Match each PEFT method to the standout advantage that defines it.

**Five PEFT methods**, each defined by one specific advantage over the others. LoRA is the general default. It adds small low-rank adapter matrices to attention projections and freezes the rest of the model. With a few hundred million trainable parameters on a 7B base, you get roughly 90% of full fine-tuning quality at a fraction of the cost. It is the right starting point for most fine-tuning work. QLoRA combines LoRA with 4-bit quantization of the frozen base model. The base weights live in 4-bit NF4 format and the LoRA adapters stay in bf16. This cuts VRAM roughly fourfold compared to LoRA on the same model, which is what lets you fit a 70B base on a single 48GB GPU. IA3 uses even fewer parameters than LoRA. It learns per-channel scaling vectors that multiply existing activations, no matrix factorisation, no rank to choose. It is ideal for very small SFT datasets where bigger adapters would overfit. DoRA decomposes each weight update into a magnitude (one scalar per column) and a direction (the column normalised). LoRA tunes only the direction. The decomposition closes most of the gap between LoRA and full fine-tuning at low rank. AdaLoRA does not fix one rank for every layer. It allocates the rank budget non-uniformly based on a sensitivity score derived from an SVD-style parameterisation, spending more rank where it matters most.

**Each of the five PEFT methods solves one specific problem.** LoRA is the standard. Low-rank BA adapters added to attention projections (and sometimes MLP projections), with the base model frozen. Trainable parameters drop by two to three orders of magnitude compared to full fine-tuning, and quality typically reaches 90% or more of full FT on standard SFT tasks. It is the right default for most fine-tuning work, supported everywhere in the 2026 ecosystem. QLoRA combines LoRA with 4-bit quantization of the frozen base, using the NF4 format and double quantization for the constants. The LoRA adapters stay in bf16 so gradients flow normally. The result is roughly fourfold VRAM reduction compared to LoRA on the same model, which makes a 70B base fit on a single 48GB GPU. The defining feature is the quantization of the frozen base, not a change to LoRA itself. IA3 (Infused Adapter by Inhibiting and Amplifying Inner Activations) learns per-channel scaling vectors only, applied to keys, values, and feedforward outputs. No matrix factorisation, no rank hyperparameter, just one scalar per channel. Trainable parameter count is far smaller than LoRA, often by another order of magnitude, which makes it ideal for very small SFT datasets where bigger adapters overfit. DoRA decomposes each weight column into a magnitude (one scalar per column) and a unit-norm direction. The magnitudes are learned directly and LoRA is applied only to the direction. The decomposition recovers the magnitude-direction correlation pattern of full fine-tuning, which closes most of the LoRA-to-full-FT gap, especially at low rank. AdaLoRA reparameterises the update in SVD form (P times Lambda times Q), then prunes low-importance Lambda entries to reallocate a fixed global rank budget across layers. Layers that need more rank get more, layers that need less get less, instead of fixing one r for all.