diff --git a/README.md b/README.md
index d68330d2a11f60..119b6f881dad8e 100644
--- a/README.md
+++ b/README.md
@@ -242,7 +242,7 @@ The project also includes many example programs and tools using the `llama` libr
 
 - Clone this repository and build locally, see [how to build](docs/build.md)
 - On MacOS or Linux, install `llama.cpp` via [brew, flox or nix](docs/install.md)
-- Use a Docker image, see [documentation for Docker](docs/docker.md)
+- Use a container image, see [documentation for containers](docs/container.md)
 - Download pre-built binaries from [releases](https://github.com/ggerganov/llama.cpp/releases)
 
 ## Obtaining and quantizing models
@@ -500,7 +500,7 @@ To learn more about model quantization, [read this documentation](examples/quant
 #### Development documentation
 
 - [How to build](docs/build.md)
-- [Running on Docker](docs/docker.md)
+- [Running in a container](docs/container.md)
 - [Build on Android](docs/android.md)
 - [Performance troubleshooting](docs/development/token_generation_performance_tips.md)
 - [GGML tips & tricks](https://github.com/ggerganov/llama.cpp/wiki/GGML-Tips-&-Tricks)
diff --git a/docs/build.md b/docs/build.md
index dd6495028b3280..a241589c48075c 100644
--- a/docs/build.md
+++ b/docs/build.md
@@ -94,13 +94,13 @@ Building through oneAPI compilers will make avx_vnni instruction set available f
 - Using manual oneAPI installation:
   By default, `GGML_BLAS_VENDOR` is set to `Generic`, so if you already sourced intel environment script and assign `-DGGML_BLAS=ON` in cmake, the mkl version of Blas will automatically been selected. Otherwise please install oneAPI and follow the below steps:
     ```bash
-    source /opt/intel/oneapi/setvars.sh # You can skip this step if  in oneapi-basekit docker image, only required for manual installation
+    source /opt/intel/oneapi/setvars.sh # You can skip this step if  in oneapi-basekit container image, only required for manual installation
     cmake -B build -DGGML_BLAS=ON -DGGML_BLAS_VENDOR=Intel10_64lp -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_NATIVE=ON
     cmake --build build --config Release
     ```
 
-- Using oneAPI docker image:
-  If you do not want to source the environment vars and install oneAPI manually, you can also build the code using intel docker container: [oneAPI-basekit](https://hub.docker.com/r/intel/oneapi-basekit). Then, you can use the commands given above.
+- Using oneAPI container image:
+  If you do not want to source the environment vars and install oneAPI manually, you can also build the code using intel container: [oneAPI-basekit](https://hub.docker.com/r/intel/oneapi-basekit). Then, you can use the commands given above.
 
 Check [Optimizing and Running LLaMA2 on Intel® CPU](https://www.intel.com/content/www/us/en/content-details/791610/optimizing-and-running-llama2-on-intel-cpu.html) for more information.
 
@@ -280,19 +280,25 @@ cmake -B build -DGGML_VULKAN=ON
 cmake --build build --config Release
 ```
 
-**With docker**:
+**With containers**:
 
 You don't need to install Vulkan SDK. It will be installed inside the container.
 
-```sh
 # Build the image
-docker build -t llama-cpp-vulkan --target light -f .devops/vulkan.Dockerfile .
+
+<details><summary>Docker example</summary>docker build -t llama-cpp-vulkan --target light -f .devops/vulkan.Dockerfile .</details>
+<details><summary>Podman example</summary>podman build -t llama-cpp-vulkan --target light -f .devops/vulkan.Dockerfile .</details>
+
 
 # Then, use it:
 docker run -it --rm -v "$(pwd):/app:Z" --device /dev/dri/renderD128:/dev/dri/renderD128 --device /dev/dri/card1:/dev/dri/card1 llama-cpp-vulkan -m "/app/models/YOUR_MODEL_FILE" -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33
+
+or
+
+podman run --security-opt label=disable -it --rm -v "$(pwd):/app:Z" --device /dev/dri/renderD128:/dev/dri/renderD128 --device /dev/dri/card1:/dev/dri/card1 llama-cpp-vulkan -m "/app/models/YOUR_MODEL_FILE" -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33
 ```
 
-**Without docker**:
+**Without a container**:
 
 Firstly, you need to make sure you have installed [Vulkan SDK](https://vulkan.lunarg.com/doc/view/latest/linux/getting_started_ubuntu.html)
 
diff --git a/docs/docker.md b/docs/container.md
similarity index 75%
rename from docs/docker.md
rename to docs/container.md
index dac9a9ec164ffe..a60fd9ea8e7d9a 100644
--- a/docs/docker.md
+++ b/docs/container.md
@@ -1,11 +1,11 @@
-# Docker
+# Container
 
 ## Prerequisites
-* Docker must be installed and running on your system.
+* A container engine, ie Docker/Podman, must be installed and running on your system.
 * Create a folder to store big models & intermediate files (ex. /llama/models)
 
 ## Images
-We have three Docker images available for this project:
+We have three container images available for this project:
 
 1. `ghcr.io/ggerganov/llama.cpp:full`: This image includes both the main executable file and the tools to convert LLaMA models into ggml and convert into 4-bit quantization. (platforms: `linux/amd64`, `linux/arm64`)
 2. `ghcr.io/ggerganov/llama.cpp:light`: This image only includes the main executable file. (platforms: `linux/amd64`, `linux/arm64`)
@@ -27,13 +27,18 @@ The GPU enabled images are not currently tested by CI beyond being built. They a
 
 ## Usage
 
-The easiest way to download the models, convert them to ggml and optimize them is with the --all-in-one command which includes the full docker image.
+The easiest way to download the models, convert them to ggml and optimize them is with the --all-in-one command which includes the full container image.
 
 Replace `/path/to/models` below with the actual path where you downloaded the models.
 
 ```bash
 docker run -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:full --all-in-one "/models/" 7B
 ```
+or
+
+```bash
+podman run --security-opt label=disable -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:full --all-in-one "/models/" 7B
+```
 
 On completion, you are ready to play!
 
@@ -41,23 +46,39 @@ On completion, you are ready to play!
 docker run -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:full --run -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512
 ```
 
+```bash
+podman run --security-opt label=disable -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:full --run -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512
+```
+
 or with a light image:
 
 ```bash
 docker run -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:light -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512
 ```
 
+or
+
+```bash
+podman run --security-opt label=disable -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:light -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512
+```
+
 or with a server image:
 
 ```bash
 docker run -v /path/to/models:/models -p 8000:8000 ghcr.io/ggerganov/llama.cpp:server -m /models/7B/ggml-model-q4_0.gguf --port 8000 --host 0.0.0.0 -n 512
 ```
 
-## Docker With CUDA
+or
+
+```bash
+podman run --security-opt label=disable -v /path/to/models:/models -p 8000:8000 ghcr.io/ggerganov/llama.cpp:server -m /models/7B/ggml-model-q4_0.gguf --port 8000 --host 0.0.0.0 -n 512
+```
+
+## Container engines With CUDA
 
 Assuming one has the [nvidia-container-toolkit](https://github.com/NVIDIA/nvidia-container-toolkit) properly installed on Linux, or is using a GPU enabled cloud, `cuBLAS` should be accessible inside the container.
 
-## Building Docker locally
+## Building Container locally
 
 ```bash
 docker build -t local/llama.cpp:full-cuda --target full -f .devops/cuda.Dockerfile .
@@ -65,6 +86,14 @@ docker build -t local/llama.cpp:light-cuda --target light -f .devops/cuda.Docker
 docker build -t local/llama.cpp:server-cuda --target server -f .devops/cuda.Dockerfile .
 ```
 
+or
+
+```bash
+podman build -t local/llama.cpp:full-cuda --target full -f .devops/cuda.Dockerfile .
+podman build -t local/llama.cpp:light-cuda --target light -f .devops/cuda.Dockerfile .
+podman build -t local/llama.cpp:server-cuda --target server -f .devops/cuda.Dockerfile .
+```
+
 You may want to pass in some different `ARGS`, depending on the CUDA environment supported by your container host, as well as the GPU architecture.
 
 The defaults are:
@@ -88,17 +117,32 @@ docker run --gpus all -v /path/to/models:/models local/llama.cpp:light-cuda -m /
 docker run --gpus all -v /path/to/models:/models local/llama.cpp:server-cuda -m /models/7B/ggml-model-q4_0.gguf --port 8000 --host 0.0.0.0 -n 512 --n-gpu-layers 1
 ```
 
-## Docker With MUSA
+or
+
+```bash
+podman run --security-opt label=disable --gpus all -v /path/to/models:/models local/llama.cpp:full-cuda --run -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1
+podman run --security-opt label=disable --gpus all -v /path/to/models:/models local/llama.cpp:light-cuda -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1
+podman run --security-opt label=disable --gpus all -v /path/to/models:/models local/llama.cpp:server-cuda -m /models/7B/ggml-model-q4_0.gguf --port 8000 --host 0.0.0.0 -n 512 --n-gpu-layers 1
+```
+
+## Container engines With MUSA
 
 Assuming one has the [mt-container-toolkit](https://developer.mthreads.com/musa/native) properly installed on Linux, `muBLAS` should be accessible inside the container.
 
-## Building Docker locally
+## Building Container images locally
 
 ```bash
 docker build -t local/llama.cpp:full-musa --target full -f .devops/musa.Dockerfile .
 docker build -t local/llama.cpp:light-musa --target light -f .devops/musa.Dockerfile .
 docker build -t local/llama.cpp:server-musa --target server -f .devops/musa.Dockerfile .
 ```
+or
+
+```bash
+podman build -t local/llama.cpp:full-musa --target full -f .devops/musa.Dockerfile .
+podman build -t local/llama.cpp:light-musa --target light -f .devops/musa.Dockerfile .
+podman build -t local/llama.cpp:server-musa --target server -f .devops/musa.Dockerfile .
+```
 
 You may want to pass in some different `ARGS`, depending on the MUSA environment supported by your container host, as well as the GPU architecture.