NVIDIA explains host offloading to reduce HBM bottlenecks in JAX LLM training

Large language model (LLM) training workloads increasingly hit GPU memory limits before compute is fully utilized. Model weights, gradients, optimizer states, communication buffers, and intermediate activations all compete for limited GPU high-bandwidth memory (HBM). As model size, sequence length, and batch size grow, HBM capacity often becomes the primary scaling bottleneck.

This article explains how host offloading can be used to mitigate these constraints in JAX-based training workflows.