February 2026: In ROCm/aiter, drove substantive GEMM enhancements and bug fixes to improve ML workloads. Delivered FP8 performance and correctness enhancements, added a new kernel instance to support additional data types, updated heuristic dispatch logic for new GEMM configurations, and corrected block size handling to boost performance and correctness. These changes strengthen ROCm GEMM reliability and throughput, enabling more accurate results and better hardware utilization.

December 2025 monthly summary for ROCm/aiter focusing on performance improvements and efficiency gains driven by targeted model tuning. The work enhances inference speed and reduces compute/memory footprint, supporting cost-effective scaling and better user experience.

Month: 2025-11. Focused on delivering high-impact MOE performance improvements, framework readiness, and robust testing across ROCm repos. Key features delivered and major bug fixes pursued to boost inference/training efficiency for large-scale MOE workloads, improve compatibility, and strengthen code quality. Delivered MOE performance optimizations and framework readiness across two repos, added a model weight shuffling feature with tests, and completed targeted parameter tuning fixes. The work enhances MOE throughput, reduces latency, and improves reliability for both training and inference. Technologies demonstrated include C++/CUDA kernel optimization, MOE (Mixture of Experts) configurations, kernel list management, Python tooling and test automation, code refactoring, and cross-repo collaboration between ROCm/composable_kernel and ROCm/aiter.