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

Large language model (LLM) training workloads increasingly run into GPU memory limits before compute is fully used. Model weights, gradients, optimizer states, communication buffers, and intermediate activations all compete for GPU high-bandwidth memory (HBM). As model size, sequence length, and batch size grow, HBM capacity often becomes the primary scaling bottleneck. This post explains how host offloading can reduce these bottlenecks in JAX-based LLM training, improving memory utilization and enabling larger model training on GPUs.