Few industries demonstrate the stakes of AI deployment better than autonomous vehicles. At Cruise, Goud, an editorial board member at ESP International Journal of Advancements in Computational Technology, led the optimization of deployment pipelines for more than fifty AV stack models spanning LiDAR, Radar, Vision, and large language models. In an environment where rollout inefficiency can delay innovation and inflate costs, he engineered systems that reduced rollout times by approximately sixty-six percent. 

This leap was not achieved through incremental tuning alone. Goud employed TensorRT accelerators, CUDA graphs, quantization, and speculative decoding to optimize inference, all while collaborating with NVIDIA to refine TensorRT pipelines. The result was faster iteration, higher real-world performance, and measurable cost savings. A Cruise blog, AV Compute: Deploying to an Edge Supercomputer, captured the industry impact of this initiative, underscoring how deployment efficiency is now as critical as model accuracy.Â