GPUs are critical for high-performance computing, particularly for neural network inference workloads, but achieving optimal GPU utilization can be challenging. This guide outlines three key metrics of GPU utilization—allocation, kernel, and model FLOP/s utilization—and discusses strategies to improve efficiency and performance in GPU applications. Modal's solutions aim to enhance GPU allocation and kernel utilization, helping users achieve better performance and cost-effectiveness.

The blog post details a reverse-engineering effort of Flash Attention 4 (FA4), a new CUDA kernel optimized for Nvidia's architecture, achieving a ~20% speedup over previous versions. It explores the kernel's architecture and asynchronous operations, making it accessible for software engineers without CUDA experience, while providing insights into its tile-based computation processes and optimizations for generative AI tasks.