A financial services company is using an AI model for fraud detection, deployed on NVIDIA GPUs. After deployment, the company notices a significant…

Implementing GPU load balancing across multiple instances is the best approach to address resource

contention and delays in a fraud detection system during peak hours. Load balancing distributes

inference workloads across multiple NVIDIA GPUs (e.g., in a DGX cluster or Kubernetes setup with

Triton Inference Server), ensuring no single GPU is overwhelmed. This maintains low latency and

high throughput, as recommended in NVIDIA’s "AI Infrastructure and Operations Fundamentals" and

"Triton Inference Server Documentation" for production environments.

Switching to CPUs (A) sacrifices GPU performance advantages. Disabling monitoring (B) doesn’t

address contention and hinders diagnostics. Increasing batch size (C) may worsen delays by

overloading GPUs. Load balancing is NVIDIA’s standard solution for peak load management.

Reference:Triton Inference Server Documentation, AI Infrastructure and Operations Fundamentals

(www.nvidia.com).