Alexander Gruber focused on stabilizing GPU memory usage and enhancing cross-architecture compatibility in the fla-org/flash-linear-attention repository. He addressed challenges in large-model training on AMD RDNA GPUs with limited 64KB shared memory by implementing shared memory guards, autotuning safeguards, and architecture-aware tiling logic. Using Python and deep learning frameworks, Alexander fixed a gating bug affecting CONST_TILING behavior and validated the improvements on RDNA4 hardware during Qwen3-Next-80B-A3B-Instruct training. His work reduced compilation and runtime failures, improved reliability for forward and backward passes, and increased portability of the linear-attention kernel across diverse GPU architectures, demonstrating strong GPU programming expertise.