Omar Pavel enhanced the pytorch/FBGEMM repository by developing a performance-focused feature for Triton table batched embeddings, introducing a configurable maximum CTA segment length accessible via the command line. Leveraging CUDA programming, CMake, and GPU performance optimization, Omar exposed this parameter for runtime tuning, defaulting to 4096 for B200 devices based on empirical testing. This adjustment improved backward pass throughput by approximately two percent for common batch sizes, while maintaining compatibility with deterministic execution controls. The work included thorough validation and traceability, reflecting a focused engineering effort to enable hardware-specific optimization and flexible configuration in high-performance deep learning workflows.