Concise monthly summary for 2025-09 focusing on ggml-org/llama.cpp work. Key features delivered: - WAN Video Models: Implemented 3D convolution and image-to-column operations to support WAN video workloads, including padding and tensor manipulation utilities to optimize data handling. Added tests to verify correctness and performance across CUDA and CPU paths. Major bugs fixed: - No major bugs reported/recorded for this period in the repository data provided. Overall impact and accomplishments: - Enabled end-to-end WAN video model support within llama.cpp, broadening deployment options across GPU and CPU environments. Improved data handling efficiency and reliability through padding utilities and targeted tests, contributing to more stable performance in video workloads. Technologies/skills demonstrated: - 3D convolution, image-to-column transformations, padding and tensor manipulation - CUDA and CPU code paths for cross-hardware support - Test-driven development with added tests for functionality and performance

August 2025 focused on CUDA-accelerated performance improvements in the im2col path for two CUDA-backed projects, delivering measurable efficiency gains and paving the way for faster model inference on image-related workloads.