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

GPU memory shortages plague AI developers when fine-tuning models, with a typical 4-billion parameter model requiring 32 GB just for basic training. Memory gets consumed by model weights, optimizer states, gradients, and activations, but several techniques can drastically reduce these requirements. Parameter-Efficient Fine-Tuning with LoRA reduces memory overhead from 24 GB to just 120 MB by freezing original weights and training only small adapter layers, while quantization can shrink model size by up to 75% using 4-bit precision instead of standard 16-bit.