Adam Rajfer developed a feature for the NVIDIA-NeMo/Eval repository that enables users to parameterize GPU memory utilization for vLLM deployments. By updating the configuration schema and documentation using YAML and Markdown, Adam allowed users to specify the fraction of GPU memory allocated to inference models, addressing the need for precise resource budgeting in GPU-bound environments. This work leveraged his skills in configuration management and DevOps, resulting in more flexible and scalable deployment options. The feature improved cost efficiency and predictability for inference workloads, demonstrating a focused, well-scoped engineering contribution with clear business value and technical depth within a short timeframe.