Parameter-Efficient Fine-Tuning (PEFT) adapts large models by training minimal parameters. Core motivation: Full fine-tuning of LLMs requires prohibitive GPU memory (70B model needs 280GB+ for optimizer states). PEFT trains 0.01-1% of parameters while achieving 90-99% of full fine-tuning quality. Major methods: LoRA (low-rank weight matrices), QLoRA (quantized base + LoRA), prompt tuning (learned soft prompts), prefix tuning (learned activations), adapters (small bottleneck layers), IA3 (learned rescaling). Benefits: Train on consumer GPUs, store tiny checkpoints per task, easily switch between tasks, avoids catastrophic forgetting. When to use each: LoRA for general fine-tuning, QLoRA when memory constrained, prompt tuning for multi-task with shared base, adapters for efficient ensemble. Tools: Hugging Face PEFT library, axolotl, llama-factory. Trade-offs: Slightly lower quality than full fine-tuning for some tasks, method selection requires experimentation. PEFT democratized LLM customization, enabling fine-tuning on single GPUs.