Month: 2025-12. In ROCm/aiter, delivered two critical contributions: a bug fix to ensure correct fused AR RMS normalization by correcting the output order in the custom_fused_ar_rms path, and performance tuning for GEMM and MoE configurations to optimize Qwen3 MoE deployments. These changes enhanced numerical accuracy and inference throughput, aligning with reliability and scalability targets for production deployments. The work reduced potential discrepancies in fused AR RMS calculations and delivered measurable performance improvements on Qwen3 MoE models. Technologies/skills demonstrated include GPU-accelerated compute optimizations, kernel-level debugging, configuration-driven performance tuning, and collaborative development (co-authored commits). Business value: more reliable model normalization, higher throughput, and easier deployment of Qwen3 MoE in production, enabling scalable, accurate inference pipelines.

November 2025 performance summary: Targeted model configuration optimizations and runtime enhancements across ROCm/aiter and vllm, plus a critical bug fix set. The work improves large-model throughput, maintainability, and reliability, with FP8 decoding now available on ROCm for ML workloads.