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

This article discusses a technique to overcome high-bandwidth memory (HBM) bottlenecks in JAX-based LLM training by offloading model states to host memory. It explains how offloading weights, gradients, and optimizer states to CPU memory can allow larger models to be trained on limited GPU memory, albeit with some performance trade-offs. The approach aims to extend the effective memory capacity for LLM training while maintaining practical training throughput.