Host overhead is killing your inference efficiency

What this post added

This post details how Modal addresses host overhead in AI inference, a key contributor to GPU inefficiency. It explains how to identify host overhead by monitoring GPU kernel utilization and discusses strategies to mitigate it, including avoiding unnecessary CPU-GPU data transfers (e.g., constructing tensors on the GPU instead of transferring them), fusing multiple kernels into a single launch to reduce kernel launch overhead, and utilizing CUDA Graphs to amortize launch overhead further. The post highlights contributions to open-source inference engines like SGLang and vLLM to implement these optimizations.