Over the last decade, the landscape of machine learning software development has undergone significant changes. Many frameworks have come and gone, but most have relied heavily on leveraging Nvidia’s CUDA and performed best on Nvidia GPUs. However, with the arrival of PyTorch 2.0 and OpenAI’s Triton, Nvidia’s dominant position in this field, mainly due to its software moat, is being disrupted.
This report will touch on topics such as why Google’s TensorFlow lost out to PyTorch, why Google hasn’t been able to capitalize publicly on its early leadership of AI, the major components of machine learning model training time, the memory capacity/bandwidth/cost wall, model optimization, why other AI hardware companies haven’t been able to make a dent in Nvidia’s dominance so far, why hardware will start to matter more, how Nvidia’s competitive advantage in CUDA is wiped away, and a major win one of Nvidia’s competitors has at a large cloud for training silicon.
Comments are closed.