I have worked out a workaround solution - How to install numpy on M1 Max, with the most accelerated performance (Apple's vecLib)? Here's the answer as of Dec 6 2021. Steps I. Install miniforge So that your Python is run natively on arm64, not translated via Rosseta. Download Miniforge3-MacOSX-arm64.sh, then Run the script, then open another shell $ bash Miniforge3-MacOSX-arm64.sh Create an environment (here I use name np_veclib) $ conda create -n np_veclib python=3.9 $ conda activate np_veclib II. Install Numpy with BLAS interface specified as vecLib To compile numpy, first need to install cython and pybind11: $ conda install cython pybind11 Compile numpy by (Thanks @Marijn's answer) - don't use conda install! $ pip install --no-binary :all: --no-use-pep517 numpy An alternative of 2. is to build from source $ git clone https://github.com/numpy/numpy $ cd numpy $ cp site.cfg.example site.cfg $ nano site.cfg Edit the copied site.cfg: add the following lines: [accelerate] libraries = Accelerate, vecLib Then build and install: $ NPY_LAPACK_ORDER=accelerate python setup.py build $ python setup.py install After either 2 or 3, now test whether numpy is using vecLib: >>> import numpy >>> numpy.show_config() Then, info like /System/Library/Frameworks/vecLib.framework/Headers should be printed. III. For further installing other packages using conda Make conda recognize packages installed by pip conda config --set pip_interop_enabled true This must be done, otherwise if e.g. conda install pandas, then numpy will be in The following packages will be installed list and installed again. But the new installed one is from conda-forge channel and is slow. Comparisons to other installations: 1. Competitors: Except for the above optimal one, I also tried several other installations A. np_default: conda create -n np_default python=3.9 numpy B. np_openblas: conda create -n np_openblas python=3.9 numpy blas=*=*openblas* C. np_netlib: conda create -n np_netlib python=3.9 numpy blas=*=*netlib* The above ABC options are directly installed from conda-forge channel. numpy.show_config() will show identical results. To see the difference, examine by conda list - e.g. openblas packages are installed in B. Note that mkl or blis is not supported on arm64. D. np_openblas_source: First install openblas by brew install openblas. Then add [openblas] path /opt/homebrew/opt/openblas to site.cfg and build Numpy from source. M1 and i9–9880H in this post. My old i5-6360U 2cores on MacBook Pro 2016 13in. 2. Benchmarks: Here I use two benchmarks: mysvd.py: My SVD decomposition import time import numpy as np np.random.seed(42) a = np.random.uniform(size=(300, 300)) runtimes = 10 timecosts = [] for _ in range(runtimes): s_time = time.time() for i in range(100): a += 1 np.linalg.svd(a) timecosts.append(time.time() - s_time) print(f'mean of {runtimes} runs: {np.mean(timecosts):.5f}s') dario.py: A benchmark script by Dario Radečić at the post above. 3. Results: +-------+-----------+------------+-------------+-----------+--------------------+----+----------+----------+ | sec | np_veclib | np_default | np_openblas | np_netlib | np_openblas_source | M1 | i9–9880H | i5-6360U | +-------+-----------+------------+-------------+-----------+--------------------+----+----------+----------+ | mysvd | 1.02300 | 4.29386 | 4.13854 | 4.75812 | 12.57879 | / | / | 2.39917 | +-------+-----------+------------+-------------+-----------+--------------------+----+----------+----------+ | dario | 21 | 41 | 39 | 323 | 40 | 33 | 23 | 78 | +-------+-----------+------------+-------------+-----------+--------------------+----+----------+----------+