diff --git a/.github/workflows/build-self-hosted.yml b/.github/workflows/build-self-hosted.yml
index dd31b1d15..9b4f5d294 100644
--- a/.github/workflows/build-self-hosted.yml
+++ b/.github/workflows/build-self-hosted.yml
@@ -115,7 +115,7 @@ jobs:
           vulkaninfo --summary
           GG_BUILD_VULKAN=1 bash ./ci/run.sh ~/results/whisper.cpp ~/mnt/whisper.cpp
 
-  amd-npu-windows:
+  npu-amd-windows:
     runs-on: [self-hosted, Windows, X64, stx, rai300-400]
     timeout-minutes: 60
     continue-on-error: true   # advisory while the runner pool is new; revisit later
@@ -208,7 +208,7 @@ jobs:
           name: vitisai-smoke-log-windows
           path: vitisai.log
 
-  amd-npu-linux:
+  npu-amd-linux:
     runs-on: [self-hosted, Linux, X64, stx, rai300-400]
     timeout-minutes: 60
     continue-on-error: true   # advisory while the runner pool is new; revisit later
diff --git a/README.md b/README.md
index fd23e1c55..18f37d563 100644
--- a/README.md
+++ b/README.md
@@ -314,47 +314,80 @@ This can result in significant speedup in encoder performance. Here are the inst
 
 For more information about the OpenVINO implementation please refer to PR [#1037](https://github.com/ggml-org/whisper.cpp/pull/1037).
 
-## AMD Ryzen™ AI support for NPU
+## AMD Ryzen™ AI NPU support
 
-On AMD's Ryzen™ AI 300 Series with dedicated NPUs for acceleration, you can now run Whisper models with the ability to fully offload the encoder to NPU. This brings significant speedup compared to CPU-only.
-> **Note:**  
-> **Ryzen™ AI NPU acceleration is currently supported on Windows only.** Linux support is planned for upcoming releases.  
-> For the latest updates on Ryzen AI, check out [the official documentation](https://ryzenai.docs.amd.com/en/latest/).
+On AMD Ryzen™ AI 300 and 400 Series processors with a dedicated NPU, whisper.cpp can fully offload the Whisper encoder to the NPU via VitisAI, delivering significant speedup over CPU-only inference.
 
-### Setup environment (Windows only)
+### Prerequisites
 
-  - Obtain the XRT package and the FlexmlRT package from AMD. Both are distributed as tarballs or wheels.
-  - Copy the downloaded archives to a local path, extract them, and run the setup script from each extracted package in your shell (for example `source /path/to/xrt/setup.sh` and `source /path/to/flexmlrt/setup.sh`). Run these in every new shell you use to build or run `whisper.cpp`.
+Install the XRT runtime and FlexML runtime for your platform:
 
-- Fetch the matching ggml model and prebuilt VitisAI encoder cache:
+- **XRT**: provides the NPU kernel driver and `xrt-smi` diagnostic tool
+- **FlexML runtime** (`flexmlrt`): VitisAI inference engine used by whisper.cpp — download from the [FlexML runtime releases](https://github.com/lemonade-sdk/whisper.cpp-rocm/releases/tag/deps)
 
-  ```bash
-  sh ./models/download-ggml-model.sh base
-  sh ./models/download-vitisai-model.sh base
-  ```
+After installing, source the setup scripts in every shell you use to build or run whisper.cpp:
 
-  ```cmd
-  .\models\download-ggml-model.cmd base
-  .\models\download-vitisai-model.cmd base
-  ```
+```bash
+# Linux
+source /opt/xilinx/xrt/setup.sh
+source /path/to/flexmlrt/setup.sh
+```
 
-  Use the same model name with both scripts. The VitisAI script queries the AMD collection on Hugging Face to list available caches, then downloads the selected `.rai` file as `ggml-<model>-encoder-vitisai.rai` alongside the matching `ggml-<model>.bin` file. You can also browse the collection manually at https://huggingface.co/collections/amd/ryzen-ai-whisper-npu-optimized-onnx-models.
+```cmd
+:: Windows
+cd /path/to/flexmlrt && call setup.bat
+```
 
-  Depending on the downloaded `.rai` cache, VitisAI may offload either the encoder only or the encoder plus cross-projection layers to the AMD NPU. `whisper.cpp` detects the cache contents at runtime and logs the selected offload mode during model initialization.
+You can verify the NPU is visible with:
 
-- Build `whisper.cpp` with VitisAI support:
+```bash
+xrt-smi examine
+```
 
-  ```bash
-  cmake -B build -DWHISPER_VITISAI=1
-  cmake --build build -j --config Release
-  ```
-Your environment is now ready.
+### Download models
 
-### Build Whisper.cpp for Ryzen™ AI support
+Download the ggml model and the matching prebuilt VitisAI encoder cache:
 
-  ```text
-  $ ./build/bin/whisper-cli -m models/ggml-base.bin -f samples/jfk.wav
-  ```
+```bash
+# Linux / macOS
+sh ./models/download-ggml-model.sh base
+sh ./models/download-vitisai-model.sh base
+```
+
+```cmd
+:: Windows
+.\models\download-ggml-model.cmd base
+.\models\download-vitisai-model.cmd base
+```
+
+Use the same model name with both scripts. To see all available VitisAI encoder caches:
+
+```bash
+sh ./models/download-vitisai-model.sh --list
+```
+
+```cmd
+.\models\download-vitisai-model.cmd --list
+```
+
+The VitisAI script queries the [AMD Ryzen AI Whisper NPU collection on Hugging Face](https://huggingface.co/collections/amd/ryzen-ai-whisper-npu-optimized-onnx-models) and downloads the `.rai` encoder cache as `models/ggml-<model>-encoder-vitisai.rai`.
+
+> Depending on the `.rai` cache, VitisAI may offload the encoder only, or the encoder plus cross-projection layers. whisper.cpp detects this at runtime and logs the selected offload mode during model initialization.
+
+### Build
+
+```bash
+cmake -B build -DWHISPER_VITISAI=1
+cmake --build build -j --config Release
+```
+
+### Run
+
+```bash
+./build/bin/whisper-cli -m models/ggml-base.bin -f samples/jfk.wav
+```
+
+For more information see the [Ryzen AI documentation](https://ryzenai.docs.amd.com/en/latest/).
 
 
 ## NVIDIA GPU support