Core Insights - The article discusses the development of a real-time visual-language-action (VLA) model that achieves a significant reduction in inference time, enabling dynamic tasks such as object grasping to be performed effectively [3][6][23]. Optimization Strategies - The research outlines a comprehensive optimization pipeline that reduces inference time from over 100ms to 27.3ms for a two-view model, achieved through four main steps: eliminating basic overhead, simplifying the computation graph, optimizing kernel depth, and tuning GEMM parameters [7][18][22]. - The first step involves removing CPU overhead by utilizing CUDA Graphs, which reduces inference time from 106.5ms to approximately 53.9ms [9][10]. - The second step simplifies the computation graph, further reducing inference time to about 45.8ms [12][14]. - The third step focuses on optimizing kernel depth, which includes techniques like weight folding and merging operations to enhance performance [15][18]. Performance Validation - The article employs the roofline model to assess the theoretical lower bound of performance, indicating that the actual inference time of 27.3ms is only 30% higher than the theoretical limit of 20.6ms, suggesting that the optimizations are close to hardware limits [20][22]. - The synchronization overhead is also analyzed, showing significant reductions when using optimized methods compared to naive implementations [21][24]. Real-World Application - A real-world experiment involving the grasping of a falling pen demonstrates the model's effectiveness, achieving a 100% success rate in trials, which highlights the model's capability to meet stringent timing constraints [36][37]. - The framework allows for high-frequency control, with the potential to run 30 VLA models at 30Hz and 480 action experts at 480Hz, showcasing its applicability in dynamic robotic tasks [31][32]. Future Directions - The article suggests future research directions, including exploring larger model sizes and finer-grained feedback loops to enhance performance and adaptability in real-time applications [37].