CPU and memory monitoring tools do not carry over to graphics processing units, or GPUs, on Kubernetes with the same ease. While the platform typically provides usage metrics at the container or Pod level, platform teams sharing GPUs need to build most of the visibility and allocation layers themselves, from data collection through determining each team’s share of the cost.
The importance of the problem stems from the fact that the GPU is typically the most expensive item on the infrastructure bill. The analysis indicates that operating costs are not limited to the price of the node; they also include the engineering time required to compensate for measurement gaps, verify the accuracy of the figures, and prevent workloads from exceeding what Kubernetes allocated to them.
Why Are Standard Metrics Not Enough?
The NVIDIA DCGM exporter, which many operators start with, provides data at the physical-device level by default. When a Pod owns an entire GPU, that may be sufficient, but the situation differs when time-slicing or MPS is used to share the device. In this case, the same value may appear for every Pod using one GPU, making it look as though three workloads consumed 300% of a unit that does not actually exist.
The problem is that the charts may look correct even though the underlying data is unsuitable for allocation. As a result, teams can build complete reports and dashboards on figures that do not actually distinguish between consumers.
What Changes in Practice for Platform Teams?
GPU also differs from CPU in its resource-allocation model. A GPU typically appears in Kubernetes as an integer resource such as nvidia.com/gpu: 1, and the scheduler does not allow fractional parts to be handled in the way they normally are with CPUs. Actual sharing occurs below the scheduler through time-slicing, MPS, or MIG, making comparisons between what a workload requested and what it used more complicated.
Teams also need independent controls to verify that what Kubernetes recorded as allocated matches what the application can actually access. The NVIDIA_VISIBLE_DEVICES environment variable may cause the container to see every GPU in the node, if an appropriate configuration does not override it, even when the scheduler allocated only one GPU to it.
The phrase “80% GPU utilization” does not necessarily have a single meaning either. It may measure only whether a kernel was running during part of the sampling window, without explaining the consumption of compute capacity or memory bandwidth. In some frameworks, such as vLLM, most GPU memory may be reserved at startup, making memory or bandwidth a more important indicator than the overall utilization figure.
Billing: Memory Is Easier to Allocate Than Compute
The NVIDIA NVML library can show the amount of memory held by each process and then link the process to the Pod and the team responsible for it, with the caveat that the sum of the values will not equal the card’s total memory because part of it is retained by the driver itself.
Assigning compute time among workloads running in parallel is less precise. Two workloads may each appear to be active 90% of the time, without the metrics explaining which one consumed 70% of the capacity and which one consumed 30%. The analysis therefore proposes using GPU memory as a practical basis for internal billing, while treating compute measurement as an operational indicator rather than an accurate reference for charging fees.
Choosing a Sharing Model Changes the Calculations
GPU sharing does not represent a single model. Time-slicing takes turns on the device without isolation, MPS allows kernels from different processes to run at the same time, while MIG divides the hardware into fixed portions. Nevertheless, cloud services may charge for the entire physical GPU even when only part of it is used, and A100 and H100-class cards can be divided into a maximum of seven instances according to the article.
Dynamic Resource Allocation (DRA) is moving toward becoming the future path for resource management, but it is still maturing, and the surrounding tools, including scaling tools such as Karpenter, are catching up with this development.
Why Does This News Matter?
On AWS, the price of the least expensive MIG-compatible A100-class GPU is about $22 per hour, or approximately $14 when using spot pricing, compared with about $5 per hour for a comparable CPU-only instance, according to the article. Therefore, an error in allocating usage is not a minor accounting issue; it may directly affect capacity and cost decisions and the fairness of charging teams.
The editorial conclusion is that running GPUs on Kubernetes still requires an operational model different from the CPU and memory model. Platform teams must determine what can be measured accurately, establish clear rules for distributing costs, and avoid basing accounting decisions on a general GPU metric that does not describe actual consumption. At the same time, the source indicates that the maturation of DRA and hardware partitioning may gradually narrow the gap, but the timing and limits of the current tools remain open questions.