June 2026 monthly performance summary focused on delivering high-impact features, stabilizing training workflows, and expanding flexible ML configurations across NVIDIA/TransformerEngine, NVIDIA-NeMo/Megatron-Bridge, and NVIDIA/Megatron-LM. Achievements emphasize measurable business value through improved training throughput, quantization readiness, and scalable architecture for large-model training.

May 2026 performance summary across NVIDIA/TransformerEngine, NVIDIA/Megatron-LM, and NVIDIA-NeMo/Megatron-Bridge. Delivered substantial performance and memory-efficiency improvements through MXFP8 grouped MLP SReLU fusion with recompute-aware backprop in TransformerEngine, enabling more efficient backpropagation for MXFP8 workflows and better utilization of GPU resources. Expanded Megatron-LM capabilities with TE grouped MLP fuser enhancements, including TEFusedDenseMLP for Dense+Grouped GEMM on SM100+ architectures and ScaledSReLU support to broaden activation options and improve training throughput. Implemented Shared Expert GLU checkpoint interleaving in Megatron-Bridge to enhance flexible handling of interleaved weights during training and saving processes. These contributions improved training throughput, reduced memory footprint, and broadened hardware compatibility, while maintaining compatibility with existing models. Demonstrated strengths in CUDA/C++ kernel development, fused-ops engineering, recompute strategies, and cross-repo collaboration, with careful attention to review feedback and code quality.

April 2026 monthly summary for NVIDIA-NeMo/Megatron-Bridge focusing on delivering a major performance-oriented feature with robust testing and safe defaults.