Parameter-Efficient Fine-Tuning (PEFT)

Parameter-efficient fine-tuning (PEFT) is a family of techniques for adapting pre-trained large language models to specific tasks or domains by updating only a small subset of the model's parameters, rather than retraining all weights. Methods include LoRA (Low-Rank Adaptation), prefix tuning, prompt tuning, and adapter layers. PEFT methods typically update less than 1–5% of total parameters while achieving performance competitive with full fine-tuning on targeted tasks.

PEFT is the dominant approach for enterprise LLM customization because it dramatically reduces the GPU memory, compute cost, and time required compared to full fine-tuning. A commerce organization can fine-tune a domain-specific model for product attribute extraction, customer intent classification, or brand-voice content generation in hours rather than days, on a fraction of the hardware. PEFT also makes it practical to maintain multiple specialized adapters — one per business domain or use case — that share a common base model, reducing storage and serving costs while enabling modular customization at scale.