A company operates a real-time fraud detection system using SageMaker. The model is deployed on an ml.c5.xlarge instance behind an Application Load Balancer (ALB). Recently, during a sales event, traffic spiked and the endpoint returned HTTP 503 errors. The team scaled the instance count from 2 to 5, but errors persisted. CloudWatch metrics show low CPU utilization (~30%) and high memory usage (~90%). The model loads a large dictionary file (2GB) into memory at startup. Which action should resolve the issue?