April 2026 monthly summary for NVIDIA-NeMo/Megatron-Bridge focused on delivering API compatibility for CUDA graphs and establishing cross-version maintainability. The primary deliverable was enabling compatibility with both old and new MCore CUDA graph APIs, including utilities for managing CUDA graph modules and adjusting configurations to support cross-version usage. No major bugs were reported fixed this period; efforts were concentrated on feature enablement and upstream readiness.

Month: 2026-03 Concise monthly summary highlighting key business value and technical achievements across two repos. Delivered targeted stability improvements and maintainability enhancements to CUDA graph workflows in PyTorch environments, enabling more reliable model training and faster iteration cycles for large-scale deployments.

February 2026: Delivered a critical bug fix in NVIDIA/Megatron-LM for the MoE layer backward pass gradient computation and latent size handling. The work focused on optimizing the backward pass for routed and shared experts and improving latent size handling during backpropagation, resulting in improved training performance and efficiency for large-scale MoE models.

January 2026 monthly summary for NVIDIA/Megatron-LM: Delivered key performance and reliability enhancements in CUDA Graphs and RNG Tracker, along with CI stability improvements. These changes reduce capture overhead, improve graph safety, and increase measurement reliability, enabling faster and more predictable large-scale training and benchmarking.

Performance and robustness improvements across two NVIDIA repositories (TransformerEngine and Megatron-LM) in December 2025. TransformerEngine: (1) Distributed RNG state management and cudagraph recomposition to ensure graph-safe RNG tracking in distributed PyTorch, boosting correctness and forward/backward throughput; (2) Flexible input handling for graphed callables, enabling sample_args/sample_kwargs to be tuples or lists for better training-mode compatibility. Megatron-LM: (3) CUDA graph capture optimization with memory reuse across non-overlapping forwards and partial capture scopes; (4) MoE refactor introducing a new CUDA graph scope enum to support partial captures, enabling more efficient training and inference. Overall impact: higher training throughput, lower peak memory, better scalability and easier integration with varied training pipelines. Technologies: PyTorch distributed, CUDA graphs, memory optimization, MoE architectures, graph scopes.

November 2025 performance summary for NVIDIA/TransformerEngine focused on feature delivery and robustness of CUDA graph execution. Delivered a new CUDA Graphs Reset Interface to manage graphed callables' state during execution, with corresponding test updates to cover reset behavior. This feature improves reliability, flexibility, and integration readiness for graph-based inference/training workflows.

Month: 2025-10.

Monthly summary for 2025-09: Focused on delivering CUDA graph-enabled training enhancements for NVIDIA/Megatron-LM and strengthening test reliability around shared expert overlap. Key work included refactoring CUDA graph functionality and introducing GraphableMegatronModule to standardize graphable modules, refining TECudaGraphHelper for reliable graph creation and replay, and updating MambaLayer and TransformerLayer to leverage the new CUDA graph capabilities. Additionally, a unit test configuration fix was implemented to ensure accurate comparisons between shared expert overlap and A2A overlap by creating two distinct configurations (reference model and overlap scenario). These efforts improve performance potential, determinism, and maintainability of graph-based training workflows.

2025-08 Monthly Performance Summary: Focused on delivering CUDA graph optimizations and cross-repo improvements to accelerate graphed workloads and improve memory efficiency. Key outcomes include feature enhancements in TransformerEngine for memory reuse and mixed-precision, plus external CUDA Graph enhancements in Megatron-LM to boost graph capture and compatibility across Transformer Engine versions. A targeted bug fix was applied to cudagraph input reuse to ensure correctness across microbatches. Business impact includes reduced memory footprint, higher throughput, and more flexible precision options for production workloads.

July 2025 monthly summary for development across ROCm/Megatron-LM and NVIDIA/TransformerEngine. Focused on correctness, efficiency, and scalability of CUDA graph-based pipelines, delivering a critical bug fix, memory and performance optimizations, and stronger test coverage to ensure reliable training results and faster iterations for large-scale models. Key features delivered and major bugs fixed: - Megatron-LM: Fixed incorrect calculation of num_warmup_microbatches for single-process pipeline parallelism under CUDA graph capture; added test test_get_pipeline_parallel_order to guard pipeline scheduling across configurations. Commit: e392d40f517ea215b9f8a6ab1a10d8af32ce1606. - TransformerEngine: CUDA Graph memory and distributed training optimizations, including memory reuse of input/output tensors, FP8 wrapper refactor, support for uneven pipeline parallelism, and optimization of static_grad_outputs reuse via pre-allocated buffers (flag-dependent). Commits: 64891899687dacb8293f8dc4ee786e16a47e1c02; e950ceb0ad5be6997a71f0e0c10c9e4a3786d692. Overall impact and accomplishments: - Improved training reliability and results when using CUDA graphs in pipeline-parallel and distributed training scenarios, enabling more stable experiments and reproducible outcomes. - Enhanced memory efficiency and throughput for CUDA graph workflows, supporting uneven pipeline parallelism and reducing memory pressure via pre-allocated buffers. - Strengthened test coverage and validation around CUDA graph-based pipelines, helping guard against regressions across configurations. Technologies/skills demonstrated: - CUDA Graphs, single-process and distributed pipeline parallelism, FP8 data types, memory reuse strategies, pre-allocated buffer optimization, and test-driven development.

April 2025 monthly summary for ROCm/Megatron-LM. No new user-facing features were released this month; the focus was on stabilizing the distributed training path on ROCm when external CUDA graphs are enabled. A critical bug fix was delivered to preserve gradients in Distributed Data Parallel (DDP) under CUDA graphs, improving reliability for large-scale training.

March 2025 monthly summary: Delivered targeted CUDA Graph-related work to enhance performance and reliability for large-scale Transformer and Mixture-of-Experts workloads. Implemented conditional CUDA Graph support for MoE in Megatron-LM with refactored pipeline parallel scheduling, improved MoE token dispatch, and added options for manual graph capture and scope control. In parallel, simplified the CUDA graph path in NVIDIA/NeMo by removing CUDA graph execution from TransformerBlock and VisionTransformerBlock, reducing complexity and potential graph-management issues in forward passes. These efforts contributed to stronger performance potential for MoE configurations, improved stability, and cleaner code paths across two key repositories.

Month: 2024-11 — Key accomplishments focused on delivering CUDA Graphs support for Mixture-of-Experts models in Transformer Engine, with refined FP8 tensor management, graph capture optimizations, and robust graphed execution. This update improves throughput, stability, and deployment ease for FP8 MoE workloads on ROCm GPUs. No major bugs reported in this period; improvements are primarily feature-driven.