diff --git a/README-sycl.md b/README-sycl.md
index de3641293..de4f23ca5 100644
--- a/README-sycl.md
+++ b/README-sycl.md
@@ -1,22 +1,16 @@
 # llama.cpp for SYCL
 
-[Background](#background)
-
-[OS](#os)
-
-[Intel GPU](#intel-gpu)
-
-[Linux](#linux)
-
-[Windows](#windows)
-
-[Environment Variable](#environment-variable)
-
-[Known Issue](#known-issue)
-
-[Q&A](#q&a)
-
-[Todo](#todo)
+- [Background](#background)
+- [OS](#os)
+- [Intel GPU](#intel-gpu)
+- Build and run:
+  - [Docker](#docker)
+  - [Linux](#linux)
+  - [Windows](#windows)
+- [Environment Variable](#environment-variable)
+- [Known Issue](#known-issue)
+- [Q&A](#q&a)
+- [Todo](#todo)
 
 ## Background
 
@@ -36,7 +30,7 @@ For Intel CPU, recommend to use llama.cpp for X86 (Intel MKL building).
 
 |OS|Status|Verified|
 |-|-|-|
-|Linux|Support|Ubuntu 22.04|
+|Linux|Support|Ubuntu 22.04, Fedora Silverblue 39|
 |Windows|Support|Windows 11|
 
 
@@ -51,6 +45,38 @@ For Intel CPU, recommend to use llama.cpp for X86 (Intel MKL building).
 |Intel iGPU| Support| iGPU in i5-1250P, i7-1260P, i7-1165G7|
 
 
+## Docker
+
+Note:
+- Only docker on Linux is tested. Docker on WSL may not work.
+- You may need to install Intel GPU driver on the host machine (See the [Linux](#linux) section to know how to do that)
+
+### Build the image
+
+You can choose between **F16** and **F32** build. F16 is faster for long-prompt inference.
+
+
+```sh
+# For F16:
+#docker build -t llama-cpp-sycl --build-arg="LLAMA_SYCL_F16=ON" -f .devops/main-intel.Dockerfile .
+
+# Or, for F32:
+docker build -t llama-cpp-sycl -f .devops/main-intel.Dockerfile .
+
+# Note: you can also use the ".devops/main-server.Dockerfile", which compiles the "server" example
+```
+
+### Run
+
+```sh
+# Firstly, find all the DRI cards:
+ls -la /dev/dri
+# Then, pick the card that you want to use.
+
+# For example with "/dev/dri/card1"
+docker run -it --rm -v "$(pwd):/app:Z" --device /dev/dri/renderD128:/dev/dri/renderD128 --device /dev/dri/card1:/dev/dri/card1 llama-cpp-sycl -m "/app/models/YOUR_MODEL_FILE" -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33
+```
+
 ## Linux
 
 ### Setup Environment
@@ -90,8 +116,6 @@ Platform #0: Intel(R) OpenCL HD Graphics
 
 2. Install Intel® oneAPI Base toolkit.
 
-Note: You can skip step this if you want to build inside docker container
-
 a. Please follow the procedure in [Get the Intel® oneAPI Base Toolkit ](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html).
 
 Recommend to install to default folder: **/opt/intel/oneapi**.
@@ -123,20 +147,6 @@ Note:
 - You can choose between **F16** and **F32** build. F16 is faster for long-prompt inference.
 - By default, it will build for all binary files. It will take more time. To reduce the time, we recommend to build for **example/main** only.
 
-Method using **docker**:
-
-```sh
-# For F16:
-#docker build -t llama-cpp-sycl:latest --build-arg="LLAMA_SYCL_F16=ON" -f .devops/main-intel.Dockerfile .
-
-# Or, for F32:
-docker build -t llama-cpp-sycl -f .devops/main-intel.Dockerfile .
-
-# Note: you can also use the ".devops/main-server.Dockerfile", which compiles the "server" example
-```
-
-or, without docker:
-
 ```sh
 mkdir -p build
 cd build
@@ -175,8 +185,6 @@ source /opt/intel/oneapi/setvars.sh
 
 3. List device ID
 
-(Skip this step if you're using docker)
-
 Run without parameter:
 
 ```sh
@@ -211,18 +219,6 @@ found 4 SYCL devices:
 
 Set device ID = 0 by **GGML_SYCL_DEVICE=0**
 
-Using docker image built from step 2:
-
-```sh
-# Firstly, find all the DRI cards:
-ls -la /dev/dri
-
-# Then, pick the card that you want to use. For example "/dev/dri/card1"
-docker run -it --rm -v "$(pwd):/app:Z" --device /dev/dri/renderD128:/dev/dri/renderD128 --device /dev/dri/card1:/dev/dri/card1 llama-cpp-sycl -m "/app/models/YOUR_MODEL_FILE" -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33
-```
-
-or, without docker:
-
 ```sh
 GGML_SYCL_DEVICE=0 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33
 ```
diff --git a/README.md b/README.md
index f26f8e91a..039f267e4 100644
--- a/README.md
+++ b/README.md
@@ -392,15 +392,15 @@ Building the program with BLAS support may lead to some performance improvements
 
   Check [BLIS.md](docs/BLIS.md) for more information.
 
-- #### SYCL (using Intel DPCPP)
+- #### SYCL
   SYCL is a higher-level programming model to improve programming productivity on various hardware accelerators.
 
   llama.cpp based on SYCL is used to **support Intel GPU** (Data Center Max series, Flex series, Arc series, Built-in GPU and iGPU).
 
-  For detailed info, please refer to [llama.cpp for SYCL](README_sycl.md).
+  For detailed info, please refer to [llama.cpp for SYCL](README-sycl.md).
 
 - #### Intel oneMKL
-  Building through oneAPI compilers will make avx_vnni instruction set available for intel processors that do not support avx512 and avx512_vnni. Please note that this build config **does not support Intel GPU**. For Intel GPU support, please refer to [llama.cpp for SYCL](./README_sycl.md).
+  Building through oneAPI compilers will make avx_vnni instruction set available for intel processors that do not support avx512 and avx512_vnni. Please note that this build config **does not support Intel GPU**. For Intel GPU support, please refer to [llama.cpp for SYCL](./README-sycl.md).
 
   - Using manual oneAPI installation:
     By default, `LLAMA_BLAS_VENDOR` is set to `Generic`, so if you already sourced intel environment script and assign `-DLLAMA_BLAS=ON` in cmake, the mkl version of Blas will automatically been selected. Otherwise please install oneAPI and follow the below steps: