Technical teams managing AI workloads on the Kubernetes platform face constant pressure as GPU clusters fill up and batch-job queues grow. Expanding GPU architecture is often the fastest option, but growing queues may reflect shortcomings in timing, model serving capacity, or the efficiency of selection between hardware and different models. Before making the decision to purchase and provision more resources, teams must define success metrics, deadlines, and the true cost of delivering results.
Distinguishing Between Utilization and Actual Throughput
The challenge is not limited to simply running resources, as an allocated GPU, a busy processor, and a unit of work that produces valuable results are entirely different concepts. Understanding the relationship between these states enables teams to utilize existing capacity more effectively.
- Defining Deadlines: Some services, such as chatbots, require continuous response service around the clock, while model evaluation tasks are linked to specific internal launch deadlines.
- Quality Goals: Every inference service must have a specific quality threshold and a response time target, as simply obtaining a completed response does not necessarily mean the target required by the customer has been met.
Using Flexible Scheduling to Reduce Peak Demand
Scheduling flexibility helps alleviate constraints without the immediate need to expand infrastructure. For example, if four GPUs are available for batch tasks for a specific period, and there are four tasks each requiring two units, running them all at once would require eight units; whereas overlapping deadlines and scheduling two tasks first, followed by the last two, results in completing the entire work on time using the same available resources.
Teams benefit from tools like Kueue to provide admission control points and policies, provided that deadlines and requirements are accurately defined and represented to prevent the need to start tasks early and stop them later.
Measuring Sustainable Serving Capacity and Performance Testing
A spike in GPU activity does not reflect the amount of additional throughput available. Therefore, measuring sustainable capacity requires conducting representative load testing while keeping hardware and service settings constant and varying the request rate to reach the highest sustainable rate that maintains quality requirements and acceptable latency ratios.
It also requires linking cost data derived from tools like OpenCost to actual results and services, while calculating the cost per achieved and verified result, to reach an operational model that achieves the required balance between cost and efficiency.
What does this approach change in practice?
The focus of teams shifts from merely tracking hardware consumption rates to evaluating the efficiency of spending linked to real business results. This shift helps avoid unnecessary capital investments in additional hardware and pushes toward improving model efficiency and scheduling settings before requesting more resources.