Host overhead is killing your inference efficiency

This post details how host overhead, characterized by low GPU kernel utilization, negatively impacts AI inference efficiency. It explains that unnecessary synchronization with the CPU and inefficient data transfers between CPU and GPU introduce host overhead. The post provides specific examples of how Modal has reduced host overhead in production inference engines by constructing tensors directly on the GPU to avoid synchronous data transfer and by fusing multiple CUDA kernels into single launches using tools like the Torch compiler and CUDA Graphs. This work aims to minimize microseconds of host overhead per inference pass, contributing to faster and more cost-effective AI inference.