1:16:24Lessons From Training Composer At Cursor And Building Meta/Nvidia Compute Clusters
The talk brings together five presentations on specialization across AI chips, GPU kernels, inference infrastructure, and simulation. Stuart explains how ParallelKittens uses different transfer mechanisms and scheduling strategies to make multi-GPU kernels fast and maintainable. John argues that improving local models and accelerators could shift much inference away from data centers, while Mark describes how AI-generated GPU kernels create new challenges for benchmarking and correctness. Misha discusses matching different inference phases to different hardware, and Brennan shows how entity-component-system design can make GPU-based game simulation more than 100 times faster than CPU baselines.