diff --git a/docs/source/installation.md b/docs/source/installation.md
index 8499e234..0e2133b1 100644
--- a/docs/source/installation.md
+++ b/docs/source/installation.md
@@ -15,6 +15,7 @@ This document describes how to install vllm-ascend manually.
     | CANN          | == 8.3.RC2                       | Required for vllm-ascend and torch-npu    |
     | torch-npu     | == 2.8.0             | Required for vllm-ascend, No need to install manually, it will be auto installed in below steps |
     | torch         | == 2.8.0                          | Required for torch-npu and vllm           |
+    | NNAL          | == 8.3.RC2                       | Required for libatb.so, enables advanced tensor operations |
 
 There are two installation methods:
 - **Using pip**: first prepare env manually or via CANN image, then install `vllm-ascend` using pip.
@@ -45,6 +46,10 @@ Refer to [Ascend Environment Setup Guide](https://ascend.github.io/docs/sources/
 
 The easiest way to prepare your software environment is using CANN image directly:
 
+```{note}
+The CANN prebuilt image includes NNAL (Ascend Neural Network Acceleration Library) which provides libatb.so for advanced tensor operations. No additional installation is required when using the prebuilt image.
+```
+
 ```{code-block} bash
    :substitutions:
 # Update DEVICE according to your device (/dev/davinci[0-7])
@@ -71,6 +76,10 @@ docker run --rm \
 :animate: fade-in-slide-down
 You can also install CANN manually:
 
+```{warning}
+If you encounter "libatb.so not found" errors during runtime, please ensure NNAL is properly installed as shown in the manual installation steps below.
+```
+
 ```bash
 # Create a virtual environment.
 python -m venv vllm-ascend-env