July 2026 performance summary for AMD-AGI/Primus: major feature work, stability fixes, and memory/perf improvements across Primus-Turbo paths, with a focus on business value and end-to-end training efficiency.

June 2026: AMD-AGI/Primus delivered quantization-enabled Megatron-LM extension improvements, memory- and layout- optimizations in Primus-Turbo, and MoE padding/quantization path refinements that collectively improve training throughput, stability, and memory efficiency for large-scale models. Key outcomes include reduced per-microbatch re-quantization, lower activation footprints in turbo attention, correct weight layout for FP8/FP4 grouped GEMMs, and streamlined MoE padding behavior aligned with the quantization pipeline. These developments strengthen business value by enabling faster training runs, reduced resource usage, and improved numerical consistency across distributed training setups.

May 2026 focused on delivering core Primus-Turbo enhancements on AMD-AGI/Primus, strengthening ROCm compatibility, deterministic execution, and CI stability. Delivered features enable faster, more reliable training with mixed-precision support, while improving reproducibility and maintainability across the Primus-Turbo path and related workflows.

In April 2026, delivered targeted enhancements to the ROCm/aiter Attention Mechanism API to improve flexibility and reliability for non-contiguous inputs on AMD GPUs. Implemented forward-result flexibility and independent backward stride handling, with extensive tests to validate non-contiguous layouts. These changes improve model training stability and broaden applicability of the attention primitives across downstream frameworks. Demonstrated strong kernel interfacing, stride manipulation, and test-driven quality assurance across CUDA/C++ backend work.

Month: 2026-03 focused on reliability, ROCm-compatibility, and quantization workflow improvements across AMD-AGI/Primus and pytorch/ao. Key outcomes included fixing a Torchtitan aiter bug by updating CI with primus-turbo, and delivering quantization enhancements with floor/ceiling scaling modes and ROCm-ready robustness tests, alongside restoration of mxfp8 quantization unit tests. These changes improve CI reliability, hardware compatibility, and validation coverage, enabling safer deployments and faster iteration on critical ML workloads.

February 2026 monthly work summary for AMD-AGI/Primus: Focused on reliability and performance improvements in core context handling and the Primus-Turbo path, enabling more stable Megatron-LM deployments and faster inference.

January 2026 — AMD-AGI/Primus: Key feature delivered MXFP4 support in Megatron-LM backend, enabling FP4 low-precision training with updated quantization config and FP4 context utilities to stay compatible with Transformer Engine. Major bug fix: removed deprecated enable_turbo_gemm_float8 option from llama4 YAML to improve compatibility and reduce configuration confusion. Overall impact: more efficient training, lower memory footprint, and cleaner configuration with smoother upgrade paths. Technologies demonstrated: Megatron-LM backend integration, FP4/quantization, Transformer Engine compatibility, and YAML configuration hygiene.

December 2025 monthly wrap-up focusing on business value and technical achievements across Primus Turbo and Megatron-LM backends. Delivered feature enhancements, stability improvements, and reproducibility improvements to support scalable experimentation and reliable deployment.

October 2025 monthly summary for AMD-AGI/Primus. Focused on extending model configurability for Llama 3.x and enabling FP8 quantization to improve inference efficiency. The work enhances model versioning, experiment reproducibility, and asset referencing, enabling faster experimentation with newer Llama models and reduced resource usage.

For September 2025, focused on FP8 quantization configuration and compatibility enhancements for AMD-AGI/Primus (Primus Turbo and Megatron Extension). Delivered alignment of FP8 linear args to Megatron, introduced FP8 global state and context managers to enable flexible FP8 configurations, and implemented dynamic GEMM selection based on FP8 config. Standardized FP8 handling with a new quant config class, updates to the global state manager, and refactored FP8 scaling configurations. Added compatibility warnings for unsupported FP8 recipes/configs by current Transformer Engine version or Primus-Turbo, and ensured safer fallbacks. These changes improve deployment flexibility, performance tuning options, and product safety across versions.

August 2025 monthly summary focusing on key accomplishments and impact for AMD-AGI/Primus. Delivered a critical bug fix to the MoE router load balancing index calculation, improving routing efficiency and reducing CPU synchronization overhead. No new features released this month; stability and performance improvements were the focus.

July 2025 monthly summary for AMD-AGI/Primus focusing on reliability, observability, and performance improvements. The team delivered deterministic training reliability enhancements, clear configuration guidance, and offline tuning reporting to drive reproducibility and data-driven optimizations. These efforts strengthen production stability and enable faster optimization cycles across models using Primus.

April 2025 for AMD-AGI/Primus delivered a critical FP8 configuration fix and expanded the performance-tuning toolkit with a new tensile tuning documentation example. The FP8 option now reliably recognized by renaming the config key from 'fp8_format' to 'fp8', improving robustness of FP8 workflows. The tensile tuning documentation provides an end-to-end offline workflow to clone/build hipblaslt, generate tensile configurations, and produce optimized GEMM kernels for AMD GPUs. These changes strengthen FP8 reliability, accelerate performance-tuning cycles, and improve developer onboarding. Committed changes are traceable to the corresponding commits.

Month 2025-03 monthly summary for ROCm/TransformerEngine: Delivered a new mask-based permutation/unpermutation and chunk-sorting utility for Mixture-of-Experts (MoE) in PyTorch. Implemented sorting of MoE chunks by index, updated API definitions, and introduced Triton kernels for optimized permutation operations, backed by comprehensive tests. Key commit: 08ad09faa3a268c3b3fbc341d46ae68fe1e878ce (Cherry-pick from PR #140). No major bugs fixed this month. Impact: Enables scalable MoE workloads on ROCm with improved routing, performance, and correctness, validated by tests. Technologies demonstrated: PyTorch MoE utilities, Triton kernel integration, API design, testing, and cherry-pick workflow.

February 2025 monthly summary for ROCm/TransformerEngine focusing on delivering a multi-process safe algorithm save feature and documenting usage to support scalable multi-GEMM workloads. Repository: ROCm/TransformerEngine.