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At our latest YC Paper Club, researchers and builders presented on multi-GPU kernel optimization, intelligence per watt for local inference, AI-generated GPU kernels and benchmarking, heterogeneous inference infrastructure design, and GPU-accelerated game engines for reinforcement learning. Thanks to the following presenters: Stuart Sul (Stanford / Cursor), John (Stanford), Mark (PyTorch / GPU Mode / CoreAuto), Misha (Marlo), and Brennan (Stanford) Transcript: https://www.ycrootaccess.com/p/multi-gpu-kernels-intelligence-per Chapters: 0:00 – Francois Chaubard: The case for chip and kernel specialization 7:16 – Stuart Sul: Parallel Kittens - Systematic and Practical Simplification of Multi-GPU Al Kernels (https://arxiv.org/abs/2511.13940) 21:29 – Jon Saad-Falcon: Intelligence per Watt - Measuring the Intelligence Efficiency of Local and Cloud AI (https://arxiv.org/abs/2511.07885) 31:05 – Mark Saroufim: When Al Starts Writing Systems Code 47:04 – Misha Smelyanskiy: Why AI Inference Needs Heterogeneous Hardware 1:04:33 – Brennan Shacklett: Building a High-Throughput Game Engine that Runs ENTIRELY on the GPU (https://madrona-engine.github.io/shacklett_siggraph23.pdf) 1:15:35 – Wrap-up & what's next Apply to Y Combinator: https://www.ycombinator.com/apply Work at a startup: https://www.ycombinator.com/jobs
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