Document DoRA inference overhead vs QLoRA across scales

README.md

@@ -229,6 +229,25 @@ Hot-swapping is safe for these adapters, in either load order.
 > (`peft` 0.18.1, `transformers` 5.3.0, `torch` 2.11.0+cu128, `bitsandbytes` 0.49.2)
 
 
+## Inference cost of DoRA
+
+DoRA recomputes weight column norms on **every forward pass through every linear
+adapter module**, which makes it slower at inference than the equivalent QLoRA
+adapter. At 72B — 8 target modules across 80 decoder layers — this overhead
+dominates inference latency.
+
+| Scale | Hardware | QLoRA | QDoRA | Overhead |
+|---|---|---|---|---|
+| 0.5B | A100 | 2.49 s/image | 4.73 s/image | **1.9x** |
+| 7B | H200 | 1.63 s/image | 5.63 s/image | **3.4x** |
+| 72B | A100 x8 | 12.5 s/image | 71.6 s/image | **5.7x** |
+
+The penalty grows with decoder depth and the number of adapted modules.
+
+**If inference latency matters to you, prefer the QLoRA adapter at the same
+scale.** The two were trained with an identical recipe and differ only in the
+adapter formulation.
+
 ## Continuing fine-tuning from this adapter
 
 You can keep training these weights on your own data. Two options: