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.