February 2026 (2026-02) monthly summary for ROCm/aiter focused on FP4 AOT MoE dispatch. Delivered a critical fix to the Ahead-Of-Time (AOT) MoE dispatch shuffling for FP4 inference, addressing correctness and performance bottlenecks in Mixture of Experts routing during inference. The work is encapsulated in commit 4c65ef097889619e8211cf98e38e10a841fa95aa, which includes updates to AOT MoE dispatch logic, fixes for build-related issues (notably AOT builds), and code improvements in aiter/ops/moe_op.py. This change also resolves stability concerns seen with 325 AOT builds, improving CI reliability and production readiness. The effort aligns with our goals to stabilize FP4 pipelines and accelerate reliable deployment of FP4 models.

Month: 2026-01 — ROCm/aiter: Key features delivered and major bug fixes with clear business and technical impact. Key features delivered: - MOE Framework Performance Enhancements: MOE tuner improvements (block_m=16) and improved stage 2 dispatch, plus MOE GEMM tile/kernel enhancements for greater flexibility and speed. Additional MOE tile/config updates. - Relevant commits: 55376c71fc070198d16b263ec8c3b04f665f31b9; fb991a7fb9caeee257c4b77a29d9e88748b791b7 Major bugs fixed: - MoE Fused Implementation Bug Fixes: Correct bias handling, dispatch logic, and quantization type adjustments to improve correctness and performance. - Relevant commit: 3fb63974a0b18bbce6423158565701682b087761 Overall impact and accomplishments: - Improved MOE throughput and reliability for large-scale models; faster tuning iterations and more predictable inference performance. - Strengthened code quality through targeted bug fixes and cohesive commits, including co-authored contributions. Technologies/skills demonstrated: - MOE tuning and performance optimization, GEMM tiling, dispatch engineering, bias/quantization handling, and cross-functional collaboration.

December 2025: Delivered MOE-focused performance enhancements and decoding optimizations across ROCm/aiter and ROCm/composable_kernel, strengthening throughput, stability, and usability for large-scale MOE deployments. Implemented robust tuning/dispatch/configuration improvements and critical bug fixes to ensure reliable behavior across varying token counts and block configurations, while advancing A4W4 decoding throughput and GEMM efficiency.

November 2025: Delivered two high-impact bug fixes across ROCm components that directly improve performance and reliability. Key features delivered: none new in this period; major value came from stabilizing core kernels and build pipelines. Major bugs fixed include a static assertion parameter bug in DeviceMoeGemmMXBPreShuffle to improve GPU thread utilization and a RTP build error fix in DeepGEMM by correcting header inclusions and essential templates. These changes reduce runtime stalls, enhance GPU throughput, and restore reliable DeepGEMM operations across the ROCm stack. The work demonstrates strong cross-repo collaboration, disciplined debugging, and solid C++ template/header hygiene with co-authored commits.

2025-10 monthly summary focusing on key accomplishments, major bugs fixed, overall impact, and technologies demonstrated. Highlights include performance-oriented kernel improvements, expanded data-type support, and reliable quantization workflows across ROCm/composable_kernel and ROCm/aiter. These efforts deliver broader business value by enabling diverse workloads with higher efficiency and stronger cross-repo collaboration.

September 2025 focused on stabilizing core subsystems and delivering measurable performance gains across ROCm/aiter and ROCm/composable_kernel. Consolidated MoE subsystem bug fixes to ensure tuner configuration correctness, blockscale configuration checks, and stage2 prebuild dispatch reliability, reducing runtime errors and improving model reliability. In CK_TILE GEMM, implemented an atomic IGLP scheduler for the weight pre-shuffle path and introduced a permuteN optimization in c_shuffle, delivering faster GEMM operations. Addressed a correctness gap in Weighted Preshuffle GEMM when permuteN is disabled by adding a TiledMMAPermuteN option and applying it in WeightPreshuffleInvoker. These efforts, supported by cross-repo collaboration and code-quality practices, improved performance and stability for large-model training and inference.

August 2025 monthly summary: Two repositories saw targeted progress aimed at stability, performance, and broader MoE support. A critical bug fix stabilizes expert offset calculation to prevent index-out-of-range crashes, while MoE-related enhancements expand prebuild options, improve code generation, and clean up dead code for maintainability. Overall, these efforts deliver measurable reliability improvements, faster prep for deployment, and broader GPU/ datatype support.

July 2025 monthly summary for ROCm/aiter: Delivered Mixture-of-Experts (MoE) functionality enhancements across quantization and hardware optimization. Key improvements include support for various quantization types, optimizations for multiple hardware architectures, refactoring of the fused MoE implementation, and improved dispatch logic for optimized kernels. The MoE work merged from the 350 launch (#580) consolidates ongoing MoE work into mainline, enabling broader deployment. No major bugs reported this month; focus was on feature maturation and performance-oriented refactors. Overall, this accelerates MoE scalability and hardware portability, enabling higher throughput and more efficient use of compute resources in large-model workloads. Demonstrated skills in MoE architecture, quantization, hardware-specific optimization, kernel dispatch, and cross-team integration.

June 2025 monthly summary for StreamHPC/rocm-libraries focusing on stabilizing the blockwise GEMM data path and tensor descriptor handling in the aiter-enabled flow. Delivered a critical bug fix for Moe i4 tensor descriptor calculations within the blockwise GEMM pipeline, along with a targeted refactor of KGroup/KRepeat logic to improve data copying and processing pathways.

A concise monthly summary for 2025-04 highlighting business value and technical achievements across StreamHPC/rocm-libraries and ROCm/aiter. The month delivered substantive MoE-focused improvements, corrected critical data-layout issues, and introduced flexible APIs to enable broader experimentation and deployment with future hardware. Key outcomes include performance-oriented enhancements, increased correctness, and expanded experimentation capabilities that directly support production workloads and research exploration.