diff --git a/docs/source/howto/installation.rst b/docs/source/howto/installation.rst
index ef01df78d..2b2a40c9b 100644
--- a/docs/source/howto/installation.rst
+++ b/docs/source/howto/installation.rst
@@ -49,4 +49,107 @@ A virtual environment can be used if the following conditions are met:
    $ CC=$(type -p mpicc) HDF5_MPI="ON" HDF5_DIR=/path/to/parallel-hdf5 pip install --no-build-isolation --no-binary=h5py h5py
    $ pip install cupy-cuda13x # install cupy-cuda13x if CUDA library/CUDA toolkit version is 13.x
    $ pip install aiofiles astra-toolbox click graypy hdf5plugin loguru nvtx pillow pyyaml scikit-image scipy tqdm
-   $ pip install --no-deps httomo httomolib httomolibgpu httomo-backends tomobar
\ No newline at end of file
+   $ pip install --no-deps httomo httomolib httomolibgpu httomo-backends tomobar
+
+
+macOS (Apple Silicon) — CPU-only Installation
+==============================================
+
+.. note::
+   HTTomo's GPU-accelerated methods (``httomolibgpu``) depend on `CuPy
+   <https://cupy.dev/>`_, which requires an NVIDIA CUDA GPU. Apple Silicon
+   Macs (M1/M2/M3/M4) have no CUDA support, so this path installs HTTomo in
+   **CPU-only mode**, using TomoPy for reconstruction instead of the GPU
+   backends. Pipelines must use CPU/TomoPy methods only (see
+   :doc:`../pipelines/yaml` for an example CPU pipeline).
+
+This guide has been tested on an M1 MacBook (16GB RAM) running native
+arm64 conda (not under Rosetta).
+
+1. Install a native arm64 conda distribution
+---------------------------------------------
+
+Make sure you install the **arm64**, not Intel/x86_64, build — otherwise
+everything below runs emulated under Rosetta and is significantly slower:
+
+.. code-block:: bash
+
+   curl -L -O https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-MacOSX-arm64.sh
+   bash Miniforge3-MacOSX-arm64.sh
+
+2. Create the environment
+---------------------------
+
+Skip ``cupy`` entirely — there is no arm64/macOS build, and it cannot be
+installed on Apple Silicon. ``astra-toolbox`` and ``tomopy`` do have
+osx-arm64 conda-forge builds (CPU-only algorithms), which is all a CPU
+pipeline needs. ``mpi4py`` is required even for a single-process run, since
+HTTomo's CLI unconditionally imports ``mpi4py.MPI``.
+Replace `conda` with `mamba` below if it's available in the environment, for a faster package resolution.
+
+.. code-block:: bash
+
+   conda create --name httomo python=3.11
+   conda activate httomo
+
+   # numpy must stay below 2.0 — HTTomo's CPU/GPU array-type detection
+   # (block.is_gpu) relies on numpy.ndarray *not* having a `.device`
+   # attribute, an assumption NumPy 2.0's Array API support breaks.
+   conda install -c conda-forge "numpy<2" mpi4py openmpi==4.1.6 \
+     "h5py=*=mpi_openmpi*" astra-toolbox tomopy==1.15.3 \
+     aiofiles click graypy loguru nvtx pillow pyyaml \
+     scikit-image scipy tqdm hdf5plugin pywavelets
+
+   # compilers needed to build httomolib's OpenMP-based C extension —
+   # macOS's system clang has no -fopenmp support
+   conda install -c conda-forge compilers llvm-openmp
+
+3. Install HTTomo
+-------------------
+
+.. code-block:: bash
+
+   pip install httomo httomo-backends httomolib tomobar --no-deps
+
+Verify:
+
+.. code-block:: bash
+
+   python -m httomo --help
+
+4. Known issues on this path (as of httomo 3.0 / httomolib 4.0.1)
+---------------------------------------------------------------------
+
+If ``h5py`` ever gets silently swapped back to a non-MPI build by a later ``conda install`` (check with
+``python -c "import h5py; print(h5py.get_config().mpi)"``), pin it:
+
+  .. code-block:: bash
+
+     conda config --env --append pinned_packages 'h5py=*=mpi_openmpi*'
+
+5. Running a CPU pipeline
+---------------------------
+
+Always validate the pipeline first:
+
+.. code-block:: bash
+
+   python -m httomo check pipeline.yaml data.nxs
+
+Run serially:
+
+.. code-block:: bash
+
+   python -m httomo run data.nxs pipeline.yaml ./output --max-memory 10G
+
+Or across multiple CPU cores with MPI (``--max-memory`` is per-process,
+so divide your budget by the process count):
+
+.. code-block:: bash
+
+   mpirun -np 4 python -m httomo run data.nxs pipeline.yaml ./output --max-memory 2G
+
+.. note::
+   With 16GB of unified memory shared with macOS itself, keep
+   ``--max-memory`` well under the physical total (8–10G total budget is a
+   safe starting point) to avoid swapping.