AI2026-05-17
QLoRA (Quantized LoRA)
Also known as: QLoRA / Quantized Low-Rank Adaptation / 量子化LoRA
A fine-tuning method that combines LoRA with 4-bit quantization (NF4), enabling fine-tuning of 65B+ parameter models on a single consumer GPU.
Overview
Introduced by the University of Washington in 2023, QLoRA loads the base model in 4-bit NF4 quantization to minimize GPU memory, then trains LoRA adapters in bfloat16. The paper demonstrated fine-tuning a 65B-parameter model on a single 48 GB GPU, democratizing large-model customization.
Difference from LoRA
Standard LoRA loads the base model in float16/bfloat16. QLoRA adds 4-bit quantization to achieve roughly 4x further memory reduction. The NF4 data type is designed to minimize accuracy loss from quantization.
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