PEFT Motivation › Summary
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Parameter-efficient fine-tuning addresses the practical challenges of adapting large language models to specific tasks. This chapter covered the motivations driving PEFT adoption and established the conceptual foundation for the specific techniques we will explore in subsequent chapters.
Storage costs multiply with tasks under full fine-tuning. A complete model copy is required per task. For LLaMA-70B with eight tasks, this means over 1 TB of weights. PEFT reduces this to approximately 130 GB by storing the base model once plus small adapters per task, changing the storage scaling from multiplicative to additive.
Training memory limits accessibility. Full fine-tuning of LLaMA-70B requires approximately 1,040 GB of GPU memory for parameters, gradients, and optimizer states. PEFT reduces this by roughly 7x by computing gradients and optimizer states only for the small trainable adapter parameters, making large-model fine-tuning accessible on a 2-GPU rather than a 14-GPU cluster.
Multi-task deployment benefits from adapter swapping. PEFT enables loading the base model once and swapping small adapters in milliseconds to switch tasks. This eliminates the 30-60 second latency of full model reloading and enables new patterns like personalized per-user model variants.
Performance trade-offs are task-dependent. PEFT achieves 90-99% of full fine-tuning performance for most tasks. Classification and extraction tasks see minimal gaps, while domain adaptation and complex reasoning show larger differences. The rank hyperparameter controls the trade-off between efficiency and performance, with ranks of 8-32 typically representing the sweet spot.
Start with PEFT for large models. Unless you have clear evidence that full fine-tuning is necessary, PEFT provides a strong efficiency-performance balance, especially for models above 7B parameters. Making large-model fine-tuning accessible on smaller hardware has been one of PEFT's most significant practical effects.
The following chapters examine specific PEFT methods, starting with LoRA's approach to low-rank weight updates.