diff --git a/book/quarto/contents/vol1/frontmatter/notation.qmd b/book/quarto/contents/vol1/frontmatter/notation.qmd
index 7fc078cdd..261347512 100644
--- a/book/quarto/contents/vol1/frontmatter/notation.qmd
+++ b/book/quarto/contents/vol1/frontmatter/notation.qmd
@@ -86,9 +86,9 @@ We follow standard deep learning conventions (@goodfellow2016deep) with explicit
 
 ## Units and Precision {#sec-notation-conventions-units-precision-fdaf}
 
-*   **Physical Units**: This book uses SI (metric) units throughout---meters, kilograms, seconds, watts, °C---consistent with standard engineering and scientific practice. Where source data was originally reported in imperial units, we convert to SI and note the original values parenthetically.
-*   **Storage**: We use decimal prefixes (1 KB = 1000 bytes, 1 GB = $10^9$ bytes) throughout for consistency with ML literature. In memory contexts, industry convention uses these same symbols for binary values ($2^{10}$, $2^{30}$); the difference is negligible for our purposes.
-*   **Compute**: We use decimal prefixes for operations.
+*   **Physical Units**: This book uses SI (metric) units throughout---meters, kilograms, seconds, watts, °C---consistent with standard engineering and scientific practice. Where source data was originally reported in imperial units, we convert to SI and note the original values parenthetically. A space always separates the number from the unit (e.g., 100 ms, 2 TB/s).
+*   **Data and memory**: We use **decimal SI prefixes only**: KB = $10^3$ bytes, MB = $10^6$, GB = $10^9$, TB = $10^{12}$. We do not use binary units (KiB, MiB, GiB) in prose; all capacities, throughputs, and model sizes are reported in decimal units (e.g., 80 GB, 2 TB/s, 102 MB).
+*   **Compute**: We use decimal prefixes for operations (e.g., GFLOPs, TFLOPs).
     *   1 TFLOP = $10^{12}$ FLOPs
 *   **Precision**:
     *   **FP32**: Single precision (4 bytes)