Developed a Benchmarking Recipe Suite for Gemma 4 31B-IT models within the aws-samples/sagemaker-genai-hosting-examples repository, focusing on deployment and performance evaluation on SageMaker g6e and g7e instances. The work introduced support for multiple quantization methods and caching strategies, enabling exploration of latency-accuracy trade-offs and optimizing inference for repeated classification workloads. Leveraging skills in AI benchmarking, AWS SageMaker, and cloud computing, the developer delivered a reproducible, parameterized benchmarking framework using YAML to standardize model evaluation. This approach established a consistent workflow for comparing model performance, informing model selection, tuning, and production readiness without reported bug fixes during the period.