March 2026 performance-focused delivery across jeejeelee/vllm and ROCm/aiter. Implemented ROCm cudagraph optimization for sparse_mla to accelerate single-token decoding, added MRoPE support in rotary embeddings for better frequency layout in multi-modal contexts, introduced shared expert scoring for top-k softmax to improve decision-making with shared experts, and performed GEMM optimizations in the qwen3.5 library to boost matrix operation performance. No major bug fixes reported this month.

February 2026: Delivered a focused unit-test benchmark refactor for qk_norm_rope_cache_quant in ROCm/aiter, moving tensor construction inside the benchmark function to boost performance and clarity, and removing unnecessary code. Also fixed the unit test issue (#2043) to improve reliability and CI stability. Overall impact: faster feedback, easier maintenance, and higher-quality benchmarks. Technologies demonstrated: Python unit tests, benchmarking, and clean Git commits with proper sign-offs.

January 2026: Delivered a critical fix to the Triton implementation of paged_pa_mqa in ROCm/aiter, along with input stride type annotations to improve stability and correctness. These changes reduce runtime errors, improve ML task reliability, and strengthen parameter handling in Triton-backed workflows.

Monthly performance summary for 2025-12 focusing on ROCm/aiter. Delivered a quantization fusion for QK norms with rotary positional embeddings, enabling per-token quantization and FP8-optimized data paths. Implemented as the qk_norm_rope_cache_quant fusion with associated type conversions, memory layout improvements, and structural enhancements to support maintainability and future optimizations.

November 2025 performance summary for IBM/vllm: Focused ROCm/AMD reliability and expanded compatibility. Key work included stabilizing ROCm cu_count retrieval in IBM/vllm through a refactor removing brittle class references and ensuring current_platform.get_cu_count() usage, along with fixes to cu_count usage in rocm_aiter_fa.py. In parallel, Deepseek V2 ROCm/AMD integration was enhanced with robust shared-experts handling under feature toggles and AMD-focused optimizations (FP8 MQA logits computation and adjusted kernels). These efforts improved stability of ROCm deployments, broadened AMD GPU support, and positioned the project for scalable performance in production environments.