Developed the InstantTensor weight loader for the jeejeelee/vllm repository, enabling efficient loading of Safetensors weights on CUDA devices. This feature introduced distributed loading and pipelined prefetching, which reduced model weight load times and improved throughput for large-scale machine learning deployments. The approach leveraged CUDA optimization and Python development to maximize GPU utilization and accelerate model startup, resulting in faster, more scalable deployments and improved end-user responsiveness. Integration and testing ensured seamless operation within the existing codebase. The work demonstrated depth in CUDA, machine learning, and Python, focusing on practical performance gains without addressing critical bug fixes during the period.