Comparison
LoRA vs QLoRA
LoRA and QLoRA are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
When you would reach for LoRA
When full fine-tuning is too expensive or you want swappable specialized adapters.
Fine-tuning Llama-3-8B for a domain on a single A100 with LoRA.
When you would reach for QLoRA
When you want to fine-tune a frontier-sized open model on a single GPU.
Fine-tuning Llama-3-70B on a domain corpus on a single A100.
Frequently asked
What is the difference between LoRA and QLoRA?
LoRA: LoRA is a parameter-efficient fine-tuning method that freezes a model's original weights and learns small low-rank update matrices alongside them. Cheap fine-tuning on a single GPU. QLoRA: QLoRA fine-tunes a 4-bit quantized base model with LoRA adapters, letting you train 70B-class models on a single 48 GB GPU at near-full fine-tuning quality.
When should I use LoRA vs QLoRA?
When full fine-tuning is too expensive or you want swappable specialized adapters. When you want to fine-tune a frontier-sized open model on a single GPU.
Are LoRA and QLoRA the same thing?
No. LoRA is training; QLoRA is training. They are related but address different parts of the AI stack.