1 link tagged with all of: memory + parallelism + fine-tuning + quantization
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This article explains the High Bandwidth Memory (HBM) needs when fine-tuning AI models, detailing what consumes memory and how to estimate requirements. It covers strategies like Parameter-Efficient Fine-Tuning (PEFT) and quantization to reduce memory usage, as well as methods for scaling training across multiple GPUs.