JAX on NVIDIA GPUs Part 2: A practical guide for ML engineers

The guide provides a comprehensive analysis of scaling JAX-based language model training across different NVIDIA GPU configurations, including single GPU, multi-GPU, and multi-node setups. Conducting controlled experiments, it reveals that while all configurations achieve similar accuracies (20-24%), the real speedup is significant, with multi-GPU training being 2.3 times faster and multi-node training being 4.1 times faster than single GPU setups. However, due to the small size of the 27M parameter model used, communication overhead limits scaling efficiency, especially on 8+ GPUs, where the time spent on gradient synchronization surpasses computation time. For larger models with 100M+ parameters, better scaling is expected, achieving 6-7x speedup on 8 GPUs and 12-14x on 16 GPUs. The guide also details the experimental setup, model architecture, dataset preparation, and provides implementation steps for conducting similar benchmarks, highlighting the importance of proper learning rate scaling to maintain convergence across different configurations.

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