Host overhead is killing your inference efficiency (modal.com)

Recent discussions highlight the critical issue of host overhead—an inefficiency in AI inference workloads resulting from the GPU being blocked by the CPU's preparation work. This phenomenon leads to low GPU kernel utilization and significantly hampers inference efficiency, impacting the growing demand for rapid responses in AI applications. As systems evolve, expectations have shifted to a few hundred milliseconds for response times, making it imperative for software engineers to optimize hardware utilization. Tools like the PyTorch Profiler and NVIDIA’s nvidia-smi can help identify instances of host overhead, described as the “gaps” in CUDA streams where the GPU sits idle. To address host overhead, developers are encouraged to minimize unnecessary synchronization between the CPU and GPU. This can be achieved by constructing tensors directly on the GPU when feasible and employing kernel fusion techniques to reduce the number of necessary kernel launches. Innovations like CUDA Graphs further allow for combining multiple kernel launches into a single execution string, significantly cutting down on launch overhead. Initiatives like these not only enhance inference efficiency but also drive down operational costs, crucial as GPU usage remains a primary expense in AI applications. As the AI landscape matures, continuous improvements in open-source inference engines will be vital for maintaining competitive performance levels.