March 2026 delivered notable performance and flexibility enhancements across ROCm/aiter and sgLang, focusing on efficient rotary positional encoding and faster top-k softmax scoring. In ROCm/aiter, we added rotary_dim support to fused QK norm operations, enabling partial rotary embeddings and better adaptability in real-time processing. The changes included interface updates (Python and C++) and a refactored test suite with improved configurability, logging, and markdown-formatted summaries to aid analysis. In sgLang, we integrated aiter's fused_topk to optimize softmax scoring in the topk path, yielding tangible throughput improvements. Together, these efforts advance model throughput, configurability, and maintainability, delivering measurable business value with lower latency and higher accuracy potential in production workloads.

February 2026 performance-focused sprint summary. Focused on delivering targeted, business-value Enhancements and efficiency improvements across ROCm/aiter and kvcache-ai/sglang, with no major bugs reported in these repos. Key outcomes: - GEMM-oriented configuration optimizations for ROCm: Added three new JSON configuration files to tailor GEMM performance for varied matrix sizes and parameters. This enables faster, more predictable throughput for common workload profiles. - Deepseek MI300X performance optimizations: Implemented FP8 batched matrix multiplication in DeepseekV2 and refined attention and quantization in Deepseek R1, targeting reduced latency and higher throughput on MI300X. - Cross-repo collaboration and code quality: Coordinated changes across two repos with AMD alignment, preserving maintainability and documentation for performance-sensitive paths. Overall impact and accomplishments: - Improved throughput and efficiency for GEMM workloads and Deepseek models on MI300X, enabling faster AI inference/training workloads and better resource utilization on AMD GPUs. - Demonstrated strong capability in GPU-accelerated optimization, JSON-driven configuration, and collaboration across teams. Technologies/skills demonstrated: - JSON-based configuration for GPU kernels (GEMM), FP8 batched matrix multiplication, attention mechanisms, and quantization optimizations, CUDA/GPU optimization patterns, and cross-team collaboration.