether-capacity-is-actuall","publisher":"Digest AI","title":"GPU utilisation shows compute activity, not whether capacity is actually available","datePublished":"2026-09-25T08:03:06Z","url":"https://digestai.news/story/gpu-utilisation-shows-compute-activity-not-whether-capacity-is-actuall"},"generatedBy":"Written by Digest AI's editorial model from the linked sources; the sources are the record.","license":"Headlines, digests and key points are written by Digest AI and may be quoted with a link to the story page. Linked articles belong to their publishers. Terms: https://digestai.news/terms#reuse"}