Reducing High-Bandwidth Memory Bottlenecks in JAX-Based LLM Training with Host Offloading
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Source: NVIDIA 技术博客Publish time unverified
Large language model (LLM) training workloads increasingly run into GPU memory limits before compute is fully used. Model weights, gradients, optimizer states,... 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).