Reducing High-Bandwidth Memory Bottlenecks in JAX-Based LLM Training with Host Offloading

JAX host offloading frees GPU memory by temporarily storing selected intermediate training results in the CPU's memory. Those results, called activations, are brought back when training needs them to calculate updates. This offers an alternative to discarding them and recomputing them later. NVIDIA explains the approach in its training article.

Moving data introduces its own cost, so scheduling matters as much as available memory. Transfers need to overlap with useful GPU computation instead of leaving the GPU waiting. The article's experiments show that throughput changes with the model and scheduling configuration. Offloading therefore exchanges memory pressure and recomputation for communication that the system must efficiently hide. The reported comparisons show that dependency.