March 2026 performance highlights focused on FP8 verification, stability, and dependency management across modular/modular and modularml/mojo. Key wins include enabling FP8 end-to-end logit verification for Qwen3-30B-A3B-Instruct-2507, stabilizing generateGoldens multiprocessing for FP8, aligning CI/Buildkite with ARM architectures, decoupling verification dependencies for faster builds, and hardening the logit verification math for robustness across hardware.

February 2026 — Delivered significant model and config improvements in modular/modular, focusing on stability, CI reliability, and clear ownership of configurations. Key features delivered include a DeepseekV3.2 integration with full layer wiring and model classes, including per-device MLP instantiation handling and tests for single-device configurations; a comprehensive overhaul of logit verification configuration management, migrating definitions to YAML as the single source of truth with Pydantic validation models, Buildkite integration (BUILD.bazel), and associated tests; and a targeted bug fix aligning the architecture name in the logit verification config from 'aarch64' to 'arm64' to satisfy CI expectations. Overall impact includes improved numerical stability, reduced configuration drift, faster and more reliable CI feedback, and clearer, test-covered deployment configurations. Technologies/skills demonstrated include Python, YAML, Pydantic, Buildkite, Bazel, and advanced model components (MoE, MLP, TopKRouter) with robust testing.

January 2026 Monthly Summary for modular/modular: DeepSeek V3.2 Architecture Enhancements focused on modularity, capacity, and numerical stability. Delivered two key architectural changes with traceable commits: Custom MLP layer for DeepSeek V3.2 and Mixture of Experts (MoE) layers to support scalable, robust inference. What was delivered: - DeepSeek V3.2 Architecture Enhancements: Added a dedicated MLP layer (float32 intermediate operations) as a new class to avoid branching in the existing nn.layers MLP implementation, enabling cleaner integration and reduced maintenance risk. Commit: f5fd567a19c023795bdda83ee82de80153909238. - MoE layers for DeepSeek V3.2: Implemented remaining Mixture of Experts components with float32 intermediates, including DeepSeekV3_2TopKRouter, MoE, and MoEFp8 modules to improve numerical stability and capacity. Commit: 9c6ce19636d94a398bd117611d7c0dbda891cc70. Major bugs fixed: - No mission-critical bugs reported this month; stability improvements were achieved via architecture changes and float32 intermediate paths, reducing numerical instability in MoE routing and aggregation. Overall impact and accomplishments: - Increased model capacity and modularity for DeepSeek V3.2, enabling more reliable experimentation with larger MoE configurations while maintaining numerical stability. - Clear separation of concerns with a dedicated MLP class and MoE modules, reducing maintenance overhead and facilitating future feature work. - Improved traceability of changes via explicit commit-based delivery, aligning with best practices for performance reviews. Technologies/skills demonstrated: - PyTorch-based architecture design, custom MLP and MoE module implementations, MoE routing (TopK), float32 intermediate computations for stability, and modular code organization to minimize branching in core layers.