Decoding high-bandwidth memory: A practical guide to GPU memory for fine-tuning AI models

GPU memory shortages plague AI developers when fine-tuning large models, but strategic techniques can dramatically reduce requirements from prohibitive levels to manageable ones. By combining Parameter-Efficient Fine-Tuning methods like LoRA with quantization and FlashAttention, developers can shrink memory needs from 32+ GB to under 8 GB for billion-parameter models, enabling training on consumer hardware.