I Built a C++ Backend So My GPU Would Stop Eating Air

In a quest to enhance the efficiency of large language model (LLM) inference, the author developed a C++ backend to minimize the overhead caused by padding in GPU operations. By implementing hardware-aware sequence packing, the backend effectively reduces wasted computation, leading to more efficient use of GPU resources. This innovation matters because it addresses a common challenge in deploying advanced machine learning models, offering a practical solution that could significantly improve performance and reduce costs for developers and organizations working with large datasets.