JAX backends and devices

This blog post shares a developer's experience while porting a PyTorch LLM code to JAX. The author encounters and resolves issues related to device memory allocation when loading a large dataset and discusses how to manipulate JAX's default device settings to optimize data processing. The transition involves understanding the nuances of hardware backends and how they impact memory usage, ultimately leading to improved code efficiency.