diff --git a/docs/source/reducing_memory_usage.md b/docs/source/reducing_memory_usage.md
index f258c0a20f..9b90fbacfb 100644
--- a/docs/source/reducing_memory_usage.md
+++ b/docs/source/reducing_memory_usage.md
@@ -90,6 +90,33 @@ from trl import SFTConfig
 training_args = SFTConfig(..., packing=True, max_length=512)
 ```
 
+## PEFT for parameter-efficient fine-tuning
+
+Parameter-Efficient Fine-Tuning (PEFT) methods like LoRA are among the most effective techniques for reducing memory usage during training. Instead of training all model parameters, PEFT methods train only a small number of adapter parameters, significantly reducing memory requirements and enabling fine-tuning of larger models on limited hardware.
+
+For comprehensive details on using PEFT with TRL, including various adapter methods, quantization options, and advanced configurations, see [PEFT Integration](peft_integration).
+
+To use PEFT for reducing memory usage:
+
+```python
+from datasets import load_dataset
+from peft import LoraConfig
+from trl import SFTTrainer
+
+dataset = load_dataset("trl-lib/Capybara", split="train")
+
+peft_config = LoraConfig()
+
+trainer = SFTTrainer(
+    model="Qwen/Qwen2.5-0.5B",
+    train_dataset=dataset,
+    peft_config=peft_config,
+)
+```
+
+PEFT can be combined with other memory reduction techniques such as quantization (4-bit or 8-bit) for even greater memory savings. See [PEFT Integration](peft_integration) for quantization examples.
+
+
 ## Liger for reducing peak memory usage
 
 > [Liger Kernel](https://github.com/linkedin/Liger-Kernel) is a collection of Triton kernels designed specifically for LLM training. It can effectively increase multi-GPU training throughput by 20% and reduce memory usage by 60%.