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Before you expand your GPU pool, measure the outcome
Expanding GPU pools without prior load testing often hides inefficiencies that flexible scheduling or better batching could solve at lower cost.
What: Teams are advised to use tools like Kueue for admission control and OpenCost to measure the cost-per-verified-outcome rather than reacting to GPU queue growth by simply adding more hardware.
Why it matters: Scaling infrastructure is frequently treated as a catch-all solution for performance bottlenecks that are actually caused by poor scheduling or suboptimal model configuration.
Takeaway: Before provisioning new GPUs, implement a representative load test to find the highest sustained request rate that meets quality and latency requirements.
Deep dive
- Distinguish between allocated GPUs, busy GPUs, and GPUs producing valuable results.
- Classify workloads by urgency and interruption tolerance to improve cluster utilization.
- Use Kueue for scheduling flexibility to reduce peak demand without increasing hardware footprint.
- High GPU activity often masks warps waiting on memory (memory-bound operations).
- Link FinOps data (OpenCost) with application-level success metrics.
- Define 'cost per verified outcome' as a key performance indicator.
Decoder
- Kueue: A Kubernetes-native job queueing controller for managing resource quotas and job scheduling.
- Warp: A group of 32 threads in NVIDIA GPU architectures that execute the same instruction in parallel.
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