April 2026 performance and reliability enhancements across the modular/modular and modularml/mojo codebases. Focus was on throughput, scalability, and robustness for distributed MoE and kernel workloads, with a strong emphasis on business-value improvements such as reduced latency, better hardware utilization, and easier observability. Key changes include kernel-level PDL defaults and targeted adjustments to communication kernels to balance performance and compatibility, centralized and scalable MoE expert-parallelism support, and memory- and quantization-path optimizations that improve both speed and accuracy in production workloads. Critical reliability fixes were addressed on AMD platforms and in the DeepSeekV3 path to ensure stable operation across common deployment scenarios.

March 2026 monthly summary for modular/modular focused on distributed training improvements, reliability, and performance optimizations across DP/TP MoE architectures. Delivered end-to-end enhancements to DeepSeek with mixed TP-attention and EP-MoE, expanded dynamic-shape scattering for multi-GPU ReduceScatter, and improved inter-model EP communication via TileTensor with deadlock prevention. Added tuned benchmarking for EP across FP8/NVFP4 and introduced per-device token distribution awareness to optimize kernels. Addressed stability and compatibility issues through MLA cache validation fix in data-parallel mode and a transformer tokenizer compatibility maintenance to address tokenization regressions. Achieved notable test and CI speedups, enabling faster validation of complex distributed setups.

February 2026 highlights: Expanded NVFP4 quantization support in Expert Parallelism (EP) with token transfers, extended Float8Config to cover quantized scales, and improved memory estimation for NVFP4 DeepSeek models; stabilized single-node EP deployments by disabling NVSHMEM and conditionally allocating resources; delivered substantial MLA/EP kernel performance and data-type optimizations, including RoPE+RMSNorm fusion, TileTensor-based batched matmul, and BF16/FP8 support; refined memory/paging alignment and expanded testing to validate NVFP4 EP dispatch on NVIDIA GPUs. These changes drive higher throughput, lower memory footprint, and easier deployment across newer hardware.

During January 2026, the modular/modular repository focused on performance, reliability, and scalability enhancements across Expert Parallelism (EP) and MoE, with kernel-level optimizations, API refinements, and data-path improvements designed to boost throughput on multi-GPU interconnects (e.g., NVLink). The work also included targeted DeepSeek-V3 model optimizations to reduce host-device transfers and improve FP8/LayoutTensor support, contributing to lower latency and higher throughput for large-scale workloads.

December 2025 (2025-12) — Delivered a targeted set of performance optimizations and stability improvements for modular/modular, focusing on faster inference, memory efficiency, and build reliability. Implemented multiple fused kernels and QKV optimizations, FP8 path support, and direct P2P transfers to reduce latency, while enhancing memory estimation and OOM resilience. Updated build infrastructure to align NVSHMEM artifacts with latest changes, enabling smoother CI/build pipelines and artifact management.

Month: 2025-11. This month delivered scalable Expert Parallelism (EP) across multi-node deployments, memory planning optimizations for DeepSeekV3, and key bug fixes that enhance scalability and deployment readiness. The work focused on delivering business value through improved throughput, robust distributed training, and accurate resource planning, while expanding our technical capabilities in parallelism, memory management, and low-level kernel reliability.

October 2025—modularml/mojo: Delivering scalable Expert Parallelism (EP) for distributed MoE/inference, strengthening performance, reliability, and model coverage across our platform. Key features delivered: - End-to-end EP for distributed MoE/inference: initialization infra, dispatch and combine pipelines, MoE sharding, token format abstractions with FP8 quantization, and integration with DeepSeek-V3; enabling direct device-to-device communication and optimized EP kernel paths across distributed GPUs. - EP integration across core modules: EP support added to the MoE SDK/module, DeepSeek-V3 model compatibility, and FP8-based token encoding through TokenFormat traits; introduced BlockwiseFP8TokenFormat for ep_dispatch kernels. - Kernel and execution flow improvements: wired up EP dispatch and combine kernels and completed the EPBatchManager with combine-related functionality to streamline end-to-end EP execution. Major bugs fixed: - Stabilized SHMEM tests and ensured correctness across test suites; robustified grouped_matmul tests; fixed static stride usage in the Gumbel kernel; improved per-device chunked request handling in data-parallel text generation. - Additional test improvements and bug fixes aimed at improving scheduling accuracy and overall runtime robustness. Overall impact and accomplishments: - Significantly improved scalability and throughput for distributed MoE workloads; introduced a robust EP-enabled path with better cross-GPU communication, reduced test flakiness, and enhanced scheduling stability for DP workloads. - Expanded model coverage with EP-enabled DeepSeek-V3 integration, broadening applicability of the EP workflow across production models. Technologies/skills demonstrated: - Distributed systems, MoE architecture, FP8 quantization, token format abstractions, and kernel-level EP optimization. - SHMEM, data-parallel DP scheduling, and DeepSeek-V3 integration. - Code quality and test discipline through targeted test improvements and reliability fixes.

September 2025 — Key distributed training enhancements and stability improvements for modularml/mojo. Delivered Expert Parallelism (EP) communications in SHMEM to enable efficient routing across ranks, introduced non-blocking SHMEM API in the SDK for asynchronous, overlap-able operations, and fixed correctness for distributed matrix multiplication with uneven partitions. These changes improve scalability, reduce synchronization bottlenecks, and improve reliability in production workloads.

August 2025 monthly summary for modularml/mojo: Delivered major features enabling non-uniform models via subgraph layer groups in DistributedTransformer, added 3D RoPE support in fused_qk_rope_ragged, improved SM90 grouped matmul dispatch for BF16 performance, and fixed FP8 matmul correctness with updated scaling and tests. These efforts expand model capabilities, boost performance, and strengthen test coverage, delivering tangible business value in throughput, accuracy, and hardware efficiency.

Overview for 2025-07: Delivered a generalized Mixture of Experts (MoE) framework and integrated it with Deepseek-V2-lite and Llama4, centralized MoE logic, and added SafeTensors handle caching for stability, enabling easier model experimentation and scalable routing. Implemented distributed tensor parallelism across MLA, MoE, and DeepSeek-V2, including Distributed Latent Attention with Rope for MLA, tensor-parallel MoE, and multi-device support for DeepSeek-V2, unlocking multi-device training and inference scalability. Aligned RMSNorm precision across Deepseek and Llama4 and fixed dtype handling in fused_qk_ragged_rope, improving numerical stability and reference-consistent behavior. Improved penalties and kernels: corrected token sampler penalty application order, enhanced H100 epilogue handling, and passed static shape information to batched matmul, boosting throughput. Removed outdated weight adapter workaround by passing expert weights directly to the graph compiler, simplifying the pipeline and improving maintainability. This work collectively improves scalability, stability, and performance, enabling broader deployment of large MoE models and faster iteration for model experimentation.

June 2025 monthly summary for modularml/mojo. Focused on delivering high-impact features, stabilizing core kernels, and boosting performance and memory efficiency to drive scalability and reliability in production workloads. Highlights include nucleus (top-p) sampling integration in Pipelines/Kernels, padded attention support for the flash attention GPU kernel, and per-token normalization in KVCache. Significant GPU performance and memory improvements were achieved via ops.fold GPU optimization, preallocated cuFFT buffers, and reduced memory footprint in ops.irfft. Exposed max_pool and avg_pool in the graph API and wired their GPU implementations, enabling broader model architectures. Implemented multiple correctness fixes across kernels and pipelines including MHA SM90 prefill masking, group_norm Welford participation, SIMD alignment, token frequency computation, RNG seed management, repetition penalty, and PyTorch DLPack/pinned memory workaround.

May 2025 (2025-05) was a feature-rich sprint for modularml/mojo, delivering expanded FP8/float8 support across Llama models, significant end-to-end and pipeline enhancements, and targeted kernel/GPU optimizations. The work focused on enabling lower-precision inference for large models, improving sampling quality and control, and expanding model and pipeline capabilities to support broader use cases and business value.

April 2025 monthly summary for modularml/mojo: Delivered a comprehensive MLA/MoE kernel suite, extended sequence handling, and reliability improvements across the pipeline, enabling higher throughput and more scalable inference/training workloads. Core focus on end-to-end performance, memory efficiency, and stability for large-model workloads. Highlights include developing an MLA prefill kernel and planning kernels (with support for merging previous attention results in MLA prefill), adding a K-cache decompression kernel, extending LatentAttentionWithRope to support max_seq_len > 1, introducing MoE indices calculation kernel with tensor parallelism for MoE layers in llama4, and enhancing SlidingWindowMask capabilities with a dedicated kernel, interface wiring, and 64-bit instruction checks. Additional improvements covered zero-filling for MLA workload planning and GPU memory transfer support.

Concise monthly summary for modularml/mojo focusing on business value and technical accomplishments in March 2025. Delivered targeted kernel and KV-cache enhancements, improving throughput, stability, and support for ragged/paged inputs across attention paths.