January 2026 monthly summary for ROCm/aiter: Focused on delivering efficiency improvements for the a4w4 MOE model by switching to a16w4 default policy, enabling split-k, and integrating the second stage of ck tile MOE. This effort included targeted bug fixes and code maintainability improvements, resulting in better throughput, lower compute footprint, and improved maintainability. Delivered in collaboration with the team with clear ownership.

December 2025 performance-focused month across ROCm/aiter and ROCm/composable_kernel. Delivered targeted MLA enhancements, MoE stage robustness, and GEMM memory utilities, plus CKTile MOE improvements. Resulting work increases model throughput and scalability while reducing memory footprint and improving stability for large-scale workloads.

Monthly summary for 2025-11 focusing on ROCm/aiter work highlights: delivered a key feature to boost ML batch processing efficiency and robustness by capping the number of key-value splits per batch, stabilizing memory usage, and improving throughput for data processing workloads. The work encompassed targeted fixes and improvements (compiled in commit 288c82f306380c98fc8d4bcc9083bcca7f64b0bf) addressing split handling, memory allocation, and kernel compatibility to support large batch sizes and reliable operation.

June 2025: Delivered AMD-optimized VLLM path by integrating Aiter chunked prefill into the VLLM framework to boost attention performance on AMD hardware. Commit 8b6e1d639c66d5828d03a7df2c3a500030a5c5cd. Repo: red-hat-data-services/vllm-cpu. Business impact: higher inference throughput and lower latency for AMD-based deployments.

Month: 2025-05 summary: Delivered a chunked prefill feature for FlashAttention in the MHA variable-length kernel (VLLM) to support small query lengths. Resolved compiler issues, added sequence-length guards to bypass problematic paths, and integrated the chunked prefill into the MHA kernel with clear comments. These changes improve reliability and performance for dynamic, variable-length workloads and contribute to more robust FlashAttention-enabled inference.