LoRA vs QLoRA: Which Fine-Tuning Method Should You Use?
LoRA vs QLoRA explained: how they work, VRAM needs, speed and quality trade-offs, and when to pick each for fine-tuning LLaMA, Mistral and other LLMs.
The idea behind both
Full fine-tuning updates every weight in a model — accurate but very memory-hungry. LoRA (Low-Rank Adaptation) freezes the base model and trains small "adapter" matrices instead, cutting trainable parameters by 100–1000×.
QLoRA goes further: it loads the base model in 4-bit quantization, then applies LoRA on top — so you fine-tune large models on modest GPUs.
Side by side
LoRA: ~12GB VRAM for a 7B model, fast, near-full quality for most tasks.
QLoRA: ~8GB VRAM, slightly slower, lets you fine-tune bigger models on smaller GPUs with minimal quality loss.
Full fine-tune: 40GB+ VRAM, highest ceiling, rarely needed for task-specific adaptation.
Which to choose
Pick LoRA if your GPU has ≥12GB and you want the fastest good result. Pick QLoRA if VRAM is tight or the base model is large. Use full fine-tuning only when you have datacenter GPUs and need maximum quality.
On Project Huginn
Project Huginn supports LoRA, QLoRA and full fine-tuning across 7 free base models (LLaMA 3.1, Mistral 7B, Phi-3, CodeLlama, Gemma 2, Qwen 2.5). Capability-aware routing sends QLoRA jobs to ~8GB GPUs and full fine-tunes to datacenter cards automatically.