JAX is becoming a preferred framework for machine learning teams seeking both research flexibility and production performance, particularly when utilizing GPUs. Unlike PyTorch, which uses an imperative style, JAX's functional approach combined with XLA compilation offers significant advantages for GPU workloads, including automatic kernel fusion and multi-accelerator scaling. This guide provides strategies for selecting the right framework, configuring GPU environments, optimizing core operations, and scaling across multiple GPUs, as well as best practices for production deployment. It contrasts JAX's capabilities with PyTorch, highlighting JAX's strengths in hardware portability, composable transformations, and memory efficiency, while noting PyTorch's mature ecosystem and intuitive style. The guide also covers essential setup steps, core optimization techniques, and scaling strategies for transitioning from single to multi-GPU deployments, alongside performance best practices like gradient checkpointing and mixed precision. By following these strategies, teams can effectively deploy JAX on GPUs, optimizing memory usage and building efficient production pipelines, with Lambda providing the necessary GPU infrastructure for enterprise machine learning workflows.