May 2026 Monthly Summary (modularml/mojo): Focused on delivering end-to-end performance improvements and correctness enhancements for large-scale FP8-enabled MoE workloads, with explicit wins in fused compute paths, kernel dispatch tuning, and decoding accuracy. The month emphasized business value through throughput gains, reduced memory bandwidth, and robust test coverage for reliability and future scalability.

April 2026 performance month focused on delivering high-impact features and reliability improvements across modular/modular and modularml/mojo, with emphasis on NVFP4 and SM100 architectures. Highlights include a new three-regime NVFP4 dispatch, an overlapped inter-tile scheduler to enable persistent-kernel throughput, and memory-traffic optimizations using predicated cp.async. Also addressed critical model-loading mismatches, stable FP8 execution, and launched programmatic dependent launch for SM100 to unlock flexible hardware-aware launches. Across both repos, the work translates to measurable DL throughput gains, reduced memory overhead, and improved launch efficiency.

March 2026 focused on performance tuning, breadth of device support, and reliability for grouped matmul in the modular repository. Deliverables span kernel optimizations, scheduling/dispatch logic, and correctness checks that translate into tangible business value: faster inference, better scaling for large models, and more robust benchmarks.

February 2026 (2026-02) modular/modular monthly summary focusing on kernel-level features and reliability improvements. Delivered AB swap support for grouped matmul and 1d1d GMM kernels, introduced 2SM UMMA in structured 1d1d GMM kernel, extended BF16 scales in FP8 grouped matmul kernels, and added BF16 batched matmul on SM100 with always-3D TMA layouts. These efforts enhance transposed-output flexibility, data integrity, and GPU throughput while expanding hardware support and test coverage.

January 2026 performance summary for modular/modular focused on delivering performance- and flexibility-enhancing kernel work and expanding support for mixture-of-experts workloads. Delivered consolidated grouped matmul kernel enhancements to boost ML workloads on GPUs, including FP8 1d1d scaling, an NVFP4 grouped matmul kernel, and a refactor to remove unused CLC code while optimizing warp usage and SM100 operand swap. Introduced comprehensive benchmarking tooling to quantify improvements. Added GMM with expert_scales support, enabling per-expert scaling during 1d1d scaling and updating tests/benchmarks to cover this feature. These efforts reduce complexity, improve kernel efficiency, and broaden data-type and workload coverage, delivering measurable business value for AI/ML training and inference pipelines.

Month: 2025-12 — Repository: modular/modular. Focused on FP8 benchmarking capabilities and GPU-accelerated benchmarking paths, laying groundwork for FP8 performance evaluation across workflows.

November 2025 monthly summary for modular/modular focused on delivering performance- and robustness-oriented matrix multiplication improvements and FP8 GMM support. The work targeted sm100-optimized paths, shared memory utilization, and scalable FP8 scheduling, with clean integration into bench and UMMA workflows.

October 2025: Focused on performance optimizations and reliability for GPU kernels in modular/modular. Delivered an environment-controlled SwapAB optimization flag to gate experimental kernel features behind USE_EXPERIMENTAL_KERNELS, enabling safer experimentation and controlled rollouts. Rolled out grouped matrix multiplication improvements including a DeepGEMM-like grouped matmul scheduler (TileScheduler), a persistent kernel optimized for SM100, and fused epilogue calculations to enable QKV fusion, driving higher throughput and lower latency. Addressed a critical N constraint in the B200 GMM kernel by correcting cta_group allocation when N is not divisible by 256, accompanied by tests with larger shapes. These changes improve hardware utilization, scalability, and reliability, delivering measurable business value in ML workloads and smoother deployment pipelines.

September 2025 (Month: 2025-09) — Kernel-level enhancements in modular/modular focused on transformer workloads on modern NVIDIA GPUs. Delivered two key features: (1) MHA kernel head_dim 80 support with padded_depth for Hopper/H100 and Blackwell GPUs, enabling head_dim = 80 with improved shared-memory alignment and general depth compatibility; (2) SwapAB optimization for SM100 matrix multiplication, including A/B swapping with internal C transpose to preserve C = A @ B', with targeted dispatch and a split into small-M/SM100 configurations. These changes introduce dedicated kernel paths and dimension/data-type conditioned dispatch, with commits traceable to specific changes. Impact: expanded head-dimension support and boosted SM100 matmul performance, enabling more efficient training/inference for larger models and targeted workloads on current generation GPUs. Technologies/skills demonstrated: GPU kernel development, memory alignment techniques, kernel dispatch design, performance tuning for SM100 and MHA workloads, and familiarity with Hopper/H100 and Blackwell architectures.

August 2025 monthly summary for modular/modular focusing on performance-oriented kernel work and memory transfer optimizations. Key kernel work delivered includes GPU-accelerated grouped matrix multiplication optimizations for B200 with tensor cores (TMA) and an FP8 path, accompanied by tests to validate correctness and performance. A naive FP8 grouped matmul kernel with blockwise scaling and FP8 input types (accumulating in float32) was added, expanding FP8 support and test coverage. Additionally, memory transfer was refactored to use shared memory via st_matrix for FA H100 kernels, with an output_reg_to_smem utility and updated thread/warp group calculations to improve organization and potential performance gains.

July 2025 monthly performance summary for modular/modular. Focused on accelerating matrix multiplication kernels and expanding cross-architecture compatibility. Delivered GPU kernel optimizations, added safe fallbacks for older GPUs, and simplified device dispatch to improve reliability across environments, enabling broader hardware support while preserving performance targets.

June 2025 performance update for modular/modular: Expanded hardware compatibility and performance for large-model inference through Flash Attention 2 (FA2) 64-head support with FP32 token generation, plus H100-optimized WGMM and matmul enhancements with FP32/TF32 dispatch. These changes broaden hardware support, improve throughput, and position us to deliver faster, more accurate token generation on diverse GPUs.

Concise May 2025 monthly summary for modular/modular: Delivered core enhancements to LayoutTensor vectorization, added coordinate offset utilities, and fixed a critical numeric stability issue. The work focused on improving performance, correctness, and build/test reliability, with a clear path to business value through faster tensor processing and robust indexing for nested layouts.

April 2025 monthly summary for modular/modular: Delivered key features to enhance cross-device accuracy testing and GPU synchronization, driving reliability and performance in matrix-multiplication workloads.

March 2025 performance-focused delivery for modular/modular. Implemented kernel-level enhancements to improve data movement and numerical robustness for large-scale matrix operations, introduced dynamic memory layouts for tiled operations, and added Householder QR factorization within kernels. Bank-conflict mitigation was addressed for SM90 paths, contributing to higher throughput and more reliable results on modern GPUs. These changes map to several targeted commits and establish a stronger foundation for scalable attention and linear algebra workloads.