Unlocking AI Potential: Solving the GPU Shortage in Your Infrastructure

In the realm of GPU utilization, it is essential to understand that compute activity does not necessarily indicate available capacity. A GPU may exhibit low utilization while being fully allocated to a specific workload, leading to a common misconception. This article delves into the reasons why workloads can queue up next to seemingly idle GPUs, exploring factors such as allocation, memory usage, placement constraints, and application bottlenecks. Additionally, it compares different GPU sharing methods and their tradeoffs.

Capsule:

Artificial intelligence workloads often find themselves waiting for GPU resources even when monitoring suggests idle compute capacity. A GPU displaying low compute activity does not automatically translate to available capacity for another workload due to various factors like allocation rules, memory requirements, hardware compatibility, isolation, and placement constraints. This article aims to clarify how to differentiate between an actual hardware shortage and issues related to allocation, placement, or application bottlenecks. While the 5% average GPU utilization finding from Cast AI’s 2026 report serves as a research hook, it should be noted that queued workloads and idle GPUs are not necessarily observed together in the same environments or that 95% of GPU capacity is immediately reclaimable.

Key takeaways

• GPU utilization metrics measure compute activity, not allocation state. A GPU reporting 5% compute utilization can still be fully allocated with zero capacity available for new workloads.
• Kubernetes assigns whole GPUs to pods by default, leading to a single workload holding a device and blocking all others regardless of actual usage.
• Workloads queuing beside idle-looking GPUs can result from at least four distinct root causes, with only one being resolved by GPU sharing.
• Time-slicing, MIG, and MPS differ in their tradeoffs on memory isolation, hardware requirements, observability, and support from cloud providers, with no single method fitting every workload.
• The diagnostic sequence is crucial: check allocation state, then memory occupancy, followed by placement constraints and application bottlenecks.
• Cast AI supports all three sharing methods with automatic bin-packing and no changes required to workload manifests.

What GPU utilization tells you and what it misses

GPU utilization encompasses various measurements, including compute activity, memory occupancy, queue wait time, and throughput/latency. It is essential to understand these metrics to avoid misdiagnosis. Compute utilisation indicates the percentage of active streaming multiprocessors but does not reflect allocation status or VRAM availability for new workloads.

Memory occupancy, queue wait time, and throughput/latency each provide valuable insights that compute activity alone cannot reveal. It is crucial to interpret these metrics accurately for a comprehensive understanding of GPU performance.

The Cast AI 2026 Kubernetes Optimization Report highlights the average GPU compute utilization of 5%, showcasing a fleet-wide pattern of overprovisioning and underuse. However, this does not imply that 95% of GPU capacity is immediately free for new workloads, emphasizing the need for a deeper diagnostic approach.

It is important to differentiate between GPU utilization metrics to gain a holistic view of GPU performance and capacity.