March 2026 monthly summary for pytorch/executorch focused on delivering a VGF/Ethos-U image classification workflow and accompanying documentation, with robust download fallbacks and clear export/run guidance to accelerate prototyping and adoption.

November 2025: Delivered KleidiAI INT4 kernels integration for PyTorch LLMs, enabling BF16 outputs and substantial efficiency gains. Implemented an INT4 symmetric quantization path with optimizations in quantization and matrix multiplication to boost decode throughput by ~15% and cut inference memory by ~50% on meta-llama/Llama-3.1-8B (Neoverse V2). BF16 precision support enabled, with improved prefill and decode performance validated through end-to-end benchmarking (including prefill, decode, and E2E timings) against a real-world LLM deployment. PR #158250 merged in PyTorch with contributions from ARM and PyTorch maintainers; reviews and approvals completed by multiple collaborators. Impact: higher inference throughput, significantly smaller memory footprint, enabling larger models and cost savings across data-center and edge deployments. Technologies/skills demonstrated: INT4/BF16 quantization, custom kernel integration, performance benchmarking, PyTorch integration, cross-team collaboration, and rigorous PR-driven validation.