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The post critiques PyTorch's effectiveness in industrial-scale scientific computing, arguing it wasn't designed for large-scale, distributed systems. In contrast, JAX, developed by DeepMind, offers a compiler-centered approach with better scalability and performance, making it more suitable for large-scale AI research. JAX's commitment to functional programming and reproducibility further enhances its utility, while PyTorch's attempts to integrate multiple backends lead to fragmentation and inefficiency. The post urges the adoption of JAX for improved research productivity and reliability.

PyTorch is dead. Long live JAX.

<p>Pytorch was very usable back when JAX was not a find and Tensorflow was being Tensorflow so the argument that it hurt the research does not make sense.
Also while it is true that JAX may be much better for large scale model training it is mainly the case for a small number of companies that require and have access to this large computing power.
For everybody else pytorch is still easier, has better community support and ecosystem. JAX has still long way to go.</p>
