In April 2025, Shah Armor developed the PeftCacheManager for the NVIDIA/TensorRT-LLM repository, focusing on efficient management of PEFT (Parameter-Efficient Fine-Tuning) weights within Torch. He implemented caching strategies and resource management hooks in Python and C++, enabling seamless handling of LoRA weights and configurations during inference. By integrating Pybind for Python bindings and leveraging Torch for batch and resource management, Shah’s work improved the scalability and reliability of PEFT model inference. This feature reduced memory usage and established a foundation for broader PEFT adoption in production, demonstrating depth in LLM inference workflows and robust engineering in model deployment.