diff --git a/docs/cudf/source/cudf_pandas/faq.md b/docs/cudf/source/cudf_pandas/faq.md
index 34b657488c1..5dd33f45216 100644
--- a/docs/cudf/source/cudf_pandas/faq.md
+++ b/docs/cudf/source/cudf_pandas/faq.md
@@ -194,3 +194,14 @@ for testing or benchmarking purposes. To do so, set the
 ```bash
 CUDF_PANDAS_FALLBACK_MODE=1 python -m cudf.pandas some_script.py
 ```
+
+## What is the recommended range of dataset size that I can process using `cudf.pandas`?
+
+`cudf.pandas` can process a wide range of dataset size. As a 
+_very rough_ rule of thumb,`cudf.pandas` shines on workflows with more than 
+10,000 - 100,000 rows of data, depending on the algorithms, data types, and other factors.
+Below this range, workflows might execute slower on GPU than CPU because of the 
+cost of data transfers. With [managed memory pool and managed memory prefetching enabled in cudf 
+by default](how-it-works.md), you can process datasets larger than GPU memory and up to a theoretical 
+limit of the combined CPU and GPU memory size. However, note that the 
+best performance with large data sizes can be data and workflow dependent.