diff --git a/README.md b/README.md
index c6fc976..190b2b0 100644
--- a/README.md
+++ b/README.md
@@ -30,6 +30,12 @@ pip install torch --index-url https://download.pytorch.org/whl/cpu  # unless you
 pip install skala
 ```
 
+Or using Conda (Mamba):
+
+```bash
+mamba install -c conda-forge skala "pytorch=*=cpu*"
+```
+
 Run an SCF calculation with Skala for a hydrogen molecule:
 
 ```python
@@ -45,15 +51,17 @@ ks.kernel()
 ```
 Go to [microsoft.github.io/skala](https://microsoft.github.io/skala) for a more detailed installation guide and further examples of how to use Skala functional with PySCF and ASE and in [Azure Foundry](https://ai.azure.com/catalog/models/Skala).
 
-## Getting started (GPU support)
+## Getting started (GPU support via Mamba, includes CUDA toolkit, Torch, CuPy)
+
+Supports CUDA version 11, 12 or 13. You can find the most recent CUDA version that is supported on your system using `nvidia-smi`.
 
-Install using Pip:
 ```bash
-cu_version=128 #or 126 or 130 depending on your CUDA version
-pip install torch cupy --extra-index-url "https://download.pytorch.org/whl/cu${cu_version}"
-pip install --no-deps "gpu4pyscf-cuda${cu_version:0:2}x>=1.0,<2" "gpu4pyscf-libxc-cuda${cu_version:0:2}x>=0.4,<1"
-pip install skala
+cu_version=12 #or 11 or 13 depending on your CUDA version
+mamba env create -n skala -f environment-gpu.yml  "cuda-version==${cu_version}.*" skala
+mamba activate skala
+pip install --no-deps "gpu4pyscf-cuda${cu_version}x>=1.0,<2" "gpu4pyscf-libxc-cuda${cu_version}x>=0.4,<1"
 ```
+
 Run an SCF calculation with Skala for a hydrogen molecule on GPU:
 
 ```python
diff --git a/docs/installation.rst b/docs/installation.rst
index 8c126f3..9b623b9 100644
--- a/docs/installation.rst
+++ b/docs/installation.rst
@@ -72,12 +72,19 @@ If you prefer to install Skala from the source code, you can clone the repositor
    mamba activate skala
    pip install -e .
 
-where `environment-cpu.yml` can be replaced for `environment-gpu.yml` for gpu support (CUDA 12) with gpu4pyscf, in which case gpu4pyscf needs to be separately installed *after creating the environment* via
+where `environment-cpu.yml` can be replaced with `environment-gpu.yml` for gpu support (specify CUDA version with `cuda_version=<version>`) with gpu4pyscf, in which case gpu4pyscf needs to be separately installed *after creating the environment* via (for CUDA 12)
 
 .. code-block:: bash
 
    pip install --no-deps 'gpu4pyscf-cuda12x>=1.0,<2' 'gpu4pyscf-libxc-cuda12x>=0.4,<1'
 
+
+or (for CUDA 13) 
+
+.. code-block:: bash
+
+   pip install --no-deps 'gpu4pyscf-cuda13x>=1.0,<2' 'gpu4pyscf-libxc-cuda13x>=0.4,<1'
+
 To install the development dependencies, you can run:
 
 .. code-block:: bash
diff --git a/environment-gpu.yml b/environment-gpu.yml
index 02b3d52..3d7e43a 100644
--- a/environment-gpu.yml
+++ b/environment-gpu.yml
@@ -17,7 +17,7 @@ dependencies:
   - cuda-toolkit
   - cupy
   - cutensor
-  - cuda-version ==12.*
+  - cuda-version
   # Testing and development
   - pre-commit
   - pytest