Recently, researchers proposed a framework called FlashDrive, which aims to address the high computational cost of vision-language-action (VLA) inference in autonomous driving. The VLA model, while promising for end-to-end reasoning, has a computational cost that is too high for real-time control. The FlashDrive framework optimizes the four bottlenecks of VLA inference: visual encoding, language model prefill, reasoning token generation, and flow-matching denoising. It uses various lightweight algorithmic shortcuts, such as temporal overlap, non-autoregressive diffusion, and adaptive step caching. Additionally, the FlashDrive framework combines system-level CUDA Graph compilation and kernel fusion, further improving performance. On the Alpamayo 1.5-10B model, the FlashDrive framework reduces end-to-end latency from 717ms to 151ms (4.7x) while maintaining essentially unchanged accuracy. This breakthrough enables a 10B-parameter VLA model to run at 6.6Hz on a single GPU, up from 1.4Hz, significantly advancing the real-time deployment of autonomous driving. Blogger's Review: The FlashDrive framework brings new hope to the autonomous driving field by achieving a crucial step towards real-time control through algorithm-system co-design, and the future development of autonomous driving will be more rapid and secure.