Nov 2025 monthly summary focused on delivering high-impact GPU-accelerated features and ensuring CUDA compatibility in PyTorch. Key deliverable: FP16 output support for torch scaled_mm when using CUTLASS on NVIDIA SM90, enabling FP16 bias and output in the scaled_mm path and aligning with CUDA 12.x improvements. Implemented data type adjustments for matrix multiplication to support FP16, enhancing performance and efficiency on SM90 workflows. Key achievements: - Delivered FP16 output support for scaled_mm with CUTLASS on SM90 (commit e3bd7bd1f4b0d9340bdb5f03c784b7e013477ac4; PR 166744). - Updated matrix multiplication data paths to properly handle FP16 (and related data types) in the scaled_mm workflow, enabling performance gains on SM90. - Validated through extensive tests; test plans executed on CUDA 12.4 and 12.9 with strong pass rates: 51 passed, 516 skipped (12.4) and 70 passed, 482 skipped (12.9). - Code review and merge: Reviewed by pranavsharma and RandySheriff; Differential Revision D84169910; Pull Request resolved and approved by maintainer slayton58. Overall impact and accomplishments: - Improves performance and CUDA compatibility for large-scale matrix operations on SM90 GPUs, enabling more efficient training and inference pipelines. - Strengthens PyTorch's position for cutting-edge NVIDIA hardware with CUTLASS integration and robust test validation across CUDA versions. Technologies/skills demonstrated: - CUDA, CUTLASS integration, FP16 data paths in PyTorch, matrix multiplication optimizations, test automation and CI validation, cross-version CUDA testing, review and collaboration in a major open-source project.